Course Description:

Semester-I:
Foundations: Mathematics and Statistics (Linear algebra, calculus,
probability, and statistical inference are crucial for understanding data
analysis and modelling)
Programming: (Python and R are the most commonly used languages in
data science. The syllabus will cover their syntax, data structures, and
libraries for data manipulation, analysis, and visualization (e.g., Pandas,
NumPy, Matplotlib, Seaborn).
Data Handling: (This includes data cleaning, wrangling, and preprocessing
techniques to prepare data for analysis)

Semester-II:
Exploratory Data Analysis (EDA): (Learning to explore and understand data
through visualization and summary statistics)
Machine Learning: Supervised Learning: Algorithms like linear regression,
logistic regression, decision trees, support vector machines, and neural
networks are typically covered.
Unsupervised Learning: Clustering algorithms (like k-means),
dimensionality reduction techniques, and anomaly detection methods are
often included.
Model Evaluation and Selection: Understanding metrics to evaluate model
performance and choosing the best model for a specific task.
Advanced Topics: Big Data Technologies ( Introduction to Hadoop, Spark,
and cloud computing platforms like AWS, Azure, or Google Cloud for
handling large datasets)

Semester-III: Natural Language Processing (NLP): Techniques for
processing and analysing text data, including text cleaning, sentiment
analysis, and topic modelling.
Deep Learning: Introduction to neural networks and deep learning
frameworks like TensorFlow and Keras.
Data Visualization: Advanced techniques beyond basic charts and graphs,
often using tools like Tableau or Power BI.
Other Important Areas:
• Data Ethics and Privacy: Understanding the ethical considerations
surrounding data collection, usage, and storage.
• Data Mining: Techniques for discovering patterns and knowledge
from large datasets.
• Data as a Service and Data Democratization: Emerging trends in
how data is accessed and used.
• Cloud Computing: Understanding cloud platforms for data storage
and processing.
• Capstone Projects: Applying learned skills to real-world data
science projects.