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.
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.
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.
About The Author
Alvaro Fuentes: Alvaro Fuentes is a senior data scientist with a background in applied mathematics and economics. He has more than 14 years of experience in various analytical roles and is an analytics consultant at one of the ‘Big Three’ global management consulting firms, leading advanced analytics projects in different industries like banking, technology, and consumer goods. Alvaro is also an author and trainer in analytics and data science and has published courses and books, such as 'Become a Python Data Analyst' and 'Hands-On Predictive Analytics with Python'. He has also taught data science and related topics to thousands of students both on-site and online through different platforms such as Springboard, Simplilearn, Udemy, and BSG Institute, among others.
Table of contents
- Chapter 1 : The Anaconda Distribution and the Jupyter Notebook
- Chapter 2 : Vectorizing Operations with NumPy
- Chapter 3 : Pandas: Everyone’s Favorite Data Analysis Library
- Chapter 4 : Visualization and Exploratory Data Analysis
- Chapter 5 : Statistical Computing with Python
- Chapter 6 : Introduction to Predictive Analytics Models
- Title: Become a Python Data Analyst
- Release date: May 2017
- Publisher(s): Packt Publishing
- ISBN: 9781787284302
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