Comfort

Data Analytics 🧮 || Data visualization 📊

[Comfort @dDharmmie]

Get insights at a glance! Check out my latest interactive dashboard

Tool: powerBI + query

[Comfort @dDharmmie]

@VephlaUni Congratulations!!! @hunteryskelis

[Dasola @Dasse_y]

I tried my hands on some resources on @tryhackme and I learnt a lot of things like understanding the roles of SOC Analysts and how they work alongside other Blue team members.
I practiced some labs, where I got to identify a malicious IP address, escalated and blocked it.

[Comfort @dDharmmie]

@dasola_olaopa @CiscoNetAcad @TechSphereAcad @ireteeh Go shawtie!!!
I'm rooting for you🥰

[Dasola @Dasse_y]

I took my first Networking Basics Quiz yesterday. I'm excited about the learning journey ahead and the new skills I'm building. @CiscoNetAcad @TechSphereAcad @ireteeh
#30DaysOfTech #LearningWithTS

[Temi ✨ @cheftee_lead]

Common errors in Excel and what they mean.
Which one do you dread the most? 😂

Knowing what these errors represent is crucial as it would inform your decision on what to do to troubleshoot and get them off your formulas 🫠

Retweet for others 🧵🤗

1. #DIV/0!: Dividing a number by zero or an empty cell. Ensure that the divisor is not zero or blank.

2. #NAME?: This means that Excel does not recognize the function name, due to misspelling a function or referring to a named range that does not exist.

3. #VALUE!: This occurs when a wrong data type has been used in a formula, such as adding text to a number.

4. #N/A: The means the value is not available, commonly seen in lookup functions when the formula can’t find a match.

5. #NUM!: This signifies Invalid numeric value, for example, when a formula has invalid number like square root of a negative.

6. #SPILL!: This happens when a formula generates multiple results, but the destination range has content blocking the output.

7. #REF!: This means a reference has become invalid, happens when you delete a cell or range that has been referenced in a formula or when you copy and paste data with underlying function elsewhere.

8. ####: Basically means that the column is too narrow to display the data.

I hope this is helpful

[Nte Daniel || Data Analyst → Engineer @iam_daniiell]

𝗚𝗼𝗼𝗱 𝗺𝗼𝗿𝗻𝗶𝗻𝗴 𝗴𝘂𝘆𝘀.

If you’re starting out as a data analyst, before you hop on any tools, you need to understand the data analysis lifecycle.

This is the framework every analyst uses, whether they realize it or not.

Let me break it down. 🧵

𝟭. 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 (𝗔𝘀𝗸)

This is where most beginners mess up.

They jump straight into tools without understanding what problem they’re solving.

Ask:
→ What question are we trying to answer?
→ Who needs this information?
→ What decision will this analysis drive?

If you don’t understand the problem, your analysis is useless.

𝟮. 𝗗𝗮𝘁𝗮 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻

Now you know what you need. Go get it.

Where is the data?
→ Database? Export it.
→ Excel file? Import it.
→ API? Pull it.
→ Manual entry? Document it.

You can���t analyze what you don’t have. Collect the right data from the right sources.

𝟯. 𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 & 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻

Real data is messy. Always.

Missing values. Duplicates. Wrong formats. Inconsistent entries.

Clean it:
→ Handle nulls
→ Remove duplicates
→ Fix data types
→ Standardize formats

This step takes 80% of your time. Accept it.

𝟰. 𝗗𝗮𝘁𝗮 𝗘𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗶𝗼𝗻

Now you start asking questions.

What patterns do you see?
→ Trends over time?
→ Outliers?
→ Correlations?
→ Unexpected values?

This is where curiosity matters more than technical skills.

Explore. Dig. Ask “why?”

𝟱. 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 / 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴

Apply your methods.

Descriptive analysis - what happened?
Diagnostic analysis - why did it happen?
Predictive analysis - what will happen?
Prescriptive analysis - what should we do?

Use the right technique for the question you’re answering.

𝟲. 𝗜𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗮𝘁𝗶𝗼𝗻 & 𝗣𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻

You found insights. Now make them understandable.

Nobody cares about your SQL query or your pivot table.

They care about:
→ What does this mean for the business?
→ What should we do about it?
→ What’s the impact?

Visualize it. Tell a story. Make it actionable.

𝟳. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻

Your analysis drives a decision. The decision leads to action.

Did it work?
→ Monitor the results
→ Track the metrics
→ Measure the impact

If it didn’t work, cycle back. Refine. Try again.

𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗽𝗮𝗿𝘁 𝗻𝗼𝗯𝗼𝗱𝘆 𝘁𝗲𝗹𝗹𝘀 𝘆𝗼𝘂:

This isn’t a straight line.

You don’t go 1 → 2 → 3 → 4 → 5 → 6 → 7 and you’re done.

You cycle back and forth.

During exploration, you find data issues → back to cleaning.
During analysis, you realize you need more data → back to collection.
During presentation, stakeholders ask new questions → back to analysis.

That’s normal. That’s how it works.

𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀:

Beginners think data analysis is about tools.

“Should I learn Excel or Python first?”
“Which BI tool is best?”

Wrong question.

The lifecycle is the same whether you use Excel, Python, Power BI, or Tableau.

Master the process first. Tools are just ways to execute it.

Understand the lifecycle. Follow the process. Get results.

That’s data analysis.

#DataAnalysis #DataEngineering #DataLifecycle #BuildingInPublic #Datafam

[Jobs with Aramide @AramideOyekunle]

Then we later complain of back pain🤣🤣🤣

[Comfort @dDharmmie]

@AramideOyekunle My data analytics journey so far...
https://t.co/ob1ZMlbOsf

[Splendor of SQL 🇬🇧🇩🇪💖 @iam_Uchenna]

- SQL + SELECT = Querying Data
- SQL + JOIN = Data Integration
- SQL + WHERE = Data Filtering
- SQL + GROUP BY = Data Aggregation
- SQL + ORDER BY = Data Sorting
- SQL + UNION = Combining Queries
- SQL + INSERT = Data Insertion
- SQL + UPDATE = Data Modification
- SQL + DELETE = Data Removal
- SQL + CREATE TABLE = Database Design
- SQL + ALTER TABLE = Schema Modification
- SQL + DROP TABLE = Table Removal
- SQL + INDEX = Query Optimization
- SQL + VIEW = Virtual Tables
- SQL + Subqueries = Nested Queries
- SQL + Stored Procedures = Task Automation
- SQL + Triggers = Automated Responses
- SQL + CTE = Recursive Queries
- SQL + Window Functions = Advanced Analytics
- SQL + Transactions = Data Integrity
- SQL + ACID Compliance = Reliable Operations
- SQL + Data Warehousing = Large Data Management
- SQL + ETL = Data Transformation
- SQL + Partitioning = Big Data Management
- SQL + Replication = High Availability
- SQL + Sharding = Database Scaling
- SQL + JSON = Semi-Structured Data
- SQL + XML = Structured Data
- SQL + Data Security = Data Protection
- SQL + Performance Tuning = Query Efficiency
- SQL + Data Governance = Data Quality

[Comfort @dDharmmie]

@Femi_forge D; Still learning!

[Comfort @dDharmmie]

@EhUdosen Order!!!!!!!!!!

[Comfort @dDharmmie]

@yilkudikang @EhUdosen 😂😂

[Comfort @dDharmmie]

@EhUdosen Favour, you can achieve this

[Comfort @dDharmmie]

@OpeyemiDad76428 @EhUdosen 😂😂 Pele engineer

[Comfort @dDharmmie]

@EhUdosen Full stack and you no pass ground level

[Comfort @dDharmmie]

@EhUdosen Omo, can someone mute this guy and let's move on

Kinni gbogbo eleyiii

[Comfort @dDharmmie]

From raw data to actionable insights

Designed this E-Commerce Dashboard in Excel to uncover trends in customer spending, satisfaction, and loyalty.

Tools: Excel + PivotTables + Slicers

#DataAnalytics #Excel #DashboardDesign #DataDriven

[Jay Yang @Jayyanginspires]

Ambition without action becomes anxiety.

[Tom Mitchell @imtommitchell]

The best data skill you can develop isn't:

- SQL
- Python
- PowerBI

It's being able to recognise when a stakeholder "wants" something vs when they "need" something.