DEV Community: Bill Muli

Introduction to Python for Data Analysis

Bill Muli — Fri, 15 May 2026 06:55:28 +0000

Python is a programming language used for data analysis, artificial intelligence, create software, apps and automation.

Reason for Python Popularity
-It is east to read and learn because of its syntax.
-It works well with large datasets thus do ETL processes.
-It is good for visualization.
-Python can also automate tasks e.g generating reports
-It also has powerful libraries e.g pandas, matplotlib, scikit-learn.

Python Libraries used in Data Analysis

Pandas- It is used for tables and datasets

Matplotlib-It is used for charts and graphs.

Numpy-It is the fundamental package for computing in python. It provides support for large, multi-dimensional arrays and matrices.

Scikit-learn-It is the go to machine learning library in python

Real World examples of Python in Data Analytics

1.Business and Finance-Used for customer and revenue analytics, customer lifetime modelling and risk and fraud detection.
2.Healthcare-Used for clinical data analysis by processing electronic health records extracts to identify patient cohorts,
treatment outcomes and adverse event patterns.
3.Operations and Supply Chain-Retailers like Walmart use python to build time-series model, predict inventory needs by region.
4.Public Sector and Research-Governments use Python to weight survey responses, impute missing data and generate population estimates.

Beginners should learn python because it removes friction at nearly every stage of learning, lets you focus on problem-solving rather than fighting the language itself

A Practical Introduction to SQL: DDL, DML, and Data Analysis

Bill Muli — Fri, 15 May 2026 06:45:54 +0000

What are DDL and DML?
DDL (Data Definition Language)

DDL refers to SQL commands used to define and manage the structure of a database. These commands control how data is stored.

Examples:

1.CREATE – used to create tables or databases
2.ALTER – used to modify existing structures
3.DROP – used to delete tables or databases

Table structure after executing CREATE statement.

DML (Data Manipulation Language)
DML deals with the data inside the database. It allows you to insert, update, retrieve, and delete data.

Examples:

1.INSERT – adds new records
2.UPDATE – modifies existing records
3.DELETE – removes records
4.SELECT – retrieves data

Difference
DDL focuses on the structure, while DML focuses on the data itself.

In simple terms,** DDL builds the house*, and* DML manages what’s inside it.**

Application of CREATE, INSERT, UPDATE, and DELETE
In this assignment, I used several SQL commands to build and manage a school database:

I used the CREATE statement to define tables such as students, subjects, and exam_results.

INSERT
The INSERT statement was used to populate tables with data.

UPDATE
I used UPDATE to modify existing data.

DELETE
Used to remove incorrect or unnecessary records.

Filtering Data with WHERE
The WHERE clause is essential for retrieving specific data.

Operators used:

= → exact match

→ greater than
BETWEEN → range filtering
IN → multiple values
LIKE → pattern matching

Using CASE WHEN for Data Transformation
The CASE WHEN statement applies conditional logic.

Example classification:

1.Marks ≥ 80 → Distinction
2.Marks ≥ 60 → Merit
3.Marks ≥ 40 → Pass
4.Below 40 → Fail

Reflection
This week was both challenging and rewarding. Errors like incorrect column names and missing tables helped me understand how strict SQL is and the importance of attention to detail.

How to Publish a Power BI Report and Embed It into a Website

Bill Muli — Mon, 13 Apr 2026 08:52:27 +0000

What Is Power BI ?

.Power BI is Microsoft’s tool for turning data into visual stories.

.No one benefits from a report that is sitting on your laptop. Publishing makes it available online so that anyone, including your lecturer and team, may view it in a browser without the requirement for Power BI Desktop. Additionally, embedding takes it a step further by dropping your interactive, live report directly into a website, much like a movie player for data

Step 1: Create a Workspace

Think of a workspace as a shared folder in the cloud. It’s where your published report lives so others can find it.

Here’s how to set one up:

1.Go to app.powerbi.com and sign in with your Microsoft account.
2.0n the left sidebar, click the Workspaces icon (it looks like a briefcase).
3.Scroll to the bottom of the panel and click + Create a workspace.
4.Give it a clear name — something like Electronics Sales Dashboard works perfectly.
5.Optionally add a short description, then click Save.

Step 2: Publish Your Report

1.Open your .pbix file in Power BI Desktop.
2.Make sure you’re signed in — look for your name in the top-right corner. If it says Sign In, click it and enter your Microsoft credentials.
3.Go to the Home tab in the ribbon and click the yellow Publish button (far right).
4.A dialog box appears asking where to publish. Select the workspace you just created.
5.Click Select and wait. It usually takes under a minute. When you see ‘Publishing to Power BI succeeded!’ — you’re done!

Step 3: Generate the Embed Code

The embed code is a small snippet of HTML that tells a web page to load your Power BI report inside it. Here’s how to get it:

1.Open your published report in app.powerbi.com.
2.Click File (top-left) or the Share button, then hover over Embed report.
3.From the submenu, select Publish to web (public).
4.Read the warning — this makes your report viewable by anyone with the link, which is fine for an academic assignment. Click Create embed code.
5.A box appears with two things: a share link (for email) and an iFrame code (for websites). Copy the iFrame code.

Step 4: Embed the Report on a Website

1.Open your HTML file in a code editor like VS Code.
2.Find the spot in the where you want the report to appear.
3.Paste your iFrame code there. Wrap it in a
if you want to add a heading above it.
4.Save the file and open it in your browser. Your live report should load right there on the page.

On WordPress

1.Open the page editor in WordPress.
2.Add a Custom HTML block.
3.Paste your iFrame code into the block.
4.Click Update or Publish — done.

Uploading Your .pbix File to GitHub

1.Go to github.com and log in (or create a free account if you don’t have one).
2.Click the + icon (top right) → New repository. Name it something like electronics-sales-powerbi. Set it to Public and click Create repository.
3.Inside the repo, click Add file → Upload files. Drag your .pbix file in and click Commit changes.
4.Create a README.md file. Add a one-line description and paste your Power BI embed link so your instructor can view the live report directly from GitHub.
5.Copy the repo URL from your browser (e.g. github.com/yourname/electronics-sales-powerbi) and submit it.

Understanding Data modeling in power BI: Joins, relationships and Schemas explained

Bill Muli — Sun, 29 Mar 2026 21:44:10 +0000

Data modeling is the backbone of every effective Power BI report. If dashboards feel slow, filters behave incorrectly, or numbers don’t match expectations, the issue is often the data model not the visuals.

This guide explains how data modeling works in Power BI step‑by‑step. You’ll learn SQL joins, relationships, schemas, fact vs dimension tables, role‑playing dimensions, and how everything is created inside Power BI itself.

This article is beginner‑friendly but structured like a professional BI reference.

SQL Joins

Although Power BI relies on relationships, understanding SQL joins helps you prepare and transform data effectively.

INNER JOINS
Returns only matching records from both tables.

.** LEFT JOIN **

Returns all records from the left table and matching ones from the right.

RIGHT JOIN (RIGHT OUTER JOIN)

Returns all records from the right table and matching ones from the left.

FULL OUTER JOIN

Returns all records from both tables, matching where possible.

LEFT ANTI JOIN

Returns records from the left table that have no match in the right.
**
RIGHT ANTI JOIN**

Returns records from the right table that have no match in the left.

How Power BI Handles Joins

Power BI does not explicitly use SQL join syntax in reports. Instead:

Power Query → Performs joins (Merge Queries)
Data Model → Uses relationships instead of joins
Key Difference:
SQL Join **→ Combines tables physically
**Power BI Relationship → Connects tables logically

Data Modeling Schemas
1*. Star Schema*

Central fact table connected to multiple dimension tables.

Example:
Customers
|
Products ─ Orders ─ Dates
|
Sales
Benefits:
Fast performance
Easy to understand
Best for Power BI

Snowflake Schema Dimension tables are further normalized into multiple related tables.

Drawbacks:
More complex
Slightly slower queries

Best Practices for Power BI Data Modeling
Use Star Schema whenever possible
Avoid many-to-many relationships unless necessary
Use clean keys (IDs) for relationships
Remove duplicate and unnecessary columns
Use Power Query for joins, not DAX

Conclusion
Understanding data modeling is essential for building reliable Power BI dashboards. While SQL joins help shape your data during preparation, Power BI relationships allow for dynamic and efficient analysis.