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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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3 answers
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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.
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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
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.