Data Science Curriculm Plan

Week	Day	Topics
Week 1	Day 1-2	Introduction to Data Science: Prelude, The Problem Landscape, Defining Data Science, Demystifying Data Science, Overview of Data Scientist’s Toolbox
	Day 3-4	Python Basics: Installation, Setup, Data Types, Functions, Data Manipulation & Visualization with Matplotlib and Seaborn
	Day 5-6	Probability and Statistics: Descriptive Statistics, Probability Distributions (Gaussian, Bernoulli, Binomial)
	Day 7-8	Holiday
Week 2	Day 9-10	Probability & Statistics: Dispersion (Variance, Standard Deviation), Hypothesis Testing (Z-test, t-test, Chi-square), Correlation Analysis (Pearson, Spearman)
	Day 11-12	Introduction to Numpy: Array Operations, Random Data Generation
	Day 13-14	Pandas: Importing Datasets, Data Wrangling, Cleaning, EDA
	Day 15-16	Holiday
	Day 14	QA Session
Week 3	Day 17-18	SQL for Data Science: Basic Queries, Joins, Subqueries, Aggregation
	Day 19-20	SQL: Group By, HAVING, Aggregation Functions, Working with SQL Databases
	Day 21-22	Scipy & Seaborn: Advanced Visualization, EDA
	Day 23-24	Holiday
Week 4	Day 25-26	Principles of Information Visualization, Applied Visualizations (Matplotlib, Seaborn)
	Day 27-28	Tableau Basics: Loading Data, Creating Charts, Basic Visual Analysis
	Day 29-30	EDA & Hypothesis Testing: Feature Engineering, Statistical Inference
	Day 31-32	Holiday
	Day 30	QA Session
Week 5	Day 33-34	Machine Learning Introduction: Supervised vs Unsupervised Learning, Overview of Clustering, Classification, and Regression
	Day 35-36	Linear Regression: Best Fit Line, Model Training, Model Evaluation
	Day 37-38	Logistic Regression: Sigmoid Curve, Model Evaluation
	Day 39-40	Holiday
Week 6	Day 41-42	Support Vector Machine (SVM): Kernel Trick, Hyperplanes, Model Evaluation
	Day 43-44	K-Nearest Neighbors (KNN): KNN Algorithm, Distance Metrics, Model Evaluation
	Day 45-46	AutoML for Model Building: Introduction, Tools, Automated Model Optimization
	Day 47-48	Holiday
	Day 42	QA Session
Week 7	Day 49-50	Unsupervised Learning: Clustering, K-Means Algorithm, Model Evaluation
	Day 51-52	Principal Component Analysis (PCA): Dimensionality Reduction, Eigenvectors, Eigenvalues
	Day 53-54	Text Mining with NLTK: Text Preprocessing, Regex, Text Classification
	Day 55-56	Holiday
Week 8	Day 57-58	Machine Learning Web App Development with Streamlit: Building Interactive ML Apps, Deploying Models
	Day 59-60	FastAPI for ML Deployment: API Building, Asynchronous Processing, Deployment & Scaling

Assignment Plan

Week	Assignment	Description	Due Date	Skills Covered
Week 1	Assignment 1: Data Science Fundamentals	- Write a report on the role of a Data Scientist, including a comparison between Data Science, AI, ML, and DL. - Complete a basic Python exercise: data types, loops, functions, and basic file handling.	Day 6	- Data Science Concepts - Python Basics (Variables, Functions, Data Types)
Week 2	Assignment 2: Probability & Statistics	- Implement basic statistical tests (z-test, t-test, Chi-square) using Python. - Conduct a descriptive analysis (mean, median, standard deviation) on a dataset.	Day 14	- Hypothesis Testing - Descriptive Statistics - Data Wrangling with Python
Week 3	Assignment 3: SQL Basics	- Create a set of SQL queries to filter, sort, and join data from multiple tables. - Perform aggregation on a dataset using SQL.	Day 22	- SQL Queries - Data Aggregation and Joins
Week 4	Assignment 4: Data Visualization & Tableau	- Create a report on information visualization principles using Matplotlib/Seaborn. - Create a dashboard in Tableau using real-world data (from Excel).	Day 30	- Data Visualization (Python, Tableau) - Data Analysis & Reporting
Week 5	Assignment 5: Regression Analysis	- Implement Linear Regression in Python to predict an outcome based on features. - Evaluate the model's performance (e.g., R-squared, Mean Absolute Error).	Day 38	- Linear Regression - Model Evaluation Techniques
Week 6	Assignment 6: Classification Models	- Implement Logistic Regression and KNN (K-Nearest Neighbors) for a classification problem. - Compare models’ performance (e.g., accuracy, precision, recall).	Day 46	- Logistic Regression - KNN Algorithm - Classification Metrics
Week 7	Assignment 7: Unsupervised Learning	- Implement K-Means clustering on a dataset. - Use PCA (Principal Component Analysis) for dimensionality reduction and explain the results.	Day 54	- K-Means Clustering - PCA (Dimensionality Reduction)
Week 8	Assignment 8: Text Mining & Web App	- Implement a text classification task using NLTK or Regex. - Build a basic ML web app using Streamlit, where users can input data for model predictions.	Day 60	- Text Mining (NLTK, Regex) - ML Web App Development (Streamlit)

Assignments :

Week 1 Assignment - Data Science Fundamentals

• Task: Report on Data Science vs. AI, ML, DL.

  • Write a comparison paper highlighting key differences.

• Python Exercise: A simple program to manipulate data and implement basic Python concepts.

  • Example: Build a small Python script that reads a file, processes some data, and outputs a summary of statistics.

Week 2 Assignment - Probability & Statistics

• Task: Implement hypothesis testing using Python:

  • Use libraries like SciPy to implement z-tests, t-tests, and Chi-square tests.

• Descriptive Statistics: Work with datasets to calculate mean, median, mode, and standard deviation.

  • Example: Download a dataset and analyze central tendency, dispersion, and test hypotheses.

Week 3 Assignment - SQL Basics

• Task: Write SQL queries to perform different tasks:

  • Task 1: Filter, sort, and join datasets from multiple tables.

  • Task 2: Use SQL aggregation functions like SUM, AVG, COUNT to analyze data.

Week 4 Assignment - Data Visualization & Tableau

• Task 1: Write a report on data visualization principles (chart types, color usage, etc.).

• Task 2: Create a Tableau dashboard that displays key insights from a dataset (e.g., sales data, customer data, etc.).

Week 5 Assignment - Regression Analysis

• Task: Implement Linear Regression:

  • Train and evaluate the model using Python (using scikit-learn).

  • Task: Evaluate the model using metrics like R-squared and Mean Absolute Error.

Week 6 Assignment - Classification Models

• Task 1: Build a Logistic Regression model for binary classification (e.g., predict if a customer will buy a product based on features).

• Task 2: Build a K-Nearest Neighbors (KNN) model and evaluate its performance.

Week 7 Assignment - Unsupervised Learning

• Task 1: Implement K-Means clustering on a dataset to identify clusters.

  • Example: Use a customer dataset to segment customers into groups.

• Task 2: Use PCA to reduce the dimensionality of the dataset and visualize the results.

Week 8 Assignment - Text Mining & Web App

• Task 1: Implement text mining techniques using NLTK or Regex:

  • Perform text cleaning, tokenization, and classification tasks.

• Task 2: Build a Streamlit web app that takes user input (like text or numbers), runs the model, and displays predictions.

Key Considerations for Assignments:

• Submission Format: Most assignments will be submitted as reports (PDF or Jupyter Notebooks) or code (Python scripts).

• Evaluation Criteria:

  • Correctness and efficiency of the code.

  • Clarity of explanations and reports.

  • Data analysis skills (EDA, feature selection, visualization).

  • Proper evaluation of machine learning models (accuracy, precision, recall, etc.).

• Extensions: Some assignments will have bonus tasks for more advanced challenges, such as working with larger datasets or using more advanced techniques like hyperparameter tuning.