This comprehensive 12-month certification program is designed to equip learners with the essential knowledge and practical skills required to excel in the field of Data Science. The curriculum progresses from foundational concepts to advanced techniques, incorporating hands-on projects and real-world case studies.

Program Overview:

Duration: 12 Months

Format: Online / Offline (Hybrid Mode) (Self-paced with live sessions and mentorship)

Target Audience: Beginners with a strong analytical aptitude, professionals looking to transition into data science, and anyone interested in building a career in data.

Classes: 2 Classes / Week

Class Duration: 2 Hours / Class

Month 1: Foundations of Programming & Data Literacy

• Week 1-2: Introduction to Python for Data Science

  • Python basics: variables, data types, operators, control flow (if/else, loops).

  • Functions, modules, and packages.

  • Introduction to Jupyter Notebooks/Google Colab.

  • Hands-on: Simple Python scripts, basic data manipulation.

• Week 3-4: Data Structures & Algorithms (Basic)

  • Lists, tuples, dictionaries, sets.

  • Introduction to basic algorithms: searching, sorting.

  • Time and space complexity (Big O notation – introductory).

  • Hands-on: Implementing basic data structures and algorithms.

Month 2: Data Manipulation & Analysis with Python

• Week 1-2: NumPy for Numerical Computing

  • Introduction to arrays, array operations, broadcasting.

  • Vectorization for performance.

  • Hands-on: Numerical computations with large datasets.

• Week 3-4: Pandas for Data Analysis

  • DataFrames and Series: creation, indexing, selection.

  • Data cleaning: handling missing values, duplicates.

  • Data transformation: merging, joining, grouping, pivoting.

  • Hands-on: Real-world data cleaning and preparation tasks.

Month 3: Data Visualization & Exploratory Data Analysis (EDA)

• Week 1-2: Matplotlib & Seaborn

  • Creating various plots: line, bar, scatter, histogram, box plots.

  • Customizing plots: titles, labels, legends, colors.

  • Statistical plots with Seaborn: distributions, relationships.

  • Hands-on: Visualizing different types of datasets.

• Week 3-4: Exploratory Data Analysis (EDA) Principles

  • Understanding data distributions, outliers, correlations.

  • Identifying patterns and anomalies.

  • Hypothesis generation from data.

  • Project: Perform a complete EDA on a given dataset, presenting insights.

Month 4: Statistics for Data Science

• Week 1-2: Descriptive Statistics

  • Measures of central tendency (mean, median, mode).

  • Measures of dispersion (variance, standard deviation, IQR).

  • Skewness and kurtosis.

  • Hands-on: Calculating and interpreting descriptive statistics.

• Week 3-4: Inferential Statistics & Probability

  • Probability theory: basic concepts, conditional probability, Bayes’ theorem.

  • Sampling distributions, Central Limit Theorem.

  • Hypothesis testing: Z-tests, T-tests, ANOVA, Chi-squared tests.

  • Confidence intervals.

  • Hands-on: Conducting hypothesis tests and interpreting results.

Month 5: Introduction to Machine Learning

• Week 1-2: Machine Learning Fundamentals

  • Types of ML: Supervised, Unsupervised, Reinforcement Learning.

  • Bias-Variance Trade-off, Overfitting, Underfitting.

  • Model evaluation metrics: accuracy, precision, recall, F1-score.

  • Cross-validation.

  • Hands-on: Setting up a basic ML pipeline.

• Week 3-4: Regression Models

  • Linear Regression: simple and multiple.

  • Polynomial Regression.

  • Regularization: Ridge, Lasso, Elastic Net.

  • Hands-on: Building and evaluating regression models on real-world data.

Month 6: Classification & Model Selection

• Week 1-2: Classification Models

  • Logistic Regression.

  • K-Nearest Neighbors (KNN).

  • Support Vector Machines (SVM).

  • Decision Trees.

  • Hands-on: Implementing and comparing various classification algorithms.

• Week 3-4: Ensemble Methods & Model Tuning

  • Random Forests, Gradient Boosting (XGBoost, LightGBM).

  • Hyperparameter tuning: Grid Search, Random Search.

  • Feature engineering and selection.

  • Project: Develop a robust classification model for a given problem.

Month 7: Unsupervised Learning & Clustering

• Week 1-2: Clustering Algorithms

  • K-Means Clustering.

  • Hierarchical Clustering.

  • DBSCAN.

  • Evaluation metrics for clustering.

  • Hands-on: Applying clustering to segment data.

• Week 3-4: Dimensionality Reduction

  • Principal Component Analysis (PCA).

  • t-SNE.

  • Hands-on: Reducing data dimensionality for visualization and model performance.

Month 8: SQL & Database Management

• Week 1-2: Relational Databases & SQL Fundamentals

  • Database concepts: tables, schemas, relationships.

  • SQL queries: SELECT, FROM, WHERE, GROUP BY, ORDER BY.

  • JOINs: INNER, LEFT, RIGHT, FULL.

  • Hands-on: Querying data from a relational database.

• Week 3-4: Advanced SQL & NoSQL Introduction

  • Subqueries, CTEs (Common Table Expressions).

  • Window functions.

  • Introduction to NoSQL databases (e.g., MongoDB, Cassandra) and their use cases.

  • Project: Design a simple database schema and populate it, then perform complex queries.

Month 9: Big Data Technologies & Cloud Platforms

• Week 1-2: Introduction to Big Data Concepts

  • Challenges of Big Data.

  • Hadoop Ecosystem (HDFS, MapReduce – conceptual understanding).

  • Introduction to Spark for distributed computing.

  • Hands-on: Basic Spark operations (using PySpark).

• Week 3-4: Cloud Platforms for Data Science

  • Overview of AWS, Google Cloud, Azure for data science.

  • Setting up virtual machines, using cloud storage.

  • Introduction to cloud-based ML services.

  • Hands-on: Deploying a simple ML model on a cloud platform (e.g., Google Colab with GPU, or a free tier cloud service).

Month 10: Deep Learning Fundamentals

• Week 1-2: Neural Networks & Keras/TensorFlow

  • Perceptrons, activation functions.

  • Feedforward Neural Networks.

  • Building and training simple neural networks with Keras/TensorFlow.

  • Hands-on: Implementing a basic multi-layer perceptron for classification.

• Week 3-4: Convolutional Neural Networks (CNNs)

  • Introduction to CNN architecture: convolutional layers, pooling layers.

  • Applications in image recognition.

  • Transfer learning.

  • Hands-on: Building a CNN for image classification.

Month 11: Natural Language Processing (NLP)

• Week 1-2: NLP Fundamentals & Text Preprocessing

  • Tokenization, stemming, lemmatization.

  • Stop words, n-grams.

  • Text representation: Bag-of-Words, TF-IDF.

  • Hands-on: Cleaning and preparing text data.

• Week 3-4: Advanced NLP & Text Models

  • Word Embeddings (Word2Vec, GloVe).

  • Recurrent Neural Networks (RNNs) – conceptual.

  • Introduction to Transformers (BERT, GPT – conceptual).

  • Sentiment analysis, text classification.

  • Project: Develop an NLP application (e.g., sentiment analyzer, spam detector).

Month 12: Deployment, Ethics & Capstone Project

• Week 1-2: Model Deployment & MLOps Concepts

  • Introduction to MLOps: version control, reproducibility.

  • API development for models (e.g., Flask/FastAPI).

  • Containerization (Docker – basic).

  • Hands-on: Creating a simple web API for a trained model.

• Week 3-4: Data Ethics, Responsible AI & Capstone Project

  • Bias in AI, fairness, transparency, privacy.

  • Ethical considerations in data collection and model deployment.

  • Capstone Project: Work on a comprehensive end-to-end data science project, from data acquisition and cleaning to model building, evaluation, and a basic deployment simulation. Present findings and insights.

  • Career Guidance: Resume building, interview preparation, portfolio development.