Data Science Program
Become a job-ready Data Scientist with hands-on projects and mentorship.
Featured Program
Why Choose This Course?
1:1 Mentorship
Personalized guidance from industry experts to clear doubts and build your career path.
Live Projects
Work on real-world case studies and build a portfolio that employers love.
Placement Support
Dedicated placement cell to help you with resume building and mock interviews.
24/7 Support
Never get stuck. Our support team is available round the clock to assist you.
Curriculum & Learning Path
Introduction to Data Science
- What are analytics & Data Science?
- Business Analytics vs. Data Analytics vs. Data Science
- Lifecycle of Data Science
- Applications in Various Industries
- Key tools and frameworks
MySQL
- Introduction to SQL and Database Types
- CRUD Operations and Data Integrity
- Data Retrieval, Filtering, String Operations
- Aggregate Functions, Joins, Constraints, ER Modelling
- Advanced Query Writing, Case Statements, CTEs
- Date Manipulation, Window Functions
- Views, Indexes, Data Partitioning
- Stored Procedures, Triggers, TCL/DCL Commands
Power BI
- Introduction to Power BI & Power Query Editor
- M Query, Data Modelling, Filter Context
- Data Transformation, Calculations, Measures
- Visualizations, Different Charts, Dashboard Creation
- DAX, Row-Level Security (RLS)
Programming for Data Science (Python)
- Python Fundamentals: Syntax, Variables, Data Types
- Operators, Strings, Data Structures (List, Tuple, Set, Dictionary)
- Control Flow (if, else, for, while, break, continue)
- Functions, Exception Handling, Modules, File Handling
- NumPy: Array Management, Statistical Analysis
- Pandas: Data Manipulation, Aggregation
- Visualization: Matplotlib, Seaborn
- OOPS Concepts, Database Connectivity
- EDA: Univariate and Bivariate Analysis
Statistics
- Descriptive Statistics: Mean, Median, Mode, Variance, SD
- Probability Distributions: Bernoulli, Binomial, Poisson, Normal
- Sampling Techniques, Central Limit Theorem
- Hypothesis Testing: Null/Alternative, Type I/II Errors, p-Value
- Statistical Tests: Z-Test, t-Test, Chi-Square, ANOVA
Machine Learning
- Supervised Learning (Regression): Linear Regression, metrics (RMSE, R²)
- Supervised Learning (Classification): Decision Trees, Logistic Regression, KNN, Naive Bayes, SVM
- Ensemble Methods: Random Forests, AdaBoost, Gradient Boosting, XGBoost
- Model Evaluation: Cross-Validation, Bias-Variance Trade-Off
- Unsupervised Learning: K-Means, DBSCAN, Hierarchical Clustering
- Dimensionality Reduction: PCA, t-SNE
Deep Learning
- Neural Networks: Neurons, Layers, Activation Functions, Backpropagation
- CNNs for Image Classification
- Transfer Learning (ResNet, VGG)
- RNNs, LSTM, GRU for Sequence Data
- NLP Basics: Tokenization, TF-IDF, Word Embeddings (Word2Vec, GloVe)
- Transformers: BERT, GPT overview
Big Data Hadoop and Spark
- Big Data Overview, Hadoop Ecosystem (HDFS, MapReduce, Hive)
- Apache Spark Architecture, RDDs
- Spark DataFrames, Spark SQL
- Data Processing & Transformations in Spark
Generative AI and Prompt Engineering
- Overview of GenAI, GANs, VAEs
- LLMs and GPT Evolution
- Prompt Engineering Basics
- ChatGPT Interface and Use Cases
- Image Generation (MidJourney, DALL-E)
- GenAI in Security & Audio Processing
Tools & Technologies
Hands-on Projects
Work on industry-relevant datasets and problem statements to build a portfolio-worthy project.
Analyze trends, forecast data, and provide actionable insights just like a professional.
Alumni Working At Top Global Companies
Our learners are transforming industries worldwide.
Student Success Stories
"The Data Science course helped me land a role as a Data Analyst at Infosys!"
"Curriculum is very practical. You actually build models and dashboards."
Frequently Asked Questions
Program Fees
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