Data Analysis with Python in Visual Studio
Python is a dynamic language popular for web development, big data, science, and scripting. Python is powerful.
The Python language is expressive and productive, it comes with a great standard library, and it’s the center of a huge universe of wonderful third-party libraries. Python’s readable style, quick edit-and-run development cycle, and “batteries included” philosophy mean that you can sit down and enjoy writing code rather than fighting compilers and thorny syntax.
In this course you will learn how to get the most from your data by combing statistical analysis, data mining and machine learning. You will gain hands on lab real life experience which will allow you easily start exploring your own data just after finishing this course.
Target audience and Prerequisites
This course is intendent for data analysts, data scientists, big data analysts, developers and IT professionals who want to get deep knowledge and skills regarding data analysis in Python.
To attend this training, you should have experience with basic statistical analysis and basic experience in programming at least in one of the modern programming languages (C++, C#, VB.NET, Java).
About Dr. Michael Jankowski-Lorek
Dr. Mike Jankowski-Lorek is a data scientist, solution architect, developer and consultant. He designs and implements solutions for Databases, data analysis and natural language processing. Mike is interested in Big data, High Availability and real time analytics especially when combined with machine learning and artificial intelligent or natural language processing.
Module 1: Python Fundamentals
- Versions and history
- Zen of Python
- Blocks and notation
Module 2: Programming in Python
- Hello world
- Data types
- Control flow
- Files and resources
Module 3: Maintaining your code
- Error handling
- Unit testing
Module 4: Data analysis and science
- Project cycle
- Preparing environment
- Working with Jupyter
- Extracting data
- Exploring data
- Processing data
- Building predictive models
- Evaluating model performance