O'Reilly logo

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Become a Python Data Analyst

Become a Python Data Analyst

2 reviews
by Alvaro Fuentes
Publisher: Packt Publishing
Release Date: May 2017
ISBN: 9781787284302
View table of contents
Start watching

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