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DataForSolution Academy

Data Science Academy

Develop the complete ability to work with data—from asking the right question and preparing information to building models, communicating results, and solving real-world problems.

What you will learn to do

The academy is designed around practical capability rather than memorizing isolated tools or definitions.

Ask better questions

Translate broad problems into clear, measurable analytical questions.

Prepare reliable data

Inspect, clean, organize, validate, and document datasets before analysis.

Build useful analysis

Use statistics, visualization, programming, and modeling to find meaningful patterns.

Explain the meaning

Communicate findings, uncertainty, limitations, and practical implications clearly.

Data Science Academy roadmap

The roadmap progresses from understanding data to completing integrated, real-world projects.

Level 1
Foundations

Understanding data and problems

Learn what data science is, how analytical questions are formed, and how data represents real people, events, systems, and processes.

  • Data science workflow
  • Problem definition
  • Data types
  • Measurement
  • Data ethics
Begin Level 1 →
Level 2
Statistics

Reasoning with uncertainty

Develop the statistical foundation needed to describe data, understand variation, test ideas, and interpret evidence.

  • Descriptive statistics
  • Probability
  • Sampling
  • Confidence intervals
  • Hypothesis testing
Level 3
SQL

Working with structured data

Learn to retrieve, join, summarize, filter, and validate data stored in relational databases.

  • SELECT queries
  • Filtering
  • Aggregation
  • Joins
  • Data quality checks
Level 4
Python

Programming for analysis

Use Python to automate repetitive work, manipulate data, perform analysis, and create reproducible workflows.

  • Python foundations
  • NumPy
  • Pandas
  • Data cleaning
  • Reusable analysis
Level 5
Visualization

Exploring and communicating data

Build visualizations and dashboards that clarify patterns, comparisons, relationships, trends, and uncertainty.

  • Chart selection
  • Exploratory analysis
  • Dashboard design
  • Data storytelling
  • Responsible visualization
Level 6
Machine Learning

Building predictive models

Learn how models are trained, evaluated, compared, interpreted, and applied without confusing prediction with certainty.

  • Supervised learning
  • Unsupervised learning
  • Model evaluation
  • Feature engineering
  • Interpretability
Level 7
Applied Work

Solving domain-specific problems

Apply the complete workflow to areas such as healthcare, business, operations, public data, research, and social impact.

  • Healthcare analytics
  • Business analytics
  • Research workflows
  • Decision support
  • Responsible deployment
Level 8
Capstone

Completing an end-to-end project

Define a problem, obtain and prepare data, conduct the analysis, validate the result, explain limitations, and present the work as a professional portfolio project.

  • Project proposal
  • Data documentation
  • Analysis and modeling
  • Validation
  • Final presentation

Learn by solving real problems

Projects will connect technical skills to decisions, outcomes, and meaningful questions.

Beginner project

Explore a public dataset

Clean and summarize a dataset, identify important patterns, and explain what the data can and cannot tell us.

Project guide coming soon
Intermediate project

Build an analytical dashboard

Create a dashboard that helps a user understand trends, comparisons, performance measures, and areas requiring attention.

Project guide coming soon
Advanced project

Develop a decision-support model

Build and evaluate a model, document uncertainty and limitations, and explain how it could responsibly support human decisions.

Project guide coming soon

How each level will work

Every stage will connect knowledge, application, reflection, and communication.

1

Learn

Understand the concept, purpose, assumptions, and practical use.

2

Practice

Apply the concept through exercises and focused examples.

3

Build

Combine skills in projects based on meaningful problems.

4

Explain

Communicate the result, evidence, uncertainty, and limitations.

Begin with the available learning resources

The full academy curriculum will be developed progressively. Current courses in SQL, machine learning, visualization, and related subjects can support your foundation today.

Browse current courses