Complete for freeIntermediateBrowser simulator included

Course 13

Data Science and Visualization

Clean, explore, summarize, visualize, and communicate CSV or JSON data with NumPy, pandas, Matplotlib, reproducible notebooks, and careful claims. The course combines structured lessons, guided browser concept simulations, and learner-created project files made with freely installable or open tools described in the course instructions. This guided page does not execute code, train models, run databases, create repositories, access hardware, or provide hosted infrastructure.

Free completion promise: The always-free pathway combines data concept simulations with learner-created analysis files made in freely installable or open tools and learner-selected non-sensitive data. The C4T page does not run notebooks.

Before you begin

Requirements & setup for Data Science and Visualization

Start with the complete free pathway below. Hardware, subscriptions, paid APIs, and cloud credits are never required.

Start here · free path

Your complete $0 setup

$0 required

Setup checklist

0 of 6 complete

This checklist is a temporary setup aid and resets when the page reloads. Saved course progress begins in the setup lessons below.

1. Choose the free learning path
Use the browser-based tools and simulations throughout this course. Paid software, APIs, cloud credits, GPUs, and physical equipment are optional.
About 2 min
2. Set up C4T Browser Data Concept Simulator
Open the concept simulator and read its scenario, assumptions, and limitations.
About 5 minOpen C4T Browser Data Concept Simulator (opens in a new tab)
3. C4T Browser Data Concept Simulator: step 2
Make a prediction, adjust the available controls, and compare the result with the explanation.
About 5 min
4. C4T Browser Data Concept Simulator: step 3
Record what changed and why; repeat with at least one different input.
About 5 min
5. C4T Browser Data Concept Simulator: step 4
For applied work, follow the course instructions to create and keep your own project files with a freely installable or open tool appropriate to the subject.
About 5 min
6. Run the setup check
C4T Browser Data Concept Simulator: Complete one prediction-and-explanation cycle and record the evidence you would carry into a learner-created project file.
About 5 min

Software

1 item

C4T Browser Data Concept Simulator

Course need

C4T Browser Data Concept Simulator

Free

A free browser concept simulator with adjustable values, a prediction prompt, deterministic feedback, and a short explanatory trace. This guided page does not execute code, train models, run databases, create repositories, access hardware, or provide hosted infrastructure. Use keyboard controls and the text trace or written alternative when a visual or pointer interaction is not suitable.

Open the concept simulator and read its scenario, assumptions, and limitations.
Make a prediction, adjust the available controls, and compare the result with the explanation.
Record what changed and why; repeat with at least one different input.
For applied work, follow the course instructions to create and keep your own project files with a freely installable or open tool appropriate to the subject.

Open C4T Browser Data Concept Simulator

No-hardware path

Data Exploration Concept Simulator

Explore course concepts through adjustable values, prediction, deterministic feedback, and an explanatory trace. No hardware is required.

Hardware

Nothing required

No physical hardware is needed for this course.

Accounts

Nothing required

No external account is needed for this course.

What you will be able to do

Learning outcomes

Inspect data types and load documented CSV and JSON datasets.
Clean, filter, group, and summarize data reproducibly.
Create accessible visualizations suited to the question and data.
Discuss sampling, bias, correlation limits, and uncertainty in a polished report.

Included downloads

Free starter assets

Original · synthetic · licensed

These small files contain no personal data, credentials, or secrets. Dataset fixtures are CC0 and starter code is MIT licensed.

Starter pack guide & licensesSee what each original synthetic dataset, fixture, and starter file contains.SQLite practice databaseOriginal schema, synthetic seed rows, constraints, and review queries.ML evaluation fixtureFixed, synthetic classification, retrieval, research, and deployment values.

Build real work

Assignments & capstone

Free tools accepted

Data cleaning and summary audit

Clean a documented dataset, explain every decision, and compare summary results before and after repair.

The always-free pathway combines data concept simulations with learner-created analysis files made in freely installable or open tools and learner-selected non-sensitive data. The C4T page does not run notebooks. Submit a project file, text trace, screenshots with descriptions, or equivalent simulator evidence.

Responsible visualization brief

Create and critique multiple charts, including accessible text summaries and limits on interpretation.

Final assessment

Reproducible data investigation

Produce a documented dataset analysis with cleaning, exploration, summary statistics, multiple appropriate visualizations, bias and limitation discussion, a notebook, and a polished dashboard or report. Deliverables: Documented dataset and provenance, Reproducible cleaning and analysis notebook, Statistical summaries and accessible visualizations, Bias, correlation, and limitation discussion, and Polished dashboard or report.

Prerequisites & pacing

Skills assumed

Python collections, functions, files, and JSON

Recommended prior courses

Python Projects and Problem Solving

These are advisory, not locks. If you already have the skills listed above, enroll directly.

Choose your pace

self paced: 9 weeks at 4 hours per week
weekly: 9 weeks at 4 hours per week

Part of this learning path

🧠 Data and Artificial Intelligence · step 1 of 7
Learn to read data honestly, then train, evaluate, and interrogate machine-learning models.

Course created by Zafir Shamsi.