Forem: Maureen Waithaka

How Analysts Translate Messy Data, DAX, and Dashboards into Action Using Power BI

Maureen Waithaka — Mon, 09 Feb 2026 11:29:45 +0000

Step 1: Taming messy Data

- Data Cleaning in Power Query
(What is Power Query?
Power Query is a data transformation and preparation tool that is part of the
Microsoft Power BI suite. It allows users to easily extract, transform, and load
(ETL) data from a wide variety of sources into a structured format for analysis
and reporting.)

Now back to Data cleaning in Power Query:
Analysts start by importing data from multiple sources (Excel sheets, databases, APIs). Power Query helps them:
• Remove duplicates and errors
• Standardize formats (dates, currencies, text cases)
• Merge or split columns for clarity

Here are some of the most common data transformation techniques used in Power BI, along with examples:

Filtering data: This involves removing unwanted data from a dataset based
on specific criteria.
For example, you may want to filter out all sales data for a certain product category that is not relevant to your analysis.
Sorting data: This involves arranging the data in a dataset in a specific order, such as by date, alphabetically, or by numerical value. For example, you may want to sort a list of customers by their total purchases.
Merging data: This involves combining data from multiple tables into one.
For example, you may have one table that contains customer information and another that contains order information. You can merge these tables to
create a single table that contains both sets of information.
Splitting data: This involves dividing a single column of data into multiple columns based on a specific delimiter or pattern.
For example, you may want to split a column of customer names into separate columns for first name and last name.
Grouping data: This involves aggregating data based on specific criteria, such as summing sales data by month or counting the number of orders by customer.
For example, you may want to group sales data by product category to see which categories are generating the most revenue.
Calculating new columns: This involves creating new columns of data based on calculations or formulas.
For example, you may want to create a new column that calculates the profit margin for each sale.
Renaming columns: This involves giving columns more descriptive names to make them easier to understand.
For example, you may want to rename a column from "col1" to "customer ID".

Step 2: Modeling with DAX

DAX stands for Data Analysis Expressions. It’s the formula language used in Power B.I, Excel Power Pivot, and SQL Server Analysis Services (SSAS) Tabular models. Think of it as the engine that powers calculations and advanced analytics inside your data models.

Data Analysis Expressions (DAX) is the language analysts use to create calculated columns, measures, and KPIs.
Key uses:
• Aggregations: SUM, AVERAGE, COUNT for quick metrics

Example: Let's say we have a table called "sales" with columns "product",
"region", and "sales_amount".

We can use the following DAX formula to calculate the total sales amount:

Total Sale amount = Sum(Sales[Sales_amount])

This will sum up the "sales_amount" column in the "sales" table and give us the total sales amount.

We can use the following DAX formula to calculate the Average sales amount:

= Average(Sales[price])

This will return the average price from the "price" column in the "sales" table

We can use the following DAX formula to calculate the Count of sales:

= Count (Sales[Product])

This will return the total number of rows in the "Sales" table that have a nonblank value in the "Product" column.

• Time Intelligence: Year-to-date, month-over-month growth
• Scenario Analysis: What-if parameters for forecasting

Step 3: Building Dashboards

• Visual Storytelling: Analysts design dashboards that highlight the most important metrics with charts, maps, and KPIs.
• Interactivity: Filters, slicers, and drill-throughs let decision-makers explore data dynamically.
• Accessibility: Dashboards can be shared across teams, embedded in apps, or refreshed automatically.

Here is an Example of a Dashboard:

A Dashboard of The Kenya Crop Performance Data

Step 4: Turning Insights into Action

Decision Support: Dashboards don’t just show numbers, they guide actions.
Examples:
• Spot declining sales - adjust marketing strategy
• Identify top customers - strengthen loyalty programs
• Track inventory trends - optimize supply chain
Outcome: Messy data becomes a strategic asset, enabling leaders to act quickly and confidently.

Power BI empowers analysts to bridge the gap between raw data and business strategy. It’s not just about charts, it’s about creating a feedback loop where data drives smarter decisions.

Schemas and Data Modelling in Power B.I

Maureen Waithaka — Sun, 01 Feb 2026 11:16:39 +0000

What is a Schema?

A schema is essentially the blueprint or structure of how data is organized in a database or data model. Think of it as the map that shows:
• What tables exist
• What fields (columns) are in those tables
• How those tables relate to each other

In other words, a schema defines the logical arrangement of data and the relationships between different parts of that data.

What is Data Modelling?

Data modeling is the process of designing how data is structured, stored, and related to each other so it can be used effectively for analysis and reporting.
It’s like creating a blueprint for your data: deciding what tables you need, what fields go into them, and how they connect.

Types of Schemas in B.I

Star Schema
Snowflake Schema
Galaxy Schema (Fact Constellation)

1. Star Schema

A Star Schema is a way of organizing data where:
• A central Fact Table holds quantitative, transactional data (numbers, metrics).
• Surrounding Dimension Tables hold descriptive attributes (categories, labels, hierarchies).
• The structure looks like a ⭐ star: fact table in the middle, dimensions radiating outward.

Components of a Star Schema

Fact Table
• Contains measures (numeric values) and foreign keys linking to dimensions.
• Usually very large because it stores detailed records.
Example:
Sales Fact Table → OrderID, CustomerID, ProductID, DateID, Quantity, Revenue.
Dimension Tables
• Contain descriptive attributes used for filtering, grouping, and slicing data.
• Smaller tables compared to fact tables.
Examples:
(i) Customer Dimension → CustomerID, Name, Age, Region.
(ii) Product Dimension → ProductID, Name, Category, Brand.
(iii) Date Dimension → DateID, Year, Quarter, Month, Day.
Relationships
• One-to-Many: Each dimension record can link to many fact records.
Example: One customer can place many orders.
• In Power BI, these relationships are defined in the Model view.

Why use Star Schema?
• Performance: Queries run faster compared to other types of schemas
• Simplicity: Easy for analysts and business users to understand.
• Flexibility: Supports hierarchies, drill-downs, and DAX measures in Power BI.
• Best Practice: Microsoft recommends star schema for Power BI semantic models.

2. Snowflake Schema

A Snowflake Schema is a type of data model where dimension tables are normalized into multiple related tables.
• Instead of having one big dimension table (like in a star schema), you split it into smaller tables to remove redundancy.
• The structure looks like a ❄️ snowflake because dimensions branch out into sub-dimensions.

Structure
• Fact Table: Holds transactional/numeric data (e.g., Sales: OrderID, ProductID, CustomerID, DateID, Revenue).
• Dimension Tables: Broken down into multiple related tables.
Example:
• Customer → City → Region → Country
• Product → Category → Department

Fact Table: Sales
• OrderID
• CustomerID
• ProductID
• DateID
• Quantity, Revenue

Dimension Tables (normalized)
• Customer (CustomerID, Name, CityID)
• City (CityID, CityName, RegionID)
• Region (RegionID, RegionName, CountryID)
• Country (CountryID, CountryName)

Here, instead of storing Customer → City → Region → Country in one table, you break it into multiple linked tables.

Advantages
• Saves storage space (no repeated values like “Kenya” in every customer row).
• Reduces redundancy and improves data integrity.
• Useful for very large datasets with complex hierarchies.

Disadvantages
• More complex to design and maintain.
• Queries are slower because they must join across many tables.
• Harder for business users to understand compared to star schema.

Relationships
• Still based on one-to-many relationships.
• But dimensions are linked through multiple levels (Customer → City → Region → Country).
• In Power BI, this means more joins and more complex relationships in the model view.

Good data modeling is critical because it directly impacts Power BI’s performance, accuracy, and scalability. A well-structured model ensures fast queries, correct calculations, and intuitive reporting, while poor modeling leads to slow dashboards, incorrect insights and wasted memory.

Summary: Importance of Good Data Modeling in Power BI
• Performance Optimization:
A well-designed model (especially using a star schema) reduces the number of joins, improves query speed, and minimizes memory usage—making dashboards faster and more responsive.
• Accurate Reporting:
Clean relationships between fact and dimension tables ensure correct aggregations, filters, and slicers. This prevents misleading totals and broken visuals.
• Scalability and Maintainability:
Structured models are easier to update, extend, and troubleshoot. They support reusable DAX measures and consistent logic across reports.
• User-Friendly Design:
A clear schema makes it easier for analysts and stakeholders to understand the data, build reports, and explore insights confidently.
• Best Practices Alignment:
Microsoft recommends using star schemas, one-to-many relationships, and dedicated date tables to ensure optimal performance and functionality.

In short: Good modeling is the foundation of reliable, fast, and scalable analytics. It turns raw data into trusted insights.

Introduction to Microsoft Excel for Data Analyst

Maureen Waithaka — Sat, 24 Jan 2026 10:39:15 +0000

What is Microsoft Excel?

Microsoft Excel is a spreadsheet program developed by Microsoft that helps you organize, analyze, and visualize data. It’s widely used for tasks like budgeting, accounting, data entry, and creating charts.
Microsoft Excel is one of the most widely used tools for data analysis. Whether you’re organizing sales numbers, tracking expenses, or analyzing survey results, Excel provides built-in features that make data analysis simple and effective.

Why Use Excel for Data Analysis?
• Easy to learn and widely available.
• Offers built-in formulas and functions for calculations.
• Provides charts and graphs for visualization.
• Allows filtering, sorting, and summarizing large datasets.

Here is an example of an Excel home screen with a dataset.

Step 1: Organizing Your Data
Before analyzing, data should be structured in rows and columns.
• Each column represents a variable (e.g., “Product,” “Sales,” “Region”).
• Each row represents a record (e.g., one product’s sales entry).

Here is an Example of an Excel table with headers.

Step 2: Using Basic Functions
Excel has built-in formulas to perform quick calculations:
• SUM: Adds up values.
• AVERAGE: Finds the mean.
• COUNT: Counts entries.
• MAX/MIN: Finds the highest or lowest value.

Worked Examples:

Sum: Adds values.

=Sum(E872:E877) // Adds all values from cell E872 to cell E877.

This is the sum value after pressing the key 'Enter'

Average: Finds the mean

=AVERAGE(E872:E877) Calculates the average of values in cells E872 to E877

This is the Average value after pressing the key 'Enter'

Count: Counts entries

=COUNT(E872:E877) Counts how many cells in E872 to E877 contain numbers.

This the Count value after pressing 'Enter'

Step 3: Sorting and Filtering

Sorting helps arrange data (e.g., highest to lowest Salary).
Filtering allows you to focus on specific data (e.g., salary, Department...).

After selecting the data you'd like to sort select 'sort and filter' in the ribbon on the home page and sort in the order you'd like.
Highest to Lowest, Lowest to Highest or Even custom.

On selecting 'Filter' the dropdown arrow appears on the selected area which allows you to filter as desired.

Step 4: PivotTables for Summarizing Data
PivotTables are one of Excel’s most powerful features. They let you quickly summarize and analyze large datasets.
• Drag and drop fields to rows, columns, values, and filters.
• Example: Salary by department

Select 'Insert' on the ribbon, then 'Pivot table', select 'New sheet' when prompted, and it takes you to a new work sheet that allows you to select the data you'd like to summarize.

This the Pivot table sheet, Label the sheet 'Department by Salary' or in a comprehendible manner to you or to the desired recipient. The highlighted dropdown allows you to find the desired numerical output of the table using 'Value field setting', like: Sum, Count, Average, Max, Min...

Step 5: Data Visualization with Charts
Charts make patterns easier to see. Common chart types:
• Column Chart: Compare categories.
• Line Chart: Show trends over time.
• Pie Chart: Show proportions.

This is an example of a Column chart, for Pivot table Department by Employee type.
Make sure to select a color theme, and to label your chart

Here is an example of a Line chart

Here is an Example of a simple Pie chart.

*****Step 6: Conditional Formatting*
Highlight important insights automatically:
• Example: Color cells red if sales are below target.
• Helps spot trends and outliers quickly.

Conclusion
MS Excel is more than just a spreadsheet—it’s a powerful data analysis tool. By learning basic functions, PivotTables, and charts, beginners can transform raw data into meaningful insights. With practice, Excel becomes a stepping stone into more advanced analytics tools like SQL, Python, or Power BI.

Git and GitHub Made Simple: A Beginner’s Guide to Push, Pull, and Track Changes

Maureen Waithaka — Mon, 19 Jan 2026 08:41:03 +0000

What is Git?

What is GitHub?

What is to Push, to pull and Track changes?

Well, easy... buckle up, get a glass of water and let us begin.

Git

Git is a free, open-source version control system that helps developers track changes in their code, collaborate with others, and manage project history efficiently.

What Git Does

Tracks changes: Git records every modification made to files over time.
Restores versions: You can roll back to earlier versions if something breaks.
Supports collaboration: Multiple people can work on the same project without overwriting each other’s work.
Keeps history: Git logs who made changes, when, and why.

Why Git Is Powerful

Distributed system: Every developer has a full copy of the project history, not just the latest version.
Speed & efficiency: Handles projects of all sizes quickly.
Flexibility: Works with command-line tools, GUIs, and hosting services like GitHub.

Example

Imagine you’re writing a research paper:

Without Git → You’d keep saving files as paper_v1.docx, paper_v2.docx, etc. Chaos!
With Git → You commit changes, add notes, and can always go back to a previous version. Much cleaner and safer.

In short: Git is your project’s memory and safety net. It ensures you never lose work, can experiment freely, and collaborate without fear of overwriting someone else’s progress.

Sip some water

Now, allow me to introduce the word Version Control

Definition: Version control (also called source control) is a system that records changes to files in a special database.

Purpose: It ensures that every modification is tracked, so you can compare, undo, or merge changes safely.

Collaboration: Multiple people can work on the same project without overwriting each other’s work.

The Correlation between Git and Version Control

• Version control is the concept.
• Git is the tool that makes version control possible.

In other words: Git is the practical implementation of version control.

Why version Control is important

• Safety net: If a mistake is made, you can roll back to a previous version.
• Transparency: You can see who made changes, when, and why.
• Efficiency: Teams work faster and smarter, especially in DevOps and agile environments.
• Organization: No more chaotic file names like project_final_really_final.py.

How to push code to GitHub

Pushing code to GitHub means sending your project from your local computer (where you’re coding) to GitHub (the cloud repository).
Here’s a clear step‑by‑step guide:

Steps to Push Code to GitHub

Initialize Git in Your Project Open Git Bash in your project folder and run:

git init

This tells Git to start tracking changes in that folder.

_ Add Your Files_ Stage all files so Git knows which ones to include:

git add .

( means “add everything in this folder.”)

Commit Your Changes Save a snapshot of your project with a message:

git commit -m "Initial commit"

The message should describe what you changed.

Connect to GitHub • Go to GitHub and create a new repository (e.g., ). • Copy the repo link (choose HTTPS or SSH). • Add it as a remote:

git remote add origin https://github.com/YourUsername/my-first-repo.git

• (Replace with your actual repo link.)

Push Your Code Finally, send your code to GitHub:

git push -u origin main

• origin → the remote repository on GitHub.
• main → the branch you’re pushing (default branch).

Quick Recap

git init → start Git in your folder
git add→ stage files
git commit-m"message"→ save snapshot
git remote add origin <repo-link>→ connect to GitHub
git push -u origin main → upload code

You have successfully pushed code to GitHub

How to pull code from GitHub

Pulling code from GitHub means downloading the latest changes from a repository on GitHub to your local computer. This keeps your local project up to date with what’s stored online.
Here’s a clear step- by -step guide:

Navigate to Your Project Folder Open Git Bash (or your terminal) and move into the folder where your project is stored:

cd path/to/your/project

Check Your Remote Connection Make sure your local repo is connected to GitHub:

git remote -v

You should see something like:

origin  https://github.com/YourUsername/repo-name.git (fetch)
origin  https://github.com/YourUsername/repo-name.git (push)

Pull the Latest Changes Run:

git pull origin main

origin → the remote repository on GitHub.
main → the branch you want to update (sometimes it’s master or another branch).

This command fetches changes from GitHub and merges them into your local project.

Resolve Merge Conflicts (if any)

If you and someone else edited the same file differently, Git will ask you to resolve conflicts manually.

Open the file, look for conflict markers (<<<<<<<, =======, >>>>>>>).
Edit the file to keep the correct version.
Then commit the resolved file:

git add filename
git commit -m "Resolved merge conflict"

Quick Recap

cd project-folder → go to your repo
git remote -v → check connection
git pull origin main → download changes
Resolve conflicts if needed

You’ve successfully pulled code from GitHub.

How to track changes using Git

Tracking changes is one of the most powerful features of Git. It lets you see what’s happening in your project, what files have been modified, and the history of every change.
Here’s a breakdown:

Key Git Commands for Tracking Changes

Check the Status

git status

• Shows which files are new, modified, or staged for commit.
• Example output:

modified:   analysis.py
untracked files: report.csv

View Commit History

git log

• Displays a list of commits with messages, authors, and dates.
• Helpful for understanding the timeline of your project.
• Add --oneline for a cleaner view:

git log --oneline

Compare Changes

git diff

• Shows the exact lines that changed in your files.
• Example:

- print("Hello World")
+ print("Hello Git")

Track Specific Files

git add filename

• Stages a file so Git starts tracking it.
• Once tracked, changes to that file will appear in git status and git diff.

Undo Staged Changes

git reset filename

• Removes a file from staging if you added it by mistake.

Workflow Example

Edit your code.
Run git status → see what changed.
Run git diff → check the exact modifications.
Run git add → stage changes.
Run git commit -m "Update analysis script" → save snapshot.
Run git log → confirm your commit is recorded.

In short: Git tracks changes through status, diffs, commits, and logs. These commands give you full visibility into your project’s evolution.

Final Thoughts
Git and GitHub may feel overwhelming at first, but once you practice the basics push, pull, commit, and track changes you’ll see how powerful they are. They’re not just tools; they’re the backbone of modern software development and data science collaboration.

Pro tip: Start small. Create a simple project (like a Python script or dataset analysis), push it to GitHub, make a few changes, and practice pulling and tracking. The more you use Git, the more natural it becomes.

Thank you for reading! If this helped you, feel free to leave a comment or share your Git journey below.

LuxDev Markdown Language Class

Maureen Waithaka — Tue, 13 Jan 2026 12:10:01 +0000

How to write a main topic using the Markdown language

To write a main Topic, use one hashtag at the beginning of the sentence

How to write a subtitle.

To write a subtitle, use two hashtags at the beginning of the sentence

How to write a Subsection under a subtitle

To write this, use three hashtags at the beginning of the sentence

To write in Italics
Use one asterisk at the beginning and at the end of the sentence

To write in bold
Use two asterisks at the beginning and at the end of the sentence

To write a code
Use the 9th from the left also the 3rd from the right icon at the top of your page,
For Example:

Name = ''Maureen''

To share a link in an article
Use the 3rd from the left and the 9th from your right icon at the top of your page,
For Example:
Find attached my LinkedIn profile Maureen Waithaka LinkedIn profile

To add a list to your Article
Use the 5th Icon from the left and the 7th from the right at the top of the page,
For Example;
My expectations from taking this bootcamp are:

to learn Data and be able to handle projects alone or in a group
to Network
to finish the course, graduate and be certified
to get the tools to be able to build a career in Data/Tech

To add an image on your article
Use the last from left or the 1st from right icon to select and photo as desired.
For Example

More to come as the course progresses.

Assignment 1.0
13th/Jan/26