Forem: willie wathagana

A Newbie's Guide to Git (Git Bash)

willie wathagana — Mon, 09 Feb 2026 19:47:30 +0000

A Newbie's Guide to Git (Git Bash)

Are you a newbie to Git like I once was? Don't worry! This guide will help you get started with Git using Git Bash on a Windows PC. We'll cover installation, configuration, connecting to GitHub, and some essential commands. By the end, you'll be ready to version control your projects confidently.

What is GitHub?

GitHub is a cloud-based platform where you can create, store, manage, and share your code. It allows collaboration with others, tracks changes, and hosts repositories (repos) for your projects. Think of it as a virtual storage for your code with superpowers for teamwork.

What is Git/Git Bash?

Git is a version control system that helps you track changes in your code over time. Git Bash is the command-line interface (CLI) that comes with Git on Windows, providing a Unix-like shell to run Git commands. It's your tool to interact with GitHub from your PC without needing a browser every time.

Fun Fact

Git uses cryptographic hashing (like SHA-1 or SHA-256) to uniquely identify each commit. This ensures your code's integrity and makes it tamper-proof!

What is a Commit?

A commit is like a snapshot of your project at a specific point. It's a permanent record of changes, including who made them and when. Once committed, you can always revert to that state if needed.

Setting Up Your GitHub Account

Go to GitHub in your browser.
Click "Sign up" and follow the prompts to create an account with your email.
Verify your email and set up your profile: add a username, bio, and profile picture for a professional touch.

Once done, you're ready to connect your local Git to GitHub.

Installing Git/Git Bash on Windows

Open your browser and search for "Git download" or go directly to git-scm.com.
Select "Windows" as your OS.
Download the installer (it's free!).
Run the .exe file and follow the setup wizard. Accept defaults unless you know what you're changing.
During installation, choose "Use Visual Studio Code as Git's default editor" if you have VS Code installed, it's beginner-friendly.
Complete the installation; Git Bash should now be in your Start menu.

Here's what the download page looks like:

Official Git downloads page, click on Windows to get the installer.

After installation, search for "Git Bash" in your Start menu and open it. It should look something like this (though this image shows settings integration):

Integrating Git Bash into Windows Terminal for a better experience (optional).

Connecting Git/Git Bash to GitHub Using Commands

Now, let's configure Git and set up SSH for secure connection.

Open Git Bash.
Check Git version:
git --version
(It should show something like git version 2.x.x)
Set your username (replace with your GitHub username):
git config --global user.name "Your Username"
text4. Set your email (must match your GitHub email):
git config --global user.email "your.email@example.com"
text5. Verify configurations:
git config --list
text6. Generate an SSH key:
ssh-keygen -t ed25519 -C "your.email@example.com"
textPress Enter for defaults (no passphrase for beginners).

Running ssh-keygen in Git Bash-enter a passphrase or leave blank.

Start the SSH agent:
eval "$(ssh-agent -s)"
text8. Add your SSH key to the agent:
ssh-add ~/.ssh/id_ed25519
text9. Display your public key:
cat ~/.ssh/id_ed25519.pub
textCopy the output (starts with ssh-ed25519...).
Go to GitHub: Settings > SSH and GPG keys > New SSH key.
- Title: Something like "My Windows PC".
- Paste the key and add it.

GitHub SSH keys page, paste your public key here.

Test the connection: ssh -T git@github.com textIt should say: "Hi username! You've successfully authenticated..."

Congrats! Your Git Bash is now connected to GitHub.

Basic Git Commands and Concepts

Version Control with Git

Git tracks changes by taking snapshots (commits). It provides an audit trail: who changed what and when. You can revert to any previous version easily.

Push and Pull

Push: Upload your local changes to GitHub. Example workflow: git add . # Stage all changes git commit -m "Your commit message" # Commit them git push origin main # Push to the main branch text- Pull: Download changes from GitHub to your local repo. git pull origin main text(Use --rebase if you want a linear history: git pull --rebase origin main)

Tracking Code Changes

git status: Shows what's changed, staged, or untracked.

Git status showing uncommitted changes.

git diff: Views differences in unstaged changes.
git diff --staged: Views staged changes.
git log: Shows commit history.
git log --oneline: Condensed history.
git log -- filename: History for a specific file.
git show commit-hash: Details of a specific commit.

Git log displaying recent commits with details.

Conclusion

You've now got the basics of Git and Git Bash on Windows! Practice by creating a repo on GitHub, cloning it locally (git clone repo-url), making changes, and pushing them back.

If you run into issues, common fixes include ensuring your email matches and regenerating SSH keys if needed. Drop a comment below with questions or feedback.

A Beginner's Guide to MS Excel for Data Analytics

willie wathagana — Mon, 09 Feb 2026 19:27:53 +0000

A Beginner's Guide to MS Excel for Data Analytics

Microsoft Excel remains one of the most powerful and accessible tools for data analytics, especially for beginners. It's user-friendly, widely available, and doesn't require coding knowledge to get meaningful insights from data.

In this beginner-friendly guide, we'll walk through the essentials: from understanding the interface to creating pivot tables and visualizations. By the end, you'll be ready to tackle real-world data tasks.

Note: This guide is based on recent versions of Excel (2016, 2019, 2021, or Microsoft 365). Some features may vary slightly in Excel Online or older versions.

1. Understanding the Excel Interface

When you launch Excel, you see a workbook with one or more worksheets. The key parts include:

Ribbon - the top toolbar with tabs (Home, Insert, Data, etc.)
Formula Bar - displays and edits cell contents
Grid - rows (numbers) and columns (letters); each box is a cell (e.g., A1)
Status Bar - shows quick calculations (sum, average, count) for selected cells

Take a moment to explore:

Excel interface overview - Ribbon, Formula Bar, Workspace, and more

Another clear labeled view:

Annotated Excel window showing all major parts

2. Entering and Formatting Data

Good analytics starts with clean, well-organized data.

Click a cell and type (text, numbers, dates).
Use the first row for headers (e.g., Name, Sales, Date).
Format cells: Select cells → Home tab → Number group (currency, date, percentage, etc.).

Pro tip: Convert your data range into an Excel Table (Ctrl + T or Insert → Table) for automatic formatting and easier referencing.

Example of formatted data:

Formatted Excel table ready for analysis

3. Basic Formulas and Functions

Formulas start with = and live in cells. Excel auto-calculates them.

Essential functions for analytics:

=SUM(A2:A100) - total
=AVERAGE(B2:B100) - mean
=COUNT(C2:C100) - count of numbers
=MIN() / =MAX() - smallest/largest value

Use AutoFill (drag the small square in the cell corner) to copy formulas down.

Simple example with SUM and AVERAGE:

Basic SUM formula example in action

Another clean AVERAGE demo:

Using AVERAGE across a column of numbers

4. Sorting and Filtering Data

Quickly organize and focus on subsets:

Sort: Select data → Data tab → Sort (by column, A-Z, smallest to largest)
Filter: Data → Filter (adds dropdown arrows to headers - filter by value, text, color, etc.)

These two features let you spot patterns and outliers instantly.

5. Creating Charts and Visualizations

Charts turn numbers into stories.

Steps:

Select your data (including headers).
Go to Insert → Recommended Charts (or pick Column, Bar, Line, Pie, etc.).
Customize title, labels, colors via Chart Tools (Design & Format tabs).

Example bar/column chart:

Clustered column chart - great for comparing categories

6. Pivot Tables - The Real Analytics Powerhouse

Pivot tables summarize, group, and analyze large datasets without writing complex formulas.

Quick start:

Select your data range.
Insert → PivotTable → OK (new or existing sheet).
Drag fields to: Rows, Columns, Values (usually Sum or Count), Filters.

You can instantly see totals by category, trends over time, percentages, etc.

Powerful pivot table example:

Pivot table showing regional sales and % of grand total

Another real-world pivot + chart combo:

Sales dashboard built with pivot table and chart

7. Quick Data Cleaning Tips

Remove duplicates → Data → Remove Duplicates
Trim extra spaces → =TRIM(A1)
Change case → =PROPER(), =UPPER(), =LOWER()
Find & Replace (Ctrl + H) for fixing typos

Always duplicate your original sheet before cleaning!

8. Next Steps - Slightly More Advanced

Once comfortable, explore:

VLOOKUP / XLOOKUP - lookup values across tables
IF — conditional logic (=IF(A2>1000, "High", "Low"))
COUNTIF / SUMIF - conditional counting/summing

Then move to Power Query (Data → Get & Transform) for advanced cleaning and Power Pivot for larger datasets.

Conclusion

Excel is still a top choice for beginner-to-intermediate data analytics. Start small:

Import or enter data
Clean and format it
Use basic formulas
Build a chart
Create your first pivot table

Practice with free sample datasets (search "Excel sample sales data CSV"). The more you experiment, the faster you'll improve.

Have questions or want to share your first pivot table? Drop a comment below!

Happy analyzing!

Getting Started with Power BI Data Modeling and Schemas: A Beginner's Guide

willie wathagana — Mon, 09 Feb 2026 19:13:30 +0000

Hey there! If you're new to Power BI, you've probably heard about data modeling and schemas, but they might sound a bit intimidating. Don't worry this article will break it down step by step in simple terms. We'll cover what data modeling is, why schemas matter, common types like star and snowflake schemas, and how to get started in Power BI. By the end, you'll feel confident building your first model. Let's dive in!

What is Data Modeling in Power BI?

Data modeling is like organizing your closet: you group similar items together and make sure everything is easy to find. In Power BI, it means structuring your data so you can create insightful reports and visuals without hassle.
Power BI has three main views: Report, Data, and Model. The Model view is where the magic happens. It's where you connect tables, define relationships, and shape your data for analysis.

Why Bother with Data Modeling?

Efficiency - Well modeled data loads faster and performs better.
Accuracy - Reduces errors in calculations and filters.
Scalability - Easier to add more data later. Without good modeling, your reports might show wrong insights or take forever to refresh.

Understanding Schemas: The Blueprint of Your Data

A schema is a design pattern for how your tables relate to each other. Think of it as the architecture of your data house. In Power BI, we often use dimensional modeling schemas borrowed from data warehousing.

Star Schema: Simple and Star-Shaped

The star schema is the most beginner-friendly. It has one central "fact" table (like sales data with numbers) surrounded by "dimension" tables (like products, customers, or dates with descriptions).

Fact table: Contains measurable data (e.g., sales amount, quantity).
Dimension tables: Provide context (e.g., product name, customer region). The name "star" comes from how it looks like a star with the fact table in the middle.

As shown in the diagram, the fact table (FactResellerSales) connects to multiple dimension tables. This setup makes queries fast because joins are simple.
Pros

Easy to understand and query.
Great for performance in Power BI.

Cons

Can lead to data redundancy if not managed.

Snowflake Schema: More Normalized but Complex

The snowflake schema is like a star schema but with extra layers. Dimension tables are "normalized" by breaking them into sub-tables to reduce redundancy.
For example, a product dimension might split into product, category, and subcategory tables.

In this diagram, you see the fact table connected to dimensions, and some dimensions branch out further like a snowflake.
Pros

Saves storage by avoiding duplicates.
Better for complex hierarchies.

Cons

More joins can slow down queries.
Harder for beginners to set up. In Power BI, star schemas are recommended for most cases because they're simpler and perform well with the tool's engine.

Building Your First Model in Power BI

Ready to try it? Here's a step-by-step guide assuming you have Power BI Desktop installed (it's free!).

Import Data: Go to "Get Data" and load your sources (e.g., Excel, SQL). Clean up in Power Query if needed.
Switch to Model View: Click the Model icon on the left pane.
Create Relationships: Drag a column from one table to a matching column in another. Power BI often auto-detects them. The example above shows relationships as lines between tables. Double-click a line to edit cardinality (one-to-many, many-to-many, etc.).
Define Measures: Use DAX (Data Analysis Expressions) for calculations, like Total Sales = SUM(Sales[Amount]).
Test It: Go back to Report view and build a visual. If filters work across tables, you're good!

Common tip: Use unique keys (like IDs) for relationships to avoid ambiguity.

Best Practices for Beginners

Start Small-Begin with 2-3 tables to practice.
Use Star Schema-Unless you have a good reason, stick to it for simplicity.
Avoid Bi-Directional Relationships-They can cause loops; use single direction unless needed.
Hide Unused Columns-In Model view, right-click and hide to keep things clean.
Document Your Model-Add descriptions to tables and columns for future you. Remember, modeling is iterative build, test, refine.

Wrapping Up

Congratulations! You've got the basics of Power BI data modeling and schemas. Start with a simple dataset, like sample sales data from Microsoft, and experiment. As you practice, you'll see how powerful (pun intended) this can be for turning raw data into stories.
If you have questions or want to share your first model, drop a comment below. Happy modeling!

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

willie wathagana — Mon, 09 Feb 2026 18:39:49 +0000

From Messy Hospital Spreadsheets to Life-Saving Decisions – Power BI in Action

(Using Realistic Hospital & Pharmacy Mock Data)

In the real world, data is rarely clean, complete, or well-structured. Analysts are often handed spreadsheets exported from multiple systems, each with its own logic and inconsistencies. Power BI becomes powerful not because it creates charts, but because it helps analysts turn disorder into decisions.

Using a mock hospital and pharmacy dataset, this article demonstrates how analysts translate messy data, DAX, and dashboards into real-world action using Power BI.

1. Starting with the Business Question (Analytics Mindset)

Before opening Power BI, analysts must understand why the analysis exists.

Mock Business Questions

Using a hospital and pharmacy dataset, stakeholders might ask:

Which hospitals have the highest patient volumes?
Are some pharmacies experiencing frequent stock-outs?
Which departments are driving higher treatment costs?
Are patient wait times increasing over time?

Without these questions, even the best dashboard becomes noise.

2. Messy Data Reality: What Analysts Actually Receive

Mock Raw Data Snapshot

Hospital Admissions

Patient_ID	Hospital	Admit_Date	Dept	Wait_Time
P-001	KNH	12/01/24	ER	45
P-002	knh	13-01-2024	Emergency	NULL
P-003	MTRH	14/1/24	OPD	30

Pharmacy Transactions

Drug_Name	Qty	Cost	Facility
Paracetamol	100	10	KNH
paracetamol	-5	10	KNH
Amoxicillin	50	25	MTRH

Issues immediately visible:

Inconsistent naming (KNH vs knh)
Different date formats
Missing values
Negative quantities
Unstandardized department names

This is where Power Query becomes essential.

3. Cleaning and Shaping Data with Power Query

Using Power Query, analysts:

Standardize hospital names (KNH, knh → Kenyatta National Hospital)
Replace null wait times with averages
Remove negative quantities
Convert dates into a consistent format
Split and rename columns for clarity

Example Transformation

4. Building Relationships: Connecting Hospital & Pharmacy Data

A star star schema is used to build the relationship on the pharmacy and hospital data

The Schema ensures:

Correct aggregation of costs and admissions
Accurate filtering across hospitals and time
Faster report performance

5. Translating Numbers into Meaning with DAX

Raw totals don’t drive decisions. Measures do.

Mock DAX Measures

Total Patients

Total Patients = COUNT(Fact_Admissions[Patient_ID])

**Average Waiting Time**

dax
Total Pharmacy Cost = SUMX(
Fact_Pharmacy,
Fact_Pharmacy[Qty] * Fact_Pharmacy[Cost]
)

Now stakeholders can finally answer key business questions:

Which hospitals have the longest waits?
Where is pharmacy spending highest?
How does performance change month to month?

6. Designing Dashboards That Drive Decisions

Mock Dashboard Layout

7. Turning Insights into Action

Mock Insight → Action Flow

Key Insights Identified

Hospital A has the highest patient load but below-average staffing
Pharmacy stock-outs spike every end of month
Emergency department wait times exceed 60 minutes

Actions Triggered

Reallocate staff during peak hours
Adjust procurement schedules to prevent end-of-month shortages
Introduce triage optimization in Emergency units

This is the true value of Power BI: evidence-backed decisions that directly improve hospital operations and patient care.

8. Why This Matters in the Real World

In healthcare, poor decisions have real, sometimes life-altering consequences. Power BI empowers analysts and decision-makers to achieve:

Faster response to operational risks
Better resource allocation across facilities and departments
Improved patient outcomes through timely interventions
Accountability through transparent, data-driven reporting

For analysts, mastering Power BI means evolving from someone who simply builds reports into a true problem-solver who influences real-world outcomes and contributes to better healthcare delivery.

Conclusion

Messy data is not a barrier - it’s the starting point.

By cleaning data with Power Query, modeling it correctly, writing purposeful DAX, and designing decision-focused dashboards, analysts transform raw hospital and pharmacy data into meaningful, actionable insight.

Power BI is not about charts.

It’s about turning complexity into clarity and clarity into impact.