A D A M S

Data Analyst

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 free.
  • It 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