A Day in techcadd's Data Analytics Classroom

Imagine walking into a classroom where the lesson begins with a simple question:

"Why did a business make more sales last month but earn less profit?"

At first, it sounds like a question for an accountant or business manager. But finding the answer requires something increasingly important in today's workplace: data analytics.

Students need to examine sales figures, compare expenses, identify patterns, and explain what the numbers reveal.

This is the kind of problem that makes data analytics interesting.

Instead of simply memorising definitions, learners can explore how spreadsheets, databases, visualisations, and analytical thinking work together to solve business problems.

For students considering a Data Analytics Course at techcadd, understanding the learning process can help them decide whether the subject matches their interests.

So, what might a day in a data analytics classroom actually look like?

Let's walk through a realistic example of how a practical training session could unfold.

Note: This article illustrates a possible hands-on data analytics classroom experience. It is not a verified observation of a particular techcadd class or a confirmed timetable. Specific tools, activities, and teaching methods should be checked against the institute's current course details.

9:30 AM – Starting With a Real Business Problem

A productive data analytics session does not necessarily begin with complicated formulas.

It can begin with a practical business question.

Imagine the instructor presenting a sample dataset from a retail store.

The file contains information about products, sales dates, quantities, prices, discounts, and profit.

The instructor asks students to investigate why revenue increased while profit declined.

Some students may immediately suggest that the business offered too many discounts.

Others might wonder whether product costs increased or customers purchased more low-margin items.

These are reasonable hypotheses, but they need evidence.

The first lesson is simple: Data analytics is not about guessing the answer. It is about investigating the available information.

Before opening any software, students should understand the problem they are trying to solve.

10:00 AM – Exploring the Dataset in Microsoft Excel

The next step is opening the sample dataset in Microsoft Excel.

Excel remains a useful tool for learning data analytics because it allows students to inspect, organise, and analyse information in a familiar spreadsheet environment.

The instructor might begin by asking students to identify the columns.

For example:

Order IDDateProductQuantityRevenueProfit100102-Oct-2026Keyboard2₹1,600₹320100203-Oct-2026Mouse5₹2,500₹600100304-Oct-2026Monitor1₹9,000₹900100405-Oct-2026Keyboard3₹2,400₹450

<text color="secondary" size="xs">Illustrative sample data created for explanation.</text>

Students might practise sorting, filtering, conditional formatting, and basic formulas.

Functions such as SUM, AVERAGE, COUNTIF, SUMIF, and XLOOKUP can help them investigate the dataset.

For example, they could calculate total revenue, average order value, and total profit.

This activity also introduces an important habit: checking whether the information is accurate before drawing conclusions.

10:45 AM – Data Cleaning: The Part Beginners Often Underestimate

One of the most important stages of data analytics is data cleaning.

Real datasets are rarely perfectly organised.

They may contain missing values, duplicate records, inconsistent product names, or incorrectly formatted dates.

For example, a dataset might contain the following product names:

• Wireless Mouse

• wireless mouse

• Wireless-Mouse

• WIRELESS MOUSE

A person can recognise that these entries probably refer to the same product.

But analytical software may treat them as different categories unless the values are standardised.

During a data cleaning exercise, students could practise removing duplicates, correcting data types, handling missing values, and standardising text.

The instructor might also demonstrate why dates sometimes appear incorrectly when a file is imported.

For example, 04/10/2026 could represent 4 October or 10 April depending on the date convention.

Students need to understand date formatting and locale settings rather than assuming the software has interpreted every value correctly.

This is particularly relevant when working with Excel, CSV files, and Power BI.

11:30 AM – Understanding SQL Through Practical Queries

After working with spreadsheets, the class might move to SQL.

SQL, or Structured Query Language, is used to work with relational databases.

Students can learn how to retrieve information, apply conditions, group records, and calculate summaries.

Imagine a database containing a table named sales.

The instructor asks students to find the total revenue for each product.

A query might look like this:

SELECT
    product_name,
    SUM(revenue) AS total_revenue
FROM sales
GROUP BY product_name
ORDER BY total_revenue DESC;

This query groups sales records by product and calculates total revenue.

Students can then explore other SQL concepts, including WHERE, ORDER BY, GROUP BY, HAVING, and JOIN operations.

An important classroom exercise is understanding why a query produces a particular result.

Rather than copying SQL statements, students should be encouraged to predict the output before running them.

That practice strengthens analytical thinking and database knowledge.

12:15 PM – Turning Raw Data Into Useful Questions

At this point, the class has worked with spreadsheets and databases.

But the main objective is still to answer the original business question.

The instructor could ask students to identify which metrics would be useful.

For example:

• Total revenue

• Total profit

• Profit margin

• Revenue by product category

• Monthly sales trends

• Average discount

• Product-wise profitability

Students might discover that revenue alone does not provide a complete picture of business performance.

A company can generate more revenue while experiencing lower profitability.

This is why analysts need to understand the difference between metrics and business outcomes.

A useful exercise would involve comparing revenue and profit across different months and identifying where the largest changes occurred.

1:00 PM – Building a Power BI Dashboard

After a break, the class might begin a dashboard-building exercise using Microsoft Power BI.

Power BI allows analysts to connect data sources, transform information, build data models, and create interactive reports.

Students could import the cleaned retail dataset and create a simple sales dashboard.

<box gap={2}>
<text weight="medium">Illustrative sales dashboard</text>
<grid columns={3} gap={2}>
{#each [{label:"Revenue",value:"₹15.5L"},{label:"Profit",value:"₹2.4L"},{label:"Margin",value:"15.5%"}] as item}
<grid-item>
<box border radius="lg" padding={3} gap={1}>
<caption>{item.label}</caption>
<title size="lg" tabularNums>{item.value}</title>
</box>
</grid-item>
{/each}
</grid>
<box border radius="lg" padding={3} gap={2}>
**Monthly revenue and profit**
<Chart content={{"chartType":"bar","meta":{"footer":"Illustrative figures, not actual business or classroom data"},"xKey":"month","series":[{"dataKey":"revenue","label":"Revenue","valuePrefix":"₹","valueFormat":"compact"},{"dataKey":"profit","label":"Profit","valuePrefix":"₹","valueFormat":"compact"}],"data":[{"month":"May","revenue":190000,"profit":41000},{"month":"Jun","revenue":220000,"profit":45000},{"month":"Jul","revenue":260000,"profit":47000},{"month":"Aug","revenue":280000,"profit":42000},{"month":"Sep","revenue":300000,"profit":35000},{"month":"Oct","revenue":300000,"profit":30000}]}}/>
</box>
</box>

A dashboard might include cards for revenue and profit, bar charts comparing products, and line charts showing monthly trends.

Students could also practise using slicers to filter the report by product category or date.

The goal is not simply to create attractive charts.

A useful dashboard should help someone understand what is happening in the business.

For example, a visualisation might reveal that revenue remained stable while profit declined over several months.

That observation could lead to further investigation of discounts, costs, or product mix.

2:00 PM – Learning Power Query and Data Transformation

Power Query is another useful component of Power BI.

It helps users import, clean, and transform data before analysis.

Students might practise changing column data types, removing unnecessary fields, splitting columns, replacing values, and merging tables.

One particularly useful exercise involves working with date formats.

Imagine importing a CSV file where dates are stored as text.

The instructor could demonstrate how to change the data type using the appropriate locale.

Students might also explore the difference between merging and appending queries.

Merging combines tables using matching columns, while appending adds rows from one table to another when their structures are compatible.

These concepts are important because analysts frequently work with data from multiple sources.

2:45 PM – Understanding DAX and Calculated Measures

Once students become comfortable with Power BI visuals, they can begin exploring DAX.

DAX stands for Data Analysis Expressions.

It is used to create calculations in Power BI and related Microsoft data modelling tools.

For example, a simple total revenue measure might look like this:

Total Revenue = SUM(Sales[Revenue])

A profit margin measure could be written as:

Profit Margin =
DIVIDE(
    SUM(Sales[Profit]),
    SUM(Sales[Revenue]),
    0
)

These measures allow calculations to respond to report filters.

Beginners may initially find the difference between calculated columns and measures confusing.

A practical classroom exercise can help demonstrate when each approach is appropriate.

Students can also learn why relationships between tables affect calculations.

This introduces the importance of data modelling rather than treating every dashboard as a collection of disconnected charts.

3:30 PM – Presenting Findings Like a Data Analyst

The final part of the session could focus on communicating results.

The instructor asks students to summarise what they discovered from the sample retail dataset.

One student might explain that sales increased because more products were sold.

Another might identify that discounts affected profit margins.

A third might notice that certain products generated strong revenue but relatively low profit.

Each student should support their conclusions with evidence.

The instructor could then ask follow-up questions:

Does the dataset contain enough information to confirm the cause?

Could other factors explain the change?

What additional data would help?

This is where students begin moving from software usage to analytical reasoning.

A data analyst's job is not just to prepare charts. It is to help people understand information and make better-informed decisions.

What Makes Practical Data Analytics Training Valuable?

A classroom experience becomes more useful when students actively work with data rather than only watching demonstrations.

Practical exercises help learners develop confidence in handling unfamiliar datasets.

They also introduce the kinds of mistakes that occur in real analytical work.

For example, students may accidentally import dates incorrectly, create duplicate records during a table merge, or use the wrong aggregation in a chart.

Identifying and correcting these problems can be an important part of learning.

Students considering data analytics training through techcadd should ask about the current syllabus, tools covered, project opportunities, and the amount of independent practice included.

What Skills Can Students Develop Through These Activities?

A well-structured data analytics learning experience can help students build several complementary skills.

SkillPractical ApplicationExcelOrganising and analysing spreadsheet dataSQLRetrieving and summarising database recordsPower QueryCleaning and transforming datasetsPower BICreating interactive reports and dashboardsDAXBuilding calculated measuresData modellingConnecting related tables correctlyAnalytical thinkingInvestigating business questionsCommunicationExplaining findings and limitations

Students do not need to master all these skills in a single day.

The activities described here represent an illustrative learning experience that may be distributed across multiple classes depending on the course structure and student level.

How Can Classroom Projects Help With Career Preparation?

Practical projects can help students demonstrate their abilities when applying for internships or entry-level data roles.

For example, a sales analytics project could include an Excel workbook, SQL queries, a Power BI dashboard, and a short report explaining the findings.

A student should be able to describe how the data was cleaned, why certain metrics were selected, and what limitations existed.

This can be more informative than simply listing Excel, SQL, and Power BI on a resume.

However, completing classroom exercises does not guarantee employment.

Employers may also evaluate problem-solving ability, communication, database knowledge, and experience with realistic datasets.

Frequently Asked Questions

Is data analytics difficult for beginners?

Data analytics can be approachable when students start with basic spreadsheets and gradually learn databases, visualisation, and analytical concepts. Regular practice is important.

Do I need coding knowledge to learn data analytics?

Not necessarily. Students can begin with Excel and Power BI. SQL is highly useful, and Python can become valuable for more advanced analysis.

Which tools should beginners learn first?

Excel, SQL, and Power BI are a practical combination for many entry-level analytics learning paths. The best sequence depends on career goals.

What is the difference between Power BI and Excel?

Excel is a general-purpose spreadsheet application with strong calculation and analysis capabilities. Power BI focuses on data modelling, interactive reporting, and business intelligence.

Can students build data analytics projects without professional experience?

Yes. Public datasets and carefully designed sample data can be used to practise cleaning, analysis, dashboard creation, and reporting.

Does techcadd's Data Analytics Course include all these classroom activities?

The exact syllabus, timetable, software coverage, and project activities should be confirmed directly with techcadd. This article illustrates a possible practical learning experience.

Conclusion

A productive day in a data analytics classroom can involve much more than learning software commands.

Students might begin with a business problem, explore a dataset in Excel, clean inconsistent information, write SQL queries, build Power BI dashboards, and present their findings.

Each activity contributes to a broader skill: turning raw information into useful insights.

For students considering a Data Analytics Course at techcadd, the most important question is not simply which tools they will learn.

It is whether they will have opportunities to practise using those tools to investigate meaningful problems.

Because becoming a data analyst is not just about knowing how to create reports. It is about knowing which questions to ask, how to investigate the evidence, and how to explain what the data actually means.