Data Science & Machine Learning With Python | Learn it with Python

Data Science & Machine Learning Course Description

Python is well known as a programming language used in a numerous do- mains — 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 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 al- low 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. 1. Accessing data (e.g., text files, databases)
  2. 2. Cleansing and normalizing data
  3. 3. Exploring data (e.g., simple statistics, correlation matrices, visualization)
  4. 4. Modeling data (e.g., statistics, machine learning)

Data Science & Machine Learning Training - Suggested Audience

This 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 Duration

  • Open-House F2F (Public): 3/4 days
  • In-House F2F (Private): 3/4 days, for commercials please send us an email with group size to

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.

This Data Science & Machine Learning with Python training course outline includes:

1. Overview of data science in Python

2. Jupyter notebook as an environment for data-science work (and collaboration)

3. NumPy
  • Data structures
  • Operations
  • Sorting, searching and retrieving • Boolean indexing techniques

4. SciPy

5. Pandas
  • Series
  • DataFrame
  • Importing and exporting data
  • Filtering data by row and column • Data manipulation
  • Time series

5. Matplotlib
  • Chart types
  • Chart styles
  • Output to a file vs. the screen
  • Multiple plots
  • Standalone Matplotlib vs. integrated with Panda

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?
  • What tools does scikit-learn provide to identify features?

11. Standardization of data

12. Machine-learning algorithms
  • How to choose
  • Why you shouldn’t be too confident

13. Supervised machine learning
  • Training
  • Predicting
  • Avoiding overfit models
  • Evaluating model success
  • Comparing models
  • Using the same classifier with different hyperparameters

14. Supervised classification problems
  • Simple classification
  • Textual classification

15. Supervised regression problems

16. Feature selection

17. Clustering with unsupervised learning

18. Future trends in machine learning
Keny White


Keny White is Professor of the Department of Computer Science at Boston University, where he has been since 2004. He also currently serves as Chief Scientist of Guavus, Inc. During 2003-2004 he was a Visiting Associate Professor at the Laboratoire d'Infomatique de Paris VI (LIP6). He received a B.S. from Cornell University in 1992, and an M.S. from the State University of New York at Buffalo.


After working as a software developer and contractor for over 8 years for a whole bunch of companies including ABX, Proit, SACC and AT&T in the US, He decided to work full-time as a private software trainer. He received his Ph.D. in Computer Science from the University of Rochester in 2001. "What I teach varies from beginner to advanced and from what I have seen, anybody can learn and grow from my courses".


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    This is great

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