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Python Data Analytics With Pandas and NumPy


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Python Data Analytics: With Pandas and NumPy

.MP4 | Video: 1280x720, 30 fps® | Audio: AAC, 44100 Hz, 2ch | 932 MB

Duration: 2 hours | Genre: eLearning Video | Language: English

Learn Get complete to handle complex data-sets and analyze your data in a principled way with Pandas, Python and NumPy.

 

What you'll learn

 

Learn to work with pandas to analyze data.

Learn to use NumPy to work with arrays and matrices of numbers.

Learn to work with Jupyter Notebook.

Learn to work with matDescriptionlib from within pandas.

 

Requirements

 

Basic Python programming experience

 

Description

 

Welcome to " Python Data Analytics: With Pandas and NumPy "

 

Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more!

 

You will learn how to:

 

Import data sets

 

Clean and prepare data for analysis

 

Manipulate pandas DataFrame

 

Summarize data

 

Build machine learning models using scikit-learn

 

Build data pipelines

 

Posing a question

 

Wrangling your data into a format you can use and fixing any problems with it

 

Exploring the data, finding patterns in it, and building your intuition about it

 

Drawing conclusions and/or making predictions

 

Communicating your findings

 

Data Analysis with Python is delivered through lecture, hands-on labs, and assignments. It includes following parts:

 

Data Analysis libraries: will learn to use Pandas DataFrames, Numpy multi-dimentional arrays, and SciPy libraries to work with a various datasets. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions

 

COURSE SYLLABUS

 

Module 1 - Installation

 

Lecture 1: Installing the Anaconda Python distribution

 

Lecture 2:Writing and running Python in the iPython notebook

 

Module 2 - Refresher Data Containers in Python

 

Lecture 3:Python containers overview

 

Lecture 4:Using Python lists and the slicing syntax

 

Lecture 5:Using Python dictionaries

 

Lecture 6:Comprehensive

 

Module - 3 Word Anagrams in Python

 

Lecture 7:Word anagram overview

 

Lecture 8:Loading the dictionary

 

Lecture 9:Finding anagrams

 

Lecture 10:Challenge

 

Lecture 11:Solution

 

Module - 4 Introduction to NumPy

 

Lecture 12:NumPy overview

 

Lecture 13:Creating Numpy Arrays

 

Lecture 14:Doing math with arrays

 

Lecture 15:Indexing and slicing

 

Lecture 16:Records and dates

 

Module - 5 Weather Data with NumPy

 

Lecture 17:Weather data overview

 

Lecture 18:Downloading and parsing data files

 

Lecture 19:Temperature analysis

 

Lecture 20:Integrating missing data

 

Lecture 21:Smoothing data

 

Lecture 22:Computing daily records

 

Lecture 23:Challenge

 

Lecture 24:weather data Solution

 

Module - 6 Introduction to Pandas

 

Lecture 25:Pandas overview

 

Lecture 26:Series in Pandas

 

Lecture 27:DataFrames in Pandas

 

Lecture 28:Using multilevel indices

 

Lecture 29:Aggregation

 

You'll also learn how to use the Python libraries NumPy, Pandas, and MatDescriptionlib to write code that's cleaner, more concise, and runs faster.

 

Take this course today and start your journey now!

 

Regards,

 

EliteHakcer Team

 

Who this course is for:

 

You should be familiar with if statements, loops, functions, lists, sets, and dictionaries. To learn about any of these topics, take the course Intro to Computer Science.

You should also be familiar with classes, objects, and modules. To learn about these topics, take the course Programming Foundations with Python.

 

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