IBM Free Courses and Certificates - Data Analysis with Python

IBM Free Courses and Certificates - Data Analysis with Python

IBM Free Courses and Certificates - Data Analysis with Python

GENERAL INFORMATION :

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

APPLY FOR THIS COURSE : CLICK HERE

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.

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 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
• ANOVA
• 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

• GENERAL INFORMATION
  

• 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

• REQUIREMENTS

• Some Python experience is expected
• Python for Data Science