OBERON GLOBAL SOLUTIONS PVT LTD

SYLLABUS - DATA SCIENCE TRAINING

First Quarter: Foundations of Data Science

1. Introduction to Data Science

• Overview of the data science process

• Understanding the role of a data scientist

2. Mathematics and Statistics for Data Science

• Probability and statistics

• Linear algebra

• Calculus

3. Programming Fundamentals

• Introduction to a programming language (e.g., Python or R)

• Data manipulation and analysis using libraries like Pandas and NumPy

4. Data Visualization

• Using tools like Matplotlib and Seaborn

• Design principles for effective data visualization

Second Quarter: Data Manipulation and Analysis

5. Database Management and SQL

• Basics of relational databases

• Querying databases using SQL

6. Data Cleaning and Preprocessing

• Dealing with missing data

• Handling outliers

• Feature scaling and normalization

7. Machine Learning Fundamentals

• Introduction to supervised and unsupervised learning

• Basic algorithms (linear regression, logistic regression, k-means clustering)

8. Introduction to Big Data Technologies

• Overview of Hadoop and Spark

• Working with large datasets

Third Quarter: Advanced Machine Learning

9. Advanced Machine Learning Algorithms

• Decision trees, random forests, support vector machines

• Neural networks and deep learning

10. Model Evaluation and Hyperparameter Tuning

• Cross-validation

• Grid search and random search for hyperparameter tuning

11. Feature Engineering

• Creating meaningful features from raw data

• Dimensionality reduction techniques

Fourth Quarter: Specialization and Capstone Project

12. Specialization Track (Choose one or more)

• Natural Language Processing (NLP)

• Computer Vision

Time Series Analysis

13. Advanced Topics in Data Science

• Ethics in data science

• Deploying machine learning models

• Interpretability and explainability

14. Capstone Project

• Apply learned skills to a real-world problem

• Develop an end-to-end data science project

Throughout the Year: Soft Skills and Industry Practices

15. Communication and Presentation Skills

• Communicating findings effectively

• Storytelling with data

16. Collaboration and Teamwork

• Working in cross-functional teams

• Version control with tools like Git

17. Industry Practices and Tools

• Understanding the data science workflow in industry

• Using tools like Jupyter Notebooks, Docker, and others

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