Data Science Course Details

Course Overview

This course offers a comprehensive introduction to data science techniques and machine learning algorithms. Students will learn how to collect, preprocess, analyze, and visualize data, as well as how to build and evaluate predictive models using machine learning algorithms. The course emphasizes hands-on experience with real-world datasets and practical applications of data science and machine learning techniques.

Learning Objectives

• Understand the principles and methodologies of data science and machine learning.
• Gain proficiency in data manipulation, visualization, and analysis using Python.
• Learn various supervised and unsupervised machine learning algorithms and their applications.
• Develop skills in model evaluation, validation, and interpretation.
• Apply data science and machine learning techniques to solve real-world problems.

Syllabus

• Induction & Course Introduction
• Impact of Data Science in today’s world & Roles in Data Science
• Python with Machine learning, Deep Learning and AI

Course Outline

Introduction to Data Science

• Introduction to data science and its applications
• Python programming basics for data science
• Introduction to data analysis libraries: NumPy, Pandas

Data Visualization and Exploratory Data Analysis (EDA)

• Data visualization with Matplotlib and Seaborn
• Exploratory data analysis (EDA) techniques
• Data preprocessing and cleaning

Statistical Methods for Data Science

• Statistical inference and hypothesis testing
• Correlation and regression analysis
• Probability distributions and sampling techniques

Introduction to Machine Learning

• What is machine learning?
• Types of machine learning (supervised, unsupervised, reinforcement learning)
• Applications of machine learning
• Overview of Python and key libraries (NumPy, Pandas, Matplotlib)

Data Pre-processing

• Data cleaning and handling missing values
• Feature scaling and normalization
• Encoding categorical variables
• Data splitting for training and testing

Supervised Learning – Regression

• Introduction to regression analysis
• Simple linear regression
• Multiple linear regression
• Polynomial regression
• Evaluation metrics for regression models (RMSE, MAE, R-squared)

Supervised Learning – Classification

• Introduction to classification
• Logistic regression
• k-Nearest Neighbors (k-NN)
• Decision trees and ensemble methods (Random Forest, Gradient Boosting)
• Evaluation metrics for classification models (accuracy, precision, recall, F1-score)

Unsupervised Learning – Clustering

• Introduction to clustering
• K-means clustering
• Hierarchical clustering
• DBSCAN clustering
• Evaluation metrics for clustering (silhouette score, Davies-Bouldin index)

Dimensionality Reduction

• Introduction to dimensionality reduction
• Principal Component Analysis (PCA)
• t-Distributed Stochastic Neighbor Embedding (t-SNE)
• Feature selection techniques

Model Evaluation and Selection

• Cross-validation techniques
• Model selection and comparison

Deep Learning

• Introduction to deep learning
• Transfer learning and pre-trained models

Natural Language Processing (NLP)

• Introduction to NLP
• Text pre-processing techniques
• Word embeddings (Word2Vec, GloVe)
• Sentiment analysis and text classification

Capstone Project

• Hands-on project applying machine learning techniques to a real-world dataset
• Data exploration, model building, evaluation, and interpretation
• Presentation of project findings