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

Frank Hahanyu — Sat, 29 Aug 2026 20:45:50 +0000

Introduction

Excel is a data entry tool, but its far more than that. It's a versatile workspace for cleaning, analyzing and visualizing data. In Data analytics, excel is all about transforming raw data into clear, actionable insights for decision-making. While specialized coding tools exist, Excel remains the primary operational engine for businesses worldwide. This guide walks you through the foundational tools, cleaning workflows, core formulas, and reporting features needed to turn messy datasets into professional analysis.

Gettting Familiar with the interface

Before writing formulas, you need to understand where everything lives. Excel’s interface centers around the top ribbon and grid system.

The Excel Ribbon Breakdown

Home: Your formatting hub for numbers, cell alignments, styles, and basic edits.
Insert: Adds visual structure, including tables, PivotTables, charts, and shapes.
Page Layout: Controls print boundaries, margins, gridlines, and page orientation.
Formulas: Houses the full function library, auditing tools, and defined names.
Data: The analytics engine. Contains sorting, filtering, deduplication, text tools, and Power Query.
Review: Manages spellcheck, comments, sheet protection, and collaboration rules.
View: Customizes workbook views, freezes panes, and toggles gridlines or formula bars.

Structural Grid Basics
Rows: Horizontal lines identified by numbers(1, 2, 3...).
Columns: Vertical sections identified by letters (A, B, C...).
Cells: The intersection of a row and column (e.g., B4).
Name Box: Displays the active cell reference.
Formula Bar: Shows the underlying value or calculation inside the selected cell.

Managing Data Types & Basic Formatting

Excel treats different data types in specific ways. Alignments and formatting dictate how calculations run under the hood.

Common Data Types

Text: Left-aligned by default. Used for descriptions, IDs, or labels.
Numbers: Right-aligned by default. Used for standard numeric calculations.
Currency/Dates: Formatted numbers displaying currency symbols or standard calendar dates (YYYY-MM-DD).
Percentages: Decimal values displayed as parts of 100 (e.g., 0.05formatted as 5%).
Pro Tip: If a number is left-aligned, Excel is treating it as text, which will cause formulas like SUM to ignore it!

Organizing Data: Tables, Sorting, & Filtering

Structured data allows for quick exploration without modifying original values permanently.
Converting Ranges to Tables (CTRL + T)
Converting a raw range into an Excel Table enables structured references, alternating row colors, and auto-expanding ranges.

Select your data range.
Go to Insert > Table (or press CTRL + T).
Ensure "My table has headers" is checked.

Sorting & Filtering

Sorting: Rearrange rows alphabetically (A-Z), numerically (Smallest to Largest), or by cell color (Data > Sort).
Filtering: Click Data > Filter to enable dropdown arrows on headers. Use these to isolate specific criteria or filter by blank values.

The Data Cleaning Workflow

Clean data is the foundation of accurate analysis. Always keep an untouched raw copy of your data before applying these steps.
Essential Data Cleaning Techniques

Handling Missing Values: Use Data > Filter to isolate blank cells, or highlight them via Find & Select > Go To Special > Blanks.
Removing Duplicates: Select your table range, go to Data > Remove Duplicates, and choose key columns.
Trimming Extra Spaces: Standardize text containing accidental leading/trailing spaces using=TRIM(cell).
Standardizing Text Case: Clean text inconsistencies using=UPPER(),=LOWER(), or =PROPER().
Find & Replace(CTRL + H): Swap out erroneous values, currency symbols, or typos across thousands of rows instantly.

Core Excel Functions

Formulas perform manual calculations, while built-in functions execute predefined logic. All entries must begin with an equals sign (=).

Basic Aggregations

SUM(range): Adds all numeric values in a range.
AVERAGE(range): Calculates the arithmetic mean.
COUNT(range): Counts cells containing numbers.
COUNTA(range): Counts non-empty cells (numbers and text).
COUNTBLANK(range): Counts empty cells.
MIN(range) / MAX(range): Returns the lowest or highest value.

Date & Time Calculations

TODAY() / NOW(): Inserts dynamic current date or timestamp.
YEAR(), MONTH(),DAY(): Extracts individual components from a date cell.
DATEDIF(start_date, end_date, unit): Calculates time elapsed in days ("D"), months ("M"), or years ("Y").
NETWORKDAYS(start_date, end_date): Counts working days excluding weekends.

Summarizing Data with PivotTables & Charts

PivotTables allow you to slice, aggregate, and analyze thousands of rows without writing complex formulas.

Creating a PivotTable

Click inside your clean dataset and select Insert > PivotTable.
Choose to place it in a New Worksheet.
Drag fields into the four main zones:

Filters: Global data boundaries.
Columns: Cross-tabulation categories.
Rows: Primary groupings down the left side.
Values: Numeric metrics to sum, average, or count.

Visualizing with Pivot Charts
To transform summary tables into visual reports:

Click anywhere inside your PivotTable.
Go to PivotTable Analyze > PivotChart (orInsert > PivotChart).
Select a bar, column, or line visualization that best represents your metrics.

Conclusion

Mastering data analytics in Excel is a step-by-step process. Once you get comfortable cleaning raw datasets and building basic summaries, explore advanced analytics tools.

GitHub Project :Understanding git work flow

Frank Hahanyu — Sun, 23 Aug 2026 03:49:13 +0000

INTRODUCTION

My first week at LuxDev Academy as a beginner in the tech field was awesome since all tutors gave us hands on tutoring , we covered installation of apps and understanding coding languages. For today I'll cover git workflow and all its components .

Version control can feel overwhelming when you first star writing code or managing data projects and for tools like git , they're designed to track changes in your projects over time using commands like git_add, git_commit, and git_push often feel like magic spells rather than logical steps.

To master or understand git you only need to understand the stages a file goes through during construction wich is :

1.The Working Directory

This is where you do the work and it consists of the actual files on your computer's files system inside your project folder. In her when you create a new file or modify an existing code those changes only exist on your working directory, meaning git only notices that changes have occurred but it doesn't track or save them automatically and for that to happen we use git status

2.The Staging Area

The staging area acts as the intermediate step between your actual workspace and your saved history.
Git selectively lets you choose which modified files belong together in the next check point . you stage a file changes from the working directory by inputting git add command , then you stage a specific file git add filename.py then stage all modified files at once git add

3.The Commit and Push

After the staging area we go ahead and commit to record those specific changes along with a clear descriptive message explaining what we updated( commit acts as a restore point).This is the commit prompt with a descriptive message i.e analysis data: git commit -m "add data analysis data"
In the push section , all your commits exist on your local computer but pushing takes all the commits and uploads them to a remote repository on GitHub and this is how you push it git push origin main

Conclusion

Git gives you full control over how your project's history is recorded, organized and shared. You need to be keen with how you key in your prompts cause once you miss a step a lot of errors pile up and with that, once you understand these four stages then you won't have a problem working with Git in your work developments.

DEV Community: Frank Hahanyu

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

Introduction

Gettting Familiar with the interface

Managing Data Types & Basic Formatting

Organizing Data: Tables, Sorting, & Filtering

The Data Cleaning Workflow

Core Excel Functions

Summarizing Data with PivotTables & Charts

Conclusion

GitHub Project :Understanding git work flow

INTRODUCTION

1.The Working Directory

2.The Staging Area

3.The Commit and Push

Conclusion