DEV Community: Noel Mwami

SQL Concepts and Practical Application

Noel Mwami — Sat, 11 Apr 2026 12:24:07 +0000

SQL is a language used to store, retrieve, and analyze data in databases.
SQL commands are grouped into categories based on their purpose.
The main two categories are;
DDL (Data Definition Language) vs DML (Data Manipulation Language) explained briefly below

DDL (Data Definition Language): Used to define and structure database objects such as tables.
DML (Data Manipulation Language): Used to manage and manipulate data inside those tables.

Difference between DDL & DML

Aspect	DDL (Data Definition Language)	DML (Data Manipulation Language)
Purpose	Defines database structure	Manipulates data in tables
Commands	CREATE, ALTER, DROP	INSERT, UPDATE, DELETE
Effect	Changes schema	Changes records/data
Example	CREATE TABLE students	INSERT INTO students VALUES (...)

2. Use of CREATE, INSERT, UPDATE, DELETE

In my assignment this week i was able to do the to use the below:

CREATE was used to define tables such as students, exam results, and subjects.
INSERT was used to add records into the tables.
UPDATE was used to correct or modify existing data, such as updating a student’s city or correcting marks in the exam results table.
DELETE was used to remove incorrect or cancelled records. For example, result_id 9 was deleted from the exam results table.

3. Filtering Data Using WHERE

The WHERE clause was used to filter records based on specific conditions. It supports several operators including:

= (equal to)
> and < (greater than / less than)
BETWEEN
IN
LIKE

Examples:

LIKE (pattern matching):
- 'A%' → starts with A
- 'E%' → starts with E
- '%Studies%' → contains "Studies"
IN (multiple values):

WHERE city IN ('Nairobi', 'Mombasa', 'Kisumu')

WHERE marks BETWEEN 50 AND 80

4. CASE WHEN for Data Transformation

CASE WHEN was used to create conditional logic in SQL queries.

It helped transform raw data into meaningful categories. For example, exam marks were classified into:
Distinction,Merit,Pass,Fail based on score ranges.

5. Reflection

This being my first week in using SQL .I learned how SQL is used to manage and analyze data efficiently.

The most interesting part was creating tables from scratch and adding data into them. Seeing the results using SELECT * was fascinating.

The most challenging part was ensuring correct syntax when writing multiple-row INSERT statements and using filtering conditions correctly. It was surprising how a small mistake like a missing comma could cause errors.

Overall, this has improved my understanding of how databases are structured and how data is manipulated in real-world scenarios. Its only the beginning and i already love it.

A Guide on Publishing Power BI Report and Embedding It on a Website Using iFrames

Noel Mwami — Fri, 10 Apr 2026 15:51:52 +0000

Introduction Power BI is a business analytics tool that allows users to visualize data and share views across organizations. It has features that allows publishing of reports online and embedding them into websites using iframes.
Requirements Before you begin or publish the power BI report , ensure you have:
A Power BI account
A completed Power BI report
Access to Power BI Service (app.powerbi.com)
Basic understanding of HTML
Step 1: Create Your Report in Power BI Desktop
Design your report with visuals, charts, and filters.

Step 2: Publish to Power BI Service Log into Power BI service Under workspace , create new work space with your name

Open the power BI report to publish
Go to file. Click "Publish" in Power BI Desktop choose the work space to publish report to

Step 3: Open Report in Power BI Service
Step 4: Generate Embed Code
Open report
Click File > Embed Report > Publish to Web

7 step 5:- Confirm and copy iframe code

Step 6: Embed in Website Paste iframe into your HTML file.

Save the file with .html extension
Click the file copy the path and paste in your browser if it works.

For example my link to the above file is a below click on it and see how a practiced power BI recently file:///C:/Users/nmwami/Desktop/PUBLISHING/Electronic%20sales%20Assignment.html
Conclusion
Embedding Power BI reports enhances interactivity and data sharing on websites.

How Excel is Used in Real-World Data Analysis

Noel Mwami — Fri, 27 Mar 2026 16:32:36 +0000

Description: Explore the enduring relevance of Microsoft Excel in professional data analysis, from auditing to business decision-making.
tags: data analysis, excel, tutorial, productivity

In today’s data-driven world, organizations rely heavily on tools that can help them organize, analyze, and interpret data efficiently. One of the most widely used tools for this purpose is Microsoft Excel. Despite the rise of advanced data analytics platforms, Excel remains a powerful and accessible solution for professionals across finance, auditing, operations, and many other fields.

What is Excel?

Excel is a spreadsheet application developed by Microsoft that allows users to store, organize, and analyze data using rows and columns. It provides a wide range of features such as:

Formulas and Functions: For automated calculations.
Charts and Graphs: For data visualization.
Pivot Tables: For summarizing large datasets.

These tools make it suitable for both simple calculations and complex data analysis.

Real-World Applications of Excel in Data Analysis

1. Internal Audit and Risk Analysis

In audit environments, Excel is essential for testing controls and identifying anomalies in data. Auditors use Excel to:

Detect duplicate transactions.
Identify gaps in voucher sequences.
Analyze unusual posting dates.

For instance, using sorting and filtering, an auditor can quickly highlight missing sequence numbers in payment vouchers, which may indicate control weaknesses.

2. Data Cleaning and Preparation

Raw data is often "messy" and needs cleaning before analysis. Excel helps users:

Remove duplicates.
Split or combine text fields.
Standardize formats.

Functions like TRIM, LEFT, RIGHT, and MID are commonly used to clean and manipulate text data.

3. Business Decision-Making

Managers and analysts use Excel dashboards and charts to visualize trends and support decision-making. For example:

Sales trends over time.
Customer purchasing patterns.
Inventory levels.
Customer satisfaction by analyzing product rejection rates.

Key Excel Features and Formulas

VLOOKUP / XLOOKUP

These functions are used to retrieve data from a table based on a matching value. For example, matching customer IDs with names from a separate dataset.

IF Function

The IF function applies logical conditions to data.
Example: Classifying transactions as “High Risk” or “Low Risk” based on value:
=IF(A2>100000,"High Risk","Low Risk")

Pivot Tables

Pivot tables summarize large datasets quickly. An auditor can summarize total expenses by department or month without writing complex formulas.

Why Excel Remains Relevant

Even with advanced tools like Python and Power BI, Excel remains a staple because:

Accessibility: It is easy to learn and widely available.
No Programming Needed: It requires no coding knowledge to get started.
Integration: It integrates well with almost every enterprise system.
Flexibility: It is effective for both small and large datasets.

Conclusion

Excel continues to be a vital tool in real-world data analysis across various industries. From financial reporting to audit testing and decision-making, its features enable users to transform raw data into meaningful insights.

Mastering Excel is an essential foundation that opens the door to more advanced analytical tools. Give it a try—you won't regret it! Start Learning Excel Today