" MAKING ODISHA EMPLOYABLE "

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This Data Science Course is designed for individuals looking to build a strong foundation in data analysis, machine learning, and artificial intelligence. Whether you’re a beginner or an experienced professional, this course covers everything from data manipulation and visualization to deep learning and model deployment.

Course Modules & Topics

Module 1: Introduction to Data Science

• What is Data Science?
• Role of a Data Scientist
• Applications of Data Science in Different Industries
• Data Science vs. Data Analytics vs. Machine Learning vs. AI

Module 2: Python & R for Data Science

• Introduction to Python & R
• Data Types, Variables, and Operators
• Control Flow and Functions
• Libraries for Data Science (NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow)
• Jupyter Notebook & IDEs

Module 3: Data Wrangling & Preprocessing

• Importing and Exporting Data (CSV, Excel, SQL, JSON)
• Data Cleaning Techniques (Handling Missing Values, Duplicates)
• Data Transformation and Feature Engineering
• Handling Categorical and Numerical Data
• Standardization & Normalization

Module 4: Exploratory Data Analysis (EDA)

• Descriptive Statistics
• Data Visualization Techniques (Matplotlib, Seaborn)
• Correlation & Covariance
• Detecting and Handling Outliers
• Hypothesis Testing

Module 5: SQL for Data Science

• SQL Basics (SELECT, INSERT, UPDATE, DELETE)
• Joins, Aggregations, and Subqueries
• Data Manipulation and Filtering
• Advanced SQL Queries for Data Analysis

Module 6: Machine Learning Basics

• Supervised vs. Unsupervised Learning
• Regression (Linear, Multiple, Polynomial)
• Classification Algorithms (Logistic Regression, Decision Tree, Random Forest, SVM, Naïve Bayes)
• Clustering (K-Means, Hierarchical Clustering)
• Model Evaluation & Performance Metrics

Module 7: Advanced Machine Learning

• Dimensionality Reduction (PCA, t-SNE)
• Ensemble Learning (Boosting, Bagging)
• Feature Selection & Engineering
• Hyperparameter Tuning (Grid Search, Random Search)
• Time Series Analysis & Forecasting

Module 8: Deep Learning & Neural Networks

• Introduction to Deep Learning
• Artificial Neural Networks (ANN)
• Convolutional Neural Networks (CNN) for Image Processing
• Recurrent Neural Networks (RNN) & LSTMs for Time Series Data
• Autoencoders & GANs
• Transfer Learning

Module 9: Natural Language Processing (NLP)

• Text Preprocessing (Tokenization, Lemmatization, Stemming)
• Sentiment Analysis & Text Classification
• Named Entity Recognition (NER)
• Topic Modeling (LDA, LSA)
• Transformers & Large Language Models (BERT, GPT)

Module 10: Big Data & Cloud Computing

• Introduction to Big Data & Hadoop
• Apache Spark for Data Processing
• Cloud Platforms (AWS, Google Cloud, Azure)
• Deploying Machine Learning Models in Cloud
• MLOps & CI/CD Pipelines

Module 11: Data Science Projects & Case Studies

• Real-World Case Studies (Finance, Healthcare, Retail, Marketing)
• End-to-End Data Science Project Development
• Model Deployment using Flask, FastAPI, and Streamlit
• Building a Data Science Portfolio
• Best Practices for Data Science Interviews

Who Should Take This Course?

✔️ Beginners interested in starting a career in Data Science
✔️ Professionals looking to switch careers to AI & ML
✔️ Students seeking practical knowledge for real-world projects
✔️ Analysts & Engineers aiming to upskill in Data Science

Certification

Upon course completion, students will receive a Data Science Certification showcasing their skills and project work.