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Week 1: Introduction To Data Science and Python Programming

1. Understanding the data science discipline
2. Why Python Programming
3. Python Programming and Jupyter notebook
4. Represent data using Python’s data types: integers, floats, booleans, strings, lists, tuples, sets, dictionaries, compound data structure
5. Perform computations and create logical statements using Python’s operators:

Week 2:  Operators and Control Flow

1. Arithmetic, Assignment, Comparison, Logical, Membership, Identity
2. Declare, assign, and reassign values using Python variables
3. Modify values using built-in functions and methods
4. Write conditional expressions using if statements and boolean expressions to add decision making to your Python programs
5. Use for and while loops along with useful built-in functions to iterate over and manipulate lists, sets, and dictionarie
6. Skip iterations in loops using break and continue
7. Condense for loops to create lists efficiently with list Comprehensions

Week 3:  Functions

1. Define your own custom functions
2. Create and reference variables using the appropriate scope
3. Add documentation to functions using docstringsDefine lambda expressions to quickly create anonymous functions
4. Use iterators and generators to create streams of dat
5. Interact with raw input from users
6. Identify and handle errors and exceptions in your code
7. Open, read, and write to files

Week 5:  Numpy

1. Create, access, modify, and sort multidimensional NumPy arrays (ndarrays)
2. Load and save ndarrays
3. Use slicing, boolean indexing, and set operations to select or change subsets of an ndarray
4. Understand difference between a view and a copy of ndarray
5. Perform element-wise operations on ndarrays
6. Use broadcasting to perform operations on ndarrays of different sizes.

Week 6:  Pandas

1. Create, access, and modify the main objects in Pandas, Series and DataFrames
2. Perform arithmetic operations on Series and DataFrames
3. Load data into a DataFrame
4. Deal with Not a Number (NaN) values

Week 7: Data Visualization

1. Matplotlib Package
2. Understanding motivations between different graphs

Week 8 – 11: Machine Learning
Module 1: Introduction to Machine Learning
Objective: Understand the basics of machine learning, its applications, and scikit-learn.
• Introduction to Machine Learning
• What is machine learning?
• Machine learning vs. traditional programming
• Introduction to Scikit-Learn
• What is scikit-learn?
• Scikit-learn’s role in machine learning

Module 2: Supervised Learning
Objective: Explore supervised learning techniques and use cases with scikit-learn.
• Regression
Predicting continuous values
• Algorithms: Linear Regression, Decision Trees, Random Forest
• Classification
• Binary and multiclass classification
• Algorithms: Logistic Regression, k-Nearest Neighbors, Support Vector Machines

Module 3: Unsupervised Learning
Objective: Learn about unsupervised learning, clustering, and dimensionality reduction in scikit-learn.
• Clustering
• K-Means, Hierarchical clustering
• Clustering evaluation and applications

Module 4: Model Evaluation and Validation
Objective: Understand how to evaluate machine learning models’ performance using scikit-learn.
• Model Evaluation Metrics
• Accuracy, precision, recall, F1-score
• ROC curves and AUC
• Cross-Validation

Module 5: Feature Engineering and Selection
Objective: Explore feature engineering and selection techniques in scikit-learn.
• Feature Preprocessing
• Scaling, normalization, and transformation
• Handling missing data
• Feature Selection
• Selecting important features
• Recursive feature elimination

Module 6: Ensembling and Random Forest
Objective: Learn about ensemble methods and the Random Forest algorithm in scikit-learn.

• Ensemble Methods
• Bagging, boosting, and stacking
• Benefits of ensemble learning
• Random Forest
• Random Forest algorithm
• Model evaluation

Module 7: Support Vector Machines (SVM)
Objective: Dive deeper into the theory and practical use of Support Vector Machines in scikit-learn.
• SVM Theory
• Understanding the SVM algorithm
• Kernel functions in SVM
• SVM Applications
• Classification and regression with SVM
• SVM in real-world use cases

Module 8: Model Deployment and Evaluation
Objective: Learn how to deploy models and evaluate their performance.
• Model Deployment
• Model Evaluation
• Evaluation metrics (accuracy, precision, recall)
• Cross-validation

Week 12 – Capstone Project :

1. Showcase all your new skills in an end-to-end data analysis project.
2. Pick the dataset, do the data munging
3. Ask the research questions, visualize the data, draw conclusions, and present your results.