DATA SCIENCE COURSE MODULE
DATA SCIENCE COURSE MODULE
INTRODUCTION TO PYTHON FOR DATA SCIENCE
Module 1: Getting started with python
Overview of Python programming language
Installing Python and essential libraries
Setting up Integrated Development Environments (IDEs)
Module 2: Python Basics
Variables, Data Types, and Basic Operations
Loops and Conditional Statement
Functions and modules
Exception handling
Module 3: Introduction to NumPy and Arrays
  • Understanding NumPy Arrays
  • Array operations and Manipulations
  • Linear Algebra with NumPy
Module 4: Data Manipulation with Pandas
  • Introduction to Pandas Data Frames
  • Loading and Cleaning Data
  • Exploratory Data Analysis with Pandas
Module 5: Data Visualization with matplotlib and seaborn
Data visualization with matplotlib
Data visualization with Seaborn
FOUNDATION OF DATA SCIENCE
Module 1: Introduction to Data Science
Understanding the Role of Data Science in various industries
Overview of data science process/workflow
Introduction to key concepts: Data, information and knowledge
Module 2: Data Exploration and Preprocessing
Exploratory Data Analysis (EDA)
Handling missing data and outliers
Data cleaning techniques
Data normalization and scaling
Module 3: Introduction to statistical concepts
  • Descriptive statistics
  • Probability distribution
  • Hypothesis testing
  • Correlation and regression analysis
Module 4: Data Visualization
  • Importance of Data Visualization in Data Science
  • Basic plotting with matplotlib and seaborn
Module 5: Data Wrangling
Introduction to Pandas Library
Data manipulation and cleaning with Panda’s library
Merging grouping and reshaping data
Module 6: Feature Engineering
Importance of feature engineering in predictive modeling
Creating new features from existing features
Handling categorical variables
Feature scaling and transformation
MACHINE LEARNING FUNDAMENTALS
Module 1: Introduction to Machine Learning
  • Definition and types of machine learning
  • Machine Learning Application in various industries
  • Overview of machine learning process/workflow
Module 2: Data Preprocessing and Exploration
  • Data cleaning and handling of missing values
  • Exploratory Data Analysis (EDA)
  • Feature scaling and normalization
  • Dealing with outliers and missing values
Module 3: Supervised Learning Algorithms
  • Linear Regression
  • Logistics Regression
  • Support Vector Machine (SVM)
  • K Nearest Neighbor (KNN)
  • Decision Trees and Random Forest
Module 4: Unsupervised Learning
  • K Means Clustering
  • Hierarchical Clustering
  • Principal Component Analysis (PCA)
  • Association Rule Learning
Module 5: Model Evaluation and Selection
  • Training and Testing data
  • Cross-validation techniques
  • Evaluation of metrics for classification and regression
Module 6: Model Deployment
Module 7: Coupon Project
Students will select and solve real-life projects under the guidance of an instructor and add them to their profile.
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