Lesson proof
Concept, demo, checklist, lab, and assignment evidence.
Python / Analytics / Data Analyst
Data Analytics with Python
Build job-ready analytics skills with Python, pandas, NumPy, statistics, data cleaning, exploratory analysis, dashboards, and business storytelling.
Python analytics readinessPortfolio notebooksDecision-ready insights
Start domain mock testPlatform-wide module outputs
Every module now feeds portfolio proof and CV readiness.
Portfolio pack
Requirement, artifact, validation, risk note, and interview story.
CV signal
Role-specific skill statement linked to a score or artifact.
Review queue
Submitted evidence can support dashboard, readiness, and career exports.
Open materials
Microsoft Power BI Data Analyst Associate (PL-300)Lesson + portfolio pack + CV evidencePython analytics foundationsLesson + portfolio pack + CV evidencePandas data cleaningLesson + portfolio pack + CV evidenceNumPy and statisticsLesson + portfolio pack + CV evidenceExploratory data analysisLesson + portfolio pack + CV evidenceData visualizationLesson + portfolio pack + CV evidenceBusiness insight storytellingLesson + portfolio pack + CV evidence
Certification objective coverage
Every provider-aligned module is connected to a lesson, labs, mock questions, and implementation proof.
This is the track-level audit view for blueprint alignment. Exact exam wording should still be checked against the current official provider guide before public exam-code claims are made.
data-analytics-python.microsoft-power-bi-data-analyst-associate-pl-300.01 / 14% weight
Apply Microsoft Power BI Data Analyst Associate (PL-300) decisions to Data Analyst scenarios
Implementation proof
- Define Microsoft Power BI Data Analyst Associate (PL-300) in plain language and explain the provider service family it belongs to.
- Show how Microsoft Power BI Data Analyst Associate (PL-300) is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
data-analytics-python.python-analytics-foundations.02 / 14% weight
Apply Python analytics foundations decisions to Data Analyst scenarios
Implementation proof
- Define Python analytics foundations in plain language and explain the provider service family it belongs to.
- Show how Python analytics foundations is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
data-analytics-python.pandas-data-cleaning.03 / 14% weight
Apply Pandas data cleaning decisions to Data Analyst scenarios
Implementation proof
- Define Pandas data cleaning in plain language and explain the provider service family it belongs to.
- Show how Pandas data cleaning is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
data-analytics-python.numpy-and-statistics.04 / 14% weight
Apply NumPy and statistics decisions to Data Analyst scenarios
Implementation proof
- Define NumPy and statistics in plain language and explain the provider service family it belongs to.
- Show how NumPy and statistics is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
data-analytics-python.exploratory-data-analysis.05 / 14% weight
Apply Exploratory data analysis decisions to Data Analyst scenarios
Implementation proof
- Define Exploratory data analysis in plain language and explain the provider service family it belongs to.
- Show how Exploratory data analysis is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
data-analytics-python.data-visualization.06 / 14% weight
Apply Data visualization decisions to Data Analyst scenarios
Implementation proof
- Define Data visualization in plain language and explain the provider service family it belongs to.
- Show how Data visualization is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
data-analytics-python.business-insight-storytelling.07 / 16% weight
Apply Business insight storytelling decisions to Data Analyst scenarios
Implementation proof
- Define Business insight storytelling in plain language and explain the provider service family it belongs to.
- Show how Business insight storytelling is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
Test readiness
Mock test by domain
Practice every domain in this track with exam-style questions, answer keys, and explanations.
Open mock testMost in-demand certification materials
High-value certificates connected to this track.
Microsoft
Microsoft Power BI Data Analyst Associate (PL-300)
Needs reviewLast verified: Not verifiedNext review: Provider source review required
Data analysts building dashboards, models, reports, and business insight workflows.
Microsoft Power BI Data Analyst Associate (PL-300) is mapped to platform lessons and labs, but still needs a dated official-source review.
- KPI and dashboard design checklist
- DAX and semantic model concept notes
- Executive insight storytelling rubric
Google Data Analytics Professional Certificate
Needs reviewLast verified: Not verifiedNext review: Provider source review required
Entry-level analysts building a portfolio across data cleaning, analysis, visualization, and communication.
Google Data Analytics Professional Certificate is mapped to platform lessons and labs, but still needs a dated official-source review.
- Data cleaning portfolio checklist
- EDA notebook template
- Stakeholder insight memo practice
Certification provider connections
Connect this learning path to the official exam provider.
Microsoft Learn Credentials
Microsoft Power BI Data Analyst Associate (PL-300)
Connect the learner's Microsoft Learn profile before booking so exam discounts, transcripts, renewals, and badges stay together.
Booking partner: Pearson VUE
- Create or confirm the Microsoft Learn profile.
- Review the official exam guide, ID policy, delivery options, and reschedule rules.
- Add target exam date, booking status, renewal date, and certificate proof to the learner record.
Certification provider
Google Data Analytics Professional Certificate
Confirm the official provider, exam code, delivery rules, ID policy, and reschedule window before booking.
Booking partner: Provider exam partner
- Create or confirm the Provider learner account.
- Review the official exam guide, ID policy, delivery options, and reschedule rules.
- Add target exam date, booking status, renewal date, and certificate proof to the learner record.
01 Match
Map each Daskerel track to the official provider, exam code, registration page, and verification route.
02 Prepare
Use provider objectives with Daskerel lessons, mock exams, labs, and evidence packs before booking.
03 Book
Send learners to the official scheduling partner while keeping target dates and next actions in the dashboard.
04 Verify
Capture certificate URL, badge, expiry, renewal plan, and portfolio proof after the learner passes.
Study plan
Start with Python data structures, notebooks, files, and repeatable analysis habits.
Practise cleaning missing values, types, duplicates, outliers, joins, and grouped aggregations.
Build visual explanations that connect metrics, trends, comparisons, and recommendations.
Hands-on labs
Clean a messy CSV dataset and produce a data quality summary.
Calculate mean, median, mode, variance, correlation, and grouped KPIs with pandas.
Create a visual report showing sales trends, top products, and underperforming segments.
Track learning assets
Templates and revision tools for this path.
Exam blueprint checklistData Analytics with Python
Weekly study plannerData Analytics with Python
Command and service cheat sheetData Analytics with Python
Architecture pattern cardsData Analytics with Python
Flashcard revision setData Analytics with Python
Mock exam review sheetData Analytics with Python
Lab evidence templateData Analytics with Python
Interview story builderData Analytics with Python
Portfolio project rubricData Analytics with Python
Final readiness checklistData Analytics with Python
Practice questions
Which Python library is most commonly used for tabular data cleaning and analysis?
pandas, because it provides DataFrame operations for filtering, joining, grouping, reshaping, and summarising data.
Why should an analyst inspect missing values before building charts?
Missing values can distort totals, averages, trends, and category comparisons, so they must be understood and handled before drawing conclusions.