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Data Science & Machine Learning Course Description

Python is well known as a programming language used in numerous domains— from system administration to Web development to test automation. In recent years, Python has become a leading language in data science and machine learning. Whether you’re looking through logfiles, calculating statistics, finding similarities between documents, or identifying buying patterns among your customers, Python provides numerous tools to solve these problems. This data science machine learning course introduces the concepts of data science and machine learning, using commonly used open-source tools built in Python. This is not a course in the Python language; participants must already have a good understanding of Python’s basics, including the built-in types, functions, list/set/dict comprehensions, and objects. The course concentrates on the practical use of data science libraries to understand existing data and make predictions with new data.

Participants in this course will learn about NumPy, SciPy, Pandas, and Matplotlib as the building blocks, and how to use them to import, transform, and filter data — all essential day-to-day aspects of a data scientist’s job.

The course then turns to machine learning, creating computer-based statistical models that allow us to make predictions using the powerful, popular, open-source “scikit-learn” library. The course looks at both supervised and unsupervised learning, using real-world data from a variety of domains. Participants will learn how to work with numeric, categorical, and textual data.

An important part of developing machine-learning models is the testing of models, to ensure that they aren’t “overfit” to the training data. Participants will learn what tools scikit-learn provides, to test their data, and to check which of their models are the most appropriate.


Data Science & Machine Learning Course Learning Outcomes;

  • Hands-on experience setting up an integrated analysis environment for doing data science with Python.
  • An understanding of how to use the Python standard library to write programs, access the various data science tools, and document and automate analytic processes.
  • Orientation to some of the most powerful and popular Python libraries for data science including Pandas (data preparation, analysis, and modeling; time series analysis), scipy.stats (statistics), scikit-learn (machine learning), and Matplotlib (data visualization).
  • Working knowledge of the Python tools ideally suited for data science tasks, including:
    1. Accessing data (e.g., text files, databases)
    2. Cleansing and normalizing data
    3. Exploring data (e.g., simple statistics, correlation matrices, visualization)
    4. Modeling data (e.g., statistics, machine learning)


Data Science & Machine Learning Training – Suggested Audience

This data science machine learning training is aimed at programmers who have day-to-day practical experience working with Python. Suggested attendees are:

  • Software developers
  • Software Engineers
  • Data Analysts
  • Python developers


Data Science Machine Learning Training – Prerequisites

  • Participants must already have a good understanding of Python’s basics, including the built-in types, functions, list/set/dict comprehensions, and objects.
  • A basic understanding of statistics will be useful, but not mandatory.


Data Science Machine Learning In-house/Corporate Group Training

Available for a group size of more than 4-5. For commercials please send us an email with group size to

Course Curriculum

1. Overview of data science in Python
2. Jupyter notebook as an environment for data-science work (and collaboration)
3. NumPy
Data structures Details 00:00:00
Operations Details 00:00:00
Sorting, searching and retrieving • Boolean indexing techniques Details 00:00:00
5. Pandas
Series Details 00:00:00
DataFrame Details 00:00:00
Importing and exporting data Details 00:00:00
Filtering data by row and column • Data manipulation Details 00:00:00
Time series Details 00:00:00
5. Matplotlib
Chart types Details 00:00:00
Chart styles Details 00:00:00
Output to a file vs. the screen Details 00:00:00
Multiple plots Details 00:00:00
Standalone Matplotlib vs. integrated with Panda Details 00:00:00
6. Machine learning
7. What is machine learning?
8. scikit-learn
9. Retrieving and using public data sets
10. Feature selection• What is it?
Why is it important? Details 00:00:00
What tools does scikit-learn provide to identify features? Details 00:00:00
11. Standardization of data
12. Machine-learning algorithms
How to choose Details 00:00:00
Why you shouldn’t be too confident Details 00:00:00
13. Supervised machine learning
Training Details 00:00:00
Predicting Details 00:00:00
Avoiding overfit models Details 00:00:00
Evaluating model success Details 00:00:00
Comparing models Details 00:00:00
Using the same classifier with different hyperparameters Details 00:00:00
14. Supervised classification problems
Simple classification Details 00:00:00
Textual classification Details 00:00:00
15. Supervised regression problems
16. Feature selection
17. Clustering with unsupervised learning
18. Future trends in machine learning

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