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Data Analysis with Python CognitiveClass

Enrollment in this course is by invitation only

About This Course

Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more! Topics covered:
  • Importing Data sets
  • Cleaning the Data
  • Data frame manipulation
  • Summarizing the Data
  • Building machine learning models
  • Building data pipelines
Data Analysis with Python will be delivered through lecture, lab, and assignments. It includes following parts:
  • Data Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with a sample dataset. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions.
You can start creating your own data science projects and collaborating with other data scientists using IBM Data Science Experience. When you sign up, you get free access to Data Science Experience. Start now and take advantage of this platform.

Recommended skills prior to taking this course

Course Syllabus

Module 1 - Importing Datasets
  • Learning Objectives
  • Understanding the Domain
  • Understanding the  Dataset
  • Python package for data science
  • Importing and Exporting Data in Python
  • Basic Insights from Datasets
Module 2 - Cleaning the Data
  • Identify and Handle Missing Values
  • Data Formatting
  • Data NormalizationSets
  • Binning
  • Indicator variables
Module 3 - Summarizing the Data Frame
  • Descriptive Statistics
  • Basic of Grouping
  • Correlation
  • Correlation 2
Module 4 - Model Development
  • Simple and Multiple Linear Regression
  • Model Evaluation Using Visualization
  • Polynomial Regression and Pipelines
  • R-squared and MSE for In-Sample Evaluation
  • Prediction and Decision Making
Module 5 - Model Evaluation
  • Model  Evaluation
  • Over Fitting, Under fitting and Model Selection
  • Ridge Regression
  • Grid Search
  • Model Refinement

General Information

      • This course is free.
      • It is self-paced.
      • It can be taken at any time.
      • It can be audited as many times as you wish.
      • There is only ONE chance to pass the course, but multiple attempts per question
      • Python programming, Statistics


  • Python programming

Course Staff

Joseph Santarcangelo Ph.D.

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

Mahdi Noorian Ph.D.

Mahdi Noorian is a Postdoctoral Fellow at the Laboratory for Systems, Software and Semantics (LS3) of the Ryerson University. He holds a Ph.D degree in Computer Science from University of New Brunswick. As a Data Scientist, he is interested in application of machine learning, data mining, optimization, and semantic data analysis for big data to solve the real-world problems.

Other Contributors

The following individual also contributed Bahare TalayianFiorella Wenver, Ke Xing , Steven Dong and Hima Vsudevan