Bootcamp: Applied Data Science with Python & AI

Learn data analysis, visualization, machine learning, and real-world data-driven decision making.

12 Weeks

Online / Offline

English & Tamil

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Course Overview

Extract actionable insights from raw data in our Data Science program. You will master statistical analysis, data visualization, and advanced machine learning algorithms. By working with real-world datasets using Python, Pandas, and Matplotlib, you will learn how to drive strategic business decisions through data.

Skills You Will Master

Data Analysis & Visualization Python for Data Science SQL & Databases Machine Learning Statistical Analysis Data Engineering Basics

Course Curriculum

1. Introduction to Data Science

What is Data Science? Importance & Applications
Data Science vs Data Analytics vs Machine Learning
Types of Data: Structured, Unstructured, Semi-structured
Overview of Data Science Workflow
Tools & Technologies: Python, R, SQL, Excel, Jupyter Notebook

2. Mathematics & Statistics for Data Science

Linear Algebra: vectors, matrices, matrix operations
Probability & Statistics: mean, median, mode, variance, standard deviation, probability distributions
Descriptive & Inferential Statistics
Hypothesis Testing: t-test, chi-square test, ANOVA
Correlation & Covariance

3. Python Programming for Data Science

Python basics: variables, loops, functions, OOP
Libraries: NumPy, pandas, matplotlib, seaborn
Data manipulation: DataFrames, indexing, slicing, filtering, aggregation
Data visualization: histograms, scatter plots, boxplots, pairplots

4. Data Cleaning & Preprocessing

Handling missing values, duplicates, outliers
Data normalization & standardization
Encoding categorical variables (Label Encoding, One-Hot Encoding)
Feature selection & dimensionality reduction (PCA)
Data transformation & scaling

5. Data Analysis & Visualization

Exploratory Data Analysis (EDA)
Descriptive statistics & graphical analysis
Correlation & pattern detection
Interactive visualizations with Plotly & Seaborn
Dashboard creation basics (Tableau / Power BI)

6. SQL & Databases

Introduction to relational databases & SQL
CRUD operations: SELECT, INSERT, UPDATE, DELETE
JOINs, GROUP BY, HAVING, ORDER BY
Subqueries & nested queries
Connecting SQL with Python (pandas + SQLAlchemy)

7. Machine Learning for Data Science

Supervised Learning: Regression (Linear, Logistic), Classification (KNN, Decision Tree, Random Forest, SVM)
Unsupervised Learning: Clustering (K-Means, Hierarchical), Dimensionality Reduction (PCA)
Model evaluation metrics: accuracy, precision, recall, F1-score, ROC-AUC
Cross-validation & hyperparameter tuning

8. Advanced Machine Learning

Ensemble Methods: Bagging, Boosting, AdaBoost, Gradient Boosting, XGBoost
Regularization techniques: Lasso, Ridge, ElasticNet
Time Series Analysis & Forecasting
Feature engineering for better model performance

9. Deep Learning (Optional)

Introduction to Neural Networks
Feedforward Neural Networks (FNN)
Convolutional Neural Networks (CNN) for images
Recurrent Neural Networks (RNN, LSTM) for sequences
Frameworks: TensorFlow, Keras, PyTorch

10. Natural Language Processing (NLP)

Text preprocessing: tokenization, stemming, lemmatization
Bag-of-Words & TF-IDF
Word embeddings: Word2Vec, GloVe
Text classification & sentiment analysis
Transformers & attention models (BERT, GPT basics)

11. Big Data & Cloud for Data Science

Introduction to Big Data concepts: Hadoop, Spark
Data pipelines & ETL
Cloud platforms for Data Science: AWS, Azure, Google Cloud
Data storage & processing on cloud

12. Data Science Project Workflow

Problem identification & data collection
Data cleaning & preprocessing
Model building, evaluation, and optimization
Visualization & insights
Deployment & reporting