Data Science
🕒 Duration: 4 Months
🎯 Level: Beginner to Advanced
🧑🏫 Mode: Online / Offline
📘 Module 1: Programming Foundations for Data Science (Weeks 1–3)
🔹 Week 1: Python Programming Fundamentals
- Day 1: Introduction to Data Science & Course Overview
- Day 2: Python Basics – Variables, Data Types, Operators
- Day 3: Control Structures – Conditionals, Loops, Functions
- Day 4: Python Data Structures – Lists, Dictionaries, Sets, Tuples
- Day 5: File Handling & Exception Management
- Day 6: Object-Oriented Programming in Python
- Day 7: Python Libraries – NumPy, Pandas, Matplotlib
🔹 Week 2: Data Manipulation with Python
- Day 8: NumPy Arrays & Mathematical Operations
- Day 9: Pandas Fundamentals – Series, DataFrames, Index Objects
- Day 10: Data Cleaning & Preprocessing
- Day 11: Data Transformation & Reshaping
- Day 12: Handling Missing Data & Outliers
- Day 13: Data Aggregation & Grouping
- Day 14: Time Series Analysis with Pandas
🔹 Week 3: Data Visualization
- Day 15: Principles of Effective Visualization
- Day 16: Matplotlib Basics
- Day 17: Statistical Visualizations with Seaborn
- Day 18: Interactive Visuals with Plotly
- Day 19: Dashboard Tools – Tableau, Power BI Intro
- Day 20: Geographic Data Visualization
- Day 21: Storytelling with Data
📊 Module 2: Statistics & Mathematics for Data Science (Weeks 4–6)
🔹 Week 4: Descriptive Statistics & Probability
- Day 22: Central Tendency & Dispersion
- Day 23: Fundamentals of Probability Theory
- Day 24: Distributions – Normal, Binomial, Poisson
- Day 25: Random Variables & Expected Values
- Day 26: Covariance & Correlation
- Day 27: Sampling Methods
- Day 28: Exploratory Data Analysis (EDA)
🔹 Week 5: Inferential Statistics
- Day 29: Confidence Intervals & Margin of Error
- Day 30: Hypothesis Testing – Z-tests, T-tests
- Day 31: ANOVA & Chi-Square Tests
- Day 32: Non-parametric Tests
- Day 33: Intro to Bayesian Statistics
- Day 34: A/B Testing
- Day 35: Statistical Fallacies
🔹 Week 6: Mathematics for Machine Learning
- Day 36: Linear Algebra – Vectors, Matrices
- Day 37: Matrix Operations
- Day 38: Eigenvalues & Eigenvectors
- Day 39: Calculus – Derivatives, Gradients
- Day 40: Multivariable Calculus
- Day 41: Optimization Techniques
- Day 42: Information Theory & Entropy
🤖 Module 3: Machine Learning (Weeks 7–9)
🔹 Week 7: Supervised Learning – Regression
- Day 43: Intro to ML & Scikit-learn
- Day 44: Linear Regression
- Day 45: Polynomial Regression & Regularization
- Day 46: Decision Trees
- Day 47: Random Forests
- Day 48: Support Vector Regression
- Day 49: Model Evaluation Metrics
🔹 Week 8: Supervised Learning – Classification
- Day 50: Logistic Regression
- Day 51: Decision Trees for Classification
- Day 52: Random Forests & Gradient Boosting
- Day 53: Support Vector Machines
- Day 54: Naive Bayes
- Day 55: Multi-class Classification
- Day 56: Handling Imbalanced Data
🔹 Week 9: Unsupervised Learning & Evaluation
- Day 57: K-Means, Hierarchical Clustering
- Day 58: DBSCAN & Density-Based Clustering
- Day 59: Dimensionality Reduction – PCA, t-SNE
- Day 60: Association Rules – Apriori
- Day 61: Cross-Validation
- Day 62: Hyperparameter Tuning
- Day 63: Feature Engineering
🧠 Module 4: Advanced Data Science Topics (Weeks 10–12)
🔹 Week 10: Deep Learning
- Day 64: Neural Networks Overview
- Day 65: TensorFlow & Keras Basics
- Day 66: Building Neural Networks
- Day 67: CNNs for Images
- Day 68: RNNs for Sequences
- Day 69: Transfer Learning
- Day 70: Model Deployment
🔹 Week 11: NLP & Time Series
- Day 71: Text Preprocessing
- Day 72: Sentiment Analysis
- Day 73: Word Embeddings – Word2Vec, GloVe
- Day 74: Transformers & BERT
- Day 75: ARIMA Forecasting
- Day 76: SARIMA & Prophet
- Day 77: Deep Learning for Time Series
🔹 Week 12: Big Data & Capstone Project
- Day 78: Big Data – Hadoop, Spark
- Day 79: PySpark for Big Data
- Day 80: Project Methodology
- Day 81: Capstone Project Planning
- Day 82: Project Execution
- Day 83: Final Presentation
- Day 84: Career Prep & Interview Guidance
📚 Additional Resources & Support
- Hands-on Assignments (Weekly)
- Kaggle Competition Participation
- Real-world Case Studies & Projects
- GitHub Portfolio Building
- Networking with Industry Experts
- Mock Interviews
- High-performance Computing Access
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Types of software development
Types of software testing strategies
Ability to identify errors
Ability to fix errors before application release
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