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What should a beginner learn before starting a career in data and technology?
I’m interested in learning more about careers in data and technology, but there seem to be many different paths, such as data analysis, data science, software development, artificial intelligence, and business analytics.
For someone who is still at the beginning of their learning journey, what skills would be most useful to develop first?
Would it be better to start with programming and SQL, or should a beginner first focus on mathematics, statistics, and understanding how data is used in real-world businesses?
I’d also like to know how students can gain practical experience while learning. Are personal projects, internships, open-source contributions, or online challenges useful for building experience?
What advice would professionals in these fields give to someone trying to decide which path is a good fit?
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13 answers
Updated
Aml’s Answer
If you're just starting, I wouldn't try to learn everything at once. Data and technology have a lot of overlapping skills, so start with a strong foundation and specialize later.
Start with these skills
1. Programming basics
Learn one language, preferably Python. Focus on problem-solving and understanding how code works rather than memorizing syntax.
2. SQL
If you're interested in anything related to data, SQL is one of the most useful skills you can learn early.
3. Basic statistics
You don't need advanced mathematics at the beginning. Start with concepts like averages, distributions, probability, correlation, and interpreting data.
4. Data literacy
Learn how to clean, visualize, analyze, and communicate data—and, importantly, understand what the data actually means in a business context.
Then you can explore different paths:
Data Analyst: SQL + Excel/Sheets + visualization + statistics
Data Scientist: Python + statistics + machine learning
Business Analyst: Business understanding + data analysis + communication
Software Developer: Programming + algorithms + software engineering
AI/ML: Programming + mathematics/statistics + machine learning
How to gain experience
Projects are probably the easiest place to start.
Don't just follow tutorials. Take a real question and try to answer it with data. For example, analyze a public dataset, build a dashboard, or create a small machine-learning project.
Then gradually add:
Projects → Competitions/challenges → Open source → Internships/freelance work
You don't need all of them immediately. A few well-documented projects are more useful than having 20 unfinished tutorials.
How do you know which path fits you?
Try each area briefly and pay attention to what kind of problems you enjoy solving.
Do you enjoy finding patterns in data? → Data/Analytics
Do you enjoy building applications? → Software Development
Do you enjoy statistics and prediction? → Data Science/AI
Do you enjoy solving business problems and communicating with stakeholders? → Business Analytics
My biggest advice would be: don't choose your specialization based only on job titles or salary. Try the work first.
Learn some Python, SQL, and basic statistics, build 2–3 small projects, and talk to people working in different roles. Your preferences will become much clearer once you've actually experienced the work.
Start with these skills
1. Programming basics
Learn one language, preferably Python. Focus on problem-solving and understanding how code works rather than memorizing syntax.
2. SQL
If you're interested in anything related to data, SQL is one of the most useful skills you can learn early.
3. Basic statistics
You don't need advanced mathematics at the beginning. Start with concepts like averages, distributions, probability, correlation, and interpreting data.
4. Data literacy
Learn how to clean, visualize, analyze, and communicate data—and, importantly, understand what the data actually means in a business context.
Then you can explore different paths:
Data Analyst: SQL + Excel/Sheets + visualization + statistics
Data Scientist: Python + statistics + machine learning
Business Analyst: Business understanding + data analysis + communication
Software Developer: Programming + algorithms + software engineering
AI/ML: Programming + mathematics/statistics + machine learning
How to gain experience
Projects are probably the easiest place to start.
Don't just follow tutorials. Take a real question and try to answer it with data. For example, analyze a public dataset, build a dashboard, or create a small machine-learning project.
Then gradually add:
Projects → Competitions/challenges → Open source → Internships/freelance work
You don't need all of them immediately. A few well-documented projects are more useful than having 20 unfinished tutorials.
How do you know which path fits you?
Try each area briefly and pay attention to what kind of problems you enjoy solving.
Do you enjoy finding patterns in data? → Data/Analytics
Do you enjoy building applications? → Software Development
Do you enjoy statistics and prediction? → Data Science/AI
Do you enjoy solving business problems and communicating with stakeholders? → Business Analytics
My biggest advice would be: don't choose your specialization based only on job titles or salary. Try the work first.
Learn some Python, SQL, and basic statistics, build 2–3 small projects, and talk to people working in different roles. Your preferences will become much clearer once you've actually experienced the work.
Updated
Allison’s Answer
The best advice I have been given is to lean into what interests you. Since you’re interested in software development, AI, and data analytics, Python would be a great first language to learn. It is beginner-friendly and widely used in all three areas. If you later become interested in building interactive websites, you could add JavaScript.
Start with small projects connected to things you enjoy. YAs you learn, you’ll be able to explore different areas and discover which ones excite you most. Saving your projects in a portfolio can also help with future college and internship applications.
For practical experience, look for coding clubs, hackathons, summer programs, internships, and volunteer opportunities. You could help a school club or local nonprofit with a website or data project, volunteer at a library technology program, teach younger students basic coding, or create a simple digital tool that helps your community.
Don’t worry about knowing everything before you begin. Curiosity, persistence, and regular practice matter much more than choosing the “perfect” first language.
Start with small projects connected to things you enjoy. YAs you learn, you’ll be able to explore different areas and discover which ones excite you most. Saving your projects in a portfolio can also help with future college and internship applications.
For practical experience, look for coding clubs, hackathons, summer programs, internships, and volunteer opportunities. You could help a school club or local nonprofit with a website or data project, volunteer at a library technology program, teach younger students basic coding, or create a simple digital tool that helps your community.
Don’t worry about knowing everything before you begin. Curiosity, persistence, and regular practice matter much more than choosing the “perfect” first language.
Updated
Patrick’s Answer
For me, I would usually start by understanding each role from a big-picture perspective. If you look at the overall data lifecycle, different roles focus on different stages of the data pipeline and have different responsibilities.
Business Analysts focus on understanding business problems and identifying what the business needs.
Data Engineers focus on collecting, processing, and preparing data so others can use it.
Data Analysts work with the prepared data to identify trends, create reports, and help businesses make informed decisions.
Data Scientists go a step further by applying statistics and machine learning to discover patterns and predict future outcomes.
Software Engineers build the applications and systems that allow people to interact with technology and data.
Of course, there may be some overlap in responsibilities between these roles, and they often work closely together throughout the data lifecycle. Don't feel pressured to decide your career path too early. Take time to understand the different roles, explore your interests, and build a strong foundation in problem-solving.
Business Analysts focus on understanding business problems and identifying what the business needs.
Data Engineers focus on collecting, processing, and preparing data so others can use it.
Data Analysts work with the prepared data to identify trends, create reports, and help businesses make informed decisions.
Data Scientists go a step further by applying statistics and machine learning to discover patterns and predict future outcomes.
Software Engineers build the applications and systems that allow people to interact with technology and data.
Of course, there may be some overlap in responsibilities between these roles, and they often work closely together throughout the data lifecycle. Don't feel pressured to decide your career path too early. Take time to understand the different roles, explore your interests, and build a strong foundation in problem-solving.
John Easton CEng FIET FBCS CITP
Design large computer systems for companies
61
Answers
Updated
John’s Answer
I think that before you try to figure out what to learn, you need to understand what things you like doing. If you hate programming, then don't bother learning to program for instance. I'd try and make two lists. One of things that you like doing and one of things that you dislike. Once you have those two lists, then you can start looking at what sorts of jobs cover most / all of your "likes" list. Every job will have things you might dislike, but as long as you have (many) more "likes" than "dislikes" you're likely headed in the right direction.
One you have identified one or more potential jobs, then you can start to look at the sorts of skills that that job needs.
One you have identified one or more potential jobs, then you can start to look at the sorts of skills that that job needs.
Updated
Shilpa’s Answer
To explore if Data Analysis is something you love, try using free tools and resources from Tableau. You can also join community projects and user groups. Visit Tableau's website to learn more.
As AI technology grows, programming becomes easier to learn. Your unique value will come from being an expert in a specific area.
As AI technology grows, programming becomes easier to learn. Your unique value will come from being an expert in a specific area.
Updated
Nadia’s Answer
Hi Smith,
When it comes to data analytics, Python and SQL are key tools to master. It's great to learn them while also exploring AI automation at the same time. Understanding Python's coding structure is important, and then you can see how AI can help automate your tasks and write code. Next, I suggest diving into Power BI or Tableau. Alongside this, watching videos and tutorials on data visualization and presentation will be beneficial. This way, your work will not only provide valuable information but also offer a great experience for your audience. Keep going—you're on the right track!
When it comes to data analytics, Python and SQL are key tools to master. It's great to learn them while also exploring AI automation at the same time. Understanding Python's coding structure is important, and then you can see how AI can help automate your tasks and write code. Next, I suggest diving into Power BI or Tableau. Alongside this, watching videos and tutorials on data visualization and presentation will be beneficial. This way, your work will not only provide valuable information but also offer a great experience for your audience. Keep going—you're on the right track!
Updated
Patrick’s Answer
There are several distinct paths within data science. To figure out where to start, ask yourself whether you lean toward Data Analysis, Modeling, or Business Analytics:
Data Analysis focuses on querying, cleaning, and visualizing data to uncover historical trends. It requires strong analytical thinking and essential tools like SQL, Excel, and visualization software.
Modeling (often Data Science or Machine Learning Engineering) requires a deeper background in advanced statistics and math, along with programming skills (Python/R) to build predictive algorithms.
Business Analytics sits at the intersection of data and decision-making. You act as the bridge between technical findings and executive strategy to drive ROI. People in this space often bring strong domain knowledge, and some hold an MBA.
My advice: figure out which aspect of the problem-solving process you enjoy first, then focus on the specific tools needed for that track.
As for an internship—100% go for it. It gives you real-world experience, helps you figure out which specialty actually fits you in practice, and gives you a major competitive edge when applying for full-time roles.
Data Analysis focuses on querying, cleaning, and visualizing data to uncover historical trends. It requires strong analytical thinking and essential tools like SQL, Excel, and visualization software.
Modeling (often Data Science or Machine Learning Engineering) requires a deeper background in advanced statistics and math, along with programming skills (Python/R) to build predictive algorithms.
Business Analytics sits at the intersection of data and decision-making. You act as the bridge between technical findings and executive strategy to drive ROI. People in this space often bring strong domain knowledge, and some hold an MBA.
My advice: figure out which aspect of the problem-solving process you enjoy first, then focus on the specific tools needed for that track.
As for an internship—100% go for it. It gives you real-world experience, helps you figure out which specialty actually fits you in practice, and gives you a major competitive edge when applying for full-time roles.
Anil Thakur
Director - Middleware and Document Management Service Owner
2
Answers
Updated
Anil’s Answer
Start by building a strong base in SQL, Excel, and basic programming like Python. Also, learn how businesses use data to make decisions. You don’t need to master advanced math right away, but having a basic grasp of statistics will be very helpful.
Here’s a simple path to follow:
- Learn SQL to manage data.
- Get comfortable with Excel and data visualization tools like Power BI or Tableau.
- Pick up Python basics for analyzing data and automating tasks.
- Study basic statistics, such as averages, distributions, and correlations.
- Focus on understanding business problems and decision-making, not just the technical tools.
To gain practical experience:
- Work on personal projects using real-world datasets.
- Join online challenges on platforms like Kaggle, DataCamp, or HackerRank.
- Apply for internships whenever you can.
- Contribute to open-source projects if you’re interested in software or data engineering.
- Create a GitHub portfolio to showcase your work and findings.
Choosing the right path:
- Data Analyst: If you like working with reports, dashboards, and business insights.
- Data Scientist: If you enjoy statistics, machine learning, and predictive modeling.
- Software Developer: If you like building applications and writing code.
- AI/ML Engineer: If you enjoy programming, algorithms, and AI systems.
- Business Analyst: If you like solving business problems and working with stakeholders.
My advice is to try small projects in each area first. The path that keeps you curious and motivated after a few months of hands-on work is usually the best fit. Focus on learning by doing, instead of trying to master everything before you start.
Here’s a simple path to follow:
- Learn SQL to manage data.
- Get comfortable with Excel and data visualization tools like Power BI or Tableau.
- Pick up Python basics for analyzing data and automating tasks.
- Study basic statistics, such as averages, distributions, and correlations.
- Focus on understanding business problems and decision-making, not just the technical tools.
To gain practical experience:
- Work on personal projects using real-world datasets.
- Join online challenges on platforms like Kaggle, DataCamp, or HackerRank.
- Apply for internships whenever you can.
- Contribute to open-source projects if you’re interested in software or data engineering.
- Create a GitHub portfolio to showcase your work and findings.
Choosing the right path:
- Data Analyst: If you like working with reports, dashboards, and business insights.
- Data Scientist: If you enjoy statistics, machine learning, and predictive modeling.
- Software Developer: If you like building applications and writing code.
- AI/ML Engineer: If you enjoy programming, algorithms, and AI systems.
- Business Analyst: If you like solving business problems and working with stakeholders.
My advice is to try small projects in each area first. The path that keeps you curious and motivated after a few months of hands-on work is usually the best fit. Focus on learning by doing, instead of trying to master everything before you start.
Updated
Swapna’s Answer
A great place to start is by building a strong foundation in problem solving, basic programming (Python) and understanding how data is used to make business decisions. Don't worry about choosing between Data Science, AI, Analytics, or Software Development immediately. Explore a little of each and see what genuinely excites you. I also recommend online learning, work on small projects, online challenges, internships, or hackathons that could impart knowledge to you even if you dont win. These experiences will teach you far more than theory alone and help you discover what kind of work you enjoy.
My advice: focus less on picking the perfect path today and more on developing curiosity, analytical thinking and technical fundamentals. The right specialization usually becomes clearer as you gain hands-on experience.
My advice: focus less on picking the perfect path today and more on developing curiosity, analytical thinking and technical fundamentals. The right specialization usually becomes clearer as you gain hands-on experience.
Updated
Sajit’s Answer
Understanding data and statistics is helpful. Also some knowledge of python is helpful. It’s not a must have but can help in the career. Also how to use AI for analytics.
Updated
Arvind’s Answer
These are questions not only from beginners but also experienced people.
1. Build the foundation first with any coding language, so it's not about coding languages but it's system design thinking process to solve a given problem. AI will write it for you. So don't get into what all you can learn but focus on longer vision
Before that decide first what one wants to achieve in life - The Vision, a Goal
2. In this era of majority of things done by AI, Keep your ears and eyes open even while you sleep. Means be on top of all these trends
3. Nowadays every JD asks for all skills and to be honest, no one can be all in one. Decide you want to be Jack of all, Master of None or vis-a-vis Master of all but jack for None
4. Always remember one rule - DIKW aka Data, Information, knowledge and Wisdom.
5. Retrospect your strengths and make one of it as your best weapon. I see there are multiple things going in mind, JD, Keywords, skills, job, coding language, databases, AI, Data etc, my advice is to know what you are best at. Yes for everything to conclude one needs Data, raw and what one makes of it is upto? Do what you are best at and success will follow.
Self retrospect
Know your strength
Give your best by investing time and commitment
Don't Give up. Keep bouncing back, there is no shortcut to success
All the best
1. Build the foundation first with any coding language, so it's not about coding languages but it's system design thinking process to solve a given problem. AI will write it for you. So don't get into what all you can learn but focus on longer vision
Before that decide first what one wants to achieve in life - The Vision, a Goal
2. In this era of majority of things done by AI, Keep your ears and eyes open even while you sleep. Means be on top of all these trends
3. Nowadays every JD asks for all skills and to be honest, no one can be all in one. Decide you want to be Jack of all, Master of None or vis-a-vis Master of all but jack for None
4. Always remember one rule - DIKW aka Data, Information, knowledge and Wisdom.
5. Retrospect your strengths and make one of it as your best weapon. I see there are multiple things going in mind, JD, Keywords, skills, job, coding language, databases, AI, Data etc, my advice is to know what you are best at. Yes for everything to conclude one needs Data, raw and what one makes of it is upto? Do what you are best at and success will follow.
Arvind recommends the following next steps:
Updated
Godfrey’s Answer
I believe it may require you to be well equipped with basic programming languages the likes of python,Msql,data capture and analysis skills like being efficient in excell,STATA, among other analysis,data cleaning and presentation skills too are important.computer operations skills too is a plus like ICDL,maximore certification.
Your can thereafter proceed for degree in computer science, computer technology,Data science among others.
Your can thereafter proceed for degree in computer science, computer technology,Data science among others.
Updated
Manohar’s Answer
If you're a student, don't worry about mastering everything before starting projects. Pick something you're genuinely interested in. For example, if you love basketball, analyze player statistics. If you enjoy running, analyze your running data. If you like movies, create a movie-rating analysis.
A first project might simply be:
Find a dataset → clean it → ask 3 questions → analyze it with Python/SQL → create charts → explain what you discovered → publish it on GitHub.
Current career roadmaps emphasize real projects alongside technical fundamentals because they give beginners something concrete to demonstrate. For a student, the goal shouldn't be "learn every technology." It should be "learn how to solve problems using technology." Once you have that foundation, learning new technologies becomes much easier.
A first project might simply be:
Find a dataset → clean it → ask 3 questions → analyze it with Python/SQL → create charts → explain what you discovered → publish it on GitHub.
Current career roadmaps emphasize real projects alongside technical fundamentals because they give beginners something concrete to demonstrate. For a student, the goal shouldn't be "learn every technology." It should be "learn how to solve problems using technology." Once you have that foundation, learning new technologies becomes much easier.