AWS Services

Course Curriculum: Your Journey from Novice to Data Scientist

Our curriculum is meticulously designed to take you on a step-by-step journey through the world of data science. Each module builds upon the last, blending core theory with practical, hands-on projects to ensure you don’t just learn—you learn by doing.

Module 1: Kickstart & Python Fundamentals (Week 1-2)

Objective: Build the foundational launchpad for your data science journey.

1.1 Introduction to Data Science:

• What is Data Science, AI, and Machine Learning?
• Roles & Responsibilities: Data Analyst vs. Data Scientist vs. ML Engineer.
• The Data Science Lifecycle: From Business Problem to Deployed Solution.

1.2 Python Programming Essentials:

• Setting up Your Environment: Anaconda, Jupyter Notebooks & VS Code.
• Python Basics: Variables, Data Types, Operators, and Control Flow (Loops & Conditionals).
• Core Data Structures: Lists, Tuples, Dictionaries, and Sets.
• Functions and Object-Oriented Programming (OOP) Concepts.
• Project 1: Create a simple command-line application (e.g., a calculator or a text-based game) to solidify Python programming logic.

Module 2: Data Analysis with NumPy & Pandas (Week 3-4)

Objective: Learn to manipulate, clean, and analyze complex datasets with industry-standard libraries.

2.1 Numerical Computing with NumPy:

• Introduction to NumPy Arrays and their advantages over Python Lists.
• Array Indexing, Slicing, and Mathematical Operations.
• Statistical functions and Linear Algebra basics.

2.2 Data Manipulation with Pandas:

• Introduction to Pandas Series and DataFrames.
• Importing Data: Reading from CSV, Excel, and other file formats.
• Data Cleaning: Handling Missing Values, Duplicates, and Inconsistent Data.
• Indexing, Filtering, and Sorting DataFrames (iloc, loc).
• Grouping and Aggregation with groupby().
• Project 2: Take a messy, real-world dataset (e.g., sales data) and perform a full data cleaning and initial analysis to uncover key insights.

Module 3: Database Management with SQL (Week 5)

Objective: Master the art of querying databases to extract exactly the data you need.

3.1 Relational Database Fundamentals:

• Understanding Tables, Primary Keys, and Foreign Keys.

3.2 Essential SQL Queries:

• SELECT, FROM, WHERE for data retrieval.
• GROUP BY, HAVING, ORDER BY for data aggregation and sorting.

3.3 Advanced SQL:

• Joining multiple tables (INNER, LEFT, OUTER JOINs).
• Subqueries and Common Table Expressions (CTEs).
• Project 3: Answer complex business questions by writing SQL queries against a sample relational database (e.g., an e-commerce store database).

Module 4: Data Visualization & Storytelling (Week 6)

Objective: Transform raw data into compelling visual stories that drive business decisions.

4.1 Principles of Effective Visualization:

• Choosing the right chart for your data.
• The art of storytelling with data.

4.2 Visualization with Matplotlib & Seaborn:

• Creating basic plots with Matplotlib (Line, Bar, Scatter).
• Building advanced statistical plots with Seaborn (Heatmaps, Box Plots, Violin Plots).
• Customizing plots for professional reports.

4.3 Interactive Dashboards:

• Introduction to interactive plotting with libraries like Plotly.
• Project 4: Create a multi-chart dashboard to present the findings from your Project 2 dataset, telling a clear story about the insights you discovered.

Module 5: Essential Statistics & Probability (Week 7)

Objective: Understand the statistical concepts that form the backbone of all data science models.

5.1 Descriptive Statistics:

• Measures of Central Tendency (Mean, Median, Mode).
• Measures of Dispersion (Variance, Standard Deviation).

5.2 Probability Distributions:

• Understanding Normal, Binomial, and Poisson distributions.

5.3 Inferential Statistics:

• Hypothesis Testing, P-values, and Confidence Intervals.
• Introduction to A/B Testing for business decision-making.
• Project 5: Analyze the results of a sample A/B test to determine if a change to a website resulted in a statistically significant improvement.

Module 6: Machine Learning Fundamentals (Week 8-9)

Objective: Build and evaluate your first predictive models.

6.1 Introduction to Machine Learning:

• Supervised vs. Unsupervised vs. Reinforcement Learning.
• The Train-Test Split and Model Evaluation Metrics.

6.2 Regression Models:

• Linear Regression: Predicting continuous values (e.g., house prices).

6.3 Classification Models:

• Logistic Regression: Predicting binary outcomes (e.g., customer churn).
• K-Nearest Neighbors (KNN) and Decision Trees.
• Project 6: Build two models: one to predict house prices from a real estate dataset and another to predict customer churn from a telecom dataset.
• Module 7: Advanced Machine Learning & Capstone Project (Week 10-12)

Objective: Tackle complex problems with advanced algorithms and complete a portfolio-worthy project.

7.1 Advanced Techniques:

• Ensemble Methods: Random Forests and Gradient Boosting (XGBoost).
• Unsupervised Learning: K-Means Clustering for customer segmentation.
• Introduction to Feature Engineering.

7.2 Capstone Project:

• Choose from a selection of real-world business problems.
• Apply the entire data science lifecycle: Data Acquisition, Cleaning, EDA, Modeling, and Interpretation.
• Present your findings and methodology in a final report.

7.3 Introduction to Deployment:

• Learn how to build a simple, interactive web app for your model using Streamlit.