DATA SCIENCE COURSE CONTENT
1. Introduction to Data Science
- What is Data Science?
- Data-driven culture & applications
- Big Data vs Analytics vs AI
- Roles, responsibilities & tools
2. Python Programming
- Python basics & Anaconda setup
- Data structures: Lists, Tuples, Dictionaries
- Conditions, Loops, Functions
- Working with files and modules
3. Python Libraries
- NumPy and SciPy
- Pandas: Series & DataFrames
- Matplotlib, Seaborn: Visualization
- Sklearn: Machine learning utilities
4. Statistics & Math
- Descriptive, Inferential Statistics
- Distributions, Hypothesis Testing
- ANOVA, t-tests, p-values
- Probability theory and Bayes theorem
5. Machine Learning
- Supervised vs Unsupervised Learning
- Regression, Classification algorithms
- Decision Trees, SVM, KNN, Naive Bayes
- PCA, Clustering: K-Means
- Model Evaluation Metrics (ROC, AUC, F1)
6. SQL Essentials
- Basics of RDBMS
- DDL, DML, DQL
- Joins, Subqueries, Aggregation
- Case statements, Functions
7. Deep Learning & AI
- Neural networks, CNNs, RNNs
- NLP: tokenization, vectorization, embeddings
- Text classification, Chatbot basics
- Computer Vision overview
8. Analytics Tools
- Power BI: Dashboards, DAX, Service
- Tableau: Storytelling with data
- Excel: Advanced Formulas & Charts
- R Programming: Tidyverse, ggplot2
