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Offered By: IBM

Data Analysis with Python

Learn modern techniques of Data Analysis using Python and popular open-source libraries like pandas, scikit-learn and numpy and transform data into insights.

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Data Analysis

50.4k+ Enrolled
(5.31k+ Reviews)

At a Glance

Learn modern techniques of Data Analysis using Python and popular open-source libraries like pandas, scikit-learn and numpy and transform data into insights.

Data Analysis has always been a very important field, a highly demanded skill and a well-paid occupation. Until recently, it has been practised using mostly closed, expensive, and limited tools like Excel or Tableau. Python, pandas, scikit-learn and other open-source libraries have changed Data Analysis forever and have become must-have tools for anyone looking to build a career as a Data Analyst.

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!

You will learn how to:
  • Import data sets
  • Clean and prepare data for analysis
  • Manipulate pandas DataFrame
  • Summarize data
  • Build machine learning models using scikit-learn
  • Build data pipelines

Data Analysis with Python is delivered through lectures, hands-on labs, and assignments. It includes the following parts:
  • Data Analysis libraries: will learn to use Pandas DataFrames, Numpy multi-dimensional arrays, and SciPy libraries to work with various datasets. 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.

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 and Preparing the Data
  • Identify and Handle Missing Values
  • Data Formatting
  • Data Normalization Sets
  • Binning
  • Indicator variables
Module 3 - Summarizing the Data Frame
  • Descriptive Statistics
  • Basic of Grouping
  • Correlation
  • More on Correlation
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

  • This course is self-paced.
  • It can be taken at any time.
  • It can be audited as many times as you wish.
  • Python programming, Statistics

  • Some Python experience is expected
  • Completing Python for Data Science course would be helpful

Estimated Effort

3 Hours



Skills You Will Learn

Data Analysis, Data Science, Numpy, Pandas, Python, sklearn



Course Code


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