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Become a Python Data Analyst

Become a Python Data Analyst

by Alvaro Fuentes
Publisher: Packt Publishing
Release Date: May 2017
ISBN: 9781787284302
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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.

    Table of Contents

    1. Chapter 1 : The Anaconda Distribution and the Jupyter Notebook
      1. The Course Overview 00:04:26
      2. The Anaconda Distribution 00:07:44
      3. Introduction to the Jupyter Notebook 00:09:51
      4. Using the Jupyter Notebook 00:11:50
    2. Chapter 2 : Vectorizing Operations with NumPy
      1. NumPy: Python’s Vectorization Solution 00:07:48
      2. NumPy Arrays: Creation, Methods and Attributes 00:23:24
      3. Using NumPy for Simulations 00:11:58
    3. Chapter 3 : Pandas: Everyone’s Favorite Data Analysis Library
      1. The Pandas Library 00:14:09
      2. Main Properties, Operations and Manipulations 00:13:37
      3. Answering Simple Questions about a Dataset – Part 1 00:11:46
      4. Answering Simple Questions about a Dataset – Part 2 00:15:56
    4. Chapter 4 : Visualization and Exploratory Data Analysis
      1. Basics of Matplotlib 00:07:01
      2. Pyplot 00:10:22
      3. The Object Oriented Interface 00:09:07
      4. Common Customizations 00:11:48
      5. EDA with Seaborn and Pandas 00:09:12
      6. Analysing Variables Individually 00:17:23
      7. Relationships between Variables 00:15:20
    5. Chapter 5 : Statistical Computing with Python
      1. SciPy and the Statistics Sub-Package 00:04:02
      2. Alcohol Consumption – Confidence Intervals and Probability Calculations 00:10:37
      3. Hypothesis Testing – Does Alcohol Consumption Affect Academic Performance? 00:08:07
      4. Hypothesis Testing – Do Male Teenagers Drink More Than Females? 00:05:23
    6. Chapter 6 : Introduction to Predictive Analytics Models
      1. Introduction to Predictive Analytics Models 00:06:12
      2. The Scikit-Learn Library – Building a Simple Predictive Model 00:06:41
      3. Classification – Predicting the Drinking Habits of Teenagers 00:08:18
      4. Regression – Predicting House Prices 00:08:07