Become a Python Data Analyst [Video]

Alvaro Fuentes
Wednesday, May 31, 2017

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

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Video Details

ISBN 139781787284302

Course Length4 hours and 30 minutes

Video Description

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.

Style and Approach

This course introduces the viewer to the main libraries of Python’s Data Science stack. Taking an applied approach, it provides many examples using real-world datasets to show how to effectively use Python’s tools to process, visualize and analyze data. It contains all you need to start analyzing data with Python and provides the foundation for more advanced topics like Predictive Analytics.

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

The Scikit-Learn Library – Building a Simple Predictive Model

Classification – Predicting the Drinking Habits of Teenagers

Regression – Predicting House Prices

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

Authors

Alvaro Fuentes

Alvaro Fuentes is a Data Scientist with an M.S. in Quantitative Economics and a M.S. in Applied Mathematics with more than 10 years of experience in analytical roles. He worked in the Central Bank of Guatemala as an Economic Analyst, building models for economic and financial data. He founded Quant Company to provide consulting and training services in Data Science topics and has been a consultant for many projects in fields such as; Business, Education, Psychology and Mass Media. He also has taught many (online and in-site) courses to students from around the world in topics like Data Science, Mathematics, Statistics, R programming and Python.

Alvaro Fuentes is a big Python fan and has been working with Python for about 4 years and uses it routinely for analyzing data and producing predictions. He also has used it in a couple of software projects. He is also a big R fan, and doesn't like the controversy between what is the “best” R or Python, he uses them both. He is also very interested in the Spark approach to Big Data, and likes the way it simplifies complicated
things. He is not a software engineer or a developer but is generally interested in web technologies.

He also has technical skills in R programming, Spark, SQL (PostgreSQL), MS Excel, machine learning, statistical analysis, econometrics, mathematical modeling.

Predictive Analytics is a topic in which he has both professional and teaching experience. Having solved practical problems in his consulting practice using the Python tools for predictive analytics and the topics of predictive analytics are part of a more general course on Data Science with Python that he teaches online.