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Sample curriculum

Data analysis with Python

Clean, explore, and communicate data with Python's analytics and visualization tools.

  • Intermediate
  • Live online and in person
Explore the curriculum Discuss your team’s training

At a glance

Who it’s for

Engineers with the listed foundations who want to deepen this skill or apply it in a different stack.

Starting point: Intermediate

Bring these foundations

  • Python basics and familiarity with tabular data.
Foundation sessions can be included when needed.

Technologies & tools

  • Python
  • Pandas
  • NumPy
  • Matplotlib
  • Seaborn
  • Jupyter
Setup and versions agreed for your environment.

A practical destination

Skills to practise.

The sample path works towards these capabilities. Final goals reflect your team’s starting point and agreed scope.

  1. 01

    Inspect and clean datasets without hiding data-quality issues.

  2. 02

    Use transformations and visualisation to answer a specific question.

  3. 03

    Present reproducible analysis with assumptions and limitations.

Sample curriculum

Follow the learning route.

Each module connects the concepts to practical work. Open a module to explore both.

01Module 1

Working with datasets

Concepts to understand

  • Arrays and numerical operations with NumPy
  • Pandas series and dataframes
  • Importing CSV, spreadsheet, and database data

Practical work

Load a dataset and inspect types, missing values and basic distributions. Write down the question the analysis should answer.

Show the result. Explain one decision. Identify what you would improve.
02Module 2

Cleaning & transformation

Concepts to understand

  • Missing values and duplicate records
  • Type conversion and date handling
  • Merging, reshaping, and grouped operations

Practical work

Clean and transform the records with Pandas. Explain how each decision changes the data and preserve a repeatable workflow.

Show the result. Explain one decision. Identify what you would improve.
03Module 3

Exploration & visualization

Concepts to understand

  • Summary statistics and distributions
  • Charts with Matplotlib and Seaborn
  • Comparisons, relationships, and misleading visuals

Practical work

Explore the question with grouped statistics and appropriate charts. Distinguish a useful observation from an unsupported conclusion.

Show the result. Explain one decision. Identify what you would improve.
04Module 4

Reproducible analysis

Concepts to understand

  • Organizing notebooks and reusable functions
  • Validating results and documenting assumptions
  • Communicating findings and limitations

Practical work

Organise the analysis into a notebook or script with clear inputs, outputs and assumptions. Re-run it on an updated dataset.

Show the result. Explain one decision. Identify what you would improve.

A sample project

Bring the skills together.

Analyze a sample business dataset and present a reproducible notebook with visual findings and limitations.

Shorter engagements can focus on selected exercises. A larger, learner-owned capstone can be scoped for a longer programme.

Review the work & reasoning

Four useful questions.

  1. 01

    Does the work meet the agreed requirements?

  2. 02

    Can the learner explain the key decisions and trade-offs?

  3. 03

    What happens with invalid input, failure or an unexpected condition?

  4. 04

    What would they change or explore next?

Designed & delivered by Ragav Kumar V.

Training since 2016 · 100+ batches · 3,000+ professionals trained

Live demonstrations, guided exercises, pair work and project reviews connect the concepts to implementation. We agree the goals, starting knowledge, cohort size, delivery and schedule before shaping the final curriculum.

Shape this for your team

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