Data Science

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

Master Python, statistics, machine learning, and data visualization to become a data scientist.

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

Take the Next Step in Your Career

Join our comprehensive Data Science program and gain real-world skills that employers demand.

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