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  1. [img]https://img76.pixhost.to/images/62/302027102_x2pkv274htab.png[/img] [b]Tsadok A Unleash Core Data Fetching Data, Migrating, 2022 | 4.71 MB[/b] [b]English | 335 Pages[/b] [b]Title:[/b] Unleash Core dаta: Fetching Data, Migrating, and Maintaining Persistent Stores [b]Author:[/b] Avi Tsadok [b]Year:[/b] 2022 [b]Description:[/b] Create apps with rich capabilities to receive, process, and intelligently store data that work across multiple devices in the Apple ecosystem. This book will show you how to organize your app's data and make it work for you and your users! With many frameworks, there's a point in the learning curve where you stop fearing the mountain of knowledge to learn and just enjoy the power to play and develop. For some reason many developers feel that point seems harder to reach with Core Data. And that's unjustified-Core Data is a great framework that with powerful, optimized tools right out of the box. So it's time you stopped fearing the journey and took your steps further out into the world of enjoying the power of Core Data. With what you learn, you can build apps to deal with a large amount of data and complex relationships in intelligent and efficient ways. Core Data has many secrets underneath the hood that can power up your persistent store like never before. Tools such as Undo, objects deletion, migration, and more. Set up your store and your data model, handle objects with a multi-threading environment, write integration tests, and share your data with an Apple Watch app and iCloud. What You'll Learn [list] [*] Work with Core Data objects across apps and platforms [*] Write integration tests with your persistent store [*] Fetch, organize, write, and process data efficiently and intelligently [/list] Who This Book Is For Experienced iOS developers with Swift knowledge creating iOS apps that require saving complex data on the device other than a few numbers or text. [b]Download Links[/b] [b]Rapidgator[/b] [code] https://rapidgator.net/file/b70df05c024c893c526c6d245678d86b/Tsadok_A._Unleash_Core_Data._Fetching_Data_Migrating...2022.rar.html [/code] [b]Nitroflare[/b] [code] https://nitroflare.com/view/EE4DB475DB65BEA/Tsadok_A._Unleash_Core_Data._Fetching_Data_Migrating...2022.rar[/code]
  2. [img]https://img79.pixhost.to/images/189/316288221_7uo1kgkgn69k.png[/img] [b]Zobitz J Exploring Modeling with Data and Diff Equations Using R 2023 | 8.5 MB[/b] [b]N/A | 379 Pages[/b] [b]Title:[/b] Exploring Modeling with Data and Differential Equations Using R [b]Author:[/b] John M. Zobitz [b]Year:[/b] 2019 [b]Description:[/b] [list] [*] Make sure this fits by entering your model number. [*] Incredibly easy to use, 30 second results! Simply dip the lead detector swab in household vinegar and wait for the yellow mustard color to appear. Next, clean the testing area and rub the vinegar moist swab over the testing areif the swab turns red, violet, or purple there's likely lead present on the surface [*] No mess or hassle, dispose after use! No need for industrial hazardous material disposal containers or protective gloves; Scitus lead testing kit swabs are non-toxic, odorless, and won't stain your clothes or skin. Use them to test countertops, vinyl, plastic, or surfaces with coats of paint. [*] 30 Lead detection swabs in every package. A simple cost comparison and you'll see that our lead tester swabs are the most cost effective lead testing product on the market. [*] Our facility manufactures these tests to ensure reliability and quality [*] Not for use on ceramics as they are more likely to hide evidence of lead within the outer glaze. [/list] [b]Download Links[/b] [b]Rapidgator[/b] [code] https://rapidgator.net/file/30c9e12d4a76d66b7278add63582e5c5/Zobitz_J._Exploring_Modeling_with_Data_and_Diff_Equations_Using_R_2023.rar.html [/code] [b]Nitroflare[/b] [code] https://nitroflare.com/view/CE66952BEFEA74D/Zobitz_J._Exploring_Modeling_with_Data_and_Diff_Equations_Using_R_2023.rar[/code]
  3. [img]https://img80.pixhost.to/images/14/318377882_dpear14z3m7w.png[/img] [b]De Villa A Causal Inference for Data Science (MEAP v 4) 2022 | 9.44 MB[/b] [b]N/A | 163 Pages[/b] [b]Title:[/b] Causal Inference for Data Science MEAP V04 [b]Author:[/b] Aleix de Villa Robert [b]Year:[/b] 2016 [b]Description:[/b] [list] [*] Cotton Polyester Blend [*] Imported [*] Pull On closure [*] Machine Wash [*] YADA YADA YADA: Binge your favorite show about nothing while wearing your new Seinfeld merch! [*] FANS ARE THE BEST: Face it, we all love a good fandom. How about representing your favorite TV show while rocking this sweet Seinfeld shirt? Our Seinfeld tees are more than just T-shirts. They are a way to bond with friends. [*] WHAT'S MY SIZE AGAIN?: Forget your size after binging all night? We've got you. This cool long sleeve shirt is available in sizes S-3XL. Body width measurements in inches: S (18), M (20), L (22), XL (24), 2XL (26), 3XL (28), and where available 4XL (30) and 5XL (32). [/list] [list] [*] SHIRTS FOR FANS: Say hello to our authentic merch. This lightweight crew-neck shirt is made from a high quality 65% Polyester, 35% Cotton blend. We know there's nothing better than hyping up your favorite themes while being comfy. Thank us later. [*] RIPPLE JUNCTION IS A WHOLE MOOD: Our story started with two dudes making and selling themed clothing from a van. We have stuck with the fun vibes and fandom obsessions over the years. Our clothes are all about wearing what you love. [/list] http://xn--jvascript-0yh [b]Download Links[/b] [b]Rapidgator[/b] [code] https://rapidgator.net/file/81fabf71c85e0ed09387afc8cd0dcd8b/De_Villa_A._Causal_Inference_for_Data_Science_MEAP_v.4_2022.rar.html [/code] [b]Nitroflare[/b] [code] https://nitroflare.com/view/BC1290D0194DAF9/De_Villa_A._Causal_Inference_for_Data_Science_MEAP_v.4_2022.rar[/code]
  4. [img]https://i120.fastpic.org/big/2022/1117/7f/c076bdefed028dfa68afc6060769877f.jpeg[/img] English | 2022 | ISBN: 1804615447 | 270 pages | True PDF | 15.03 MB Learn concepts, methodologies, and applications of deep learning for building predictive models from complex genomics data sets to overcome challenges in the life sciences and biotechnology industries [b]Key Features[/b] Apply deep learning algorithms to solve real-world problems in the field of genomicsExtract biological insights from deep learning models built from genomic datasetsTrain, tune, evaluate, deploy, and monitor deep learning models for enabling predictions in genomics [b]Book Description[/b] Deep learning has shown remarkable promise in the field of genomics; however, there is a lack of a skilled deep learning workforce in this discipline. This book will help researchers and data scientists to stand out from the rest of the crowd and solve real-world problems in genomics by developing the necessary skill set. Starting with an introduction to the essential concepts, this book highlights the power of deep learning in handling big data in genomics. First, you'll learn about conventional genomics analysis, then transition to state-of-the-art machine learning-based genomics applications, and finally dive into deep learning approaches for genomics. The book covers all of the important deep learning algorithms commonly used by the research community and goes into the details of what they are, how they work, and their practical applications in genomics. The book dedicates an entire section to operationalizing deep learning models, which will provide the necessary hands-on tutorials for researchers and any deep learning practitioners to build, tune, interpret, deploy, evaluate, and monitor deep learning models from genomics big data sets. By the end of this book, you'll have learned about the challenges, best practices, and pitfalls of deep learning for genomics. [b]What you will learn[/b] Discover the machine learning applications for genomicsExplore deep learning concepts and methodologies for genomics applicationsUnderstand supervised deep learning algorithms for genomics applicationsGet to grips with unsupervised deep learning with autoencodersImprove deep learning models using generative modelsOperationalize deep learning models from genomics datasetsVisualize and interpret deep learning modelsUnderstand deep learning challenges, pitfalls, and best practices [b]Who this book is for[/b] This deep learning book is for machine learning engineers, data scientists, and academicians practicing in the field of genomics. It assumes that readers have intermediate Python programming knowledge, basic knowledge of Python libraries such as NumPy and Pandas to manipulate and parse data, Matplotlib, and Seaborn for visualizing data, along with a base in genomics and genomic analysis concepts. [b]Table of Contents[/b] Introducing Machine Learning for GenomicsGenomics Data AnalysisMachine Learning Methods for Genomic ApplicationsDeep Learning for GenomicsIntroducing Convolutional Neural Networks for GenomicsRecurrent Neural Networks in GenomicsUnsupervised Deep Learning with AutoencodersGANs for Improving Models in GenomicsBuilding and Tuning Deep Learning ModelsModel Interpretability in GenomicsModel Deployment and MonitoringChallenges, Pitfalls, and Best Practices for Deep Learning in Genomics [b]Download Links[/b] [b]Rapidgator[/b] [code] https://rapidgator.net/file/035419e2e3653b2d9c6c2036f79e42d2/uheis.D.L.f.G.D.a.f.g.a.i.l.s.a.b.rar.html [/code] [b]Nitroflare[/b] [code] https://nitroflare.com/view/D075166E2D4FC61/uheis.D.L.f.G.D.a.f.g.a.i.l.s.a.b.rar[/code]
  5. [img]https://i.postimg.cc/RCc9kWTS/437950b5-6d4b-4909-b6e6-25c4b249e88b.png[/img] English | 2022 | ISBN: ‎ 1804615447 | 470 pages | True EPUB | 8.26 MB Learn concepts, methodologies, and applications of deep learning for building predictive models from complex genomics data sets to overcome challenges in the life sciences and biotechnology industries Key Features Apply deep learning algorithms to solve real-world problems in the field of genomics Extract biological insights from deep learning models built from genomic datasets Train, tune, evaluate, deploy, and monitor deep learning models for enabling predictions in genomics Book Description Deep learning has shown remarkable promise in the field of genomics; however, there is a lack of a skilled deep learning workforce in this discipline. This book will help researchers and data scientists to stand out from the rest of the crowd and solve real-world problems in genomics by developing the necessary skill set. Starting with an introduction to the essential concepts, this book highlights the power of deep learning in handling big data in genomics. First, you'll learn about conventional genomics analysis, then transition to state-of-the-art machine learning-based genomics applications, and finally dive into deep learning approaches for genomics. The book covers all of the important deep learning algorithms commonly used by the research community and goes into the details of what they are, how they work, and their practical applications in genomics. The book dedicates an entire section to operationalizing deep learning models, which will provide the necessary hands-on tutorials for researchers and any deep learning practitioners to build, tune, interpret, deploy, evaluate, and monitor deep learning models from genomics big data sets. By the end of this book, you'll have learned about the challenges, best practices, and pitfalls of deep learning for genomics. What you will learn Discover the machine learning applications for genomics Explore deep learning concepts and methodologies for genomics applications Understand supervised deep learning algorithms for genomics applications Get to grips with unsupervised deep learning with autoencoders Improve deep learning models using generative models Operationalize deep learning models from genomics datasets Visualize and interpret deep learning models Understand deep learning challenges, pitfalls, and best practices Who this book is for This deep learning book is for machine learning eeers, data scientists, and acadians practicing in the field of genomics. It assumes that readers have intermediate Python programming knowledge, basic knowledge of Python libraries such as NumPy and Pandas to manipulate and parse data, Matplotlib, and Seaborn for visualizing data, along with a base in genomics and genomic analysis concepts. [b]Download Links[/b] [b]Rapidgator[/b] [code] https://rapidgator.net/file/6051c3809bd5120dda085eac8adca7a1/6Eg1BVg5_Deep_Learning_for_Genomics_Datadriven_approaches_for_genomics_applications_in_life_sciences.rar.html [/code] [b]Nitroflare[/b] [code] https://nitroflare.com/view/CD226F42D046C49/6Eg1BVg5_Deep_Learning_for_Genomics_Datadriven_approaches_for_genomics_applications_in_life_sciences.rar[/code]
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  7. Last updated 11/2022 Duration: 5h 33m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 1.73 GB Genre: eLearning | Language: English Build your Data Analysis and Visualization skills with Python's Seaborn Library What you'll learn Learn Installation of python and related libraries. Why Should you use Data Visualization in Analytics and Business Intelligence projects? Create beautiful visualizations with Seaborn Learn Data Visualization in Python Learn to use Seaborn for statistical plots Distribution Plot, Histograms, KDE Plots, Scatter Plot, Rug Plot, Joint Plot, Pair Plot, Bar Plot, Count Plot, Box Plot, Violin Plot, Strip Plot, Swarm Plot Heat Map, Pair Plot, Sub Plot Requirements No introductory skill level of Python programming required Desire to learn! Description ***** Enrol now in the most comprehensive and up-to-date (Nov 2022) course available for Data Visualization with Python using Seaborn ***** About the Instructor. This course is led by Aditya Dhandi - an international trainer, consultant, and data analyst with over 100 000 enrollments on Udemy. Aditya specializes in teaching data analysis techniques, Excel Pivot Tables, Power Pivot, Microsoft Power BI, & Google Data Studio & his courses average 4+ stars out of 5. He's also the founder of the popular website, Jobshie. Why Learn Seaborn Library? "A picture is worth a thousand words". We are all familiar with this expression. It especially applies when trying to explain the insight obtained from the analysis of increasingly large datasets. Data visualization plays an essential role in the representation of both small and large-scale data. This course is designed to teach analysts, students interested in data science, statisticians, and data scientists how to analyze real-world data by creating professional-looking charts and using numerical descriptive statistics techniques in Python 3. We'll teach you how to program with Python, how to analyze and create amazing data visualizations with Python! You can use this course as your ready-to-go reference for your own project. How is this course designed Describe what makes a good or bad visualization Understand best practices for creating basic charts Identify the functions that are best for particular problems Create a visualization seaborn Distribution Plot Histograms KDE Plots Scatter Plot Rug Plot Joint Plot Pair Plot Bar Plot Count Plot Box Plot Violin Plot Strip Plot Swarm Plot Heat Map Pair Plot Sub Plot SKILLS YOU WILL GAIN Python Programming Data Virtualization Data Visualization (DataViz) seaborn Key Features Video Lectures: Learn the best ways to learn seaborn Course Certificate: Complete the course and show off your skills with the course certificate. Learn from experts: Your expert instructor will teach you seaborn skills you can apply immediately. Full Excess: There is no time limit, so take the course at your own pace and retake lessons as you need. Use any Device: Join the course using any modern browser on your phone, tablet and computer. Ask Question: Ask questions and share ideas with other students in the course community. What's included? High quality video lessons to build your knowledge and skills. Guided walk-throughs with techniques and tips. 14 pre-built dashboards you can use and customize. Practical exercises to apply your skills. Quizzes to reinforce learnings and test your knowledge. Private discussion area where you can ask questions. Course certificate for completing the lessons and assignment. Full access lets you review lessons whenever you need. Updates when lessons in the course are refreshed. Jobshie Academy Reviews 300+ learner reviews | 4+ average rating We help learners across the globe develop new skills and achieve their personal and professional goals. Browse learner reviews below to discover how people enjoy the online learning experience at Jobshie Academy. "I have gained useful skills while learning this course. I can consider myself a data analysis specialist. Thank you" by Joseph Israel "I was a nice experience about this course as it introduces with many topics which are related to data analysis ." by Sreeraj Mopkar "very good explaination , bohot achhe se sab samajh aata hai ...100% Understanding all concept very much....excellent explanation" by Moosa Khan "The course is very instructive and didactic, I liked it, it is all the information I need for my activities" by Jaime Ronald Palma Aguilar "yeah it was such a great course, the teacher took time out to disect all angles of Excel, he touched all the essential parts, needed for our daily use of excel. Thanks a lot" by Unyime Joshua "The instructor is very knowledgeable and teach in a very engaging way. I totally recommend this course.????" by Aditya FAQs When does the seaborn course start and finish? The course starts as soon as you join! It is a completely self-paced online course, so you decide when you start and when you finish. How long does it take to complete the course? We recommend taking the course over two to three weeks, so you have time to apply the lessons to your account (or your client's account). That being said we've seen people complete the course in a week and others that spread the lessons over a couple of months. How long do I have access to the seaborn course? You receive full access to the course so that you can take the lessons at any time. You can rewatch lessons whenever you like and any lesson updates are included. Will I receive a certificate? Yes, once you complete all of the lessons, exercises, quizzes, and course assignment, you'll receive your course certificate. What are you waiting for? There's never been a better time to add a skill like programming to your toolbox and seaborn to get started. So check out the free preview and get enrolled! You've got nothing to lose and everything to gain! If you need to analyze, present or communicate data professionally at some point, this course is a must. I really encourage you to deepen your knowledge on Data Visualization. It's not a difficult topic, and we will start from the basics. You don't need any previous knowledge. I'll teach you everything you need to know along the way and we'll go straight to the point. No rambling. I really hope to see you in class! Who this course is for Data Scientists, Data Analysts. Who wants to start their data visualization journey Who Is curious about how to build effective and impactful graphs Who Is looking to break into the business intelligence, analytics or data visualization field Anyone who want to express thoughts, findings, insights from any kind of data using data Visualization. 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  8. Last updated 11/2022 Duration: 7h 54m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 2.53 GB Genre: eLearning | Language: English Analyze and Visualization data quickly and easily with Python's powerful Numpy, Pandas, Seaborn Library What you'll learn You will be able to program in Python professionally Be able to use Python for data science and machine learning You will be able to use Python for your own work problems or personal projects. Importing and creating data frame in python Learn everything there is to know about pandas - from absolute scratch! Perform a multitude of data operations in Python's popular pandas library including grouping, pivoting, joining and more! Create beautiful visualizations with Seaborn Distribution Plot, Histograms, KDE Plots, Scatter Plot, Rug Plot, Joint Plot, Pair Plot, Bar Plot, Count Plot, Box Plot, Violin Plot, Strip Plot, Swarm Plot Heat Map, Pair Plot, Sub Plot Requirements No introductory skill level of Python programming required Desire to learn! Description ***** Enrol now in the most comprehensive and up-to-date (Nov 2022) course available for the Data Science Prerequisites Concepts! ***** About the Instructor. This course is led by Aditya Dhandi - an international trainer, consultant, and data analyst with over 100 000 enrollments on Udemy. Aditya specializes in teaching data analysis techniques, Excel Pivot Tables, Power Pivot, Microsoft Power BI, & Google Data Studio & his courses average 4+ stars out of 5. He's also the founder of the popular website, Jobshie. Why Learn Data Science Prerequisites? If you've spent time in a spreadsheet software like MS Excel or Google Sheets and want to take your data analysis skills to the next level, this course is for you! Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Pandas is the most powerful and flexible open source data analysis/manipulation tool available in any language. pandas is well suited for many different kinds of data Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet Ordered and unordered (not necessarily fixed-frequency) time series data. Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels Any other form of observational / statistical data sets. The data need not be labeled at all to be placed into a pandas data structure Why Learn Numpy? The central object in Numpy is the Numpy array, on which you can do various operations. The key is that a Numpy array isn't just a regular array you'd see in a language like Java or C++, but instead is like a mathematical object like a vector or a matrix. That means you can do vector and matrix operations like addition, subtraction, and multiplication. The most important aspect of Numpy arrays is that they are optimized for speed. So we're going to do a demo where I prove to you that using a Numpy vectorized operation is faster than using a Python list. Then we'll look at some more complicated matrix operations, like products, inverses, determinants, and solving linear systems. Why Learn Seaborn Library? "A picture is worth a thousand words". We are all familiar with this expression. It especially applies when trying to explain the insight obtained from the analysis of increasingly large datasets. Data visualization plays an essential role in the representation of both small and large-scale data. This course is designed to teach analysts, students interested in data science, statisticians, and data scientists how to analyze real-world data by creating professional-looking charts and using numerical descriptive statistics techniques in Python 3. We'll teach you how to program with Python, how to analyze and create amazing data visualizations with Python! You can use this course as your ready-to-go reference for your own project. Key Features Video Lectures: Learn the best ways to learn Data Science Prerequisites Course Certificate: Complete the course and show off your skills with the course certificate. Learn from experts: Your expert instructor will teach you Data Science Prerequisites skills you can apply immediately. Full Excess: There is no time limit, so take the course at your own pace and retake lessons as you need. Use any Device: Join the course using any modern browser on your phone, tablet and computer. Ask Question: Ask questions and share ideas with other students in the course community. What's included? High quality video lessons to build your knowledge and skills. Guided walk-throughs with techniques and tips. 14 pre-built dashboards you can use and customize. Practical exercises to apply your skills. Quizzes to reinforce learnings and test your knowledge. Private discussion area where you can ask questions. Course certificate for completing the lessons and assignment. Full access lets you review lessons whenever you need. Updates when lessons in the course are refreshed. Jobshie Academy Reviews 300+ learner reviews | 4+ average rating We help learners across the globe develop new skills and achieve their personal and professional goals. Browse learner reviews below to discover how people enjoy the online learning experience at Jobshie Academy. "I have gained useful skills while learning this course. I can consider myself a data analysis specialist. Thank you" by Joseph Israel "I was a nice experience about this course as it introduces with many topics which are related to data analysis ." by Sreeraj Mopkar "very good explaination , bohot achhe se sab samajh aata hai ...100% Understanding all concept very much....excellent explanation" by Moosa Khan "The course is very instructive and didactic, I liked it, it is all the information I need for my activities" by Jaime Ronald Palma Aguilar "yeah it was such a great course, the teacher took time out to disect all angles of Excel, he touched all the essential parts, needed for our daily use of excel. Thanks a lot" by Unyime Joshua "The instructor is very knowledgeable and teach in a very engaging way. I totally recommend this course.????" by Aditya FAQs When does the Data Science Prerequisites course start and finish? The course starts as soon as you join! It is a completely self-paced online course, so you decide when you start and when you finish. How long does it take to complete the course? We recommend taking the course over two to three weeks, so you have time to apply the lessons to your account (or your client's account). That being said we've seen people complete the course in a week and others that spread the lessons over a couple of months. How long do I have access to the Data Science Prerequisites course? You receive full access to the course so that you can take the lessons at any time. You can rewatch lessons whenever you like and any lesson updates are included. Will I receive a certificate? Yes, once you complete all of the lessons, exercises, quizzes, and course assignment, you'll receive your course certificate. What are you waiting for? There's never been a better time to add a skill like programming to your toolbox and Data Science Prerequisites to get started. So check out the free preview and get enrolled! You've got nothing to lose and everything to gain! Who this course is for Data Scientists, Data Analysts. Students and professionals who wants to do data analysis using python. Students and professionals who have tried machine learning and data science but are having trouble putting the ideas down in code Python developer who wants to do analysis of tabular data. Who wants to start their data visualization journey Who Is curious about how to build effective and impactful graphs Who Is looking to break into the business intelligence, analytics or data visualization field Anyone who want to express thoughts, findings, insights from any kind of data using data Visualization. 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  9. Published 11/2022 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 258.58 MB | Duration: 0h 48m JavaScript course for complete beginners that will cover the Data Structures and Algorithms. Great for interviews. JavaScript course for complete beginners that will cover the Data Structures and Algorithms. Great for interviews. What you'll learn Important JavaScript Data Structures. Make your code Clean and Effective. Answers to all of the technical interview questions. Use Data Structures like a Professional. Use algorithms confidently. Requirements You have an attitude to learn and have fun :) You want to learn JavaScript. You want to learn about data structures. You want to learn about algorithms. Description This course teaches data structures such as linked lists, binary search trees and hash tables from the ground up. Data structures allow you to improve the efficiency, performance, speed, and scalability of your code/programs/applications. You will learn what data structures are, why they are important, and how to code them out in JavaScript. You will also learn other important programming concepts along the way such as recursion, time complexity, the "this" keyword and the prototype object, since data structures use these concepts by their very nature. This course uses diagrams and animations to help make understanding the material easier and faster.This course is also very good for anyone who is interviewing for developer/engineering jobs at both large and small companies. Interviewers will very often ask candidates to write data structures out in code, and this course will prepare you very well to do that. Besides that you'll learn about different algorithms that will be helpful to make your code more efficient and robust.This course is more suitable for JavaScript beginners, but if you already have some experience using this programming language but you don't know much about data structures and algorithms, don't be shy to join us and learn about them together with us! :) Overview Section 1: Introduction to Course Lecture 1 What you'll learn in this Course? Lecture 2 What are Data Structures? Lecture 3 Big O Notation Section 2: Basics of Data Structures Lecture 4 this Keyword Lecture 5 Constructor Functions Lecture 6 What is a Node? Section 3: Linked List Lecture 7 What is a Linked List? Lecture 8 Head Lecture 9 Insert Lecture 10 Get Lecture 11 Remove Lecture 12 Big O Notation Section 4: Hash Tables Lecture 13 What is a Hash Table? Lecture 14 Hash Function Lecture 15 Insert Lecture 16 Get Lecture 17 Remove Lecture 18 Big O Notation Section 5: Binary Search Trees Lecture 19 What is a Binary Search Tree? Lecture 20 Recursion Lecture 21 Insert Lecture 22 Search Lecture 23 Inorder Traversal Lecture 24 Preorder Traversal Lecture 25 Postorder Traversal Beginner JavaScript Programmers.,JavaScript Programmers who want to learn something new.,JavaScript Programmers who are preparing for technical interviews. https://www.udemy.com/course/javascript-data-structures-and-algorithms-for-beginners/ Download From 1DL https://1dl.net/rqvi9mja2xly/dqujt.Javascript.Data.Structures.And.Algorithms.For.Beginners.rar.html Download ( Rapidgator ) https://rapidgator.net/file/b498083702b368ddd4b0763d314e5bc5/dqujt.Javascript.Data.Structures.And.Algorithms.For.Beginners.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/e3561Aad1c5dA9c7/dqujt.Javascript.Data.Structures.And.Algorithms.For.Beginners.rar Download ( NitroFlare ) https://nitroflare.com/view/7D89E282DF432FC/dqujt.Javascript.Data.Structures.And.Algorithms.For.Beginners.rar Links are Interchangeable - No Password - Single Extraction
  10. Last updated 11/2022 Duration: 7h 46m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 2.79 GB Genre: eLearning | Language: English aster the powerful Pandas, NumPy and PostgreSQL library to analyze, manipulate and visualize data. What you'll learn You will be able to program in Python professionally Be able to use Python for data science and machine learning You will be able to use Python for your own work problems or personal projects. Importing and creating data frame in python Learn everything there is to know about pandas - from absolute scratch! Perform a multitude of data operations in Python's popular pandas library including grouping, pivoting, joining and more! Master the essentials of NumPy Learn how to explore, transform, aggregate and join NumPy arrays Create your own database or interact with existing databases You will write basic to advance SQL queries Requirements Basic Python Description ***** Enrol now in the most comprehensive and up-to-date (Nov 2022) course available for the Data Analysis with Pandas, NumPy and PostgreSQL Concepts! ***** About the Instructor. This course is led by Aditya Dhandi - an international trainer, consultant, and data analyst with over 100 000 enrollments on Udemy. Aditya specializes in teaching data analysis techniques, Excel Pivot Tables, Power Pivot, Microsoft Power BI, & Google Data Studio & his courses average 4+ stars out of 5. He's also the founder of the popular website, Jobshie. Why learn Pandas? If you've spent time in a spreadsheet software like MS Excel or Google Sheets and want to take your data analysis skills to the next level, this course is for you! Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Pandas is the most powerful and flexible open source data analysis/manipulation tool available in any language. pandas is well suited for many different kinds of data Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet Ordered and unordered (not necessarily fixed-frequency) time series data. Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels Any other form of observational / statistical data sets. The data need not be labeled at all to be placed into a pandas data structure Why Learn Numpy? The central object in Numpy is the Numpy array, on which you can do various operations. The key is that a Numpy array isn't just a regular array you'd see in a language like Java or C++, but instead is like a mathematical object like a vector or a matrix. That means you can do vector and matrix operations like addition, subtraction, and multiplication. The most important aspect of Numpy arrays is that they are optimized for speed. So we're going to do a demo where I prove to you that using a Numpy vectorized operation is faster than using a Python list. Then we'll look at some more complicated matrix operations, like products, inverses, determinants, and solving linear systems. Why Learn Advanced SQL and PostgreSQL? It is almost impossible not to interact with a database daily, Every Mobile Application or Website you use uses a database to commit your request, You just don't know it, Which makes you wonder, What is a database? Why is it important? how can I communicate with it? and most importantly, What's SQL? SQL is a Standard Structured Query Language that you can use to code queries to interact with a database. PostgreSQL is a database management system that uses SQL to interact with the database. SQL is by far the most coding skill in demand in the tech industry and it is one of the fastest ways to improve your career prospects, Whether you're a Mobile developer, A Web Developer, A Data Scientist, A Data Analyst, or Even New to the tech industry, You have to learn Databases and SQL. Finally, This course is concise and to the point, It aims to teach you the basics of Databases and SQL from Beginner to Intermediate bearing in mind how valuable is your time. Key Features Video Lectures: Learn the best ways to learn Data Analysis with Pandas, NumPy and PostgreSQL Course Certificate: Complete the course and show off your skills with the course certificate. Learn from experts: Your expert instructor will teach you Data Analysis with Pandas, NumPy and PostgreSQL skills you can apply immediately. Full Excess: There is no time limit, so take the course at your own pace and retake lessons as you need. Use any Device: Join the course using any modern browser on your phone, tablet and computer. Ask Question: Ask questions and share ideas with other students in the course community. What's included? High quality video lessons to build your knowledge and skills. Guided walk-throughs with techniques and tips. 14 pre-built dashboards you can use and customize. Practical exercises to apply your skills. Quizzes to reinforce learnings and test your knowledge. Private discussion area where you can ask questions. Course certificate for completing the lessons and assignment. Full access lets you review lessons whenever you need. Updates when lessons in the course are refreshed. Jobshie Academy Reviews 300+ learner reviews | 4+ average rating We help learners across the globe develop new skills and achieve their personal and professional goals. Browse learner reviews below to discover how people enjoy the online learning experience at Jobshie Academy. "I have gained useful skills while learning this course. I can consider myself a data analysis specialist. Thank you" by Joseph Israel "I was a nice experience about this course as it introduces with many topics which are related to data analysis ." by Sreeraj Mopkar "very good explaination , bohot achhe se sab samajh aata hai ...100% Understanding all concept very much....excellent explanation" by Moosa Khan "The course is very instructive and didactic, I liked it, it is all the information I need for my activities" by Jaime Ronald Palma Aguilar "yeah it was such a great course, the teacher took time out to disect all angles of Excel, he touched all the essential parts, needed for our daily use of excel. Thanks a lot" by Unyime Joshua "The instructor is very knowledgeable and teach in a very engaging way. I totally recommend this course.????" by Aditya FAQs When does the Data Analysis with Pandas, NumPy and PostgreSQL course start and finish? The course starts as soon as you join! It is a completely self-paced online course, so you decide when you start and when you finish. How long does it take to complete the course? We recommend taking the course over two to three weeks, so you have time to apply the lessons to your account (or your client's account). That being said we've seen people complete the course in a week and others that spread the lessons over a couple of months. How long do I have access to the Data Analysis with Pandas, NumPy and PostgreSQL course? You receive full access to the course so that you can take the lessons at any time. You can rewatch lessons whenever you like and any lesson updates are included. Will I receive a certificate? Yes, once you complete all of the lessons, exercises, quizzes, and course assignment, you'll receive your course certificate. What are you waiting for? There's never been a better time to add a skill like programming to your toolbox and Data Analysis with Pandas, NumPy and PostgreSQL to get started. So check out the free preview and get enrolled! You've got nothing to lose and everything to gain! Who this course is for Students and professionals with little Numpy experience who plan to learn deep learning and machine learning later Students and professionals who have tried machine learning and data science but are having trouble putting the ideas down in code Students and professionals who wants to do data analysis using python. Python developer who wants to do analysis of tabular data. Anyone interested in learning more about SQL, PostgreSQL, or basic data analysis! 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  11. Last updated 8/2022 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 590.89 MB | Duration: 2h 6m Statistics introduction applied to data science. Focus on Exploratory Data Analysis (EDA). What you'll learn Descriptive Statistics. Pivot Table. HeatMap. Histograms. Box-Plot. Regression and Correlation. Anova. Chi-Square. Introduction to Time Series. And much more. Requirements Basic training in mathematics and use of a web browser. Knowledge of the Python language is desirable but not essential. Description Do you need help with statistics?. In this course we will learn the basic statistical techniques to perform an Exploratory Data Analysis in a professional way. Data analysis is a broad and multidisciplinary concept. With this course, you will learn to take your first steps in the world of data analysis. It combines both theory and practice.The course begins by explaining basic concepts about data and its properties. Univariate measures as measures of central tendency and dispersion. And it ends with more advanced applications like regression, correlation, analysis of variance, and other important statistical techniques.You can review the first lessons that I have published totally free for you and you can evaluate the content of the course in detail.We use Python Jupyter Notebooks as a technology tool of support. Knowledge of the Python language is desirable, but not essential, since during the course the necessary knowledge to carry out the labs and exercises will be provided.If you need improve your statistics ability, this course is for you.if you are interested in learning or improving your skills in data analysis, this course is for you.If you are a student interested in learning data analysis, this course is for you too.This course, have six modules, and six laboratories for practices.Module one. We will look at the most basic topics of the course.Module two. We will see some data types that we will use in python language.Module three. We will see some of the main properties of quantitative data.Module four. We will see what data preprocessing is, using the python language.Module five. We will begin with basics, of exploratory data analysis.Module six. We will see more advanced topics, of exploratory data analysis. Overview Section 1: Summary Lecture 1 Summary Section 2: Module 1 Lecture 2 Basic Concepts Lecture 3 Anaconda Individual Edition Installation Lecture 4 Lecture 4: Install Anaconda Suite on Windows 10 Section 3: Module 2 Lecture 5 Python data types - Part 1 Lecture 6 Python data types - Lab 1 Lecture 7 Python data types - Part 2 Lecture 8 Python data types - Lab 2 Lecture 9 Python data types - Part 3 Lecture 10 Python data types - Lab 3 Section 4: Module 3 Lecture 11 Quantitative Data Properties Lecture 12 Quantitative Data Properties - Lab 4 Section 5: Module 4 Lecture 13 Pre-Processing Data in Python Section 6: Module 5 Lecture 14 Exploratory Data Analysis. Part one Section 7: Module 6 Lecture 15 Exploratory Data Analysis. Part Two Section 8: Final Test Section 9: Bonus One - Chi Square Lecture 16 Chi-Square Test Section 10: Bonus Two - Time Series Lecture 17 What are Time Series? Lecture 18 Time Series - Date and Time Lecture 19 Transformation, Indexing and Resampling Lecture 20 Time Series - Basic Calculations Lecture 21 Time Series - Decomposition Students and professionals who wish to acquire or improve their skills in data analysis through statistical techniques.,Python developers who want to improve their skills using statistical techniques.,Data analysts.,Beginning python developers interested in data science. Homepage https://www.udemy.com/course/statistics-introduction-applied-to-data-science/ Download From 1DL https://1dl.net/sepj4ndc2acn/dpdni.Data.Analysis.With.Python.2022.rar.html Download ( Rapidgator ) https://rapidgator.net/file/40214fe924a842858de5e368ca57110e/dpdni.Data.Analysis.With.Python.2022.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/a95315753f54Ab1c/dpdni.Data.Analysis.With.Python.2022.rar Download ( NitroFlare ) https://nitroflare.com/view/BA3D8C2B35BFB79/dpdni.Data.Analysis.With.Python.2022.rar Links are Interchangeable - No Password - Single Extraction
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  13. Last updated 5/2021 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 2.43 GB | Duration: 4h 53m Master Statistics for Data Science, Probability and Statistics, and excel in careers of Data Science & Business Analysis What you'll learn Learn the fundamentals of Statistics Learn how to organize and display different types of Data Interpret and Draw Histograms, Pie Charts, Stem-and-Leaf Displays, Box Plots and much more! Understand Average, Mode, Median, Standard Deviation and Variance Understand Z-Scores and their usage Take informed business decisions based on insights from Data. Build a strong foundation for a career in R, Python and Data Science Requirements Basic knowledge of High School Math is needed Bring your passion to learn and practice the Statistics concepts We start from scratch and build the concepts gradually. All topics are covered in detail. Description Are you seeking a career in Business Analytics, Business Analysis, Data Analysis, Machine Learning, or you want to learn Probability and Statistics for Data Science? Then you really need a solid background in Statistics! And .....This is the perfect course for you!Learning Statistics can be challenging, if you are not in a university setting. But with this course, you take a university-level Statistics course at your convenience, that will equip you with ALL the FUNDAMENTAL CONCEPTS critically important in Probability and Statistics, Statistics for Data Science and Business Analysis.One of the most comprehensive Business Analytics course online!This course has both breadth of Statistics topics, and depth of content. By the end of this course, you would have mastered all the important fundamentals of Statistics.A hands-on course!This is NOT just another boring and theoretical course. It is a very practical course:· With lots of insightful practice problems and exercises· Full of real-world examples and case studiesWhy learn from me?I've designed this Statistics course for beginners in Probability and Statistics, Data Science, Data Analysis, Business Analysis and Business Analytics!I will provide premium support - so if you ever get stuck or have a question, just post it to the course dashboard and I'll be there to help you out!My goal is to make this the best Probability and Statistics, and Statistics for Data Science course online, and I'll do anything possible to help you learn.My Happiness GuaranteeIf you aren't happy with your purchase, I have a 30-day 100% money back guarantee.So what are you waiting for?Enroll now to this Statistics for Probability, Data Science and Data Analysis course and open doors to new careers! Feel free to check out the entire course outline or watch the free preview lessons.I'll see you inside the course!Cheers,Kashif Who this course is for Anybody who wants to master the fundamentals of Statistics from scratch,Anybody seeking a career in Data Science, Business Analytics and Business Intelligence,Anybody wanting to learn the applications of Statistics in Business using real-world examples,Business Analysts, Managers and Executives Homepage https://www.udemy.com/course/statistics-for-business-analytics-data-science/ Download From 1DL https://1dl.net/hdgxfqez42qi/fuwbe.Statistics.For.Data.Science.Data.And.Business.Analysis.2022.part1.rar.html https://1dl.net/236h79lxabqv/fuwbe.Statistics.For.Data.Science.Data.And.Business.Analysis.2022.part2.rar.html https://1dl.net/ilgq4keku7zi/fuwbe.Statistics.For.Data.Science.Data.And.Business.Analysis.2022.part3.rar.html Download ( Rapidgator ) https://rapidgator.net/file/22a9827ea3f997b67ed28633d396358b/fuwbe.Statistics.For.Data.Science.Data.And.Business.Analysis.2022.part1.rar.html https://rapidgator.net/file/32c58672729e1b4ec0dde7f19441862d/fuwbe.Statistics.For.Data.Science.Data.And.Business.Analysis.2022.part2.rar.html https://rapidgator.net/file/c2c7f64a1bc97a19d3f816f299e32b96/fuwbe.Statistics.For.Data.Science.Data.And.Business.Analysis.2022.part3.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/F688867722a97416/fuwbe.Statistics.For.Data.Science.Data.And.Business.Analysis.2022.part1.rar https://uploadgig.com/file/download/Dad890FBc8c4C50D/fuwbe.Statistics.For.Data.Science.Data.And.Business.Analysis.2022.part2.rar https://uploadgig.com/file/download/CAe7aeaB5b25B795/fuwbe.Statistics.For.Data.Science.Data.And.Business.Analysis.2022.part3.rar Download ( NitroFlare ) https://nitroflare.com/view/8902407ED77EF7B/fuwbe.Statistics.For.Data.Science.Data.And.Business.Analysis.2022.part1.rar https://nitroflare.com/view/EEA6B7252E26D5F/fuwbe.Statistics.For.Data.Science.Data.And.Business.Analysis.2022.part2.rar https://nitroflare.com/view/9A670F5C83E1C11/fuwbe.Statistics.For.Data.Science.Data.And.Business.Analysis.2022.part3.rar Links are Interchangeable - No Password - Single Extraction
  14. Published 11/2022 Created by Salman Alhiary MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 11 Lectures ( 4h 34m ) | Size: 2.5 GB A Professional Development and Foundational Learning Course for VXLAN EVPN Technology in Cisco Nexus Switches. What you'll learn Understand why VXLAN existsa and what problems does it solve? Describe how does VXLAN EVPN solve the problems? Explain how the VXLAN EVPN components interact. Recognize the VXLAN EVPN encapsulation and packet format. Understand the packet forwarding in VXLAN EVPN. Learn and practice how to implement VXLAN EVPN in Nexus Switches. Learn how to troubleshoot VXLAN EVPN in Nexus Switches. Explain how VXLAN EVPN works with VPC and practice how to implement it. Learn and practice how to implement VXLAN EVPN in a multi-site environment. In summary, you will learn and practice ALL VXLAN knowledge you need for the CCIE Data Center LAB Exam. Requirements Basic routing and switching knowledge are required to get benefits from this course. Understanding Multicast is required for a better understanding of BUM traffic forwarding in VXLAN. Understanding the VPC feature is required for a better understanding of the "VXLAN EVPN with Dual-Homed Endpoints" section. Description This course aims to provide a resource for network engineers to understand and deploy VXLAN EVPN technology for today's data centers. And to help CCIE/CCNP Data Center certification candidates prepare well for their exams.It begins with an introduction to the current data center challenges before going into the technology building blocks. It also provides an overview of the evolution of the data center fabric. The course takes a deep dive into the various fabric semantics, including the underlay, control plane & data plane interaction, unicast and multicast forwarding flows, and external data center interconnect deployments.It has been designed with a broad audience in mind while specifically targeting Cisco certification candidates, network architects, engineers, and operators. You do not have to be a networking professional or data center administrator to benefit from this course.This learning material is an authoritative guide for network professionals with an in-depth understanding of various networking areas, explaining detailed control and data plane concepts, with VXLAN and BGP EVPN being the primary focus. Detailed packet flows are presented, covering numerous functions, features, and deployments.This professional development and foundational learning course contain the following:High-quality, technical on-demand videos cover all essential topics.Lab demos for better implementation understanding. Quizzes for knowledge review.Workbook assignments for practical exercises.External resources that allow you to extend your knowledge.Packet captures that enable a deep understanding of VXLAN encapsulation.Configuration examples which can be used later as VXLAN EVPN configuration references. Who this course is for Network engineer professionals and data center specialists. Candidates preparing for their CCIE Data Center v3.0 LAB or Core exams. This course can be for candidates with basic networking knowledge who desire more expertise in VXLAN EVPN technology. Homepage https://www.udemy.com/course/ccie-data-center-vxlan-evpn/ Download From 1DL https://1dl.net/5vltn8d5nr9p/oxifw.CCIE.Data.Center.v3.0..VXLAN.EVPN.part1.rar.html https://1dl.net/51n4m5fotc5g/oxifw.CCIE.Data.Center.v3.0..VXLAN.EVPN.part2.rar.html https://1dl.net/00h6y0g8f82j/oxifw.CCIE.Data.Center.v3.0..VXLAN.EVPN.part3.rar.html Download ( Rapidgator ) https://rapidgator.net/file/6c54dfbae5cd6f23bd444698799f3ff8/oxifw.CCIE.Data.Center.v3.0..VXLAN.EVPN.part1.rar.html https://rapidgator.net/file/b898c65fba5b2df5b6f0ac3ac2d3e260/oxifw.CCIE.Data.Center.v3.0..VXLAN.EVPN.part2.rar.html https://rapidgator.net/file/a43fb7cc9eaab5905a3ed42e4a84dcf3/oxifw.CCIE.Data.Center.v3.0..VXLAN.EVPN.part3.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/3c3398aB2C2da22b/oxifw.CCIE.Data.Center.v3.0..VXLAN.EVPN.part1.rar https://uploadgig.com/file/download/0d84825ee43c1C55/oxifw.CCIE.Data.Center.v3.0..VXLAN.EVPN.part2.rar https://uploadgig.com/file/download/7c36f929c65b2327/oxifw.CCIE.Data.Center.v3.0..VXLAN.EVPN.part3.rar Download ( NitroFlare ) https://nitroflare.com/view/ECD78A93FFDA897/oxifw.CCIE.Data.Center.v3.0..VXLAN.EVPN.part1.rar https://nitroflare.com/view/628FB52CCBDB1D0/oxifw.CCIE.Data.Center.v3.0..VXLAN.EVPN.part2.rar https://nitroflare.com/view/224DA84567BC5B6/oxifw.CCIE.Data.Center.v3.0..VXLAN.EVPN.part3.rar Links are Interchangeable - No Password - Single Extraction
  15. Instructors: Ed Spot 7 sections • 27 lectures • 1h 57m total length Video: MP4 1280x720 44 KHz | English + Sub Updated 7/2022 | Size: 880 MB Practical way to learn Data Science and Machine Learning with STATA . Examples and real data are provided What you'll learn Data Science Machine Learning Programming Language STATA Credit Risk Modelling Requirements No programming knowledge is needed. We will learn everything from zero Description Hello and welcome to the Machine Learning with STATA course. Machine Learning is influencing our daily lives and is one of the most significant aspects of technological advancements. The goal of this course is to provide you with the most up-to-date Machine Learning methodologies using STATA . It will teach you how to think about data science and machine learning in a new way. This is an excellent approach to begin a career in Machine Learning because you will learn some fundamental principles and receive practical experience. I'm thrilled to share what I know about Machine Learning using STATA with you. I assure you that it will be well worth your time and effort, and that you will gain a vital skill. Based on our research this is the only course that uses STATA to apply Machine Learning Models in Credit Risk Scenario. Because we know that many of you are already familiar with STATA or want to be familiar, we chose it as our platform. From the beginning to the finish of the course, we will start from scratch and work together to build new abilities. In this course, we will work together to create a complete data science project utilizing Credit Risk Data from start to finish. For this course, we have information on around 40,000 consumers, including their level of education, age, marital status, where they live, if they own a home, and other pertinent information. We'll get our hands filthy with these numbers and dig deep into them, and you'll be able to practice on your own. Additionally, you will have access to essential resources like as lectures, homework, quizzes, slides, and a literature analysis on modeling methodologies. Let's see what the course structure looks like right now! Who this course is for This course is designed for people that want to learn Data Science and Machine Learning. The course is created using the statistical software STATA Homepage https://www.udemy.com/course/machine-learning-and-data-science-in-stata/ Download From 1DL https://1dl.net/27gs8yldfw4f/lihqr.Machine.Learning.and.Data.Science.in.STATA.rar.html Download ( Rapidgator ) https://rapidgator.net/file/f7c46d67cae6b6141952840ac66e3228/lihqr.Machine.Learning.and.Data.Science.in.STATA.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/8336293d3c0d3BBb/lihqr.Machine.Learning.and.Data.Science.in.STATA.rar Download ( NitroFlare ) https://nitroflare.com/view/4DE2FB88DB5ED1A/lihqr.Machine.Learning.and.Data.Science.in.STATA.rar Links are Interchangeable - No Password - Single Extraction
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  17. Published 11/2022 Created by Woody Lewenstein,Starweaver Team,Paul Siegel MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 33 Lectures ( 5h 19m ) | Size: 4.65 GB Learn the concepts and techniques required to use statistics effectively to make better decisions. What you'll learn Data Analysis - Learn the most important tools for making sense of data. Data Visualization - Learn how to use Excel to create effective visuals. Interpretation - Learn to understand and comment on data visualizations. Cleaning Data - Get your data ready for analysis Sampling - Learn different methods, and when they work. Probability - Learn all the most important techniques in basic probability theory. Requirements Nothing beyond basic arithmetic and a desire to learn. Description This is a course for people who want to use statistics to make better decisions. In the course I cover all the foundational concepts needed to begin your journey in data science, business analytics, or any area in which you need to use data to understand the world. No prior knowledge of the subject is assumed, and you'll need nothing more than basic arithmetic and a desire to learn to get everything from this course.The course is hands on and practical, with a focus on showing you how to use the skills taught. Wherever possible, I show you how to implement the techniques in Microsoft Excel.Key concepts taught in the course include:· Descriptive Statistics - the nuts and bolts needed when analyzing and working with data, from averages to measures of spread and correlation, to more advanced measures such as skew.· Cleaning Data - the world sometimes gives us messy data, and I'll show you how to clean it up and make sense of it.· Visualizing Data - I'll show you all the standard ways to usefully visualize your data, and in each case show you how to use Excel to create beautiful visualizations.· Probability - I'll teach you all the fundamentals of probability, up to and including conditional probability and an intro to Bayesian statistics.As always, if you ever have questions about the content I'm waiting in the Q&A to help you out, and try to answer all my students questions.I really hope you enjoy this course!Woody Who this course is for People who want to use statistics to make better decisions. Homepage https://www.udemy.com/course/practical-statistics-for-business-and-data-science/ Download From 1DL https://1dl.net/0jfq5w630qwq/usmxa.Practical.Statistics.for.Business.and.Data.Science.part5.rar.html https://1dl.net/fg03nfwksac2/usmxa.Practical.Statistics.for.Business.and.Data.Science.part3.rar.html https://1dl.net/l7powj6z6oi8/usmxa.Practical.Statistics.for.Business.and.Data.Science.part1.rar.html https://1dl.net/mmrorwj3gbxn/usmxa.Practical.Statistics.for.Business.and.Data.Science.part2.rar.html https://1dl.net/q5ucq4eldbb4/usmxa.Practical.Statistics.for.Business.and.Data.Science.part4.rar.html Download ( Rapidgator ) https://rapidgator.net/file/0b7edf35bd2c07ccf1d3d1f4a5f002e8/usmxa.Practical.Statistics.for.Business.and.Data.Science.part2.rar.html https://rapidgator.net/file/6c9369d2f3aac2adf1517e4581eeca3e/usmxa.Practical.Statistics.for.Business.and.Data.Science.part3.rar.html https://rapidgator.net/file/8585145aae9bf7f88c0f38294d4e73fd/usmxa.Practical.Statistics.for.Business.and.Data.Science.part5.rar.html https://rapidgator.net/file/86e2b3494b0b756bf6524e58a84f2b0d/usmxa.Practical.Statistics.for.Business.and.Data.Science.part4.rar.html https://rapidgator.net/file/fbe362a3a6df2417bfe10f26ec105bbe/usmxa.Practical.Statistics.for.Business.and.Data.Science.part1.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/3029d0A9e21bf4c1/usmxa.Practical.Statistics.for.Business.and.Data.Science.part4.rar https://uploadgig.com/file/download/799b30cb6Ec3C3c5/usmxa.Practical.Statistics.for.Business.and.Data.Science.part5.rar https://uploadgig.com/file/download/a3ed790dd99D55b0/usmxa.Practical.Statistics.for.Business.and.Data.Science.part2.rar https://uploadgig.com/file/download/aa858f7a9FaDB8f9/usmxa.Practical.Statistics.for.Business.and.Data.Science.part3.rar https://uploadgig.com/file/download/af8D87cdAaF6ff36/usmxa.Practical.Statistics.for.Business.and.Data.Science.part1.rar Download ( NitroFlare ) https://nitroflare.com/view/6148B0E40E0BF4D/usmxa.Practical.Statistics.for.Business.and.Data.Science.part4.rar https://nitroflare.com/view/80E405EFC254554/usmxa.Practical.Statistics.for.Business.and.Data.Science.part3.rar https://nitroflare.com/view/D42C855E768C41B/usmxa.Practical.Statistics.for.Business.and.Data.Science.part2.rar https://nitroflare.com/view/E3D1C76F302E2C6/usmxa.Practical.Statistics.for.Business.and.Data.Science.part1.rar https://nitroflare.com/view/F2B73C7519A24E6/usmxa.Practical.Statistics.for.Business.and.Data.Science.part5.rar Links are Interchangeable - No Password - Single Extraction
  18. Published 11/2022 Created by ajay parmar MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 65 Lectures ( 11h 14m ) | Size: 4.7 GB Python programming For Beginners What you'll learn In this Part1, You will learn everything which a beginner needs to know before start working with real time data You will be able to get the idea how to program every type of project professionally in Python and what do we need to use and when to use it. You will understand Varaibles in Python, IF statements, different types of operators, Loops How to use Lists, Tuples, Dictionaries , error handlers, Constructors, Functions,Strings ,Random modules You will get many projects to create and they are discussed in lectures too so you can tally your codes with solutions and become the best Requirements No prior programming knowledge is needed because this course has started from zero level. You need to download Pycharm IDLE to do programming and this is free of cost. Though ,course codes can be used in your other favorite IDLEs too,if you wish to like Jupyter notebook or spyder. Entire course is at right pace , projects are discussed to gain confidence. Description In this Part1, I will teach you Python on my favorite software Pycharm however you can use any IDLE ,If you wish to. It does not matter at all.What are variables and data type used in PythonDeclare variables and function to know the data typeHow to use constructors and why do we need to learn them. Know in and not in operators, logical operators, comparison and arithmetic operatorsHow to pick up the error descriptions and solve the errors using themHow to use for loops and do loops and use them with IF statementsHow to use Nested IFs and how to take care of indentations - Very important conceptWhat are functions in Python and how to create user defined functionsRules to know before creating a FunctionScope of a variable - Local and Global conceptsDeep dive into Lists and use of lists in Loops with IF StatementsIn detail learning about Tuples and use with Loops and every crucial method we should know.Strings data types and their accessing approach plus methods to handle dataWhat are error handlers and how to use them.Introduction to a random module and where you can use it.Create a Guess a number game using Loops and listsCreate a project to identify odd and even numbersCreate a project to guess and number but this time user should be given 3 attemptsFind out which game has won the highest medals using two lists.Print function with every small detail . 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  19. Published 11/2022 Created by Trulabz Technologies MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 54 Lectures ( 5h 21m ) | Size: 4.1 GB A Complete Guide to Microsoft Excel Pivot Tables & Charts in just 5 Hours. Most Powerful, Compact& Useful course. What you'll learn Learn data analysis skills from ZERO to HERO using Excel Pivot Tables and charts. How to streamline your data analysis workflow in Excel How to create amazing looking dashboards using Pivot Tables Advanced Sorting and Filtering in PivotTables Quick Pivot Table Tricks Data based CASE STUDIES using Pivot Table Requirements Basic Excel knowledge Microsoft Excel 2010-2019 or Office 365 (ideally for PC/Windows) Description In this Microsoft Excel - Data Analysis using PivotTables & Charts Course, we teach you how to make the most of this raw data and convert it back to powerful reports and dynamic excel dashboards.PivotTables are ideal if you are looking to perform data analysis tasks quickly and efficiently in Excel.Pivot Tables allow you to quickly explore and analyze your raw data in Excel depicting powerful information help you and your company to scale your business taking wise business decisions.With the use of pivot tables in Excel (one of my personal favourites) you can skilfully summarize data from a worksheet into an illustrative chart.This Business Data Analysis using Pivot Table Excel course will provide you with an in-depth understanding of:Excel functions and pivot tables, pivot charting, and pivot reporting, to group, slice, calculate and summarize large syndicated datasets in order to answer specific questionsSorting, filtering and grouping tools in Excel.Pivot Table Grouping (text, number, dates).Charting, graphing, and illustrating data trends in Excel, amongst others!We will also take you to the Excel Pivot Table Tricks and Latest Excel Pivot Hacks used now a days.Pivot Table Options - A way to make your pivot table more powerful.Conditional Formatting in Pivot Table - a good way to visualize strong datasets.GETPIVOTTABLE Formula adds flexibility to your pivots.Exploring power of "Report Filter Pages".We'll start by covering everything you need to know to get up and running with Excel Pivot Tables, including:Raw data structure in ExcelCreating your very First Pivot Table.Pivot Table layouts & stylesDesign & formatting optionsSorting, filtering, & Grouping toolsCalculated fields, items & valuesExploring power of "Report Filter Pages"Pivot Charts, Slicers & TimelinesInteractive Dynamic Excel dashboards**********************************************<<<<Real Time Excel Data which is used in the industry.>>>Practice Files, Assignments and Case- StudiesE-Book guide to assist while watching the videos Who this course is for ANYONE & EVERYONE who works with Microsoft Excel on a regular basis (even if you've never used a Pivot Table!) 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  20. Last updated 7/2020 Created by Marious Kuriata MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 32 Lectures ( 1h 49m ) | Size: 464.6 MB A vendor-neutral introduction to DLP with hands-on labs and examples. Network security in action! What you'll learn Designs of Data Loss Prevention systems Basic and advanced features of DLP Create a basic regular expression (Regex) Implement your own DLP system using a VM Create and implement content-aware policies Learn how to protect against data leaks Integrate DLP with Microsoft Active Directory Requirements Basic understanding of IT security concepts Understanding of general networking terms Basic understanding of virtualization and servers Description Did you know that almost 50% of employees transfer business files to a private laptop? Is there anything we can do about it? Yes - implement a Data Loss Prevention (DLP) system to address potential data loss or theft on Windows, (Mac OSX), Linux, and mobile devices. Protect your sensitive files, clients' information, credit card numbers, and more.With a DLP system you decide when, from where, and by whom data can or cannot be accessed and copied! About this course: This is a vendor-neutral introductory course providing a high-level overview of DLP systems, architecture, components, and features. Hands-on labs included!What I will learn: How to start with a DLP system, create a basic policy, understand DLP components, and even create your first regular expression (regex).Special topics: Even though this is a vendor-neutral course, I show you a lot of real world examples and DLP systems that you can try at home - for free!Join this course to learn:Data Loss Prevention (DLP) architectureEndpoint vs Network protectionData at rest, Data in motion, Data in useDeploy your own DLP system in minutes using a virtual machineSee a lot of examples and real world applications (e.g. blocking uploads, sharing programming codes)Write your first regular expressionLearn about more advanced DLP features (SSL interception, OCR, integration with Active Directory)See a lot of real world examples and practical tipsYou will know enough to understand how DLP protects your networkJoin now and see what a DLP system can do for you and your company! Protect USB drives, clipboard, network drives, email clients, Skype, and more. Make sure nobody can share or copy your internal files and data ever again.This is a vendor-neutral course that will make you ready to start with any DLP system, including enterprise solutions from Symantec or McAfee. Hands-on labs are based on two DLP systems: DeviceLock and EndpointProtector. A real-world course.Join now and learn all the basic about DLP! Who this course is for Anyone looking for a high-level overview of DLP systems Engineers and managers interesting in implementing DLP Homepage https://www.udemy.com/course/data-loss-prevention-dlp-security-breaches-under-control/ Download From 1DL https://1dl.net/h90b9oulxbwf/wmorw.Data.Loss.Prevention.DLP..Security.breaches.under.control.rar.html Download ( Rapidgator ) https://rapidgator.net/file/9850486019551488f531ddff2f9dfe32/wmorw.Data.Loss.Prevention.DLP..Security.breaches.under.control.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/Efe2Ab9357eAfae9/wmorw.Data.Loss.Prevention.DLP..Security.breaches.under.control.rar Download ( NitroFlare ) https://nitroflare.com/view/614F49D912B8D71/wmorw.Data.Loss.Prevention.DLP..Security.breaches.under.control.rar Links are Interchangeable - No Password - Single Extraction
  21. Elliot Forbes | Duration: 1 h | Video: H264 3840x2160 | Audio: AAC 48 kHz 2ch | 481 MB | Language: English Knowing what data structures to use in certain scenarios is invaluable and can save heaps (.sorry) of time and effort. In this course, we'll be covering everything from arrays and slices in go, all the way through to stacks and queues, before tackling some of the more complex data structures such as linked-lists, binary trees, and graphs. Once we have laid this foundational knowledge, we'll be expanding upon it to then look at some of the most common sorting and searching algorithms and how you can effectively implement these in Go! What You'll Learn • Arrays and Slices • Sorting in Go • Queues • Linked-Lists • Stacks • Graphs • Binary Trees • Priority Queues By the end of this course you should have a solid understanding of some of the fundamental data structures and you should be able to smash those technical interviews. https://tutorialedge.net/courses/go-data-structures-course/ Download From 1DL https://1dl.net/uz0s2yibw8b0/hoxmf.Elliot.Forbes..Go.Data.Structures.Course.rar.html Download ( Rapidgator ) https://rapidgator.net/file/ca8e0a4a35c32e3aed8129147b0043ee/hoxmf.Elliot.Forbes..Go.Data.Structures.Course.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/990E87ee38003b26/hoxmf.Elliot.Forbes..Go.Data.Structures.Course.rar Download ( NitroFlare ) https://nitroflare.com/view/386B2D8F3356E80/hoxmf.Elliot.Forbes..Go.Data.Structures.Course.rar Links are Interchangeable - No Password - Single Extraction
  22. FoneLab Data Retriever 1.3.8 | macOS | 25 mb FoneLab Data Retriever is a safe and convenient tool to recover deleted or lost data (like photos, documents, emails, videos, etc.) on windows/mac, hard drive, flash drive, memory card, digital camera and more. FoneLab Data Retriever is a safe and convenient tool to recover deleted or lost data (like photos, documents, emails, videos, etc.) on windows/mac, hard drive, flash drive, memory card, digital camera and more. There are different methods you can try to recover lost data on computer, hard drive and other devices due to deletion, formatted partition, RAW hard drive or other crashed problems, FoneLab data retriever will be the best choice for you because of its convenience and safety. It will never store and modify your data. FoneLab Data Retriever is a safe and convenient tool to recover deleted or lost data (like photos, documents, emails, videos, etc.) on windows/mac, hard drive, flash drive, memory card, digital camera and more. Easy & Convenient to Use - It won't store or modify your data - Suitable for different situations - Offers two scan modes, Quick Scan & Deep Scan Compatibility: OS X 10.9 or later (Apple Silicon compatible) Homepage:https://www.fonelab.com Download From 1DL https://1dl.net/9tj0p7hpmaea/w6120.FoneLab.Data.Retriever.1.3.8.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/bD22eed5553882f8/w6120.FoneLab.Data.Retriever.1.3.8.rar Download ( Rapidgator ) https://rapidgator.net/file/f83fa5e215d43e7aaeb72ae3c69bf1b8/w6120.FoneLab.Data.Retriever.1.3.8.rar.html Download ( NitroFlare ) https://nitroflare.com/view/3B2622941987E8E/w6120.FoneLab.Data.Retriever.1.3.8.rar Links are Interchangeable - No Password - Single Extraction
  23. Last updated 3/2020 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 4.35 GB | Duration: 10h 21m The practical guide to master real world Data science building projects What you'll learn Learn the fundamentals of R programming Learn the core concepts of Data science Learn data concepts building real world projects Requirements Basic knolwedge of R programming will be helpful for completion of the course Description Data Science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems for gaining insights by analyzing the structured or unstructured data. Basically, it helps in finding hidden patterns from the raw data by using technologies like R, Hadoop, Machine Learning and others.With its use from the healthcare to retail, it has one of the greatest potentials to change numerous sectors to its entirety. Similar to the rise of data in recent years, the demands of data scientists have also exploded with average salaries being offered up to $110,000 depending upon the locality.Why you should learn Data Science?Desired in different fields like business, healthcare, finance and othersIn order to perform complicated data analysisTo find the hidden patterns by data manipulationFor making precise predictionsWhy you should take this course?The regular need for storing, modifying and analyzing data have made data science one of the most important field. From big to small companies, all are in a constant search for the data scientists or the individuals who understand and can work with a huge pool of data. Knowing all these facts, we have designed this comprehensive online tutorial which will help you in building different real-world projects. This tutorial with over 5 hours of videos will be sufficient enough to make you explain different aspects of data science in the most simplest, easiest and practical way.Projects covered in the course :Data Transformations on Iris DatasetProject on Wide and Long DataPerforming Joins on DatasetsProject on Facets, Geoms and TansformationsTake this course for building different real-world projects in Data Science which has great potential in the world ruled by data. Overview Section 1: Introduction to Data Science Using R Lecture 1 Introduction Lecture 2 Intro to R studio Lecture 3 The Assignment Operator Lecture 4 Basic Data Types in R Lecture 5 Vectors Lecture 6 Matrices and Data Frames Lecture 7 Subsetting Syntax Lecture 8 Project 1 : Introduction to R - Problem Statement Lecture 9 Project 1 Solution Section 2: Data Transformation Lecture 10 Data Transformations on Rows Lecture 11 Data Transformations on Columns Lecture 12 Data Transformations on Iris Dataset - Project Problem Statement Lecture 13 Data Transformations on Iris Dataset - Project Solution Lecture 14 Wide and Long Data Lecture 15 Grouped Transposes Lecture 16 Project 2 : Wide and Long Data - Problem statement Lecture 17 Project 2 Solution Lecture 18 What are Joins Lecture 19 Programming Joins Part 1 Lecture 20 Programming Joins Part 2 Lecture 21 Project 3 :Performing Joins - Problem Statement Lecture 22 Project 3 Solution Section 3: Data Visualization Lecture 23 GGPLOT Basics Lecture 24 Aesthetic Mappings in GGPLOT Lecture 25 Facets in GGPLOT Lecture 26 Geoms in GGPLOT Lecture 27 Statistical Transformations in GGPLOT Lecture 28 Project 4 : GGPLOT - Problem Statement Lecture 29 Project 4 Solution Lecture 30 Project 5: Facets, Geoms and Tansformations Lecture 31 Project 5 Solution Section 4: Exploratory Data Analysis Lecture 32 How to Identify Missing Values Lecture 33 How to Identify Outliers Lecture 34 What to do with Missing Values and Outliers Lecture 35 Functional Transformations Section 5: Regression Models Lecture 36 Intro to Regression Problem and Data Set Lecture 37 Exploratory Data Analysis Lecture 38 Correlations and Final Data Set Lecture 39 What is Multiple Regression Lecture 40 Building a Multiple Regression Model Lecture 41 Measuring Regression Model Accuracy Section 6: KNN Model Lecture 42 What is KNN Lecture 43 Building a KNN Model Lecture 44 Assessing KNN Model Performance Lecture 45 Assessing Training and Test Error for KNN Lecture 46 What is a Decision Tree Lecture 47 Creating a Decision Tree Lecture 48 Assessing Performance of a Decision Tree Lecture 49 Model Comparison Lecture 50 Project: Build a model that is better than our multiple regression and KNN model Section 7: Classification Dataset Lecture 51 Intro to Classification Dataset and Problem Lecture 52 EDA Part 1 Lecture 53 EDA Part 2 Lecture 54 What is Logistic Regression Lecture 55 Building a Logistic Regression Model Lecture 56 Building a Classification Tree Lecture 57 Building a Random Forest Lecture 58 Project: Build a model better than logistic regression, decision and RF model Anyone who wants to learn R programming and fundamentals of Data Science will find this course very useful Homepage https://www.udemy.com/course/projects-in-data-science-using-r/ Download From 1DL https://1dl.net/40rc26mvb8px/ddkwj.Projects.In.Data.Science.Using.R.part5.rar.html https://1dl.net/884p07o975tg/ddkwj.Projects.In.Data.Science.Using.R.part1.rar.html https://1dl.net/g1nj301mzt7d/ddkwj.Projects.In.Data.Science.Using.R.part3.rar.html https://1dl.net/gj8nho709am6/ddkwj.Projects.In.Data.Science.Using.R.part4.rar.html https://1dl.net/ng7rdgvjvkjt/ddkwj.Projects.In.Data.Science.Using.R.part2.rar.html Download ( Rapidgator ) https://rapidgator.net/file/0428377ba18d18e3c1679a3e3681a581/ddkwj.Projects.In.Data.Science.Using.R.part1.rar.html https://rapidgator.net/file/166cc878ed07d584f3f9db7a92e6ed34/ddkwj.Projects.In.Data.Science.Using.R.part5.rar.html https://rapidgator.net/file/303491c369196c4848542fe4b48bf8ac/ddkwj.Projects.In.Data.Science.Using.R.part3.rar.html https://rapidgator.net/file/8abeaca87689edbe6c570902f590f360/ddkwj.Projects.In.Data.Science.Using.R.part2.rar.html https://rapidgator.net/file/d521d46d9064ed8572b690a63bc46ed5/ddkwj.Projects.In.Data.Science.Using.R.part4.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/1e4a60f726bedEc2/ddkwj.Projects.In.Data.Science.Using.R.part5.rar https://uploadgig.com/file/download/8C63189202a5517D/ddkwj.Projects.In.Data.Science.Using.R.part2.rar https://uploadgig.com/file/download/910a67Fde13e2913/ddkwj.Projects.In.Data.Science.Using.R.part4.rar https://uploadgig.com/file/download/a9b9d5c91927FD1b/ddkwj.Projects.In.Data.Science.Using.R.part1.rar https://uploadgig.com/file/download/c501B3b5988c67f6/ddkwj.Projects.In.Data.Science.Using.R.part3.rar Links are Interchangeable - No Password - Single Extraction
  24. Published 11/2022 Created by Peter Alkema MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 290 Lectures ( 29h 31m ) | Size: 9 GB Learn tools, techniques, careers, companies, k-means, clustering, deep learning, neural networks, machine learning ++ What you'll learn Data Science and Its Types Top 10 Jobs in Data Science Tools of Data Science Variables and Data in Python Introduction to Python Probability and Statistics Functions in Python Operator in Python DataFrame with Excel Dictionaries in Python Tuples and loops Conditional Statement in Python Sequences in Python Iterations in Python Multiple Regression in Python Linear Regression Libraries in Python Numpy and SK Learn Pandas in Python K-Means Clustering Clustering of Data Data Visualization with Matplotlib Data Preprocessing in Python Mathematics in Python Data Visualization with Plotly What is Deep Learning? Deep Learning Neural Network Tensor Flow PostgreSQL Machine Learning and Data Science Machine Learning Models Data Science Projects: Real World Problems Requirements Internet connection PC, Laptop, Mobile No prior knowledge is required. We shall start from basics and finish on a pro level. Description Get instant access to a workbook on Data Science, follow along, and keep for referenceIntroduce yourself to our community of students in this course and tell us your goals with data scienceEncouragement and celebration of your progress every step of the way: 25% > 50% > 75% & 100%30 hours of clear and concise step-by-step instructions, lessons, and engagementThis data science course provides participants with the knowledge, skills, and experience associated with Data Science. Students will explore a range of data science tools, algorithms, Machine Learning, and statistical techniques, with the aim of discovering hidden insights and patterns from raw data in order to inform scientific business decision-making.What you will learn:Introduce data and information conceptIdentify the difference between business intelligence and data scienceUnderstand and learn the process of data scienceDefine demand and challenges for people working in data scienceIdentify the difference between dispersion and descriptive vs inferential statistics discussionLearn after installing anaconda steps to be followedLearn spread of data discussion and interquartile rangeDefine advantages of getting conditional probability based on exampleIdentify the advantage of calculating z scoresLearn calculating p-value and learning factors on p-valueKnow the Prerequisites and Questions for a Data ScientistTypes of Data AcquisitionKnow Career Aspects for Data ScienceDiscuss Mathematical and Statistical Concepts and ExamplesDescriptive and Inferential Statisticshow to use the Jupyter applicationCalculate varianceGet Conditional Probability based on exampleDistribution and Probability DensityZ test and finding percentage under the curveCompare Mean and Variable discussionchi-squared test and discussing based on example dataData Preprocessing in PythonChecking Array and Dimension Shape and Discussing on Encode WindowWhy is data visualization important in data science and how to use itParametric Methods and Algorithm Trade-OffClassification and Concept of learningK means Clustering and AlgorithmDoing cluster and using sklearn on it and encodingTP,TN,FP and FN of Confusion Matrix and Discussing accuracyClassification report and calculation on encoding window on Python...and more!Contents and OverviewYou'll start with Data and Information Concept; Difference between Business Intelligence and Data Science; Business Intelligence vs Data Science based on parameters factors; Prerequisites and Questions for a Data Scientist; Questions on applying as a Data Scientist - Statistics and Data Domain; Prerequisite on Business Intelligence and discussing tools on Data Science; Types of Data Acquisition; Data Preparation, Exploration, and its factors; Process of Data Science; Know Career Aspects for a Data Scientist; Demand and Challenges for Data Science; Discussion of Mathematical and Statistical Concepts and Examples; Discussing Variables - Numerical and Categorical; Discussing Qualitative Variables and Central Tendency; Dispersion and Descriptive vs Inferential Statistics Discussion.Descriptive and Inferential Statistics; Descriptive Statistics, Examples and steps on installing Anaconda; Steps to follow after installing Anaconda; Using Jupyter on Anaconda Application; how to use Jupyter application; Continuation of Jupyter application, explanation, and discussion; Getting data and putting data on Jupyter; Minimizing data to be see on Jupyter app and bringing data from Excel; Explaining modes used on Jupyter app on Data statistics and Analysis; Variables - continues and categorical variable; Inputting and typing data on Jupyter app; Getting mean data on Jupyter based on example; How to summarize data of median and mean; Inputting quantiles data and explaining factors; Spread of data discussion and interquartile range; Interquartile range and inputting data; Variance averaged deviation on the mean; Calculating variance; Discussing degree of freedom based on variables and calculation; Introduction to probability and overview of the lesson; Getting Conditional Probability based on example; Continuation of example based on students data on probability; Make a new column for absences and column for pivot table; Calculating and encoding of the result of condition probability of students.We will also cover Inferential statistics; Distribution and Probability Density; Gaussion Distribution; Define distribution parameters and graphing normal distribution; PDF and CDF - Cumulative Distribution Function; Learn what is Correlation Coefficient, Z score, and Z test; Calculating Z scores; What does Z scores tell you?; Z test and finding percentage under the curve; Getting the mean, getting data, hypothesis and comparing mean; Comparing Mean and Variable discussion; Continuing Z test, Calculating P test and continuing steps on Z test; Doing small Z test, Stats, and discussing factors; Null Hypothesis, run Z test, finding and defining P value; Calculating P value and learning factors on P value; T test, Diamond data test and mean of concerned value; how to import data set, t test and learning; Learn what is correlation coefficients, scatter plot , calculation; Getting scatter plot data correlation.This course will also tackle chi squared test and discussing based on example data; Chi square test , getting data set and discussing factor; Chi2 contingency method discussion and result on data ci square test; Data Preprocessing in Python - Step 1: Importing the libraries; Step 2 importing data set; Step 3 handling the missing values; Step 3 continuation and factors; Step 4 Encoding categorical data; step 4 label encoding; step 5 Normalizing the data set; step 6 Splitting the data set; numpy and pandas and The numpy ndarray A multidimensional; Learn Checking Array and Dimension Shape and Discussing on Encode Window; Learn panda series and creating a panda series; data frame on panda series and know how to use reindex function; Learn Pandas Dataframe; Learn what is data visualization; why is data visualization is important and how to use it; Learn plotting libraries and know its steps; Learn what is machine learning; Learn Examples of Learning Problems, Research Fields, and Applications; Discussing the Learning Problem; Learn what is Prediction and its examples; Parametric Methods and Algorithm Trade Off; Supervised and Unsupervised Learning Terminology, and Regression vs Classification; Assessing model accuracy, Bias and Variance learning of methods and Test MS; Doing linear regression on code window; Doing scatter plot method to get linear regression; from sklearn linear model to linear regression regressor; Finding intercept regression or regressor and learning other factor; Sklearn import metrics and getting the final data on linear regression.Next, we will discuss Learning Classification and Concept on learning; machine learning areas and Important concepts; Example of spam filter, Label data and unlabelled data, Training vs error; Classification has 2 step process, Issues Data preparation; Learning decision trees and sample problem; Learn Decision Tree Induction - Training dataset and discussing examples; Doing decision tree classification on Python; Importing some libraries and data, factors and format ; Continuation with understanding the data and discussing it; Checking on train test split and creating decision tree classified; Solution on tree plot tree too interpret data and what is Gini index, K means Clustering and Algorithm; Stopping/Convergence Criterion giving examples and Algorithm K means; Strength and weakness of K means and discussing factors; how clustering K means method works and learning factors; Combining data processing and getting data and encoding factors; Label encoding code to use, data encoding, using transform ; Doing cluster and using sklearn; Continuation of k-means clustering and other factor on coding Python; Preview on Data in sales and other factors and topic; Data science use cases in sales , Case study - future sales prediction ; Describing the data on mean standard deviation and factors; Load data, Removing the index column and Relationship between Predictor.Then, how to change the default policy; Accuracy, MSE, RMSE, RSquare, Seaborn Library; machine learning model building; Evaluation metrics and different evaluation matrix and confusion matrix; TP,TN,FP and FN of Confusion Matrix and Discussing accuracy; precision, recall and F1 score in data science; Learn Classification report and calculation on encoding window on Python.We can't wait to see you on the course!Enrol now, and we'll help you improve your data science skills!Peter and Laika Who this course is for Those who want to have career in data science. Those who have interest in data science and want to apply their knowledge in their field or profession. Those who want to learn the application of data science using python. 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  25. Last updated 1/2020 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 1.96 GB | Duration: 4h 55m Explore all the concepts of Data Cleaning for AI & Data Science to become an expert with this complete online tutorial. What you'll learn Professional ways for handling the data Learn Standard visualization techniques like Histograms, Scatterplots etc How to locate discrepancies, and deal with issues Requirements Basic Knowledge of Python Description One of the most essential aspects of Data Science or Machine Learning is Data Cleaning. In order to get the most out of the data, your data must be clean as uncleaned data can make it harder for you to train ML models. In regard to ML & Data Science, data cleaning generally filters & modifies your data making it easier for you to explore, understand and model.A good statistician or a researcher must spend at least 90% of his/her time on collecting or cleaning data for developing a hypothesis and remaining 10% on the actual manipulation of the data for analyzing or deriving the results. Despite these facts, data cleaning is not commonly discussed or taught in detail in most of the data science or ML courses. With the rise of big data & ML, now data cleaning has also become equally important.Why should you learn Data Cleaning?Improve decision makingImprove the efficiencyIncrease productivityRemove the errors and inconsistencies from the datasetIdentifying missing valuesRemove duplicationWhy should you take this course?Data Cleaning is an essential part of Data Science & AI, and it has become an equally important skill for a programmer. It's true that you will find hundreds of online tutorials on Data Science and Artificial Intelligence but only a few of them cover data cleaning or just give the basic overview. This online guide for data cleaning includes numerous sections having over 5 hours of video which are enough to teach anyone about all its concepts from the very beginning. Enroll in this course now to learn all the concepts of Data Cleaning. This course teaches you everything including the basics of Data Cleaning, Data Reading, merging or splitting datasets, different visualization tools, locate or handling missing/absurd values and hands-on sessions where you'll be introduced to the dataset for ensuring complete learning of Data Cleaning.Enroll in this course now to learn about data cleaning concepts and techniques in detail! Overview Section 1: Introduction Lecture 1 Identifying the task Lecture 2 Model building Lecture 3 Some common solutions Lecture 4 Training and test data Lecture 5 Cross validation Lecture 6 Feature selection Lecture 7 Accuracy measures Lecture 8 Overfitting Section 2: Playing with the Data Lecture 9 Reading the data Lecture 10 Structure of the data Lecture 11 Merging/Splitting Lecture 12 Integrity check Lecture 13 Knowing the domain Lecture 14 Range of variables Lecture 15 Inquiring dependencies Section 3: Variables and Correlations Lecture 16 Type of variables Lecture 17 More variable types Lecture 18 Single variable plots Lecture 19 Plotting interrelations Lecture 20 Measuring correlations Lecture 21 Need for transformation Lecture 22 Discretizing features Section 4: Missing Values and Outliers Lecture 23 Absurd or Missing values Lecture 24 Finding their distribution in the dataset Lecture 25 Deciding what to do with them Lecture 26 Looking for outliers Section 5: Exercises Lecture 27 Exercise-1 Lecture 28 Exercise-2 Lecture 29 Exercise-3 Lecture 30 Exercise-4 Students who want to learn the basics of Data Cleaning Homepage https://www.udemy.com/course/data-science-and-ml-data-cleaning-techniques/ Download From 1DL https://1dl.net/f3h1lbi8ogib/gvsug.Data.Cleaning.Techniques.In.Data.Science..Machine.Learning.part1.rar.html https://1dl.net/h3h6frc56nfo/gvsug.Data.Cleaning.Techniques.In.Data.Science..Machine.Learning.part3.rar.html https://1dl.net/p4hy57as4y19/gvsug.Data.Cleaning.Techniques.In.Data.Science..Machine.Learning.part2.rar.html Download ( Rapidgator ) https://rapidgator.net/file/4066d7dd760c7d03c359f9b6fd118756/gvsug.Data.Cleaning.Techniques.In.Data.Science..Machine.Learning.part1.rar.html https://rapidgator.net/file/9d43e3d2c4df5cb80dd88099a3d67895/gvsug.Data.Cleaning.Techniques.In.Data.Science..Machine.Learning.part2.rar.html https://rapidgator.net/file/9e42a6039086ea3e76749e41cd1be442/gvsug.Data.Cleaning.Techniques.In.Data.Science..Machine.Learning.part3.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/3e4ccbbac7Ef30cd/gvsug.Data.Cleaning.Techniques.In.Data.Science..Machine.Learning.part3.rar https://uploadgig.com/file/download/813bFE21cd80Dfaf/gvsug.Data.Cleaning.Techniques.In.Data.Science..Machine.Learning.part2.rar https://uploadgig.com/file/download/98Dee935cc063308/gvsug.Data.Cleaning.Techniques.In.Data.Science..Machine.Learning.part1.rar Links are Interchangeable - No Password - Single Extraction
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