Data Science Course Syllabus @ Softzone

1. Introduction to Data Science

• Overview of Data Science

• Data Science Life Cycle

• Role of a Data Scientist

2. Mathematics & Statistics

• Probability & Distributions

• Descriptive & Inferential Statistics

• Linear Algebra & Calculus Basics

• Hypothesis Testing

3. Programming for Data Science

• Python Programming Essentials

• Libraries: NumPy, Pandas, Matplotlib, Seaborn

• SQL for Data Retrieval

4. Data Wrangling & Cleaning

• Handling Missing Data & Outliers

• Data Transformation & Encoding

• Data Normalization & Scaling

5. Exploratory Data Analysis (EDA)

• Summary Statistics

• Correlation Analysis

• Visualization Techniques (Matplotlib, Seaborn, Plotly)

6. Data Visualization & Dashboarding

• Data Storytelling

• Tools: Tableau / Power BI / Python Visualizations

7. Machine Learning

• Supervised Learning: Regression, Classification

• Unsupervised Learning: Clustering, Dimensionality Reduction

• Model Evaluation & Validation

8. Advanced Topics (Optional / Extended)

• Deep Learning (Neural Networks, CNN, RNN)

• Natural Language Processing (NLP)

• Big Data Tools (Hadoop, Spark)

• Model Deployment & Cloud Basics

9. Databases & Big Data

• SQL & NoSQL Databases

• Data Handling at Scale

10. Ethics & Data Privacy

• Responsible Data Use

• Bias in Algorithms

• Data Privacy Laws (GDPR etc.)

11. Capstone / Final Project

• Real-world dataset projects

• Predictive modeling, dashboards, reporting

• Portfolio development for job readiness