DEV Community: MwendeMugambi

Introduction to Python: A Practical Starting Point for Data Work

MwendeMugambi — Sun, 29 Mar 2026 14:29:35 +0000

I'm sure every time you listen to people in tech talk about coding languages, you have heard the mention "Python" several times. If you're like me, I'm sure you are wondering what Python is.
Python is a simple and powerful programming language used to automate tasks, analyze data, and build software by giving clear instructions to a computer.

If you’ve worked with Excel long enough, you’ve probably hit at least one of these limits:

Your file crashes when it gets too big
Formulas become impossible to track
You spend hours repeating the same cleaning steps every week

Python usually enters the picture at that exact moment.
It’s not that Excel is bad, it’s just that it wasn’t designed for scale or automation. Python, on the other hand, is built for handling large datasets, repeating tasks reliably, and giving you full control over how data is processed.

What surprises most beginners is this:
You don’t need to become a “software developer” to use Python effectively. For data work, you’re mostly writing small, practical scripts.

Step 1: Setting Up Your Environment

To get started, you need:
Python installed (Anaconda is the easiest option for beginners). Mine is version 3.12
A tool to write and run code (Jupyter Notebook is recommended)
Once installed, open Jupyter Notebook.

Click New - Python 3 Notebook

Step 2: Writing Your First Python Code
In the notebook, you’ll see a blank cell. This is where you write code.
Try this:
print("Hello, World!")
Click Run.

Step 3: Working with Data Using Pandas
For data analysis, Python uses a library called pandas.
First, import it:
import pandas as pd

Loading a Dataset
Let’s load a CSV file (similar to opening an Excel file):
df = pd.read_csv("sales.csv") df.head()
What’s happening here:
read_csv() loads your data
head() shows the first few rows
This table is called a DataFrame - your main workspace in Python.

Step 4: Basic Data Operations (What You Already Do in Excel)

Creating a New Column
In Excel, you would write a formula and drag it down.
In Python:
df['Total'] = df['Price'] * df['Quantity']
Filtering Data
To filter rows:
df[df['Region'] == 'Nairobi']
This is like applying a filter in Excel, but faster and reusable.
Summarizing Data (Like a Pivot Table)
df.groupby('Region')['Total'].sum()
This shows total sales per region.

Step 5: Using Variables (Making Your Work Clear)
Instead of hardcoding values, Python lets you name them:
tax_rate = 0.16 df['Tax'] = df['Total'] * tax_rate
This makes your work easier to understand and update.

Step 6: Automating Decisions with Logic
Let’s say you want to label customers as Active or Inactive.
df['Status'] = df['Last_Purchase_Date'].apply( lambda x: 'Inactive' if x < '2025-01-01' else 'Active' )
This replaces manual sorting and tagging.

Step 7: Saving Your Results
Once you’re done, you can export your data:
df.to_csv("processed_data.csv", index=False)

Beginner Tips (Based on Real Experience)

Start with datasets you already understand
Don’t try to learn everything at once
Expect errors- they’re part of the process
Focus on doing, not memorizing

I hope that this article makes it a bit easier for you to try out Python. See you in the next article as I shed more light to what Python is.

Connecting Power BI to SQL Databases

MwendeMugambi — Sun, 29 Mar 2026 13:49:13 +0000

If you have taken time to go through my articles, then you will notice that I had given a defined Power BI a few weeks back. But for the sake of any newcomers, Power BI is a tool created by Microsoft to turn raw data into interactive insights. It is widely used by analysts, organizations, and decision-makers to track performance, identify trends, and generate insights in real time.
It is a data consumption and visualization tool, and that's why it is commonly connected to databases rather than static files. While Excel or CSV files may work for small datasets, modern organizations rely on structured databases to store, manage, and update large volumes of data efficiently.
SQL databases, such as PostgreSQL, are especially important in this ecosystem. They provide:

Structured storage for relational data
Efficient querying using SQL
Data integrity and consistency
Scalability for growing datasets

By connecting Power BI directly to a SQL database, companies ensure that their reports are built on reliable, up-to-date, and well-organized data.

Connecting Power BI to a Local PostgreSQL Database

Step 1: Open Power BI Desktop

Launch Power BI Desktop.

On the home screen, locate the “Get Data” button in the top ribbon.

Step 2: Select PostgreSQL Database

Click Get Data
Search for or select PostgreSQL database
Click Connect

Step 3: Enter Connection Details
A dialogue box will appear prompting you to enter:

Server: localhost (if your database is running locally)
Database: (e.g., saleslux)
Click OK to proceed.

Step 4: Authenticate
Enter your PostgreSQL credentials:

Username
Password
Choose the appropriate authentication method (typically Database authentication).

Step 5: Load Data into Power BI
Once connected, Power BI will display a Navigator window showing available tables.
For example:
customers
products
sales
inventory
Select the tables you need and click Load (or Transform Data if you want to clean them first).

Connecting Power BI to a Cloud Database (Aiven PostgreSQL)

When working with cloud-hosted databases like Aiven PostgreSQL, the process is similar like above, but it includes additional security steps.

Step 1: Obtain Connection Details from Aiven
From your Aiven dashboard, retrieve:
Host (e.g., your-db.aivencloud.com) - pg-726720-mwendemugambi8-cc78.c.aivencloud.com
Port (usually 5432)- 12155
Database name- defaultdb
Username- avnadmin
Password- **********

These replace the “localhost” setup used in local connections.

Step 2: Download the SSL Certificate
Aiven requires secure connections using SSL.
Download the CA certificate (often named something like ca.pem) from the Aiven console.

Why this matters:
SSL certificates encrypt the connection between Power BI and the database. This prevents sensitive data (like credentials or query results) from being intercepted during transmission.

Step 3: Configure the Connection in Power BI
Open Get Data → PostgreSQL
Enter:
Server: host:port (e.g., your-db.aivencloud.com:5432)
Database name

During connection:
Enable SSL mode (if prompted)
Provide credentials

Loading Tables and Building Relationships
After connecting (locally or via cloud), Power BI imports your selected tables into the data model.

Typical Tables
customers containing customer details
products containing product catalog
sales containing transaction records
inventory containing stock levels

Creating Relationships
Power BI attempts to automatically detect relationships, but you should always verify them in the Model View.
PS: Different Power BI Relationships are covered in the Schemas article.

Common relationships include:
customers.customer_id connected to sales.customer_id
products.product_id connected to sales.product_id
products.product_id connected inventory.product_id

Importance of Relationships
Relationships define how data flows between tables.
For example:

When you filter by a specific customer, Power BI uses the relationship to show only that customer’s sales
When analyzing product performance, sales data is grouped correctly by product
Without proper relationships:

Data may be duplicated
Aggregations may be incorrect
Visuals may show misleading results

Why SQL Skills Matter for Power BI Analysts

As we end this article, it is important we all understand why understanding SQL is important for all analysts. While Power BI provides powerful visualization tools, SQL remains a foundational skill for any serious analyst.

SQL allows you to:

Retrieve data efficiently using SELECT statements
Filter datasets using WHERE clauses
Aggregate data using functions like SUM, COUNT, and AVG
Join multiple tables to create meaningful datasets
Preprocess data before it reaches Power BI In practice, the most effective Power BI workflows involve: Preparing and shaping data in SQL Visualizing and analyzing it in Power BI

This division of work ensures that dashboards are not only visually appealing, but also accurate, scalable, and high performing.

Thank you for passing by and I hope connecting Power BI to SQL is a bit easier especially after going through this article.

Joins and Window Functions in SQL

MwendeMugambi — Tue, 17 Mar 2026 12:01:32 +0000

Most people start using SQL to retrieve data. The real shift happens when you begin using it to shape relationships within that data.

Joins as Data Logic, Not Just Combination

A join is not just about “bringing tables together”, it’s about defining which relationships matter and which don’t.

Types of Joins
Inner Join - Returns records that have matching values in both tables.
Left Join - Returns all records from them left table, and matched records from the right.
Right Join - Returns all records from the right table, and matched records from the left.
Full Join - Returns all records when there is a match in either table.

Expounding more on Left and Inner Joins:
Left Join
It guarantees that every record from your primary table survives the query, even when there is no matching data in the second table.

For example, in a student system, if you join Students to Borrowed_Books, a Left Join ensures students who have never borrowed a book still appear in your results. Their book-related fields will return NULL, which is not “empty”; it’s meaningful. It tells you no relationship exists.

This is critical in real analysis: drop-offs, non-participation, and missing relationships are often more important than the matches.

Inner Join
It filters your data to only records where a relationship exists in both tables.

Using the same example, only students who have borrowed books will appear. Everyone else is excluded entirely.

This is useful when your analysis depends strictly on confirmed interactions; such as completed transactions, logged activities, or verified matches.

The key difference:

LEFT JOIN answers: “Who exists, regardless of activity?”

INNER JOIN answers: “Who has participated?”

Window Functions: Calculations Without Losing Detail

Traditional aggregation functions like SUM() or COUNT() collapse rows into a summary. That’s useful, but limiting.

Window functions solve this by allowing you to compute aggregates while retaining every individual row.

The core idea sits in the OVER() clause.

PARTITION BY: Controlled Context
When you write:

SUM(sales) OVER (PARTITION BY region)

you’re telling SQL:
“Calculate totals within each region, but do not merge the rows.”

Each row still represents an individual transaction—but now carries additional context: the total performance of its group.

Why this matters in practice
Imagine a sales dataset. With a window function, each row can simultaneously show:

The value of that specific deal
The total sales for that salesperson’s region
The ranking of that deal within the region All without losing granularity.

This is what makes window functions powerful. They allow you to move from static summaries to context-aware analysis.

Solving Messy Data the Analyst way

MwendeMugambi — Mon, 09 Feb 2026 09:02:22 +0000

Everyone understand the feeling when you open a spreadsheet full of data and nothing makes sense. Some rows are empty, dates are in the wrong format, numbers don’t add up, and there are duplicates everywhere. This is what we call Messy data,and it’s how most real-world data arrives.

If this data is used as it is, it can lead to confusion and poor decisions. So the analyst’s job is to turn this mess into something meaningful. And you get to become an analyst just for this article.

Once you open the data, the journey to transform it usually starts in Power Query, where the data is cleaned and organized. Here, the analyst removes duplicate and empty rows, fixes column names, corrects data types, and deals with missing values. Sometimes columns are split, merged, or reshaped so the data finally makes sense. At this stage, the chaos begins to look a little like order.

Next comes Data Modeling, the process of creating a structured, visual representation of data and its relationships. Instead of working with scattered tables, the analyst connects them in a clear structure called a schema with Star Schema being the most preferred for Power BI. If there was any Sales data, it is linked to customers, products, and dates. This step is important because it ensures the numbers tell the right story and calculations work correctly.

Once the data is structured, the analyst uses DAX(Data Analysis Expressions),a formula expression language that makes it easy for you to perform calculations and queries with your data therefore bringing it to life. Simple formulas answer big questions: How much profit did we make? Are sales growing compared to last year? Which customers bring the most value? DAX turns raw data into insights that actually matter to the business.

Now comes the most visually appealing part the Dashboard, a single page, often called a canvas, that tells a story through visualizations. Because it's limited to one page, a well-designed dashboard contains only the highlights of that story. Instead of long tables, the story is told using charts, KPIs, and trends. A quick glance shows what’s going well, what’s not, and where attention is needed. The goal is clarity, not clutter.

In the end, the dashboard helps decision-makers move from questions to action. They can see what happened, understand why it happened, and decide what to do next.

That’s how analysts use Power BI to translate messy data, DAX, and dashboards into action thus turning confusion into clarity and data into decisions.

So my question to you, do you now feel like an analyst? Do you feel like you are ready to handle Messy Data like a pro? I really hope the answer is a resounding Yes!

Schemas(more like Schemes) & Data Modelling in Power BI.

MwendeMugambi — Mon, 02 Feb 2026 10:38:22 +0000

Schemas and data modelling in Power BI involve structuring data into a logical and efficient format to enable accurate analysis, strong relationships, and fast reporting. A well-designed model is the backbone of any reliable Power BI report as it determines how easily users can explore data and how efficiently visuals perform.

Key Definitions

Power BI
It is a tool created by Microsoft to turn raw data into interactive insights.

Schema
Refers to the logical blueprint of how data is organized in a database. It defines tables, fields, data types, and the relationships between them.

Data Modelling
The process of analyzing, defining, and structuring data entities and their relationships based on how a business collects, stores, and uses data.

Fact Tables and Dimension Tables

Fact and dimension tables are the foundation of data warehousing and Power BI modelling. Together, they support efficient querying, filtering, and aggregation.

Differences Between Fact and Dimension Tables

Together, fact and dimension tables form a Star Schema, where one central fact table connects to multiple dimension tables. Before exploring the Star Schema further, it is important we understand schemas more broadly.

Understanding Schemas

A schema (database schema), is a structured framework that defines how data is organized and how different data elements relate to one another.

Core Components of a Schema

Table – A collection of related data organized in rows and columns
Field (Column) – A single piece of information within a table
Data Type – Specifies the type of data stored in a field (e.g., integer, text, date)

A well-designed schema ensures:

Data accuracy and integrity
Optimized query performance
Scalability to support business growth

Good schemas also include:

Constraints to enforce data rules
Efficient indexing to speed up data retrieval
Automation support for updates and system growth

Types of Database Schemas
Different schema types serve different stages of database design:

a) Conceptual Schema
A high-level representation that shows what data exists and how it flows, without technical details. It is useful for both technical and non-technical stakeholders.

b) Logical Schema
Defines the logical structure of data, including entities, attributes, relationships, and constraints—without specifying physical storage details.

c) Physical Schema
Describes how and where data is physically stored, including file locations, indexes, and storage strategies to improve performance.

Styles of Database Schemas
Different business needs require different schema designs:

1. Star Schema
This is the most common schema used in Power BI. It consists of:

One central fact table
Multiple surrounding dimension tables The structure is simple, intuitive, and optimized for fast querying and reporting.

2. Snowflake Schema
Similar to the star schema, but dimension tables are normalized into multiple related tables.
While it reduces data redundancy, it can be more complex and slower in Power BI.

3. Relational Schema
Used primarily in transactional systems (OLTP). Each entity has its own table, and tables are highly normalized. This structure is less optimal for analytical reporting.

4. Hierarchical Schema
Organized in a tree-like structure where one parent table connects to multiple child tables. Each child has only one parent.

Benefits of Using Schemas

Organize data clearly and consistently
Enforce data accuracy using keys and constraints
Improve security by controlling access at the schema level
Support scalability and growth
Enable better collaboration between designers and administrators
Allow structural changes without disrupting applications

Data Modelling Explained
Data modelling defines how data is structured, related, and used within a system. Using diagrams and symbols, it visually represents how data flows from capture to reporting.

Benefits of Data Modelling

Improves collaboration between business and IT teams
Identifies opportunities to optimize business processes
Saves time and costs through proper planning
Reduces errors and redundant data entry
Enhances performance of analytics and reporting
Helps define and track key performance indicators (KPIs)

Relationships in Schemas and Data Modelling
Relationships define how tables connect and interact. They are essential for:

Data integrity
Accurate joins and filters
Reflecting real-world business processes

Types of Relationships

One-to-One (1:1): One record relates to exactly one other record (e.g., user and user profile)
One-to-Many (1:N): One record relates to many others (e.g., customer and orders)
Many-to-Many (M:N): Many records relate to many others and require a bridge (junction) table.

Good Modelling and Why It Matters
Good modelling simplifies complexity and improves clarity across systems.

Why Good Modelling Is Important
I. Enhances Understanding – Focuses on essential elements and removes unnecessary complexity
II. Improves Communication – Acts as a shared language among stakeholders
III. Supports Decision-Making – Enables scenario testing and risk identification
IV. Drives Efficiency and Quality – Prevents messy structures and improves data reliability
V. Enhances Learning and Teaching – Builds confidence and independence through clarity

As we come to the end of today's article, Did you catch something new?
If you did, then you know what to do...look out for my next piece next week.

For now, let me go learn some more. 😊

MS Excel?! Simple beginner steps for basic Data Analysis using Excel.

MwendeMugambi — Sun, 25 Jan 2026 19:18:44 +0000

Let me tell you a story of a lady.

A lady who, whenever she receives any Excel-related task, immediately grabs her laptop and runs to the nearest person for help. In her mind, one thought keeps ringing: “MS Excel is too hard. This thing is not for me.”

She asks for help, and the person helping her looks at her wondering, “But… has she even tried?” But since that can't be said out loud, the person just quietly judges her.

I would loved to tell you her name, but I fear I'm still a student data analyst who might not be able to afford a lawyer yet. Let’s just say… you might know her. You might be her. And honestly? That’s okay.

That’s why I’m here; to help you get a beginner-friendly understanding of Excel, without the stress, panic, or running around the office looking for rescue.

Because the truth is, data is everywhere. We use it when tracking monthly expenses, monitoring attendance at events, or collecting survey responses, often without even realizing it.

Today, I introduce you to Microsoft Excel (MS Excel), one of the most common and beginner-friendly tools for organizing and analyzing data. By the end of this article, I promise you’ll understand how to use Excel for basic data analysis, and maybe even stop running away from it.

Opening MS Excel and finding your way around.

When you open MS Excel, you find a blank workbook made up of rows and columns

Key parts of the Excel interface:

Workbook – the Excel file you are working on
Worksheet – a single page inside the workbook
Rows – horizontal lines, labeled with numbers (1, 2, 3…)
Columns – vertical lines, labeled with letters (A, B, C…)
Cells – small boxes where rows and columns meet (e.g., A1, B2)

Each cell is where you enter data such as names, numbers, or text.

Red= Column
Yellow/Lime= Row

What is Data in Excel?

Data, in Excel simply means information that has been entered into cells.
For example:

Names of people
Ages
Counties
Test scores
Amounts of money
Dates
Gender

Let's assume we have the following data:

The above table is a simple dataset that can be analyzed.

Basic Data Analysis tasks in Excel

1 Sorting Data
Sorting helps you arrange data in a specific order, such as highest to lowest scores.
For example, to see who scored the highest:

Select the entire table
Click on the Data tab
Choose Sort
Sort by Test Score from largest to smallest

After sorting, you can now easily identify top and low performers.

2 Filtering Data
Filtering allows you to view specific information without deleting other data.
For example, to view only students from Nairobi:

Select the table
Click Filter under the Data tab
Click the dropdown arrow in the County column
Select Nairobi

Now, excel only shows students from Nairobi, which is according to our selection.

3. Using simple Formulas
Excel can automatically calculate values using formulas.

3.1: Calculating Total (SUM)
To calculate the total of numbers (like test scores):

Click an empty cell
Type =SUM(C2:C6)
Press Enter

A column of numbers with the SUM formula entered in an empty cell and the total is displayed.

3.2: Calculating Average (AVERAGE)
To find the average of numbers:

Click an empty cell
Type =AVERAGE(C2:C6)
Press Enter

3.3: Subtracting Numbers (-)
To subtract numbers (like finding the difference between scores or expenses):

Click an empty cell
Type =C2-C3
Press Enter

3.4: Multiplying Numbers (*)
To multiply numbers (like calculating total cost or combining quantities):

Click an empty cell
Type =C2*C3
Press Enter

3.5: Counting Numbers (COUNT)
To count how many numeric entries are in your data:

Click an empty cell
Type =COUNT(C2:C6)
Press Enter

3.6 Counting Everything (COUNTA)
To count all non-empty cells (numbers or text):

Click an empty cell
Type =COUNTA(A2:A6)
Press Enter

3.7: Finding the Highest Value (MAX)
To find the highest number:

Click an empty cell
Type =MAX(C2:C5)
Press Enter

3.8: Finding the Lowest Value (MIN)

To find the lowest number:
Click an empty cell
Type =MIN(C2:C6)
Press Enter

3.9: Making Decisions (IF)
To assign labels like Pass/Fail based on a condition:

Click an empty cell
Type =IF(C2>=50,"Pass","Fail")
Press Enter

Creating a Simple Chart
Charts help turn numbers into visuals that are easier to understand.
To create a chart:
Select the Name and Test Score columns
Click Insert
Choose a Column Chart

Why Excel Is a Good Tool for Beginners in Data Analytics

Excel is ideal for beginners because:
It is easy to use
It requires no programming
It is widely available
It helps build strong data foundations

Skills learned in Excel, such as sorting, filtering, and using formulas, are transferable to advanced tools like Power BI, SQL, and Python.

Conclusion

Microsoft Excel is a powerful starting point for anyone interested in data analytics. With just basic skills, you can organize information, analyze trends, and make informed decisions.
As a beginner, focus on:

Understanding rows, columns, and cells
Practicing simple formulas
Exploring charts and filters

Once comfortable, you can move on to advanced Excel features like Pivot Tables and Dashboards.

You don’t need to know everything to start. Just start, Excel will grow with you.

You too can Git it: A beginners guide to connecting Git and GitHub

MwendeMugambi — Sun, 18 Jan 2026 16:38:32 +0000

If you’re new to Git and GitHub, welcome! Don’t worry, you don’t need to be a coding wizard. This guide will walk you through everything, step by step, from downloading the tools and creating your GitHub account to pushing your first project online, and understanding the basics of version control.
And yes…there will be frustration and mistakes, but don't be like me, a person who tried to understand Git when hungry.

Step 1: Understand the Goal
The goal of Git is simple:

Track changes in your project on your computer
Push these changes to GitHub so the world (or just your future self) can see them Think of Git as your personal diary for your project, but with a very strict editor

Step 2: Create a GitHub Account
Before you can push anything online, you need a GitHub account:

Go to https://github.com
Click Sign Up
Fill in your username, email, and password
Verify your email

Congratulations! You now have a GitHub account—the gateway to version control fame.

Step 3: Download Git and Git Bash
Next, download the tools you need to communicate with GitHub:

Download Git: https://git-scm.com/downloads
Install Git Bash (it usually comes with Git) Git Bash is your terminal and in simpler terms, the “control room”, where all the magic happens. Accept the defaults during installation.

Optional: You can also download GitHub Desktop (https://desktop.github.com) if you prefer a GUI version, but this guide focuses on Git Bash to understand the basics.

Step 4: Configure Git (Set Your Identity)
Git needs to know who you are, so it can attach your name and email to commits. In Git Bash, type:
git config --global user.name "Your Name" git config --global user.email "your-email@example.com"
Check whether the code has worked with:
git config --list

Think of this as signing your work. Without it, GitHub may show your commits as “unknown user.”

Step 5: Create a Project Folder
Your “project” is just a folder with at least one file. It can be as simple as a README.md.

Open Documents (or anywhere safe).
Create a folder called: my-first-repo
Inside, create a file called README.md with some text: My first GitHub project

Pro tip: Don’t try to be fancy. Keep it simple. One folder, one file, and you’re good to go

Step 6: Initialize Git in the Right Place
Here’s where I learned my first painful lesson. I accidentally ran the following in my home directory (C:\Users\YourName).:
git init git add .
The result? My poor computer started complaining:
warning: could not open directory 'AppData/Local/...': Permission denied fatal: adding files failed
What was happening? Git was trying to track my entire computer profile, including:

AppData (system files)
Windows temp files
Eclipse configuration files
SSH keys
My soul And Windows was like: “Nope. Not today, buddy.”

Lesson learned: Never, ever run Git commands in your home folder. Or any system folder. Unless you want to face the wrath of permission-denied errors

Step 7: Open Git Bash in the Project Folder
Right-click inside my-first-repo → Open Git Bash here.
Check your location by the typing the following in Git Bash:
pwd
You should see:
/c/Users/YourName/Documents/my-first-repo
If you see /c/Users/YourName instead, STOP.Close Git Bash. Walk away. Grab a snack. You’ll thank me later. Then later come and initialize it in the correct place.

Step 8: Initialize Git
Inside your project folder:
git init
You should see:
Initialized empty Git repository
This creates a Git “tracking system” for your project.

Step 9: Add and Commit Your Files
Stage your files for tracking:
git add .
Ignore harmless warnings like:
LF will be replaced by CRLF
Commit your changes:
git commit -m "Initial commit"
Then check the status with:
git status
You should see:
nothing to commit, working tree clean

Think of commits as screenshots of your project. They let you go back in time if needed.

Step 10: Create a GitHub Repository

Go to GitHub and create a new repository Name: my-first-repo Leave it empty (no README, no license)
Copy the SSH link in the repository. It will be written as: git@github.com:YourUsername/my-first-repo.git

Step 11: Connect Local Repository to GitHub
In Git Bash, run the following codes:
git remote add origin git@github.com:YourUsername/my-first-repo.git git branch -M main git push -u origin main
remote add origin → links your local project to GitHub
branch -M main → sets your main branch
push -u origin main → sends your files online
Refresh your GitHub repository, and there is your README.md! 🎉

Step 12: Pulling Code and Tracking Changes
Once your project grows, you’ll want to track changes and collaborate.

To check the status of your folder: git status Shows you what’s changed, what’s staged, and what’s untracked.
To pull updates from GitHub: git pull origin main This gets changes others (or you from another computer) pushed online.
To stage and commit new changes: git add . git commit -m "Updated file X" git push Think of this as: Add → “I want to track this change” Commit → “Save this change snapshot” Push → “Send it to GitHub”
To view commit history git log Shows all previous snapshots with author, date, and message.

Step 13: Understand Version Control
Version control is like a time machine for your code.

Every commit is a snapshot
You can go back to previous snapshots if something breaks
You can collaborate with others without overwriting each other’s work
Branches let you work on new features separately, then merge when ready

In short: Git = your code diary + time machine + collaboration hub.

Step 14: Lessons Learnt.

Location matters. Always run Git in your project folder.
Ignore harmless warnings. Line-ending messages won’t hurt your files.
Do not try to understand Git while hungry. Starvation leads to home folder disasters.
Start small. One folder, one file, one commit.
Have fun. Mistakes happen—laugh at them, learn, and push forward.

Git and GitHub can be intimidating at first, but step-by-step practice makes them approachable. Follow this guide, and soon you’ll feel like a Git superhero.
Commit responsibly, push wisely, and never Git hungry.