Become a Python Data Analyst

Name: Become a Python Data Analyst
Rating: 4.0 (510 reviews)

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

Created byPackt Publishing

Last updated 6/2017

English

What you'll 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

Course content

6 sections • 26 lectures • 4h 29m total length

The Course Overview4:25
This video provides an overview of the entire course.
The Anaconda Distribution7:43
Explain what Anaconda Distribution is and why we are using it in this course. Also show how to get and install the software.
Introduction to the Jupyter Notebook9:51
Introduce the computing environment in which we will work for the rest of the course.
Using the Jupyter Notebook11:49
Use the Jupyter notebook for basic Python code and explain the basics of using markdown and code cells in the Jupyter Notebook.

The Pandas Library14:09
Explain what pandas is and what we can do with it. An introduction to the main objects: Series and DataFrames.
Main Properties, Operations and Manipulations13:36
Show how to use pandas Series and DataFrames with a real-world data set.
Answering Simple Questions about a Dataset – Part 111:46
Show the viewer how to use pandas by doing real-world data analysis tasks and answering questions.
Answering Simple Questions about a Dataset – Part 215:55
Show the viewer how to use pandas by doing real-world data analysis tasks and answering questions.

Basics of Matplotlib7:00
Explain to the viewer what matplotlib is and the main concepts needed for using it.
Pyplot10:21
Explain what pyplot is, how to use the pyplot interface, and its limitations.
The Object Oriented Interface9:06
Explain how to use the Object-Oriented Interface and how it compares with the plyplot interface.
Common Customizations11:48
Show some of the common customizations that can be done to plots.
EDA with Seaborn and Pandas9:12
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.
Analysing Variables Individually17:22
Show how to analyze and make sense of individual variables depending on their type.
Relationships between Variables15:20
Show how to produce the main plots used to show relationships between variables.

SciPy and the Statistics Sub-Package4:01
Give a quick introduction to the Scipy package and all the different sub-packages it contains.
Alcohol Consumption – Confidence Intervals and Probability Calculations10:37
Show how to perform statistical calculations with the stats package like confidence intervals and probabilities of events.
Hypothesis Testing – Does Alcohol Consumption Affect Academic Performance?8:07
Explain how to perform one of the most common statistical tests using the stats package.
Hypothesis Testing – Do Male Teenagers Drink More Than Females?5:22
Show how to perform a chi-square test using the stats package.

Introduction to Predictive Analytics Models6:11
Present an overview of the section. Discuss the concepts of Predictive Analytics and its relationship with Machine Learning and give some characteristics of ML models.
The Scikit-Learn Library – Building a Simple Predictive Model6:41
Introduce the Scikit-Learn library and show the workflow traditionally used to build a Predictive Model with this library.
Classification – Predicting the Drinking Habits of Teenagers8:18
Explain how to build classification models using a dataset containing real-world data; then evaluate the model and use it to make predictions.
Regression – Predicting House Prices8:07
Explain how to build a regression model using a dataset containing real-world data; then evaluate the model and use it to make predictions.

Requirements

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.

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.

About the author :

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.

Who this course is for:

Data analysts or data scientists interested in learning Python’s tools for doing Data Science. Business Analysts and Business Intelligence experts who would like to learn how to use Python for doing their data own analysis tasks will also find this tutorial very helpful. Software engineers and developers interested in Python’s capabilities for analyzing data gain a lot from this course. A basic (beginner’s level) familiarity with Python language is assumed.

Become a Python Data Analyst

What you'll learn

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Course content

The Anaconda Distribution and the Jupyter Notebook4 lectures • 34min

Vectorizing Operations with NumPy3 lectures • 43min

Pandas: Everyone’s Favorite Data Analysis Library4 lectures • 55min

Visualization and Exploratory Data Analysis7 lectures • 1hr 20min

Statistical Computing with Python4 lectures • 28min

Introduction to Predictive Analytics Models4 lectures • 29min

Requirements

Description

Who this course is for: