Data Science
Description
Data Science is an interdisciplinary field that focuses on extracting insights and knowledge from structured and unstructured data using statistical methods, programming, and machine learning techniques. It combines elements of mathematics, computer science, and domain expertise to help organizations make data-driven decisions.
What You will Learn
During this program, you will learn how to collect, analyze, and visualize data to extract meaningful insights and make data-driven decisions. You will gain both theoretical knowledge and practical hands-on experience in using tools like Python, SQL, and machine learning libraries to perform statistical analysis, build predictive models, and apply data science techniques to real-world problems.
After this training, you will be able to:
Course Syllabus
Data Science
- Linear Algebra: Vectors, matrices, matrix operations, eigenvalues, eigenvectors
- Calculus: Differentiation, integration, optimisation techniques (Gradient Descent)
- Probability & Statistics: Probability distributions, Bayes’ theorem, hypothesis testing, correlation, regression analysis
- Overview of Data Science & AI
- Applications in Various Industries
- Data Science Lifecycle & Project Workflow
- Tools & Technologies Used (Python, Jupyter Notebook, Google Colab)
- Understanding Structured & Unstructured Data
- Python Basics & Data Structures
- Control Statements & Functions
- File Handling & Exception Handling
- Object-Oriented Programming (OOP) in Python
- NumPy for Numerical Computing
- Pandas for Data Manipulation & Cleaning
- Matplotlib & Seaborn for Data Visualisation
- Introduction to Databases & SQL
- Data Modeling & Normalisation
- SQL Queries (SELECT, INSERT, UPDATE, DELETE)
- Joins, Subqueries, and Window Functions
- Stored Procedures & Performance Optimisation
- Connecting Python with SQL
- Data Cleaning Techniques
- Handling Missing Data & Outliers
- Feature Engineering & Transformation
- Scaling & Normalisation Techniques
- Correlation Analysis & Hypothesis Testing
- Creating Reports & Dashboards
- Excel for Data Analysis & Visualisation
- Pivot Tables & Advanced Excel Functions
- Introduction to Power BI & Data Transformation
- DAX Functions & Power BI Dashboard Creation
- Introduction to Machine Learning & AI
- Supervised Learning Algorithms
- Linear & Logistic Regression
- Decision Trees & Random Forest
- Support Vector Machines (SVM)
- K-Nearest Neighbors (KNN)
- Unsupervised Learning Techniques
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
- Model Evaluation & Hyper parameter Tuning
- Real-World ML Applications
- Basics of Artificial Neural Networks (ANN)
- Convolutional Neural Networks (CNN) for Image Processing
- Recurrent Neural Networks (RNN) & LSTMs for Sequence Data
- Transfer Learning & Pre-trained Models
- AI Model Deployment using Flask & FastAPI
- Text Processing & Tokenisation
- Word Embeddings (Word2Vec, GloVe, BERT)
- Sentiment Analysis & Named Entity Recognition (NER)
- Transformers & Pre-trained Language Models
- Chatbot Development & AI-Based Text Processing
- Introduction to Big Data & Hadoop
- Apache Spark for Data Processing
- NoSQL Databases (MongoDB, Cassandra)
- Data Engineering Pipelines & ETL Process
- Cloud Computing with AWS, GCP, Azure
- Deploying Data Science Models on Cloud
- End-to-End Data Science Project Lifecycle
- Industry-Specific Case Studies
- Model Deployment & Performance Monitoring
- Resume Building & Interview Preparation
- Git & Version Control for Data Science
- StartsThis Week
- Duration6 Months, 6 hrs / week