DEV Community: Carobecky chepngeno

HOW TO TRANSLATE MESSY DATA,DAX AND DASHBOARDS INTO ACTION USING POWER BI

Carobecky chepngeno — Mon, 09 Feb 2026 12:21:07 +0000

Power BI is a 3 step journey:
Messy data->meaningful calculations->clear visuals->smart decisions

Step 1:Translating messy data

First use power query to:

remove duplicates
Rename columns
Fix data types(dates, numbers, texts)
Slit or merge columns
Filter out invalid rows Power Query is about preparing data.This cleans data turning it to trustworthy insights.

Step 2:Turning data into meaning(DAX)

DAX is how you ask business questions in Power BI. It's purpose is to turn raw numbers into insights.
For example:

What is the total sales?
Which product performs best?
What is the percentage growth rate? ### Common DAX ideas
Measures- total sales, profit margin, average profit
Time intelligence- month-to-date, year-over-year growth Always ask, what decision will this number support?

Step 3: Building Dashboards that tell a story

A good dashboard answers these questions:

What is happening?
Why is it happening?
What should we do next?

key dashboard elements

KPIs(cards): sales, revenue, ratings
Trends: line charts(growth over time)
Comparisons: Bar charts(products, regions)
Filters(slicers): Date, category, location Pro tip; ** Have clear titles,focus on action and not decoration.

Turning dashboards into action

If the dashboard shows;

Sales dropped
Ratings went down
Discount increased Action to be taken will involve;
Review discount strategy
Increase product quality
Target high performing regions

In summary, Power Bi is not about charts but how you turn messy data into decisions.

- Power Query- Prepare the truth
- DAX -Explain the truth
- Dashboards- Communicate the truth
- Action- Business value

Schemas and Data Modelling in Power BI

Carobecky chepngeno — Mon, 02 Feb 2026 12:53:29 +0000

Data modelling is the backbone of every Power BI report. A well curated model provides fast performance, accurate calculations and easily understood reports
This article explains:

Power BI data modelling
Fact and Dimension Tables
Relationships
Star schema
Snowflake schema
Importance of good modelling

Data modelling in Power BI

Data Modelling is like a foundation. It involves the process of structuring,organizing and connecting data tables so that Power BI can easily analyze them. You;

Define relationships between tables
Decide how data flows
Prepare the model for DAX calculations A good model answers the questions, what happens, when, where and under what conditions? ## Fact Tables These are tables that store measurable and quantitative data. They contain very many rows,numeric values used in calculations and are linked to multiple dimensions.

Examples

this tables links;

dim Customer-> who made the purchase
dim Product-> what product was purchased
dim Territory-> where the purchase was made
dim Date-> when the events occurred

Dimension tables

These store descriptive attributes that provide context to facts. They are smaller than fact tables,contain text and categorical data and are used for filtering,grouping and slicing data.
Example;

Star Schema

This is a foundational data modelling technique for data warehousing featuring a central fact table surrounded by multiple dimensional tables. It resembles a star.

Benefits

Has a faster query performance
simpler DAX formulas
It's easier to understand and maintain
Ideal for Power BI

Snowflake Schema

This is a logical data modelling technique where a central fact table connects to normalized dimension tables which are further broken down into sub-dimensions to reduce data redundancy.
It is an extension of star schema and is often used for complex and hierarchical data to enable storage efficiency at the cost of query performance due to many required joints.

Benefits

Efficient storage utilization
Cleaner data structures
Better handling of complex data
Easier maintenance ### Disadvantages
Slower performance due to increases query
More complex relationships
Harder DAX calculations
Commonly used in warehouses not ideal for Power BI

Relationships in Power BI

Relationships define how tables are connected enabling data modelling, filtering and analyzing without merging.
Relationship types include:

One to many (1:*)- commonly used
many-to-many (:)
one-to-one (1:1)- rarely used

Importance of data modelling

Performance

Faster visualization due to fewer tables and joins
Star schemas reduce query complexity
Efficient models use less memory

Accuracy

Proper relationships ensure correct totals
DAX behaves predictably

Scalability

Easy to add new measures
Model supports large datasets
Reports remain responsive as data grows

Usability

Dimensions are easy to understand
Clear fields for slicing and filtering
Clean and professional report structure

In summary, investing time in proper modelling saves time of debugging and ensures your report tells the right story with confidence.

Introduction to MS Excel for Data Analytics

Carobecky chepngeno — Sun, 25 Jan 2026 19:12:57 +0000

Learning data science and analytics as a beginner? This article got you covered. Microsoft Excel is one of the most powerful tools used in data analytics and it is beginner-friendly.With wide range of uses in corporate worlds,Excel can turn raw data into meaningful and interpretable insights.
By the end of this article, I expect you to:

Understand Microsoft Excel and its uses in data analytics
Identify rows,columns and cells in a worksheet
Create and organize data in table format
Use basic Excel functions
Learn Data cleaning by sorting,filtering and removing duplicates
Create pivot tables for data summarizing
Learn data visualization by use of simple charts
Understand creation of dashboards for data reporting

Commonly used Excel Terms

Cell- A single box in Excel where data is entered.*(example:E1)
Column - A vertical group of cells labelled using alphabets (example:A,B,C)
Row - A horizontal group of cells labelled numerically (example:1,2,3)
Worksheet -A single page in Excel where data is entered and analyzed.
Workbook -This is an Excel file that contains one or more worksheets.
Dataset - A collection of related data normally in columns and rows.
Formula -An equation in Excel used to do calculations (example:B1+C2)
Function -A built-in formula that executes specific tasks. *(example: SUM,MAX,MIN)
Filter -This is used to display data that meets specific criteria
Sort - This is arranging data in a specific data (example; A-Z)
Pivot Table -A tool that is used to summarize and analyze large datasets since it calculates data automatically.
Charts -Visual representation of data
Dashboard -A single page that shows summaries of key data insights using charts and numbers.

1.Introduction to Excel

microsoft excel is a spreadsheet application by Microsoft used to organize,calculate,analyze and visualize data in rows and columns.
Its main functions include:

Performing calculations using formulas and built-in functions.
Organizing and managing data in tables
Analyzing data by sorting, filtering and pivot tables.
Data visualization by creating charts and graphs.
Automating tasks using macros and basic scripting. Excel spreadsheet is made up of rows, columns, cells where each cell contains;
Text(name,categories)
Numbers
Dates
Formulas

Example of dataset

## organizing data for analysis
It is really important to ensure data being analysed is correctly structured i.e the columns and rows. Avoid having empty cells.

2.Data Cleaning

This involves:

Removing duplicates data->remove duplicates
Sorting data->sort A-Z OR Z-A
Filtering data->filter

3.Basic Excel functions

Sum- adds numbers
Example

=SUM(E2:E877)
Average- finding the mean
Example

=AVERAGE(E2:E877)
Count- finding how many cells contain numbers
Example

=COUNT(E2:E877)
MIN- shows the minimum value in the cells
example

=MIN(E2:E877)
MAX- shows the maximum value in the cells
example

=MAX(E2:E877)

4. Pivot Tables

Pivot tables are used to summarize large datasets.
A pivot table is created by:
data->insert->pivot table and then you drag fields into rows,columns and values.

5.Charts in Excel

They help turn numbers into visuals that are easier to understand
We insert a chart by; Select data->insert->choose chart type
There are many chart types:

column charts that compares categories
line charts that shows trends over time
pie chart that shows proportions and many more.

6.Dashboards

This is a single interactive worksheet that visually summarizes key data using charts,tables and KPIs.

In summary, mastering of excel is not difficult. It gives you a strong basis in order to advance to analytical tools such as Power BI and SQL.
I hope this guide provides a strong basic understanding of Excel and it's importance in data analytics.

Understanding version control, pushing and pulling code and tracking changes using Git.

Carobecky chepngeno — Fri, 16 Jan 2026 09:28:21 +0000

If you are new to programming and data analytics,keep your worries down.This article explains Git in a simple and practical way.By the end of this guide,you will grasp:

What version control is and it uses
What is git and how it works
How to push and pull code using git
How to track changes in your code
Beginner friendly common git commands

Version Control

This is a system that helps you:

Track changes and shows what changed,when and by whom
Go back to previous versions and undo mistakes
work on the same project with other people without overwriting each other's work.

Git

This is basically a version control system that runs on your computer ( a tool on your computer)
Git has three main areas:

Working Directory that is a normal folder where you edit files.
staging area where you select the changes you want to save.
Repository where commits (your project at a specific time) are stored permanently. Git helps you:
Show your work professionally
Collaborate effectively
Build a strong portfolio on Github

Github

This is an online platform that stores Git repositories.

Installing Git

How to check if it is installed:

git --version

if not, go to:

windows download and install Git from [git-scm.com](https://git-scm.com)
mac brew install git
linux sudo apt install git

Creating a Git repository

option 1. initialize git in an existing project

git init

option 2. clone an existing repository

git clone
https://github.com/username/project-name.git

Tracking changes with Git

To check file status,

git status

This shows;

New files
Modified files
Files ready to commit To add files to the staging area;

git add file_name

Or add everything

git add .

To commit changes;

git commit -m "describe what you changed"

To view commit history;

git log

Commit history shows:

Author
Dates
commit messages and IDs

Pushing Code

This means sending your local commits to a repository like Github.It helps

To back up your work online
To share your code
To collaborate with a team First time connecting:


git remote add origin

To push code:


git push origin main

origin means remote repository
main is the branch name

Pulling Code

This means getting the changes from the remote repository to your computer.This is done in order to;

Download updates made by team mates
Sync and merge your local code with the remote version


git pull origin main

This is the comparison

Action	Meaning
`git push`	upload your changes
`git pull`	download other's changes

Always pull before working and push when you are done.Also make sure to commit to allow Git to save your changes.
You don't need to know every Git command to get started. Master the basics:

git status
git add
git commit
git push
git pull With overtime practice, Git will become an efficient tool for your everyday use.

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Carobecky chepngeno — Tue, 13 Jan 2026 11:02:16 +0000