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Become a Python Data Analyst [Video]

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  • 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

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.

  • 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
Course Length 4 hours 30 minutes
ISBN 9781787284302
Date Of Publication 30 May 2017
Main Properties, Operations and Manipulations
Answering Simple Questions about a Dataset – Part 1
Answering Simple Questions about a Dataset – Part 2
Alcohol Consumption – Confidence Intervals and Probability Calculations
Hypothesis Testing – Does Alcohol Consumption Affect Academic Performance?
Hypothesis Testing – Do Male Teenagers Drink More Than Females?
The Scikit-Learn Library – Building a Simple Predictive Model
Classification – Predicting the Drinking Habits of Teenagers
Regression – Predicting House Prices


Alvaro Fuentes

Alvaro Fuentes is a data scientist with more than 12 years of experience in analytical roles. He holds an M.S. in applied mathematics and an M.S. in quantitative economics. He worked for many years 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, medicine, and mass media, among others.

He is a big Python fan and has been using it routinely for five years to analyze data, build models, produce reports, make predictions, and build interactive applications that transform data into intelligence.