Course Syllabus
Download SyllabusCore Python
- Introduction to Python, features, and applications
- Variables, data types, strings, and operators
- Decision-making and looping statements
- Functions (built-in, user-defined, lambda, recursion)
- Object-Oriented Programming (classes, objects, inheritance, polymorphism)
- Exception handling and file handling basics
Data Science Phase 1
- Data analysis with Pandas & NumPy
- Data cleaning, mapping, grouping, and handling duplicates
- Reading/writing data from CSV
- Basic data visualizations
- NumPy arrays, attributes, and functions
Data Science Phase 2
- Key data science terms and exploratory data analysis
- Machine learning introduction (classification, regression, prediction)
- Data visualization using Matplotlib and Seaborn (charts, plots, heatmaps, scatterplots)
EDA & Machine Learning with Scikit-Learn
- Types of ML algorithms (supervised, unsupervised, ensemble)
- Workflow: dataset preparation, feature scaling, model building, evaluation
- Popular ML algorithms: Linear/Logistic Regression, Naïve Bayes, KNN, K-Means, SVM, PCA, Decision Trees, Random Forest, XGBoost
Deep Learning & AI
- Neural networks (ANN, CNN, RNN) and activation functions
- Model training, evaluation, and optimization
- Hands-on projects with datasets like MNIST, CIFAR, IMDB Reviews, Boston Housing
- Natural Language Processing (NLP) using NLTK & SpaCy
- Computer Vision with OpenCV (image processing, face detection, augmentation)
Generative AI & LLMs
- Introduction to Generative AI concepts and applications
- Large Language Models (LLMs) – GPT, BERT, T5
- Prompt engineering basics and ethical AI practices
- Transformer architecture and attention mechanism
- Retrieval-Augmented Generation (RAG) with FAISS/Chroma
Hands-on with LLMs
- Working with OpenAI APIs, HuggingFace, and LangChain
- Building workflows, chatbots, and Q&A systems
- Streamlit integration for LLM apps (front-end + back-end)
- Deploying Streamlit apps locally and on the cloud
In conclusion, the Data Science Course Syllabus at SLA Institute helps learners build the right mix of technical skills, problem-solving ability, and practical knowledge. It trains you to clean data, apply machine learning models, and visualize insights, preparing you for roles like Data Analyst, Data Engineer, and Machine Learning Engineer. With the growing demand for data-driven decisions, this course opens up strong and rewarding career opportunities in the tech industry.
