**Become a Python Data Analyst**

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 4.5 Hours | 1.01 GB

Take your data analytics and predictive modeling skills to the next level using the popular tools and libraries in Python

The Python programming language has become a major player in the world of Data Science and Analytics. This course introduces Python’s most important tools and libraries for doing Data Science; they are known in the community as “Python’s Data Science Stack”.

This is a practical course where the viewer will learn through real-world examples how to use the most popular tools for doing Data Science and Analytics with Python.

What You Will Learn

- Learn about the most important libraries for doing Data Science with Python and how they can be easily installed with the Anaconda distribution.
- Understand the basics of Numpy which is the foundation of all the other analytical tools in Python.
- Produce informative, useful and beautiful visualizations for analyzing data.
- Analyze, answer questions and derive conclusions from real world data sets using the Pandas library.
- Perform common statistical calculations and use the results to reach conclusions about the data.
- Learn how to build predictive models and understand the principles of Predictive Analytics

**Table of Contents**

**The Anaconda Distribution and the Jupyter Notebook**

The Course Overview

The Anaconda Distribution

Introduction to the Jupyter Notebook

Using the Jupyter Notebook

**Vectorizing Operations with NumPy**

NumPy: Python’s Vectorization Solution

NumPy Arrays: Creation, Methods and Attributes

Using NumPy for Simulations

**Pandas - Everyone’s Favorite Data Analysis Library**

The Pandas Library

Main Properties, Operations and Manipulations

Answering Simple Questions about a Dataset – Part 1

Answering Simple Questions about a Dataset – Part 2

**Visualization and Exploratory Data Analysis**

Basics of Matplotlib

Pyplot

The Object-Oriented Interface

Common Customizations

EDA with Seaborn and Pandas

Analysing Variables Individually

Relationships between Variables

**Statistical Computing with Python**

SciPy and the Statistics Sub-Package

Alcohol Consumption – Confidence Intervals and Probability Calculations

Hypothesis Testing – Does Alcohol Consumption Affect Academic Performance?

Hypothesis Testing – Do Male Teenagers Drink More Than Females?

**Introduction to Predictive Analytics Models**

Introduction to Predictive Analytics Models

The Scikit-Learn Library – Building a Simple Predictive Model

Classification – Predicting the Drinking Habits of Teenagers

Regression – Predicting House Prices

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