kingers Posted April 25 Report Share Posted April 25 Mastering Data Science: From Analysis To Application Published 4/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 680.29 MB[/center] | Duration: 1h 48m Empowering Data Enthusiasts: From Exploration to Deployment with Python What you'll learn Data Exploration and Analysis Proficiency Data Cleaning and Preprocessing Skills Model Building and Evaluation Techniques Deployment and Application Development Understanding Data Science Workflow Effective Communication of Data Insights Requirements A basic comprehension of data ideas, experience with Jupyter Notebook, basic Python programming ability, and a strong interest in data analysis and machine learning are prerequisites for this course. Description Embark on a journey of transformation into the field of data science with comprehensive courses designed to provide you with the skills and knowledge you need to navigate the complexities of data analysis, machine learning, and model deployment. . This immersive experience delves into fundamental data science concepts and practical techniques using Python, the industry-leading language for data manipulation and analysis. Introduce the basic principles of data science from the beginning, understand its importance in today's data-driven world, and explore its diverse applications in different fields. Led by an experienced instructor, you'll learn how to harness the power of Python to load datasets, manipulate data with Pandas, and gain insights through exploratory data analysis. Through hands-on exercises and real-world examples, master the art of data visualization using Matplotlib and Seaborn and gain the ability to communicate complex insights clearly and accurately. Then, embark on a fascinating exploration of the legendary Titanic dataset and uncover its secrets through data cleaning, visualization, and feature engineering. Over the course of the course, you will delve into the intricacies of machine learning, use logistic regression to build predictive models, and evaluate their performance using industry standard metrics. But the journey doesn't end here. Step into the realm of model deployment and learn how to use Streamlit to build interactive user interfaces and host predictive models on your web server for seamless access. By the end of this course, you will be a competent data scientist with the skills to tackle real-world data challenges with confidence and accuracy. Whether you're an experienced professional looking to improve your skills or an aspiring data enthusiast looking to embark on a new journey, this course will help you discover the full potential of data science. and make a meaningful impact on weight gain in today's data-driven world. Join us and embark on a journey of data discovery, innovation, and endless possibilities. Overview Section 1: Introduction to data science Lecture 1 Introduction to Data Science Section 2: Loading dataset in Jupyter Notebook Lecture 2 Loading dataset in Jupyter Notebook Section 3: Introduction to Pandas Lecture 3 Introduction to Pandas Section 4: Exploring Columns in Pandas Lecture 4 Exploring Columns in Pandas Section 5: Data Visualization using Matplotlib Lecture 5 Data Visualization using Matplotlib Section 6: Data Visualization with Seaborn Lecture 6 Data Visualization with Seaborn Section 7: Exploring the Titanic Dataset Lecture 7 Exploring the Titanic Dataset Section 8: Data Cleaning on Training Dataset Lecture 8 Data Cleaning on Training Dataset Section 9: Data Visualization on Titanic Dataset Lecture 9 Data Visualization on Titanic Dataset Section 10: Encoding Categorical Columns on Titanic Dataset Lecture 10 Encoding Categorical Columns on Titanic Dataset Section 11: Separating Features and Target Variables in Titanic dataset Lecture 11 Separating Features and Target Variables in Titanic dataset Section 12: Building the Logistic Regression Model Lecture 12 Building the Logistic Regression Model Section 13: Evaluating and Saving the Logistic Regression model Lecture 13 Evaluating and Saving the Logistic Regression model Section 14: Building a User Interface with Streamlit Lecture 14 Building a User Interface with Streamlit Section 15: Hosting the Titanic Survival Prediction Model on Streamlit Lecture 15 Hosting the Titanic Survival Prediction Model on Streamlit Regardless of your experience level, this course is intended for aspiring data scientists, analysts, and professionals looking to improve their abilities in Python-based data analysis, machine learning, and model deployment.https://voltupload.com/k7n9gorlg9qe/Mastering_Data_Science_From_Analysis_to_Application.ziphttps://rapidgator.net/file/8d874e4f6612b7d33b13f5ed6c162789/Mastering_Data_Science_From_Analysis_to_Application.zipFree search engine download: Mastering Data Science From Analysis to Application Link to comment Share on other sites More sharing options...
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