AnalystOS

Job-Ready Analytics Lab

AnalystOS Role Path

Data Scientist

Learn Python basics, data exploration, checks, and simple result explanations.

A guided track for learners who want to explore data, find patterns, and explain results clearly.

Junior Data ScientistMachine Learning AnalystApplied Data AnalystDecision Scientist

Difficulty

Advanced

Duration

10-12 weeks

Weekly modules

Start Path

Skills

Capabilities You Will Build

PythonPandasFinding patternsChoosing useful cluesChecking SQLGrouping usersExplaining model resultsTesting ideas

Tools

Tooling And Work Surfaces

PythonPandasSQLNotebooksVisualization

Weekly Modules

A structured timeline from fundamentals to proof.

Each week connects topics, goals, outcomes, and recommended practice so progress stays visible.

Module

Explore Data With Python

Use notebooks to inspect, clean, chart, and summarize data.

4 topics

Topics

Python basics

Notebook work

Data checks

Charts

Goals

Use notebooks to inspect, clean, chart, and summarize data.

Outcomes

A notebook with clean notes, a few charts, findings, and ideas to test.

Module

Check And Fix SQL Answers

Check that a query is correct and fix it when it is not.

3 topics

Topics

Fixing SQL

Checking logic

Explaining fixes

Goals

Check that a query is correct and fix it when it is not.

Outcomes

A short risk summary, a small evidence table, and one suggested action.

A fixed answer, what changed, a quick check, and a short business summary.

Module

Choose Useful Data Clues

Turn patterns into useful clues and simple test ideas.

3 topics

Topics

Finding clues

Grouping users

Testing ideas

Goals

Turn patterns into useful clues and simple test ideas.

Outcomes

A cleaned data dictionary, transformation notes, and a quality-check summary.

A notebook with clean notes, a few charts, findings, and ideas to test.

Module

Explain Results To Non-Technical People

Explain results, risks, and next steps in plain language.

3 topics

Topics

Explaining results

Testing ideas

Business communication

Goals

Explain results, risks, and next steps in plain language.

Outcomes

A short risk summary, a small evidence table, and one suggested action.

A prioritized set of user stories with personas, acceptance criteria, and implementation notes.

Recommended Labs

Practice work mapped to this path.

These local-content labs are designed to create evidence for the path outcome before capstone work.

SQL

Beginner

Combine Two Tables

Combine simple customer and order tables to find where money is being lost.

Output

A working answer, a small results table, and three plain sentences about what to fix first.

Start Lab

SQL

Intermediate

Find At-Risk Customers

Find which customers may leave and suggest one helpful action.

Output

A short risk summary, a small evidence table, and one suggested action.

Start Lab

Excel

Beginner

Excel Cleaning Challenge

Clean a messy operational spreadsheet so it can support reliable reporting and stakeholder decisions.

Output

A cleaned data dictionary, transformation notes, and a quality-check summary.

Start Lab

Python

Intermediate

Explore Data in a Notebook

Look through messy customer activity data and find useful patterns.

Output

A notebook with clean notes, a few charts, findings, and ideas to test.

Start Lab

SQL

Intermediate

Fix a Broken Query

Fix a broken SQL query and explain what was wrong in plain language.

Output

A fixed answer, what changed, a quick check, and a short business summary.

Start Lab

Capstone

Graduate Placement Prediction Model

A career services team wants to identify placement readiness signals and predict which graduates may need targeted intervention before recruitment season.

Portfolio summary

A graduate placement prediction case showing EDA, modeling judgment, interpretation, and intervention design.

View Capstone

Required Artefacts

01EDA notebook

02Feature quality notes

03Baseline prediction model

04Model interpretation summary

05Intervention recommendation memo

Evaluation Criteria

01Sound data quality and leakage checks

02Useful exploratory analysis

03Reasonable baseline model and validation logic

04Clear interpretation for non-technical stakeholders

05Business-safe recommendation for student support

Outcome

Build proof recruiters can scan.

A proof page with a notebook, pattern findings, simple model explanation, and next-step recommendations.

After completing this path, you will be able to...

Explore messy datasets with Python and SQL validation.

Explain model-ready signals and assumptions.

Recommend experiments from interpretable analysis.