MLD103 · Exploratory Data Analysis and Visualization

Categories: AI & Automation
Wishlist Share

About Course

Explore data systematically to find patterns, problems and opportunities, then turn findings into clear charts that support business decisions.

Build · 6 hours · 4 lessons, a lab, an assessed assignment and a portfolio piece

Who it is for

Analysts and aspiring data scientists who can use pandas and want to explore data rigorously and communicate it well.

Prerequisites

MLD102 or equivalent experience loading and cleaning data with pandas.

What you will be able to do

  • Apply a structured EDA checklist covering distributions, missing data, outliers and relationships
  • Choose the right chart for comparisons, distributions, relationships and trends
  • Detect misleading patterns such as confounding, Simpson's paradox and seasonality
  • Produce an EDA report and a short dashboard that state findings and recommended actions

Lessons

  • Lesson 1 · A systematic approach to exploration
  • Lesson 2 · Relationships, segments and time
  • Lesson 3 · Visualization that communicates
  • Lesson 4 · Lab and assignment

Lab: Exploring a restaurant's orders and reviews

You will explore a year of orders and customer reviews from a small restaurant group, looking for what drives order value, ratings and slow periods. You will finish by building a one-page dashboard in Looker Studio from your cleaned data.

Free tools: Google Colab (free) with pandas, matplotlib and seaborn, plus Looker Studio (free) for the dashboard

Assessed assignment

Complete an exploratory analysis of a dataset of your choice from a small business setting, or the provided bike-rental dataset. Deliver a notebook that follows the EDA checklist and a three-page report with at least five findings, each supported by a well-designed chart and a business implication. Include one finding that changes or reverses when you segment the data, and a short list of open questions that need more data or a controlled test.

Criterion Weight What excellent work looks like
Systematic exploration 25% Distributions, missing data, outliers, relationships and time patterns are all examined, following a clear checklist.
Chart quality 25% Each chart type fits its purpose, with clear titles, labeled axes, honest scales and minimal clutter.
Insight and reasoning 30% Findings are specific, quantified and correctly interpreted, with segmentation used to avoid misleading conclusions.
Communication 20% The report leads with findings and actions, is easy to scan and separates evidence from hypotheses.

Portfolio piece

An exploratory analysis report and Looker Studio dashboard that turn raw business data into actionable findings.

Self-paced, 12-month access. Questions are answered in writing through the student panel. A completion record is issued when you finish the course.

Next step

Continue with MLD104 · Statistics for Machine Learning · 6 hours · $42.00

Continue to the next course

Prefer the whole program? Program in Machine Learning & Applied Data Science · $378.00 (10% less than buying the courses separately)

Show More

What Will You Learn?

  • Apply a structured EDA checklist covering distributions, missing data, outliers and relationships
  • Choose the right chart for comparisons, distributions, relationships and trends
  • Detect misleading patterns such as confounding, Simpson's paradox and seasonality
  • Produce an EDA report and a short dashboard that state findings and recommended actions

Course Content

MLD103 · Exploratory Data Analysis and Visualization (6 h)
Explore data systematically to find patterns, problems and opportunities, then turn findings into clear charts that support business decisions.

  • Lesson 1 · A systematic approach to exploration
  • Lesson 2 · Relationships, segments and time
  • Lesson 3 · Visualization that communicates
  • Lesson 4 · Lab and assignment
  • MLD103 Assessment · Exploratory Data Analysis and Visualization
Scroll to Top