Data Science

Section 1: Introduction to Data Science

Duration:1 week

Topics:

• Role of a data scientist and key responsibilities
• Data science workflow: Data collection, cleaning, analysis, modeling
• Overview of tools: Python, SQL, and Jupyter Notebook
• Types of data: Structured, unstructured, time-series

Learning Outcomes:

• Understand the data science process and its applications
• Set up a data science environment

Activities:

• Case study: Framing a business problem as a data science task
• Tool setup: Install Python (Anaconda), MySQL, and Jupyter Notebook

Resources:

• Anaconda, MySQL Workbench
• Sample datasets (e.g., Kaggle)

Section 2: SQL for Data Science

Duration:2 weeks

Topics:

– Week 1:SQL Basics

• Introduction to relational databases and MySQL
• SELECT queries, WHERE, ORDER BY, LIMIT
• Filtering with operators: AND, OR, LIKE, IN
• Aggregations: COUNT, SUM, AVG, MIN, MAX

– Week 2:Intermediate SQL

• Joins: INNER JOIN, LEFT JOIN, RIGHT JOIN
• Grouping with GROUP BY and HAVING
• Subqueries and CTEs
• Working with dates and NULL values

Learning Outcomes:

• Query and aggregate data from databases
• Prepare data for analysis using SQL

Activities:

• Hands-on: Query a retail dataset
• Project: Analyze sales trends using SQL

Resources:

• MySQL Workbench
• Sample datasets (e.g., e-commerce, finance)

Section 3: Python for Data Science

Duration:4 weeks

Topics:

– Week 1:Python Fundamentals

• Variables, data types, loops, and functions
• Lists, dictionaries, and sets
• Introduction to NumPy for numerical operations
• Setting up Jupyter Notebook

– Week 2:Data Manipulation with Pandas

• Importing and cleaning data
• Handling missing values, duplicates, and outliers
• Merging, grouping, and reshaping datasets
• Time-series operations

– Week 3:Data Visualization with Python

• Creating plots with Matplotlib and Seaborn
• Visualizing distributions, correlations, and trends
• Customizing plots for effective communication

– Week 4:Advanced Python

• Working with APIs and web scraping (Requests, BeautifulSoup)
• Automating data workflows
• Version control with Git

Learning Outcomes:

• Manipulate and clean data with Pandas
• Create insightful visualizations
• Automate data collection and preprocessing

Activities:

• Hands-on: Clean and visualize a dataset
• Project: Build a pipeline to analyze social media data

Resources:

• Libraries: NumPy, Pandas, Matplotlib, Seaborn, Requests
• Sample datasets (e.g., Kaggle, UCI Repository)

Section 4: Statistics and Probability

Duration:2 weeks

Topics:

– Week 1:Descriptive and Inferential Statistics

• Measures: Mean, median, mode, variance, standard deviation
• Hypothesis testing: t-tests, chi-square tests
• Confidence intervals and p-values

– Week 2:Probability and Distributions

• Probability concepts: Conditional probability, Bayes’ theorem
• Distributions: Normal, binomial, Poisson
• Correlation and linear regression
• Statistical analysis with Python (SciPy, StatsModels)

Learning Outcomes:

• Apply statistical methods to interpret data
• Understand probability for machine learning

Activities:

• Exercises: Hypothesis testing in Python
• Case study: Analyze A/B test results

Section 5: Machine Learning

Duration:4 weeks

Topics:

– Week 1:Introduction to Machine Learning

• Supervised vs. unsupervised learning
• Model evaluation: Accuracy, precision, recall, F1-score
• Linear regression and logistic regression
• Introduction to Scikit-learn

– Week 2:Supervised Learning

• Decision trees, random forests, gradient boosting
• Classification: KNN, SVM
• Cross-validation and confusion matrix

– Week 3:Unsupervised Learning

• Clustering: K-means, hierarchical clustering
• Dimensionality reduction: PCA
• Anomaly detection

– Week 4:Model Tuning and Deployment

• Hyperparameter tuning with GridSearchCV
• Introduction to neural networks (TensorFlow basics)
• Deploying models with Flask or Streamlit

Learning Outcomes:

• Build and evaluate machine learning models
• Apply unsupervised techniques for data exploration
• Deploy models for practical use

Activities:

• Hands-on: Predict customer churn with classification
• Project: Build a price prediction model

Resources:

• Libraries: Scikit-learn, TensorFlow
• Sample datasets (e.g., Kaggle)

Section 6: Capstone Project

Duration:2 weeks

Objective:Apply SQL, Python, statistics, and machine learning to solve a real-world problem

Project Examples:

• Predict customer retention using classification
• Forecast sales with regression models
• Cluster customers for targeted marketing

Deliverables:

• SQL queries for data extraction
• Python scripts for data cleaning, visualization, and modeling
• Report summarizing insights and model performance

Learning Outcomes:

• Integrate core data science skills
• Communicate findings effectively

Resources:

• Kaggle datasets, UCI Repository

Section 7: Career Preparation

Duration:1 week

Topics:

• Building a data science portfolio
• Optimizing resume and LinkedIn
• Preparing for technical interviews (SQL, Python, ML)
• Overview of certifications: Google Data Analytics, TensorFlow Developer

Learning Outcomes:

• Create a professional portfolio
• Prepare for data science job applications

Activities:

• Build a portfolio with capstone project
• Mock interviews with coding and ML challenges

Course Duration

• Total: 16 weeks (assuming 10-15 hours per week)
• Format: Self-paced with optional instructor-led sessions

Prerequisites

• Basic computer literacy
• Familiarity with high school-level mathematics
• No prior programming experience required

Tools and Software

• SQL: MySQL Workbench (free)
• Python: Anaconda, Jupyter Notebook (free)
• Libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow
• Git: Version control (free)

Recommended Resources

• Online platforms: Coursera, edX, Kaggle, DataCamp
• Books: “Python for Data Analysis” by Wes McKinney, “Introduction to Statistical Learning” by James et al.
• Datasets: Kaggle, UC Irvine ML Repository

Certification Preparation

• Google Data Analytics Professional Certificate
• TensorFlow Developer Certificate
• AWS Certified Data Analytics – Specialty