Best Free Data Analysis Courses with Certificates (2026)
Data is everywhere.
Businesses collect information about customers, sales, employees, finances, products, websites and operations every day. The challenge is turning that information into useful insights that can help organisations make better decisions.
This is where data analysis comes in.
Data analysts examine information, identify patterns, find trends, create reports and communicate insights that can help businesses understand what is happening and what they should do next.
The good news is that you don't necessarily need a university degree or an expensive bootcamp to begin learning data analysis.
There are many free data analysis courses online that can introduce you to important skills such as Excel, statistics, data visualisation, SQL, Power BI and Python.
For South African job seekers, these skills can open pathways into administration, finance, business analysis, reporting, operations, marketing, information technology and dedicated data-analysis roles.
One of the platforms worth exploring is Alison, which currently offers a range of free data-analysis and data-analytics courses covering Excel, Power BI, Python, SQL, business analytics and other related subjects.
In this guide, we'll explain:
What data analysis is
Why data analysis skills matter
The difference between data analysis and data analytics
The best free data analysis courses
Excel for data analysis
Power BI
SQL
Python
Statistics
Data visualisation
Artificial intelligence and data analysis
Data analyst career opportunities
Freelance opportunities
South African salary expectations
How to build a data-analysis portfolio
How to add data-analysis courses to your CV
Interview questions
Frequently asked questions
Let's get started.
What Is Data Analysis?
Data analysis is the process of examining information to discover useful patterns, trends, relationships and insights.
A data analyst might take a spreadsheet containing thousands of sales transactions and answer questions such as:
Which products sell the most?
Which month generated the highest revenue?
Which region performs best?
Which customers spend the most?
Why have sales decreased?
Which marketing campaign generated the most leads?
The analyst then communicates these findings to decision-makers.
In simple terms:
Data analysis turns raw information into useful information.
What Is Data Analytics?
Data analytics is a broader term that includes collecting, processing, analysing and interpreting data.
It can involve:
Data collection
Data cleaning
Data analysis
Statistical analysis
Data visualisation
Predictive analysis
Reporting
Business intelligence
Decision-making
The terms "data analysis" and "data analytics" are often used interchangeably, particularly in job advertisements and online courses.
Why Learn Data Analysis?
Data-driven decision-making has become important across almost every industry.
Companies want to understand their customers, control costs, improve operations and make better decisions.
Learning data analysis can therefore give you a valuable combination of:
Technology + Business + Problem-Solving
These skills can be useful even if you don't become a professional data analyst.
For example, an HR administrator can analyse employee information.
A finance clerk can analyse expenses.
A marketing assistant can analyse campaign performance.
A sales employee can analyse revenue.
A logistics employee can analyse inventory and delivery information.
Benefits of Taking Free Data Analysis Courses
Learn Without Paying Expensive Tuition
Free online courses make it possible to explore data analysis before investing in more expensive education.
This is particularly useful if you're unemployed, changing careers or simply curious about the field.
Study at Your Own Pace
Online courses allow you to study around your existing responsibilities.
Build Practical Skills
You can practise using real or fictional datasets.
Strengthen Your CV
Relevant data-analysis training can demonstrate initiative and technical development.
Explore a New Career
A free introductory course allows you to discover whether you actually enjoy working with data before committing to a longer qualification.
Best Free Data Analysis Courses Online in 2026
There isn't one single "best" data-analysis course for everyone.
Your ideal starting point depends on your current skills.
If you're completely new, begin with data-analysis fundamentals.
If you're comfortable with Excel, move into spreadsheet analysis.
If you want to work with databases, learn SQL.
If you want to create dashboards, learn Power BI.
If you're interested in programming and advanced analytics, learn Python.
1. Introduction to Data Analysis
Best for: Beginners who want to understand data analysis
Level: Intermediate
Estimated duration: 1.5–3 hours
A good starting point is Alison's Introduction to Data Analysis course.
The course introduces data-analysis concepts and explores tools such as Pareto charts, histograms, run charts and control charts. It also explains how data can be used to drive improvements in business.
You can learn about:
Data analysis principles
Performance measurement
Data-analysis tools
Variation
Control charts
Business improvement
Customer and employee perspectives
Data-driven decision-making
This is a useful course if you want to understand what data analysis actually involves before moving into technical tools.
2. Mastering Data Analysis Using Microsoft Excel
Best for: Beginners who want to use Excel for data analysis
Excel is one of the best places to start because it is widely used in offices and businesses.
Alison's Mastering Data Analysis Using Microsoft Excel course introduces Excel's interface, data entry, formatting, formulas and functions.
Topics include:
Excel navigation
Data entry
Cell formatting
Formulas
Functions
Data organisation
Spreadsheet analysis
This is particularly useful if you are interested in combining Excel skills with data analysis.
3. Mastering Data Analysis Using Excel: Formulas and Pivot Tables
Best for: Learners progressing beyond basic Excel
Once you've learned basic Excel, PivotTables become an extremely useful data-analysis tool.
Alison's Mastering Data Analysis Using Microsoft Excel: Formulas and Pivot Tables course focuses specifically on formulas and PivotTables for data analysis.
You can use these skills to:
Summarise large datasets
Calculate totals
Compare categories
Identify trends
Analyse business information
Build reports
PivotTables are particularly useful in administrative, finance, sales and reporting roles.
4. Basics of Data Analysis with Excel
Best for: Learners who want practical Excel data skills
Alison's Basics of Data Analysis with Excel course focuses on handling data and using Excel functions. The course includes data entry, formulas and data cleaning.
Topics include:
Entering data
Copying data
Basic formulas
More complex formulas
Data cleaning
Working with Excel
Data cleaning is an important part of real-world data analysis because datasets are rarely perfect.
5. Introduction to Data Analytics Using Microsoft Power BI
Best for: Beginners interested in dashboards and business intelligence
Power BI is a Microsoft business-intelligence platform that can be used to transform data into interactive reports and visualisations.
Alison's Introduction to Data Analytics Using Microsoft Power BI course introduces Power BI Online and Power BI Desktop and explains how to transform data and create visual reports.
You can learn about:
Power BI
Power BI Desktop
Power BI Online
Data transformation
Data visualisation
Interactive reports
Dashboards
Working with large datasets
Power BI is a valuable skill to add after learning Excel.
6. Fundamental Data Analysis Using Power BI
Best for: Learners wanting additional Power BI training
Alison's Fundamental Data Analysis using Power BI course explores Power BI tools and functions used for data analysis.
This course covers:
Power BI fundamentals
Data types
Power BI tools
Data analysis
Reporting
Power BI can be particularly valuable for people who want to work in business intelligence, reporting or data analysis.
7. Master Microsoft Power BI
Best for: Learners moving towards advanced Power BI
If you're already comfortable with the fundamentals, Alison's Master Microsoft Power BI course provides another pathway into business intelligence and data visualisation.
The course covers Power BI components and workflows, reports, dashboards, visualisations, Power Query and other features.
These are useful skills for people interested in:
Data analysis
Business intelligence
Reporting
Management information
Dashboard development
8. Introduction to Data Analytics with Python
Best for: Beginners interested in programming
Python is one of the most popular programming languages used in data analytics.
Alison's Introduction to Data Analytics with Python course introduces data analytics fundamentals and explains the relationship between data analytics, statistics and data science. It also introduces Python programming and data visualisation.
Topics include:
Data and its importance
Data analytics
Statistics
Data science
Python
Data visualisation
Central tendency
Dispersion
Regression concepts
Consumer-market analysis
This is a useful bridge between basic data analysis and programming-based analytics.
9. SQL Server for Data Analysis
Best for: Learners interested in databases
SQL is one of the most important technical skills for data analysts because much business information is stored in databases.
Alison's SQL Server for Data Analysis course introduces SQL Server and explains how to write SQL queries for filtering, sorting and aggregating data.
The course also covers:
SQL fundamentals
Queries
Tables
Filtering
Sorting
Aggregation
Database relationships
Joins
Subqueries
SQL functions
Query optimisation
Learning SQL can significantly expand your data-analysis opportunities.
10. Business Data Analytics: Strategies and Tools
Best for: Learners interested in business analytics
Alison's Business Data Analytics: Strategies and Tools course introduces business data, data structures, data sources, data cleaning, data mining, predictive analytics and analytical thinking.
This is particularly relevant if you want to understand the business side of data analysis rather than focusing entirely on programming.
11. Data Analysis Using SPSS
Best for: Learners interested in statistics and research
SPSS is widely used for statistical analysis and research.
Alison's Diploma in Data Analysis Using SPSS covers variables, measurement scales, summary statistics, exploratory data analysis, data transformation, correlation, regression and other statistical methods.
This course may be particularly relevant to people interested in:
Research
Social sciences
Market research
Statistics
Academic analysis
12. Data Analytics and Machine Learning
Best for: Learners progressing towards advanced analytics
Machine learning is a more advanced area of data analytics.
Alison's Data Analytics – Introduction to Machine Learning course introduces concepts including supervised and unsupervised learning, regression, regularisation and other machine-learning principles.
You should ideally learn the fundamentals of data analysis and statistics before moving into machine learning.
The Data Analysis Skills You Should Learn
If you're starting from zero, don't try to learn every tool at once.
Build your skills gradually.
1. Excel
Learn:
Formulas
Functions
Sorting
Filtering
Tables
PivotTables
Charts
Data cleaning
2. Statistics
Learn:
Mean
Median
Mode
Range
Variance
Standard deviation
Correlation
Probability
3. Data Cleaning
Learn how to:
Remove duplicates
Handle missing information
Correct inconsistent formats
Standardise data
Identify errors
4. Data Visualisation
Learn how to communicate information using:
Charts
Graphs
Dashboards
Tables
5. SQL
Learn how to:
SELECT data
Filter information
Sort results
Group data
Use aggregate functions
Join tables
6. Power BI
Learn:
Data importing
Data transformation
Visualisations
Dashboards
Reports
Power Query
Basic DAX
7. Python
Eventually learn:
Python fundamentals
Pandas
NumPy
Matplotlib
Data cleaning
Data analysis
What Is Data Cleaning?
Data cleaning is the process of identifying and correcting problems within a dataset.
For example, imagine a customer database contains:
Johannesburg
Johannesburg
JHB
Joburg
These could all refer to the same location.
A data analyst needs to standardise the information before analysing it.
Other common problems include:
Missing values
Duplicate records
Spelling mistakes
Incorrect dates
Incorrect numbers
Inconsistent categories
Formatting problems
Poor-quality data can lead to poor conclusions.
What Is Data Visualisation?
Data visualisation means presenting information visually.
Instead of giving a manager a spreadsheet containing 10,000 rows, you might create a dashboard showing:
Monthly sales
Top products
Regional performance
Customer growth
Profit trends
Charts and dashboards make complex information easier to understand.
Common visualisations include:
Bar charts
Line charts
Pie charts
Scatter plots
Tables
Heat maps
Dashboards
The best visualisation depends on the question you're trying to answer.
Why Excel Is Important for Data Analysis
Excel remains an excellent starting point.
It is easier to learn than programming and is widely used in workplaces.
You should consider learning:
SUM
AVERAGE
COUNT
IF
COUNTIF
SUMIF
XLOOKUP
VLOOKUP
Sorting
Filtering
Conditional formatting
Tables
PivotTables
Charts
Once you've mastered these, you can move into Power BI, SQL and Python.
SQL for Data Analysts
SQL stands for Structured Query Language.
It allows users to communicate with relational databases.
For example, you might have a database containing millions of sales transactions.
SQL can help you ask:
Which products sold the most?
Which customers spent the most?
What were sales last month?
Which region generated the most revenue?
How many orders were completed?
Basic SQL concepts include:
SELECT
FROM
WHERE
ORDER BY
GROUP BY
JOIN
COUNT
SUM
AVG
SQL becomes particularly valuable when datasets are too large or complex for ordinary spreadsheets.
Power BI for Data Analysts
Power BI allows you to turn data into interactive reports and dashboards.
A typical Power BI workflow might look like:
Import Data → Clean Data → Transform Data → Build Relationships → Create Visualisations → Build Dashboard → Share Insights
This makes Power BI a powerful complement to Excel.
A useful learning pathway is:
Excel → Power BI → SQL → Python
You don't necessarily have to follow that exact order, but it provides a practical progression for many beginners.
Python for Data Analysis
Python is useful when you want to work with larger datasets or automate analysis.
Popular Python libraries include:
Pandas
NumPy
Matplotlib
Seaborn
Scikit-learn
For a beginner, don't worry about learning every library immediately.
Start with basic Python programming.
Then learn Pandas for data manipulation.
Then move into visualisation and more advanced analytics.
Careers in Data Analysis
Data analysis can lead to several different careers.
Data Analyst
A data analyst collects, cleans, analyses and presents information.
Responsibilities can include:
Analysing datasets
Creating reports
Building dashboards
Identifying trends
Preparing recommendations
Communicating findings
Junior Data Analyst
A junior data analyst is often an entry-level position.
You may assist senior analysts with:
Data cleaning
Spreadsheet analysis
Reporting
SQL queries
Dashboard updates
Data validation
This can be a good starting point for someone entering the field.
Business Analyst
Business analysts examine business processes and information to identify improvements.
They may work with:
Data
Processes
Customers
Systems
Operations
Business requirements
Excel, SQL and Power BI can all be useful.
Business Intelligence Analyst
Business intelligence analysts help organisations understand performance through reports and dashboards.
Useful skills include:
SQL
Power BI
Excel
Data modelling
Data visualisation
Data Capturer
Data capturing is more entry-level than professional data analysis, but it can provide valuable experience working with information.
You may:
Enter information
Check accuracy
Update records
Maintain spreadsheets
Prepare data
Excel skills can make you more effective in this type of role.
Reporting Analyst
Reporting analysts prepare reports that help managers understand business performance.
You may create:
Sales reports
Financial reports
Performance reports
Customer reports
Operational dashboards
Excel and Power BI are particularly useful.
Marketing Analyst
Marketing analysts examine:
Website traffic
Campaign performance
Customer behaviour
Sales
Advertising results
Data-analysis skills can therefore be combined with digital marketing.
Financial Analyst
Financial analysts work with financial information to evaluate performance and support business decisions.
Excel is particularly important in many finance roles.
You can strengthen this career pathway by combining:
Excel + Accounting + Finance + Data Analysis
Freelance Data Analysis Opportunities
Data analysis can also create opportunities for freelance work.
Small businesses may need help with:
Excel spreadsheets
Data cleaning
Sales reports
Dashboards
Inventory analysis
Customer analysis
Data visualisation
Business reporting
For example, a small retailer might have months of sales information but no organised reporting system.
A freelancer could clean the data, build an Excel report and create a dashboard showing sales trends.
Potential freelance services include:
Excel Spreadsheet Services
Spreadsheet creation
Formula development
Data cleaning
Report automation
Dashboard Creation
Build dashboards using:
Excel
Power BI
Data Cleaning
Clean and organise messy spreadsheets.
Business Reports
Turn raw information into easy-to-understand reports.
How to Start Freelancing as a Data Analyst
You don't need to wait until you have years of experience.
Start by building a portfolio.
Create fictional projects such as:
Retail Sales Dashboard
Customer Analysis Report
Monthly Expense Analysis
Inventory Dashboard
Marketing Campaign Analysis
Employee Turnover Report
These demonstrate what you can do.
You can then offer services through freelance platforms or directly approach small businesses.
Always be honest about your experience and don't claim professional expertise you don't yet have.
Build a Data Analysis Portfolio
A portfolio can be more powerful than a list of certificates.
Project 1: Sales Analysis
Create a dataset containing:
Product
Salesperson
Region
Date
Quantity
Revenue
Then calculate:
Total sales
Average sales
Best-performing product
Best-performing salesperson
Monthly trends
Create a dashboard.
Project 2: Customer Analysis
Analyse fictional customer information.
Look at:
Customer location
Purchase frequency
Spending
Product preferences
Then identify patterns.
Project 3: Inventory Analysis
Analyse:
Stock levels
Sales
Reorder levels
Product categories
Identify products that may require reordering.
Project 4: HR Analytics
Create fictional employee data.
Analyse:
Department
Employee tenure
Absenteeism
Turnover
Training
Create charts showing trends.
This project can demonstrate how data analysis applies to HR.
Project 5: Financial Analysis
Create a fictional business income-and-expense dataset.
Analyse:
Revenue
Expenses
Profit
Monthly performance
Expense categories
This is an excellent project if you are interested in finance.
How to Put Data Analysis Skills on Your CV
Don't simply write:
Data Analysis
Be more specific.
For example:
DATA ANALYSIS SKILLS
Microsoft Excel
Data cleaning
Data validation
Sorting and filtering
PivotTables
Data visualisation
Power BI
SQL
Basic statistics
Dashboard development
Report preparation
Business analysis
Only include technologies you have actually studied and can explain.
Example CV Training Section
Data Analysis Training
Introduction to Data Analysis – AlisonCompleted 2026
Data-analysis fundamentals
Data-driven decision-making
Performance analysis
Data visualisation concepts
Microsoft Excel Data Analysis – AlisonCompleted 2026
Excel formulas
Data organisation
Data cleaning
Spreadsheet analysis
Power BI Data Analytics – AlisonCompleted 2026
Data transformation
Data visualisation
Interactive reports
Dashboard development
South African Salary Expectations
Data-related salaries vary considerably in South Africa.
Factors affecting salary include:
Job title
Experience
Qualification
Technical skills
Industry
Employer
Location
Level of responsibility
Entry-level data-related positions generally pay less than experienced specialist positions.
A junior data analyst may have a very different salary from a senior data analyst, business intelligence specialist or data scientist.
You should therefore treat online salary figures as guidelines rather than guaranteed rates.
As your skills develop, combining data analysis with technical tools can increase your potential career options.
For example:
Excel
can lead to:
Excel + Power BI
which can lead to:
Excel + Power BI + SQL
which can eventually lead to:
Excel + Power BI + SQL + Python + Statistics
The more advanced your technical and analytical capabilities become, the more specialised roles you can potentially target.
How to Become a Data Analyst
You don't need to learn everything at once.
Follow a structured pathway.
Step 1: Learn Computer Skills
Make sure you're comfortable using computers and managing files.
Step 2: Learn Excel
Master spreadsheets, formulas and PivotTables.
Step 3: Learn Statistics
Understand basic statistical concepts.
Step 4: Learn Data Cleaning
Practise correcting messy datasets.
Step 5: Learn Data Visualisation
Learn how to communicate insights.
Step 6: Learn Power BI
Build interactive dashboards.
Step 7: Learn SQL
Learn how to extract information from databases.
Step 8: Learn Python
Add programming and automation capabilities.
Step 9: Build a Portfolio
Create several practical projects.
Step 10: Apply for Entry-Level Roles
Search for:
Junior Data Analyst
Reporting Analyst
Business Analyst
Data Capturer
Reporting Assistant
Business Intelligence Intern
Data Intern
Data Analysis Interview Questions
"What is data analysis?"
Example answer:
“Data analysis is the process of examining, cleaning and interpreting information to identify useful patterns, trends and insights that can support decision-making.”
"What Excel functions do you know?"
Example answer:
“I have worked with functions such as SUM, AVERAGE, COUNT, IF, COUNTIF and SUMIF. I have also learned sorting, filtering, PivotTables, charts and lookup functions.”
"What is data cleaning?"
Example answer:
“Data cleaning is the process of identifying and correcting errors or inconsistencies in a dataset, such as duplicate records, missing values, incorrect formats or inconsistent information.”
"What is SQL?"
Example answer:
“SQL is a language used to work with relational databases. It can be used to retrieve, filter, sort, aggregate and join information stored in databases.”
"What is Power BI?"
Example answer:
“Power BI is a business-intelligence and data-visualisation platform used to transform data into reports and interactive dashboards.”
Common Mistakes Beginners Make
Trying to Learn Everything at Once
Focus on one tool at a time.
Ignoring Statistics
Statistics helps you understand whether your conclusions are meaningful.
Collecting Certificates Without Practising
A certificate doesn't replace practical ability.
Not Building a Portfolio
Projects demonstrate what you can actually do.
Focusing Only on Tools
A good analyst doesn't just know Excel or Power BI.
They understand the business question behind the data.
Creating Complicated Dashboards
A dashboard should communicate information clearly.
More charts don't automatically mean better analysis.
Free Data Analysis Courses vs Formal Qualifications
Free courses are an excellent way to begin.
They can help you:
Explore the field
Develop foundational skills
Build confidence
Create portfolio projects
Strengthen your CV
However, some employers may require:
A degree
Diploma
Professional certification
Industry experience
Specific technical skills
A free course should therefore be viewed as a starting point, not necessarily a replacement for every formal qualification.
Are Alison Data Analysis Certificates Worth It?
Alison provides free learning across a wide range of data-analysis subjects, and many course pages provide options to certify your learning. Alison also states that its certificates can be used to showcase completed learning on CVs, LinkedIn and profiles.
However, certificate recognition varies by employer.
The strongest approach is to combine:
Certificate + Practical Skills + Portfolio + Relevant Experience
For example, saying:
“Completed an Alison Power BI course”
is useful.
But saying:
“Completed Power BI training and created three dashboards analysing fictional retail, inventory and sales datasets”
provides much stronger evidence of practical ability.
Frequently Asked Questions
What are the best free data analysis courses?
Good starting options include Alison's Introduction to Data Analysis, Excel-based data-analysis courses, Power BI courses, SQL Server for Data Analysis and Python data analytics courses.
Can I learn data analysis for free?
Yes. There are free online courses covering Excel, statistics, SQL, Power BI, Python and other data-analysis topics.
Can I become a data analyst with free courses?
Free courses can help you develop foundational skills, but becoming employable as a data analyst usually requires practical experience, portfolio projects and potentially additional qualifications depending on the employer.
What should I learn first for data analysis?
Start with Excel and basic statistics.
Then move into data cleaning, visualisation, Power BI and SQL.
Python can be added later.
Is Excel enough to become a data analyst?
Excel is an excellent starting point, but professional data-analysis roles often require additional tools such as SQL, Power BI, Python or other technologies.
Is SQL difficult to learn?
SQL has a learning curve, but beginners can learn basic queries relatively quickly with consistent practice.
Should I learn Power BI or Tableau?
Both are useful data-visualisation tools.
Power BI is particularly worth considering if you're already interested in Microsoft's Excel ecosystem.
Is Python necessary for data analysis?
Not for every role.
Some analysts work primarily with Excel, SQL and Power BI.
Python becomes increasingly useful for automation, advanced analysis and data science.
Can I freelance as a data analyst?
Yes. Freelancers can offer services such as Excel reporting, data cleaning, dashboard development and business reporting.
Can data analysis be done remotely?
Many data-analysis tasks can be performed remotely because the work is largely computer-based, although remote availability depends on the employer.
What Excel skills should a data analyst know?
Useful skills include:
Formulas
Functions
Sorting
Filtering
PivotTables
Charts
Data cleaning
Lookup functions
Conditional formatting
What is the difference between data analysis and data science?
Data analysis generally focuses on examining existing information and finding insights.
Data science is broader and can involve programming, statistics, machine learning, predictive modelling and large-scale data systems.
Do I need mathematics to learn data analysis?
You don't need to be a mathematics expert to start.
However, understanding basic statistics becomes increasingly important as you progress.
The Best Free Data Analysis Learning Path for Beginners
If you're completely new, follow this pathway:
Level 1 – Computer Skills
Learn basic computer literacy.
Level 2 – Excel
Learn:
Formulas
Functions
Sorting
Filtering
PivotTables
Charts
Level 3 – Statistics
Learn:
Mean
Median
Mode
Standard deviation
Correlation
Level 4 – Data Cleaning
Learn how to prepare messy information.
Level 5 – Power BI
Learn how to create interactive dashboards.
Level 6 – SQL
Learn how to work with databases.
Level 7 – Python
Learn programming for data analysis.
Level 8 – Portfolio
Create five practical projects.
Level 9 – Job Applications
Apply for internships, junior positions and related entry-level roles.
This approach is much more effective than trying to learn every data tool simultaneously.
Final Thoughts: Start Learning Data Analysis Today
Data analysis is one of the most valuable digital skills you can begin developing in 2026.
You don't have to start with advanced programming.
You don't need to understand machine learning.
You don't need to know complicated statistics.
Start with the basics.
Learn Excel.
Understand data.
Practise cleaning spreadsheets.
Learn how to create charts.
Move into Power BI.
Learn SQL.
Then consider Python.
The most important thing is to keep practising.
Don't simply collect certificates.
Build projects.
Take a fictional dataset and ask questions.
Analyse the information.
Find patterns.
Create a report.
Explain what the numbers mean.
That is how you begin thinking like a data analyst.
A strong learning pathway could look like:
Computer Literacy → Excel → Statistics → Data Cleaning → Power BI → SQL → Python → Portfolio → Employment
You can also combine data analysis with another career.
For example:
Data Analysis + Finance
Data Analysis + Accounting
Data Analysis + Marketing
Data Analysis + HR
Data Analysis + Business
Data Analysis + Logistics
This combination can make your skills even more useful.
Start Learning for Free in 2026
You don't need to wait until you can afford an expensive data-analytics bootcamp.
Start with a free course.
If you're a complete beginner, begin with Alison's Introduction to Data Analysis.
If you already know Excel, consider Mastering Data Analysis Using Microsoft Excel.
If you want to improve your PivotTable and formula skills, try Mastering Data Analysis Using Microsoft Excel: Formulas and Pivot Tables.
If you want to learn dashboards, start with Introduction to Data Analytics Using Microsoft Power BI.
If you're interested in programming, explore Introduction to Data Analytics with Python.
And if databases interest you, explore SQL Server for Data Analysis.
These courses provide different starting points depending on your existing knowledge and career goals.
Choose one course today. Complete it. Practise what you learn. Build a project. Add the skills to your CV. Then take the next step.
Your data-analysis career can start with something as simple as a spreadsheet.
Need Help Improving Your CV?
Completing data-analysis courses is only part of the process.
Your CV should clearly show:
Your courses
Excel skills
Power BI skills
SQL knowledge
Portfolio projects
Previous experience
Education
Relevant achievements
Instead of simply writing:
"Completed a data-analysis course,"
show employers what you can actually do.
For example:
Data Analysis Skills: Excel, PivotTables, data cleaning, Power BI dashboards, SQL fundamentals and data visualisation.
If you need help improving your CV and presenting your new skills professionally, contact:
Learn a skill. Build a portfolio. Improve your CV. Apply for opportunities. Start building your data-analysis career today.
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