How Data Analytics Becomes Accessible for Students without a Technical Background

Nearly 73% of employers planned to hire freshers during the first half of 2026, according to a February 2026 news report. The same report showed that employers were paying greater attention to internships and practical project experience while selecting young candidates. (TOI)

This change is important for students who think about modern business careers. Companies collect information about sales, customers, costs, employees and market behaviour. They need people who can understand this information and use it for better decisions.

However, many students might think data analytics requires advanced coding or engineering knowledge. The field certainly has technical areas, but beginners need not start there.

A student can first learn how data answers a business question and then slowly move towards tools and methods.

The word “data” itself can make some students nervous. They may imagine long formulas, difficult programming and large computer systems. This image is not completely correct.

At the beginner level, analytics often begins with simple questions:

• Why did sales fall in one month?
• Which product is bought most often?
• Which advertisement brings more enquiries?
• Why are customers leaving a service?

These are business questions before they become technical questions. Students who can think clearly, compare information and explain patterns already have a useful starting point.

A non-technical student may also understand customer behaviour, finance, marketing or business operations better than someone who only knows software. This business knowledge is valuable because numbers have little meaning without proper context.

Students need not master everything together. They can build the required skills in a simple order.

Percentages, averages, ratios and simple comparisons are commonly used in beginner-level analysis.

Students should learn to separate facts from assumptions and ask why a result has changed.

Sorting, filtering, charts and basic formulas can help students work with small datasets.

An analyst must explain findings in words that managers and clients can understand.

Knowing how marketing, finance, HR and operations work makes the analysis more useful.

Advanced statistics and programming may become important for certain roles later. However, students can first develop confidence with smaller tasks before entering those areas.

Coding is helpful in Data Analytics, but it need not be the first step. Many beginner exercises can be completed through spreadsheets and visual reporting tools.

After becoming comfortable, students may begin learning database queries or a programming language used for analytics. At this stage, coding becomes easier because they already understand why it is being used.

The aim should not be to memorise hundreds of commands. Students should learn how a tool helps them solve a problem. This approach reduces fear and makes technical learning more meaningful.

Students can begin with familiar situations instead of waiting for a company internship.

A college fest, local shop, family business or student club can provide useful examples. A student may analyse event registrations, compare campaign responses or study which activities received the highest participation.

Beginners can also work with publicly available datasets. They may study population changes, product reviews, transport patterns or spending habits. The topic should be simple enough for them to understand.

A small project can follow four steps:

• Choose one clear question instead of trying to study everything.
• Arrange the available information and remove repeated or incomplete entries.
• Use a chart or table to identify an important pattern.

Explain what the finding means and what action may be considered.

A BBA introduces students to areas such as marketing, accounting, finance, human resources, economics and operations. These subjects provide the business context required for analytics.

A marketing student can study customer groups and campaign results. A finance student can examine costs, revenue and risk. An HR student can analyse attendance, employee satisfaction or hiring trends.

This connection makes Data Analytics less abstract. Students not only work with rows and columns. They also try to understand a real business situation.

Parul University offers a specialized BBA in Data Analytics for students who have passed Class 12 in any stream with English as a subject from a recognised board. This eligibility route allows commerce, arts and science students to consider the programme instead of limiting it only to those with a technical background.

The programme can help students connect business education with data-based decision-making. Subjects related to management can provide context, while analytics learning can help students examine business information more carefully.

Data Analytics is not about becoming highly technical overnight. It begins when a student learns to ask a clear question and look for an answer through reliable information.

There will be mistakes. A chart may be confusing, a formula may fail, or a conclusion may be too broad. Checking the work and correcting such errors is part of learning. Students should also avoid changing data to support the result they expected.