Become a Python Data Analyst

Video description

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

About This Video

Aimed for the beginner, this course contains in one place all you need to start analyzing data with Python

Learn the foundations for doing Data Science and Predictive Analytics with Python through real-world examples

Learn how ask questions and answer them effectively with the most widely used visualization and …

Become a Python Data Analyst

Video description

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

About This Video

Aimed for the beginner, this course contains in one place all you need to start analyzing data with Python

Learn the foundations for doing Data Science and Predictive Analytics with Python through real-world examples

Learn how ask questions and answer them effectively with the most widely used visualization and data analysis techniques

In Detail

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.

Audience

Data analysts or data scientists interested in learning Python’s tools for doing Data Science. Business Analysts and Business Intelligence experts who would like to learn how to use Python for doing their data own analysis tasks will also find this tutorial very helpful. Software engineers and developers interested in Python’s capabilities for analyzing data gain a lot from this course. A basic (beginner’s level) familiarity with Python language is assumed.

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Chapter 1 : The Anaconda Distribution and the Jupyter Notebook

The Course Overview

The Anaconda Distribution

Introduction to the Jupyter Notebook

Using the Jupyter Notebook

Chapter 2 : Vectorizing Operations with NumPy

NumPy: Python’s Vectorization Solution

NumPy Arrays: Creation, Methods and Attributes

Using NumPy for Simulations

Chapter 3 : 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

Chapter 4 : 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

Chapter 5 : 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?

Chapter 6 : 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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