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Domain 06

Data Science

Turning data into a decision someone can act on — analysis, statistics and visualization, ending in a conclusion you can defend in a room full of people who disagree.

No prior programming experience required2 programs available
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Learning objectives

What you will be able to do.

  • Acquire, clean and shape messy real-world data
  • Apply statistics to answer a specific question
  • Build visualizations that communicate rather than decorate
  • Produce predictive models where they add value
  • Present findings, assumptions and limitations clearly

Who this is for

  • Learners who enjoy finding the answer as much as building the tool
  • Students from analytics, commerce, mathematics or science backgrounds

Prerequisites: No prior programming experience required

Tools and technologies

  • Python
  • Pandas
  • SQL
  • Matplotlib / Seaborn
  • Jupyter
  • Excel
  • Power BI / Tableau

Roles this prepares you for

  • Data Analyst
  • Data Scientist
  • Business Analyst

Curriculum

5 modules, in the order they build on each other.

Each module ends in something you have made work, not just something you have watched.

  1. 01

    Working with data

    • Data sources and formats
    • Cleaning and reshaping
    • SQL for analysis
    • Reproducible notebooks
  2. 02

    Statistics

    • Descriptive statistics and distributions
    • Sampling and uncertainty
    • Hypothesis testing
    • Correlation and causation
  3. 03

    Visualization

    • Choosing the right chart
    • Designing for the reader
    • Dashboards
    • Patterns that mislead
  4. 04

    Predictive modelling

    • Regression and classification for analysis
    • Validation
    • Interpreting model output
  5. 05

    Communication

    • Structuring an analysis
    • Writing up findings
    • Presenting to non-technical stakeholders

Practical work

What you could build in this domain.

Project briefs are agreed with your mentor at the start, so the work suits the level you are actually at.

  • 01

    A sales analysis with a stakeholder-facing dashboard

  • 02

    A cohort study answering a specific business question

  • 03

    An exploratory analysis of a public dataset with stated conclusions

Programs

How to study Data Science.

The same domain, in the format that fits your situation.

All programs
  • Data ScienceBeginner to IntermediateInternship included

    Data Science Internship

    Take a raw dataset through cleaning, analysis and visualization to a finding you can defend.

    • Data cleaning and exploratory analysis
    • Descriptive and inferential statistics
    • Visualization that communicates a result
    • A written analysis with stated limitations

    You finish with: An analysis notebook and dashboard with documented findings.

    Program details
  • Data ScienceBeginner to job-readyInternship included

    Data Science & Analytics Career Program

    From data handling and statistics through to predictive modelling and communicating results to a non-technical audience.

    • Data wrangling, SQL and exploratory analysis
    • Statistics for decision-making
    • Predictive modelling and validation
    • Visualization and stakeholder communication
    • Mock assessments, interview practice and portfolio review

    You finish with: An analytics portfolio: a cleaned dataset, a predictive model and a stakeholder-facing dashboard.

    Program details

Keep looking

Other domains.

Learn Smart. Build Practical. Grow with MindXcel.

Learn the technologies shaping tomorrow — AI, ML, Data Science, Generative AI, LLMs and AI Agents.

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