Introduction & Programming Basics

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Data Exploration & Visualization

★★☆☆☆

Machine Learning & Applications

★★★☆☆

SECTION

TITLE

DURATION

Module 1

Module 1: Introduction & Programming Basics

104 min

Module 2

Module 2: Data Exploration & Visualization

103 min

Module 3

Module 3: Machine Learning & Applications

105 min

Final Exam

Final Exam

118 min

TOTAL

430 min

TYPE

TITLE

DESCRIPTION

DURATION

VIDEO

Video 1: What is Data Science?

This introductory video defines data science and situates it within business and research contexts. It explains the end-to-end workflow, from data collection to communicating insights, with concrete examples. You’ll see how data problems are framed, the roles of data scientists, and the value of an iterative approach. Real-world contexts illustrate how data science informs decisions, improves processes, and supports outcomes. Practical tips help you start thinking like a data scientist from the first lesson.

8 min

VIDEO

Video 2: The Data Science Workflow

This video breaks down the data science workflow into manageable stages: problem definition, data collection, cleaning, exploration, modeling, evaluation, and storytelling. It covers how to translate questions into data tasks and how to plan analyses. The narrative emphasizes reproducibility, versioning, and collaboration as core practices. It also highlights common pitfalls and how to avoid them through simple, structured processes. You’ll see how each step connects to practical outcomes and project milestones.

7 min

VIDEO

Video 3: Python Essentials for Data Science

In this video you’ll learn essential Python fundamentals for data work, including data structures, control flow, and simple functions. The session demonstrates how Python enables data manipulation, quick analyses, and rapid experimentation. Examples feature common libraries used in data science workflows and how to run small, repeatable scripts. The goal is to build confidence with Python basics to support hands-on practice in later modules.

7 min

READING

Reading: Foundations of Data Handling

This reading introduces data types, data sources, and data quality concepts. It explains how data is collected, stored, and accessed, and why data cleaning is a critical first step in any analysis. The material covers basic data manipulation operations and the importance of reproducible workflows. It provides practical examples of common data issues such as missing values, outliers, and inconsistent formatting. Key takeaways include aligning data preparation with analysis goals and documenting assumptions for future analyses. The reading also outlines a simple data pipeline concept to frame subsequent modules.

28 min

CASE STUDY

Case Study 1: A Practical Data Problem

This case study presents a small dataset and a real‑world problem that requires framing, data collection, cleaning, and initial exploration. You will identify the data sources, assess data quality, and outline a simple approach to obtaining initial insights. The scenario emphasizes clear problem statements, stakeholder needs, and the role of data in informing decisions. It provides a structured environment to practice translating a business question into data tasks. The narrative invites you to consider ethical and privacy considerations in data handling and to propose a high‑level plan for analysis.
LEARNING OUTCOMES:
• Identify data sources and data types involved in a simple problem
• Explain the importance of data cleaning and preparation
• Outline a basic data pipeline for a small dataset
• Summarize initial insights and potential next steps
QUESTIONS (4):
1. What is the core data science problem described in the case?
2. Which data sources are involved and what types of data do they contain?
3. What is the first data cleaning step you would apply and why?
4. What initial insights could be shared with stakeholders based on the data?

40 min

QUIZ

QUESTIONS

14 min

TYPE

TITLE

DESCRIPTION

DURATION

VIDEO

Video 1: Data Cleaning Essentials

This video highlights practical data cleaning techniques, including handling missing values, correcting data formats, and validating data quality. You’ll see examples of simple rules to detect anomalies and ensure consistency across datasets. The session emphasizes how clean data enables reliable analyses and better visualizations. Realistic scenarios show how to document cleaning steps for reproducibility. The goal is to build a toolkit you can apply to diverse datasets from the start.

8 min

VIDEO

Video 2: Exploratory Data Analysis (EDA)

In this video you’ll learn how to summarize and explore data to reveal patterns and relationships. Topics include distribution checks, correlations, and identifying outliers. The session demonstrates practical approaches to formulating hypotheses and validating them with quick visual checks. You’ll see how EDA informs feature selection and modeling decisions. Concrete examples illustrate how to iterate from questions to insights.

7 min

VIDEO

Video 3: Visualization Basics

This video covers the basics of effective data visualization, including choosing appropriate chart types and storytelling with visuals. You’ll learn common pitfalls to avoid and techniques to enhance clarity and interpretability. The session includes examples of dashboards and simple visual narratives you can apply to real datasets. Practical tips help you communicate results to diverse audiences. The aim is to equip you with visuals that support actionable insights.

6 min

READING

Reading: Intro to Statistics for Data Science

This reading introduces core statistical ideas essential for data exploration, including measures of central tendency, variability, and basic distributions. It explains how to interpret summary statistics and how they relate to data visualization. Concepts such as p-values and confidence intervals are discussed at a beginner level to build intuition. The material also covers the role of sampling and the importance of avoiding overfitting in early analyses. Practical examples link statistical ideas to real datasets, enabling you to apply what you learn directly. The reading concludes with guidance on using statistics to inform decisions and storytelling.

28 min

CASE STUDY

Case Study 2: Clean, Explore, and Visualize

This case study walks through a dataset requiring cleaning, exploratory analysis, and visualization to uncover trends. You’ll identify data quality issues, apply basic cleaning steps, and perform visual explorations to reveal patterns. The scenario emphasizes choosing appropriate visual representations to communicate insights. It also highlights how EDA informs next steps and feature considerations for simple models. Throughout, you’ll practice documenting your process and presenting findings clearly.
LEARNING OUTCOMES:
• Explain the purpose of data cleaning in preparation for exploration
• Apply basic exploratory data analysis techniques to identify patterns
• Select appropriate visualization methods to communicate findings
• Summarize insights and potential actions based on data patterns
QUESTIONS (4):
1. What cleaning steps would you apply to this dataset and why?
2. Which variable shows the strongest relationship with the target and how would you interpret it?
3. Which visualization best reveals a potential trend in the data and why?
4. What are two potential data quality issues you should address before modeling?

40 min

QUIZ

QUESTIONS

14 min

TOTAL

103 min

TYPE

TITLE

DESCRIPTION

DURATION

VIDEO

Video 1: ML Fundamentals

This video introduces core machine learning concepts, including supervised vs. unsupervised learning, model training, and evaluation. It explains how to frame a predictive task, select a simple modelling approach, and interpret results. Practical examples show how data preparation influences model performance. The session emphasizes intuition and caution to avoid common pitfalls like overfitting. By the end, you’ll understand the basic workflow for applying ML to small problems.

9 min

VIDEO

Video 2: Regression & Classification

In this session you’ll explore two foundational modelling tasks: regression for predicting continuous values and classification for categorizing outcomes. You’ll see how to split data, train simple models, and evaluate performance with common metrics. The video includes practical tips for feature selection and model interpretation. Examples demonstrate how model choices affect results and stakeholder communications. The focus is on applying simple ML concepts to real datasets.

8 min

VIDEO

Video 3: Advanced ML Overview

This overview presents a broader view of machine learning applications, including model selection trade‑offs, feature engineering ideas, and practical deployment considerations. It highlights the importance of evaluating models on unseen data and communicating results responsibly. Real‑world examples illustrate how ML can be used for decision support, automation, and insight generation. The aim is to give you a high‑level sense of how ML fits into broader data projects and strategies.

6 min

READING

Reading: Introduction to Machine Learning Concepts

This reading covers foundational ML ideas, including the difference between training and testing, common algorithms, and the importance of data quality. It explains performance metrics for regression and classification and provides beginner-friendly examples. The material also discusses model interpretation and communicating results to non-technical stakeholders. Practical tips help you think critically about when to apply ML and how to avoid common missteps. The reading closes with a short roadmap for progressing from basics to more advanced topics.

28 min

CASE STUDY

Case Study 3: Building a Simple Predictive Model

This case study guides you through building a straightforward predictive model on a small dataset. You’ll define the problem, prepare data, select a simple algorithm, and evaluate performance. The scenario emphasizes understanding feature importance and communicating results clearly. It also highlights the iterative nature of model development and the need for careful validation. You’ll practice documenting steps and justifying modelling choices for stakeholders.
LEARNING OUTCOMES:
• Explain how a simple predictive model is built and evaluated
• Identify features that influence predictions and justify their inclusion
• Communicate model results and limitations to a non-technical audience
• Demonstrate an iterative approach to improving a model
QUESTIONS (4):
1. What is the target variable in the case study and why is it chosen?
2. Which features are most impactful and how would you justify their inclusion?
3. What evaluation metric is most appropriate for this task and why?
4. What limitations should be communicated to stakeholders about the model?

40 min

QUIZ

QUESTIONS

14 min

TOTAL

105 min

TYPE

TITLE

DESCRIPTION

DURATION

CASE STUDY

Exam Case Study: Predicting House Prices

This exam case study challenges you to apply data science literacy to a predictive modeling task. You’ll outline a structured approach, identify relevant features, and discuss model selection and evaluation. The scenario emphasizes clear justification of choices and communication of results. You will consider data quality issues and potential biases that could affect the interpretation of outcomes. The case is designed to test your ability to translate knowledge into a practical, defensible solution under exam conditions.
LEARNING OUTCOMES:
• Explain a structured approach to a predictive modeling problem
• Identify relevant features and justify their inclusion
• Describe evaluation strategies for model performance
• Discuss data quality and bias considerations in a real-world task
QUESTIONS (4):
1. What is the primary objective of the house prices prediction task and how would you frame it for modeling?
2. Which features would you consider first and why might they be predictive?
3. What evaluation metric would you choose and why is it appropriate for this task?
4. What data quality issues could affect the results and how would you address them?

40 min

QUIZ

QUESTIONS

78 min

TOTAL

118 min