DEV Community: Ryan Murunga

Data Modelling in Power Bi

Ryan Murunga — Mon, 29 Jun 2026 10:52:50 +0000

INTRODUCTION

Power Bi is a comprehensive business analytic tool developed by microsoft.

Power Bi is used for:

Connect data .
Clean and transform data.
Create relationships between tables
Analyze data
Build Reports
Create dashboards and share insights

Some of the importance of Power Bi is;

Cleaning messy data
Analyze large amounts of data
Create Interactive dashboards
Identify trends & patterns
Make data driven decisions
Present insights visually

Core components of power bi include:

Power BI Desktop - Is a free Windows application used to connect to data sources, clean and transform information, model relationships, and build reports.
Power BI Service - Is a cloud-based platform accessible via web browsers.
Power BI Mobile - Is a Native iOS and Android apps that allow you to monitor business metrics and interact with reports on the go.

DAX FUNCTIONS

DAX - Data Analysis Expression (Is a formula language used in power Bi to create calculations and analyze logics.)

DAX is used to create ;

Calculated columns
Calculated tables

DAX functions allow us to create calculations that are not direclty available in the raw dataset.

Here are some of few common DAX function;

Sum Is a measure used to calculate a total value.

Total Sales = SUM(Sales[SalesAmount])

Average Is a measure used to calculate the average of values.

Average Sales = AVERAGE(Sales[Amount])

Count Is used to count the number of rows in a column that contain numbers.

Total Orders = COUNT(Sales[OrderID])

IF Is used to create conditions. It checks if a condition is true or false and returns a specific result based on that.

Sales Category = IF(Sales[Amount] > 10000, “High”, “Low”)

RANKX It assigns ranks to values based on a specific measure.

Sales Rank = RANKX(ALL(Products), SUM(Sales[Amount]))

ISBLANK It checks if a value is blank.

Check Sales = IF(ISBLANK(Sales[Amount]), “No Data”, Sales[Amount])

NOW It returns the current date and time.

Current DateTime = NOW()

After a short overview of PowerBi lets now understand what is Data Modelling.

Data Modelling - is the process of structuring and defining relationships between your tables to enable fast, accurate, and scalable data analysis.

Relationships - link data across different tables so your visuals can aggregate, filter, and slice data correctly.

There are 3 main relationships

One to Many - A database where a single record in a one table is linked to a table with multiple records
e.g
One patient can have many hospital visits
One to One - A database where a single records corresponds to exactly one unique in another record.
e.g
One patient can have one patient profile
Many to Many - A database where multiple records in one entity are associated with multiple records in another entity.
e.g
One patient can have many diagnosis

SCHEMAS
This refers to how data is organized in a database.

The main types of schema are ;

Flat Table
Star Schema
Snowflake Schema

1. Flat table
Is a single large table that contain all the information in one place

Characteristics

Has many columns
Combines both ; Descriptive columns, Numeric Columns, ID columns,Repeated values

Example of a flat table

Advantages

Simple for small datasets
Easy to load in PowerBI
Easy to read

Disdvantages

May slow down reports
Can create data quality problems 3.Contain reported data
Can become very large datasets

2. Star Schema
Is a data model where a central fact table connects directly to multiple dimension table.

Fact-table Sits in the middle
Dimension Tables surround it

Example of a star schema

Fact table
Stores events , transaction or activities that happened
The fact table contains:

Keys that connnect to dimension tables
Numeric Values that can be calculated

Dimension Table
Stores descriptive information
Helps us filter , group and describe the fact table

Advantages of Star schema

Reduces repeated data
Improves report performance
Easier to understand
Makes DAX measures easier

Star schema is recommended because

Dimension tables contain descreption
Filter flow from dimensions to facts
Fact tables contains numbers and keys

3. SNOWFLAKE SCHEMA
This is when dimension tables are normalized and split into multiple, related sub-dimension tables.
Dimension tables are split into smaller related tables

Example of a snowflake schema

Snowflake schema is useful when;

Creating a more normalized model
Reduce repeated data
Have very large dimension tables

Differences between Star Schema and Snowflake schema

AREA	STAR SCHEMA	SNOWFLAKE SCHEMA
Performance in Power BI	Usually Faster	Can be slower if too many relationships
Simplicity	Easier for beginners	More Complex
Structure	Fact table connected directly to dimensions	Dimensions are split into smaller dimensions
Storage	More repeated dimension data	Less data
Best for	Reporting and dashboards	More normalized database style model

How I Built a 2026 World Cup Predictor Dashboard

Ryan Murunga — Fri, 19 Jun 2026 05:59:46 +0000

Let’s be honest: there is nothing quite like the feeling of moving a machine learning model off a messy local Jupyter Notebook and seeing it live, breathing, and clickable on the internet.

A few weeks ago, I decided to tackle a fun challenge: predicting the knockout stages of the upcoming 2026 FIFA World Cup. Instead of just looking at standard text readouts on my terminal, I wanted to build a complete, interactive visual experience that anyone could play with.

Here is the story of how I built the data pipeline, why I chose my model, and the cheeky trick I used to keep the app online 24/7 for free.

🏗️ Breaking Down the Pipeline

I wanted to keep this workspace modular and clean. Instead of one massive script that does everything, I split the workflow across four dedicated stages:

The Ingestion (01_data_alignment.ipynb): Cleaning up raw data and aligning historical match histories.
The Feature Store (02_historical_feature_store.ipynb): This is where the magic happens. I engineered metrics to track team form, "attacking velocity," and defensive stability over time.
The Baseline (03_local_validation_and_baseline.ipynb): Setting up cross-validation loops to ensure the model wasn't just guessing blindly.
The Interface (app.py): The final production script handling the simulation logic and rendering the front-end layout.

🤖 The Engine: Why LightGBM?

For the predictive heavy lifting, I went with LightGBM.

International football data is highly tabular but filled with tricky, non-linear relationships (e.g., how a team's current attacking form clashes with an opponent's specific defensive structure). LightGBM handles these interactions beautifully. Plus, it trains incredibly fast and allows me to output precise match probability distributions rather than just a boring binary "Win/Loss" result.

🎨 Fighting with Streamlit and CSS

I wanted this app to be intuitive for regular football fans, not just techies.

I built a sidebar playground where you can match any two qualified nations head-to-head and tweak "volatility sliders" to simulate massive underdog upsets.

But the hardest part? The bracket. Streamlit is amazing, but drawing a fully responsive, clean 16-team tournament tree using standard components is tough. To fix it, I injected raw custom CSS blocks directly into the layout. Now, it draws crisp vector grid lines mapping progression straight from the Round of 16 down to a gold-bordered podium crowning the grand champion.

😴 The "Never Sleep" Secret

I deployed the app for free using Streamlit Community Cloud. It’s an incredible service, but there is a catch: if your app doesn't get traffic for 12 hours, the server puts it to sleep. The next visitor gets a slow "This app is hibernating" loading screen.

To bypass this for my portfolio, I set up a simple GitHub Actions workflow (.github/workflows/keep_alive.yml). Every 10 hours, it automatically pushes an invisible, empty commit to the repository. This tricks the cloud server into seeing active development, resetting the hibernation timer to zero. Result? The app stays awake 24/7!

🎮 Take it for a Spin!

The entire project is public, and I’d love for you to play around with it.

🌍 Live App: Test Your 2026 World Cup Predictions
📂 GitHub Source: MouroshK/World_cup_2026

🏆 The Ultimate Prediction

To give you a sneak peek of the dashboard in action, here is the full knockout bracket generated by the pipeline's baseline run:

2026 World Cup Predictive Bracket Simulation

As you can see, the LightGBM engine forecasts an absolute heavyweight clash for the final, ultimately predicting France to edge out Argentina to lift the trophy, while nations like England and Brazil fall just short in the semifinals.

How Excel is used in Real World Data Analysis

Ryan Murunga — Fri, 05 Jun 2026 08:43:32 +0000

EXCEL

This is a spreadsheet software developed by Microsoft used to insert , read and manipulate data.

This is an example of how an excel page looks like. It is called a workbook

Basics of excel

Formula bar - Area above the grid where the contents or formula of the selected cell appear.
Column header - Is the label at the very top of a column in a spreadsheet, database, or web table.
Row header - Is the first column in a data table that identifies the specific information, category, or ID for the corresponding data to its right.
Selected cell/Active Cell - Is the currently selected cell in a spreadsheet where you can directly enter data or formulas.
Sheet tab - Tabs at the bottom (Sheet1, Sheet2) that switch between spreadsheets within a workbook.
Cell - A single box where a row and a column intersect eg D9.
Row - Labelled 1,2,3... down the left side horizontally .
Columns - Labelled A,B,C... across the top vertically.

Ways in which Excel is used in real world data analytics

1. Financial reporting

Most business uses excel to create financial reports, forecast revenue and analyze profitability. Excel can also help track expenses , manage cash flow and optimize financial decision making.

This is an example of a profit and loss statement

2. Healthcare & Research

Patients records, Lab results , clinical results data are stored in excel . Lab results can be used to predict future outbreaks of diseases

This is an example of a medical research KPI dashboard in excell

3. Marketing and Sales

Excel is used in sales tracking, inventory control,revenue targets, conversion rates and supply chain management. Businesses can use formulas to calculate stock levels, reorder points, and sales growth trends. It helps companies reduce stockouts, prevent overstocking, and improve operational efficiency.

This is an example of an excel sales forecasting

4. Business forecasting & Decision making

This is predicting what will happen in the future to be able to make decision based on the prediction.By using historical data and forecasting models, businesses can predict market trends,plan for future growth, assess risk and make data-driven decisions.

This is an example of a forecast sheet

5. Automation & Productivity

Excel automation features like templates and predefined functions help businesses save time and reduce manual errors. It allows companies to automate repetitive tasks such as data entry, invoicing, and payroll processing, increasing overall productivity.

This is an example of an automated Periods Table.

Formulas in Excel

In excel they are very many function that makes data analysis simple . Here a few of the main functions one should know.

1. Sum

Adds all the values in the said range
E.g in the below it add all values from B2,B3,...,B8

=SUM(B2:B8)

2. Average

Finds the average of values in the said range
E.g In the below cell it finds the average of all values between cell D2,D3,...,D22

=AVERAGE(D2:D22)

3. Max

Finds the max number in said range of values
E.g In the below cell it finds the maximum number in all cells from F3,F4,...,F10

=MAX(F3:F10)

4. Count

Count cells with numbers
E.g It returns the number of cells with numbers

=COUNT(H2:H10)

5. Filter

Extracts data from a table based on criteria you specify.
E.g Checks the data and instantly spill out a list of only the rows where sales (Column C) are greater than 5000.

=FILTER(A2:C100, C2:C100>5000, "No Sales")

6. Sort

Sorts the contents of a range or array automatically.
E.g Sorts the data range based on the 2nd column, in descending order (highest to lowest).

=SORT(A2:B100, 2, -1)

7. IF

It checks if a condition is true or false and gives you a different result depending on the outcome.
E.g If a student's grade in cell B2 is 70 or higher, Excel prints "Pass". If it's 69 or lower, it prints "Fail".

=IF(B2>=70, "Pass", "Fail")

8. Median

Returns the exact middle number in a given set of numbers.
E.g Finds the middle value of all numbers in that range.

=MEDIAN(C2:C50)

9. Mode

Returns the most frequently occurring or repetitive value in a dataset.
E.g It shows you which number is most frequently.

=MODE(D2:D100)

10. Proper

It capitalizes the first letter of every word in a text string and converts all other letters to lowercase.
E.g If cell A2 contains the messy text: "jOHN mCcLAIN" the formula will cleanly output: "John Mcclain"

=PROPER(A2)

11.COUNTIF

Returns the value in a given a range given one condition
E.g How many total deals did the North region close?

=COUNTIF(B2:B100, "North")

12. COUNTIFS

Returns the value in a given range with more than one condition
E.g Returns the number of deals in the North region are still 'Pending'

=COUNTIFS(B2:B100, "North", D2:D100, "Pending")

13.SUMIF

Adds the values in a range that meet a single, specified condition
E.g Adds the total revenue brought in by the sales rep 'Sarah'?

=SUMIF(A2:A100, "Sarah", C2:C100)

14.SUMIFS

Adds all numbers in a range that meet multiple conditions
E.g Adds total revenue brought in by 'Sarah' for deals larger than $10,000?

=SUMIFS(C2:C100, A2:A100, "Sarah", C2:C100, ">10000")

15. AVERAGEIF

Calculates the average of numbers in a range that meet a specific condition
E.g Calculate the average deal size in the South region?

=AVERAGEIF(B2:B100, "South", C2:C100)

16. AVERAGEIFS

Calculates the averages of numbers in a specific range based on more than one conditions.
E.g Calculates the average size of 'Closed' deals in the South region?

=AVERAGEIFS(C2:C100, D2:D100, "Closed", B2:B100, "South")

17. ROUND

Used to round a number to a specified number of digits
E.g A sales commission calculates out to $1,250.4567. You need to round it to standard currency (2 decimal places) for payroll.

=ROUND(1250.4567, 2) returns 1250.46

18. ROUNDUP

Used to round a number up
E.g You need to ship items to a client. A box holds 10 items, and they ordered 41 items ($41 / 10 = 4.1$). You can't ship 4.1 boxes; you must round up to ensure you have enough physical boxes.

=ROUNDUP(4.1, 0) returns 5.

19. INT

Rounds a number down to the nearest whole interger by removing its decimals
E.g An employee clocked 38.75 hours this week. You want to extract just the full, completed hours they worked.

=INT(38.75) returns 38

20.ABS

Returns the positive value of a number by removing its negative sign
E.g If a refund is logged as -$450.00, you want to know the absolute dollar volume of the transaction.

=ABS(-450) returns 450

21. TODAY()

Is a formula that updates the day automatically
E.g Finding out exactly what day it is today to calculate invoice aging.

=TODAY() returns 06/05/2026 (based on today's date)

22. NOW

Inserts the current date and time
E.g Stamping a report so stakeholders know exactly what minute the data was pulled from the server.

=NOW() returns 06/05/2026 15:02

23. YEAR

To extract year in a date
E.g You want to group sales by their calendar year.If cell A2 holds 06/05/2026.

=YEAR(A2) returns 2026

24.WEEKDAY

To extract the day of the week in excel
E.g Checking if a client submitted a payment on a weekend so you can account for bank processing delays.

=WEEKDAY(A2) returns 6 (which represents Friday)

25. WEEKNUM

Finds the week number of a specific date
E.g

Excel is a very important tool in Data analysis. We can say it the mother of all since it can do almost all the analysis. Excel needs to be one of the most important tools one has to learn to be able to understand data analysis.

My Data Journey

Ryan Murunga — Wed, 03 Jun 2026 16:41:41 +0000

Background

As a young kid i realized i had a passion with Mathematics. I would really struggle understanding the other subjects but Math to me was so easy . When i finished highschool the big question was what i wanted to do . I already new that i wanted to do something associated with Math. I did my research and the course that was coming up more frequent was Acturial Science . I applied for the course later on while still doing more research Data science came up and i was curious about it . Looked it up and so that the career opportunities were what i would be intrested . I was later called for Acturial Science but did a course transfer to Data Science

Life in Data Science

Back to September 2023 and i began my degree in Data Science . At first i was happy with what i was learning and enjoyed . The only challenge i faced is i expected alot of practical work and i only recieved the theory part of most my units. I wanted to learn the theory but i didn't know where to start. I tried out different online contents but i was never satisfied with whatever i was learning . I still was eager to get my hands on real data.As this was the reason i joined Lux academy to get hands on skill on Data Science

All my best as i begin this journey