Forem: Trizah Ogeto

The 4 Types of Data Analytics Every Beginner Must Know

Trizah Ogeto — Thu, 12 Mar 2026 13:33:12 +0000

Imagine running a business and suddenly noticing sales drop.

The first questions that pop up are:

What happened?
Why did it happen?
What will happen next?
What should we do about it?

These four questions represent the four types of data analytics.

Let’s break them down in the simplest, most practical way.

1. Descriptive Analytics >> What happened?

This is the foundation of all analytics.

It looks at past data to understand what has already occurred.

Companies use reports, dashboards, and summaries to answer these questions.

Examples:

Monthly sales reports
Website traffic reports
Number of customers last quarter

Example questions you can ask:

What were our sales last month?
Which product sold the most?
Which region generated the most revenue?
How many visitors did our website get this week?

2.Diagnostic Analytics >> Why did it happen?

Once we know what happened, we need to understand why.

Diagnostic analytics digs into root causes and explains trends or problems.

Examples:

Why did sales drop last month?
Why did website traffic spike suddenly?
Why did app downloads decrease this week?

Example questions you can ask:

Why did our sales decrease in March?
Why are customers abandoning their carts?
Why did website traffic suddenly increase?

3. Predictive Analytics >> What will happen?

Once we know what happened, we need to understand why.

Diagnostic analytics digs into root causes and explains trends or problems.

Examples:

Why did sales drop last month?
Why did website traffic spike suddenly?
Why did app downloads decrease this week?

Example questions you can ask:

Why did our sales decrease in March?
Why are customers abandoning their carts?
Why did website traffic suddenly increase?

4.Prescriptive Analytics >> What should we do?

This is the most advanced type of analytics.

It doesn’t just predict the future,it suggests the best actions to take.

Think of it as data-driven decision-making on autopilot.

Examples:

Best pricing strategy
Optimal time to run a promotion
Best products to recommend to customers

Example questions you can ask:

What is the best action to increase sales?
Which marketing campaign should we run next?
What pricing strategy will maximize revenue?
Which products should we recommend to customers?
When is the best time to launch a promotion?

Quick Summary Table

Type	Question	Example
Descriptive	What happened?	Monthly sales report
Diagnostic	Why did it happen?	Why sales dropped
Predictive	What will happen?	Next month sales forecast
Prescriptive	What should we do?	Best pricing strategy

Understanding the four types of data analytics is essential for anyone starting a career in data, business intelligence, or analytics.

Start with Descriptive Analytics, but the real power comes from Predictive and Prescriptive Analytics, which help businesses make smarter, faster decisions.

Basics of Data Analytics

Trizah Ogeto — Wed, 11 Mar 2026 13:35:40 +0000

Data Analytics is the process of collecting, cleaning, organizing, and analyzing data to uncover meaningful insights that help businesses make informed decisions.
Simply put, it is turning raw data into meaningful information

Every single day, companies and organizations produce large amounts of data ranging from sales and customer information to website visits and transactions.It is the role of a data analyst to take this raw data and turn it into insights that will boost performance and solve business problems.

Why Data Analytics is Important

Data analytics is essential because it helps businesses:

1.Make decisions based on real data rather than guesswork

2.Improve overall performance and efficiency

3.Identify trends and patterns in their operations

4.Predict future outcomes and plan ahead

5.Reduce risks by spotting potential issues early

6.Enhance the customer experience by understanding their needs

Data analytics is closer and more present in our daily lives than we often realize.

For example:

1.Uber is able to adjust pricing and predict demand during busy hours because of data analytics.

2.Spotify recommends songs and playlists tailored to each user’s listening habits using data analytics.

3.Starbucks tracks which drinks are most popular in different locations and adjusts stock and promotions using data analytics.

4.Airbnb analyzes booking patterns to suggest optimal pricing and popular locations to hosts and guests through data analytics.

5.LinkedIn shows personalized job suggestions and connection recommendations by leveraging data analytics.

Data Analytics Process
1. Data Collection
Data collection is the process of gathering information from different sources to work with.
The Common sources of data include:

Databases
Excel or CSV files
Websites
Surveys and forms
Business applications

Example: Starbucks collects information on which drinks are most popular in each store, customer feedback from surveys, and daily sales numbers. This data helps them understand customer preferences and manage inventory more effectively.

2. Data Cleaning
This is the most time-consuming Step especially if you have hundreds or even thousands of rows and columns to clean.
Raw data is often messy, incomplete, and inconsistent. Cleaning the data means fixing these issues so it’s accurate and ready for analysis.

Common cleaning tasks include:

Removing duplicate records
Handling missing values
Correcting errors
Standardizing formats

Example: Starbucks might find customer names written differently in different stores, like “Sarah,” “sarah,” or “SARA.” Cleaning the data would standardize all of them to one consistent format.

Fun Fact: Data analysts spend around 70–80% of their time cleaning data before they can analyze it. More like sharpening an axe before cutting a tree.

3.Data Analysis
Once the data is clean, the next step is to analyze it to uncover trends, patterns, and useful insights.
Common tasks during analysis include:

Finding trends over time
Comparing values across different categories
Calculating metrics like totals, averages, or percentages
Identifying patterns or correlations in the data

Example: Starbucks might analyze sales data to find out which drink is the most popular in each region, or which store has the highest daily sales. This helps them make decisions about inventory, promotions, and staffing.

4.Finding Insights

After analyzing the data, the next step is to turn your findings into meaningful conclusions that explain what’s happening and why.

Example:

Starbucks notices that cold drinks sell more on hot days

Insight: weather affects drink choices.

Uber sees that rides rise during early morning hours

Insight: demand varies by time of day.

Spotify finds that certain playlists are streamed more on weekends

Insight: user behavior changes by day.

Insights answer the key question: “Why is this happening?” and help guide smarter decisions.

5. Supporting Decision Making
The final goal of data analytics is to use insights to help businesses make informed decisions and take effective action.

Example:

Increasing marketing efforts in regions with high sales
Improving products that are not performing well
Adjusting pricing strategies to stay competitive

The ultimate purpose of data analytics is to help businesses make better, data-driven decisions.

Introduction to Git Bash, GitHub, and Version Control

Trizah Ogeto — Mon, 19 Jan 2026 12:39:22 +0000

1. Introduction

In today’s technology world, developers and data analysts work on projects that change often. Files are updated, mistakes happen, and sometimes we need to go back to an earlier version of our work. This is where Git, Git Bash, and GitHub come in.

Git is a version control system that helps track changes in files.
Git Bash is a terminal that allows Windows users to run Git commands easily.
GitHub is an online platform where Git repositories are stored and shared.

In this article, I explain how to install Git Bash, connect it to GitHub, use Git Bash commands, pull code from GitHub, and understand version control.

2. Installing Git Bash on Windows

Git Bash is required to use Git commands on a Windows computer.

Steps to install Git Bash:

Open a web browser
Search for Download Git Bash
Open the official website official website
Download Git for Windows
Run the installer

Keep the default settings and click Next until installation is complete

How to confirm installation:

Right-click anywhere on your computer

Select Git Bash Here

If a terminal window opens, Git Bash is successfully installed

3. Configuring Git Bash

After installation, Git must be configured with your name and email. This information is used to identify who made changes to files.

Commands used:

Name("git config --global user.name "Your Name")
Email("git config --global user.email "your@email.com")

To confirm the configuration:

Confirm("git config --list")
This command displays the saved Git configuration details.

4. Connecting Git Bash to GitHub

Git Bash can be securely connected to GitHub using SSH keys.

Step 1: Create a GitHub account

create

Step 2:Generate an SSH key

In Git Bash, run:

code to run("ssh-keygen -t ed25519 -C "your@email.com")

Press Enter to accept default options

Step 3:Add SSH key to GitHub

Copy the SSH key:

code to SSH key("cat ~/.ssh/id_ed25519.pub")

Go to GitHub → Settings → SSH and GPG keys

Click New SSH key

Paste the key and save

Step 4: Test the connection

Test connection("ssh -T git@github.com")

If the connection is successful, GitHub will confirm authentication.

5. Basic Git Bash Commands

6. Tracking Changes Using Git

Tracking changes is one of Git’s most important features.
Steps to track changes:

git status
git add .
git commit -m "Initial commit"

whereby
git status checks file changes

git add . prepares files for saving

git commit saves a snapshot of the project

This process allows developers to keep a record of every change made.

7. Pulling Code from GitHub

Git Bash allows users to download and update code from GitHub.

Clone a repository("git clone git@github.com:username/repository.git")

Pull latest changes("git pull")

These commands ensure the local project is always up to date with the remote repository.

8. Understanding Version Control

Version control is the process of managing changes to files over time.

It helps to:

Track changes
Collaborate with others
Recover previous versions
Prevent loss of work

Git acts as the version control system, while GitHub stores projects online for collaboration and backup.

9. Conclusion

Git Bash and GitHub are essential tools for developers and data analysts. Git Bash allows users to interact with Git through commands, while GitHub provides a platform to store and share projects. Understanding version control makes it easier to track changes, collaborate, and work efficiently. Learning these tools is an important step toward becoming a skilled technical professional.