Book Image

Become a Python Data Analyst [Video]

By : Alvaro Fuentes
Book Image

Become a Python Data Analyst [Video]

By: Alvaro Fuentes

Overview of this book

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. All the resource files are added to the GitHub repository at: https://github.com/PacktPublishing/Become-a-python-data-analyst

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Become a Python Data Analyst [Video]
Alvaro Fuentes
May, 2017 | 4 hours 30 minutes
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Table of Contents (6 chapters)
Free Chapter
1
The Anaconda Distribution and the Jupyter Notebook
The Course Overview
The Anaconda Distribution
Introduction to the Jupyter Notebook
Using the Jupyter Notebook
2
Vectorizing Operations with NumPy
NumPy: Python’s Vectorization Solution
NumPy Arrays: Creation, Methods and Attributes
Using NumPy for Simulations
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
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
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?
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
Chapter 4
Visualization and Exploratory Data Analysis
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Section 5
EDA with Seaborn and Pandas
Explain what Exploratory Data Analysis (EDA) is and how to perform it in a real-world dataset; in the process, introduce the Seaborn plotting library. - Explain the general purpose of EDA; introduce Seaborn and the dataset to be used - Explain the types of variables that can be found in data analysis - Present the variables that will be used in the EDA example
Previous Section
End of Section 5
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