Getting Started with Excel for Data Analytics From Basics to Data Cleaning

CLEOPAS RONO — Sun, 30 Aug 2026 21:37:09 +0000

Introduction

During my first week of learning Data Science and Analytics, I was introduced to Microsoft Excel and how it can be used when working with data. I learned that Excel can also be useful for data analytics because it provides different tools for organizing, checking, cleaning, and analyzing data.
Also I learned is that data needs to be prepared before it can be analyzed properly. When we receive raw data, it may contain missing information, duplicate records, spelling mistakes, extra spaces, or values that are not in the correct format. If these problems are not corrected, they can affect the results of the analysis.
These include the basic structure of Excel, entering and organizing data, formulas and functions, sorting and filtering, and some basic data-cleaning techniques and its importances on data sciences and analytics;

1. Excel structure

An Excel file is called a workbook, and inside a workbook there can be one or more worksheets. A worksheet is where the actual data is entered. I noticed that the worksheet is made up of rows and columns. The columns are labeled with letters such as A, B, C, and D, while the rows are labeled with numbers such as 1, 2, 3, and 4.
Also, Where a row and column meet, there is a cell. Each cell has a reference, such as A1 or B5. I learned that these cell references are important because they allow me to use the contents of one cell in a formula.
For example, if I have a quantity in cell B2 and a price in cell C2, I can use the formula:
=B2*C2 this is to calculate the total.
This was one of the first things that helped me understand that Excel can be used for more than just storing information.

2. Understanding columns and rows

I also learned how rows and columns are normally used when working with a dataset. A column usually represents a variable or a type of information, while a row represents one record or observation.
I learned that keeping this structure is important when preparing data for analysis. If the information is not organized properly, it becomes more difficult to perform calculations or find information.

3. Different Types of Data

I also learned that Excel can work with different types of data. The main types I came across were text, numbers, and dates. This showed me that data preparation is not just about how the spreadsheet looks. It is also about making sure Excel understands the information correctly.

4. Using Formulas

A formula tells Excel to perform a calculation. Formulas normally start with an equals sign (=).
For example, if I have a quantity in B2 and a price in C2, I can calculate the total using:
=B2*C2
This is useful because Excel can do the calculation for me instead of requiring me to calculate it manually. I also learned that formulas can be copied down to other rows. This is useful when working with a dataset containing many records.

5.Basic Functions

some basic Excel functions. Functions are built-in commands that perform common calculations.
Some of the functions I learned include;
SUM, AVERAGE, COUNT, MIN, and MAX.
SUM -This function adds numbers together
AVERAGE - This function calculates the average of a group of numbers.
COUNT-This function counts cells that contain numbers and can tell me how many numerical entries are in the selected range.
MIN and MAX- MIN finds the smallest number, while MAX finds the largest number.

6. Sorting data

Sorting means arranging data in a particular order. For example, I can sort a list of sales from the smallest amount to the largest amount. I can also sort it from the largest amount to the smallest. With that I can also sort names alphabetically.

7. filtering data

Filtering allows me to display only the records that meet a particular condition. For example, if I have a dataset containing customers from different locations, I can filter the Location column to display only customers from Nairobi.

8. Data cleaning

One of the most important things I learned during Week 1 was the idea of data cleaning.
Data cleaning means checking raw data for errors and making corrections so that the data is more suitable for analysis.
When data is collected, it is not always perfect. There may be mistakes because people entered the information manually, because different systems were used, or because information was collected from different sources.
Some common problems include:
-Duplicate records
-Missing values
-Spelling mistakes
-Extra spaces
-Different capitalization
-Incorrect formats

My Two-Week Journey into Git and GitHub,From Local Projects to Version Control

CLEOPAS RONO — Sat, 22 Aug 2026 21:33:40 +0000

Introduction

Over the past two weeks,I have been learning the basics of Git and GitHub and how they are used in software development.During the classes, I learned that Git and GitHub are not the same thing. Git is a version control system that runs on my computer and helps me track changes in my project, while GitHub is an online platform where I can store and manage Git repositories and collaborate with other developers.
One of the most useful things I learned was how to take a project that is stored in a local folder on my computer and connect it to GitHub. I also learned how SSH can be used to securely connect my computer to GitHub.

Understanding Git

One of the first things I learned was that Git helps me keep track of changes in my files. Instead of manually keeping different copies of a project whenever I make changes, Git allows me to record those changes and go back to previous versions when necessary.
For example, if I create a project and later make changes that cause something to stop working, Git allows me to see what changed and restore an earlier version of the project.
Git works mainly through the command line, where I use commands to tell Git what I want it to do.
One of the first commands I learned was:

1. git --version

This allows me to check whether Git is installed on my computer and see the version that is currently installed.

2. git init

This creates a Git repository in my project. It means Git can now start tracking the files and changes inside that folder.
I also learned that Git does not automatically save every change as a version. I have to tell Git which changes I want to include and then create a commit.

3. git status

This command is useful because it shows me which files have been changed, which files are untracked, and which files are ready to be committed.

Connecting My Local Project to GitHub

After learning how Git works locally, I learned how to connect my local repository to a repository on GitHub.
First, I need to create a repository on GitHub. Once the repository is created, GitHub provides a remote repository that I can connect to my local project.
I can connect the local repository using:
git remote add origin git@github.com:USERNAME/REPOSITORY.git
I can check whether the remote connection has been added correctly by using: git remote -v This shows me the remote repository that my local Git repository is connected to.

Understanding SSH

I also learn on how to create the SSH keys and know that SSH keys can be used to authenticate my computer.With that i can used this command :
ssh-keygen -t ed25519 -C "myemail@example.com"
This creates a pair of keys: a private key and a public key. After adding the public SSH key to GitHub, I can test the connection using:
ssh -T git@github.com

Pushing My Project to GitHub

After connecting my local repository to GitHub, the next step was to send my commits from my computer to the GitHub repository.
I learned that this is done using the git push command.

Conclusion

My two weeks of learning Git and GitHub gave me a better understanding of one of the basic tools used in modern software development. I learned how to create a Git repository, track changes, stage files, create commits, connect a local project to GitHub, configure SSH, and push my work to a remote repository.
The most important thing for me was not just memorizing commands but understanding the workflow behind them. I can now see how a project moves from being a simple folder on my computer to becoming a properly tracked project on GitHub.
This experience has given me a foundation that I can continue building on as I learn more about software development, collaboration, and version control.
Here is a summary of everything i learn in my first two weeks at LuxdevHq:

DEV Community: CLEOPAS RONO

Getting Started with Excel for Data Analytics From Basics to Data Cleaning

Introduction

1. Excel structure

2. Understanding columns and rows

3. Different Types of Data

4. Using Formulas

5.Basic Functions

6. Sorting data

7. filtering data

8. Data cleaning

My Two-Week Journey into Git and GitHub,From Local Projects to Version Control

Introduction

Understanding Git

1. git --version

2. git init

3. git status

Connecting My Local Project to GitHub

Understanding SSH

Pushing My Project to GitHub

Conclusion