Data Science & Analytics

Module – 1 Introduction to Data Science
What is Data Science?
Introduction to the Data Science Workflow
Importance and applications of data science

Module – 2 Introduction to Data
Understanding data types: structured vs. unstructured
Basics of data collection and data entry

Module – 3 Data Wrangling
Data cleaning and pre-processing techniques
Handling missing data
Data manipulation with Pandas in Python

Module – 4 Exploratory Data Analysis (EDA)
Descriptive statistics: mean, median, mode, standard deviation, etc.
Data visualization basics with Matplotlib and Seaborn

Module – 5 Data Visualization
Data visualization using Matplotlib and Seaborn libraries
Visualizing distributions, relationships, and trends in data

Module – 6 Statistical Analysis
Probability theory and distributions
Hypothesis testing (t-tests, chi-square tests)
Correlation and regression analysis
Introduction to statistical inference

Module – 7 Machine Learning Fundamentals
Introduction to machine learning
Supervised learning
Unsupervised learning
Model evaluation and validation techniques

Module – 8 Model Evaluation
Train/test split
Cross-validation
Performance metrics: accuracy, recall, precision, sensitivity

Module – 9 Data Engineering
Database systems (SQL and NoSQL)
Data warehousing
Introduction to Power BI: overview, Power Query Editor, visualizing loaded data, creating story, creating dashboards

Module – 10 Advanced Topics and Applications 
Clustering algorithms (K-means, hierarchical clustering)
Dimensionality reduction techniques (PCA, t-SNE)
Natural Language Processing (NLP) basics
Introduction to deep learning and neural networks

Module 11 – Project
Project: applying learned concepts and tools to solve a real-world problem