Course 1: Introduction to Data Science

The Data Science Lifecycle

Course 1: Introduction to Data Science

0 (0)
Overview
Curriculum
Reviews

Curriculum

  • 9 Sections
  • 168 Lessons
  • 26 Quizzes
  • 18 Assignments
  • 0m Duration
Expand All
Section 1: The Data Science Lifecycle
17 Lessons3 Quizzes
  1. Module 1.1: What Is Data Science? Definitions & Scope
  2. 1.1.1 Definition and Core Components
  3. 1.1.2 Distinction from Related Fields
  4. 1.1.3 The Data Science Workflow
  5. 1.1.4 Real-World Applications
  6. 1.1 Quiz
  7. Module 1.2: Stages of a Data Science Project
  8. 1.2.1 Stage 1: Problem Definition
  9. 1.2.2 Stage 2: Data Acquisition
  10. 1.2.3 Stage 3: Data Cleaning & Preparation
  11. 1.2.4 Stage 4: Exploratory Data Analysis (EDA)
  12. 1.2.5 Stage 5: Modeling
  13. 1.2.6 Stage 6: Interpretation & Communication
  14. 1.2.7 Stage 7: Deployment & Monitoring
  15. 1.2 Quiz
  16. Module 1.3: Roles & Responsibilities in Data Science Teams
  17. 1.3.1 Key Roles
  18. 1.3.2 Collaboration Across Stages
  19. 1.3.3 Skills Matrix & Growth Path
  20. 1.3 Quiz
Section 2: Tools & Environments
21 Lessons3 Quizzes
  1. Module 2.1: Python Ecosystem Overview (NumPy, pandas, Matplotlib)
  2. 2.1.1 Why Python for Data Science?
  3. 2.1.2 NumPy: Numerical Computing Foundation
  4. 2.1.3 pandas: Tabular Data Manipulation
  5. 2.1.4 Matplotlib: Basic Visualization
  6. 2.1.5 Best Practices & Tips
  7. 2.1.6 Hands-On Assignment 2.1
  8. 2.1 Quiz
  9. Module 2.2: Jupyter Notebooks & VS Code Setup
  10. 2.2.1 Installing Jupyter bash Copy Edit
  11. 2.2.2 Launching & Navigating bash Copy Edit
  12. 2.2.3 VS Code Setup
  13. 2.2.4 Notebooks vs. Scripts
  14. 2.2.5 Best Practices
  15. 2.2.6 Hands-On Assignment 2.2
  16. 2.2 Quiz
  17. Module 2.3: Version Control with Git & GitHub
  18. 2.3.1 Git Basics
  19. 2.3.2 Core Git Workflow
  20. 2.3.3 Branching & Merging
  21. 2.3.4 Remote Repositories & GitHub
  22. 2.3.5 Collaboration Best Practices
  23. 2.3.6 Hands-On Assignment 2.3
  24. 2.3 Quiz
Section 3: Data Exploration & Visualization
25 Lessons3 Quizzes
  1. Module 3.1: Exploratory Data Analysis (EDA) Principles
  2. 3.1.1 Purpose of EDA
  3. 3.1.2 Statistical Summaries
  4. 3.1.3 Univariate Analysis
  5. 3.1.4 Bivariate Analysis
  6. 3.1.5 Multivariate Analysis
  7. 3.1.6 EDA Workflow Best Practices
  8. 3.1.7 Hands-On Assignment 3.1
  9. 3.1 Quiz
  10. Module 3.2: Descriptive Statistics & Basic Plots
  11. 3.2.1 Descriptive Statistics Refresher
  12. 3.2.2 Histogram
  13. 3.2.3 Bar Chart
  14. 3.2.4 Box Plot
  15. 3.2.5 Line Chart
  16. 3.2.6 Choosing the Right Plot
  17. 3.2.7 Hands-On Assignment 3.2
  18. 3.2 Quiz
  19. Module 3.3: Case Study – EDA on Sample Dataset
  20. 3.3.1 Dataset Overview
  21. 3.3.2 Step 1: Data Loading & Initial Inspection python Copy Edit
  22. 3.3.3 Step 2: Data Cleaning
  23. 3.3.4 Step 3: Univariate Analysis
  24. 3.3.5 Step 4: Bivariate Analysis
  25. 3.3.6 Step 5: Correlation Analysis python Copy Edit
  26. 3.3.7 Step 6: Summary of Findings
  27. 3.3.8 Hands-On Assignment 3.3
  28. 3.3 Quiz
Section 4: Data Ethics & Reproducibility
18 Lessons3 Quizzes3 Assignments
  1. Module 4.1: Data Ethics, Privacy & Reproducibility
  2. 4.1.1 Ethical Principles in Data Science
  3. 4.1.2 Sources of Bias & Mitigation
  4. 4.1.3 Data Privacy & Protection
  5. 4.1.4 Reproducible Workflows
  6. 4.1.5 Documentation & Collaboration
  7. Hands-On Assignment 4.1
  8. 4.1 Quiz
  9. Module 4.2: Documentation & Reproducible Pipelines
  10. 4.2.1 Project Documentation Essentials
  11. 4.2.2 Workflow Automation with Makefiles
  12. 4.2.3 Orchestrating with Prefect (or Airflow)
  13. 4.2.4 Data Versioning with DVC
  14. 4.2.5 Best Practices for Reproducibility
  15. Hands-On Assignment 4.2
  16. 4.2 Quiz
  17. Module 4.3: Collaboration Best Practices & Versioning
  18. 4.3.1 Branching Strategies
  19. 4.3.2 Pull Requests & Code Review
  20. 4.3.3 Issues & Project Boards
  21. 4.3.4 Collaborative Communication
  22. 4.3.5 CI/CD Integration
  23. Hands-On Assignment 4.3
  24. 4.3 Quiz
Section 5: Modeling
19 Lessons2 Quizzes3 Assignments
  1. Module 5.1: Supervised Learning Algorithms
  2. 5.1.1 What Is Supervised Learning?
  3. 5.1.2 Common Regression Algorithms
  4. 5.1.3 Common Classification Algorithms
  5. 5.1.4 Workflow: Train–Test Split & Evaluation
  6. 5.1.5 Bias–Variance Trade-off
  7. 5.1.6 Hands-On Assignment 5.1
  8. 5.1 Quiz
  9. Module 5.2: Model Selection & Hyperparameter Tuning
  10. 5.2.1 The Need for Model Selection
  11. 5.2.2 Cross-Validation Techniques
  12. 5.2.3 Hyperparameter Tuning Strategies
  13. 5.2.4 Avoiding Data Leakage
  14. 5.2.5 Interpreting Tuning Results
  15. 5.2.6 Hands-On Assignment 5.2
  16. 5.2 Quiz
  17. Module 5.3: Bias–Variance Trade-off & Regularization
  18. 5.3.1 Understanding Bias and Variance
  19. 5.3.2 Visualizing the Trade-off
  20. 5.3.3 Regularization Techniques
  21. 5.3.4 Choosing Regularization Strength
  22. 5.3.5 When to Use L1 vs. L2
  23. 5.3.6 Hands-On Assignment 5.3
  24. 5.3 Quiz
Section 6: Deep Learning Specialization
21 Lessons3 Quizzes3 Assignments
  1. Module 6.1: Neural Networks & Deep Learning Basics
  2. 6.1.1 Neural Network Architecture
  3. 6.1.2 Activation Functions
  4. 6.1.3 Forward & Backward Propagation
  5. 6.1.4 Implementing a Simple Network (TensorFlow) python Copy Edit
  6. 6.1.5 Key Hyperparameters
  7. 6.1.6 Hands-On Assignment 6.1
  8. 6.1 Quiz
  9. Module 6.2: Convolutional Neural Networks (CNNs)
  10. 6.2.1 Convolutional Layers
  11. 6.2.2 Activation & Nonlinearity
  12. 6.2.3 Pooling Layers
  13. 6.2.4 Classic CNN Architectures
  14. 6.2.5 Building a Simple CNN (Keras) python Copy Edit
  15. 6.2.6 Tips & Best Practices
  16. Hands-On Assignment 6.2
  17. 6.2 Quiz
  18. Module 6.3: Recurrent Neural Networks (RNNs) & Transformers
  19. 6.3.1 Recurrent Neural Networks (RNNs)
  20. 6.3.2 LSTM & GRU Cells
  21. 6.3.3 Sequence Modeling Tasks
  22. 6.3.4 Implementing an RNN for Text Classification python Copy Edit
  23. 6.3.5 Transformer & Self-Attention
  24. 6.3.6 Building a Simple Transformer Classifier python Copy Edit
  25. 6.3.7 Comparison: RNN vs. Transformer
  26. Hands-On Assignment 6.3
  27. 6.3 Quiz
Section 7: Natural Language Processing (NLP)
17 Lessons3 Quizzes3 Assignments
  1. Module 7.1: NLP Fundamentals & Text Preprocessing
  2. 7.1.1 What Is NLP?
  3. 7.1.2 The NLP Pipeline
  4. 7.1.3 Text Preprocessing Steps
  5. 7.1.4 Feature Extraction: Bag-of-Words & TF-IDF
  6. 7.1.5 Choosing Preprocessing & Features
  7. Hands-On Assignment 7.1
  8. 7.1 Quiz
  9. Module 7.2: Text Classification with Naïve Bayes & Logistic Regression
  10. 7.2.1 Problem Setup: Text Classification
  11. 7.2.2 Multinomial Naïve Bayes
  12. 7.2.3 Logistic Regression for Text
  13. 7.2.4 Model Comparison
  14. 7.2.5 Hands-On Assignment 7.2
  15. 7.2 Quiz
  16. Module 7.3: Word Embeddings & Text Generation with RNNs/Transformers
  17. 7.3.1 Word Embeddings: Motivation & Methods
  18. 7.3.2 Training an Embedding Layer in Keras python Copy Edit
  19. 7.3.3 Using Pretrained Embeddings python Copy Edit
  20. 7.3.4 Text Generation with LSTM python Copy Edit
  21. 7.3.5 Text Generation with a Transformer python Copy Edit
  22. Hands-On Assignment 7.3
  23. 7.3 Quiz
Section 8: Applied AI Use-Cases
12 Lessons2 Quizzes2 Assignments
  1. Module 8.1: Computer Vision Applications
  2. 8.1.1 Core Vision Tasks
  3. 8.1.2 Pretrained Backbone for Classification python Copy Edit
  4. 8.1.3 Object Detection Overview
  5. 8.1.4 Semantic Segmentation Snapshot
  6. 8.1.5 Evaluation Metrics
  7. 8.1.6 Hands-On Assignment 8.1
  8. 8.1 Quiz
  9. Module 8.2: Deploying Models with MLOps & APIs
  10. 8.2.1 MLOps Lifecycle Overview
  11. 8.2.2 Containerizing with Docker
  12. 8.2.3 Building a FastAPI Prediction Service python Copy Edit
  13. 8.2.4 Orchestrating with Kubernetes (Optional)
  14. 8.2.5 Monitoring & Logging
  15. Hands-On Assignment 8.2
  16. 8.2 Quiz
Section 9: Capstone Project Kickoff & Data Challenge
18 Lessons4 Quizzes4 Assignments
  1. Module 9.1: Capstone Project Kickoff & Data Challenge
  2. 9.1.1 Capstone Overview
  3. 9.1.2 Data Challenge Description
  4. 9.1.3 Project Plan & Milestones
  5. 9.1.4 Roles & Collaboration (Team Option)
  6. 9.1.5 Hands-On Assignment 9.1
  7. 9.1 Quiz
  8. Module 9.2: Project Development & Interim Report
  9. 9.2.1 Data Ingestion & Cleaning Pipeline
  10. 9.2.2 Baseline Modeling
  11. 9.2.3 Interim Report Structure
  12. 9.2.4 Hands-On Assignment 9.2
  13. 9.2 Quiz
  14. Module 9.3: Model Refinement, Tuning & Advanced Techniques
  15. 9.3.1 Feature Engineering Enhancements
  16. 9.3.2 Advanced Algorithms
  17. 9.3.3 Hyperparameter Tuning Revisited
  18. 9.3.4 Evaluation of Refined Models
  19. Hands-On Assignment 9.3
  20. 9.3 Quiz
  21. Module 9.4: Final Presentation & Submission
  22. 9.4.1 Final Presentation Structure
  23. 9.4.2 Submission Checklist
  24. 9.4.3 Best Practices for Delivery
  25. Hands-On Assignment 9.4
  26. 9.4 Quiz
0 out of 5

0 user ratings

×

Free Lesson Videos:

Deleting Course Review

Are you sure? You can't restore this back

Course Access

This course is password protected. To access it please enter your password below:

Scroll to top