The Job Ladder

If I were to start my Data Analyst career from scratch, here’s the 6-step roadmap I’d follow:


𝗦𝘁𝗲𝗽 𝟭 - 𝗘𝘅𝗰𝗲𝗹 & 𝗦𝗽𝗿𝗲𝗮𝗱𝘀𝗵𝗲𝗲𝘁𝘀
• Free: Excel Skills for Business (Coursera) – lnkd.in/e5tCSGyw
• Paid: Data Analysis in Excel – lnkd.in/eRKBUWKA
• Tools: Excel, Google Sheets

𝗦𝘁𝗲𝗽 𝟮 - 𝗦𝗤𝗟 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗤𝘂𝗲𝗿𝘆𝗶𝗻𝗴
• Free: Intro to SQL (Khan Academy) – lnkd.in/erHcstcU
• Paid Cert: SQL (Datacamp) – lnkd.in/dMU2SUbm
• Tools: MySQL, PostgreSQL, BigQuery

𝗦𝘁𝗲𝗽 𝟯 - 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻
• Free: Power BI Learn (Microsoft) – lnkd.in/ea_EvTh9
• Paid Cert: Data Visualization in PowerBI - lnkd.in/eKPUw-xW
• Tableau Desktop Specialist – lnkd.in/eJ4FrcpM
• Tools: Power BI, Tableau, Looker

𝗦𝘁𝗲𝗽 𝟰 - 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀
• Free: Python for Everybody (Coursera) – lnkd.in/efB4wiUK
• Paid Cert: EDA in Python: lnkd.in/egR7_Pzc
• IBM Python for Data Science – lnkd.in/ed-JHTbF
• Tools: pandas, NumPy, Jupyter Notebook

𝗦𝘁𝗲𝗽 𝟱 - 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 & 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗦𝗲𝗻𝘀𝗲
• Free: Statistics & Probability (Khan Academy) – lnkd.in/ex9wA7TN
• Paid Cert: HarvardX Data Science: Probability – lnkd.in/eWj_tswb
• Tools: A/B Testing, Hypothesis Testing, Business Metrics

𝗦𝘁𝗲𝗽 𝟲 - 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 & 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼
• Data Analyst/Scientist Portfolio Ideas – lnkd.in/eKtaYMjr
• Analysing customer Churn: lnkd.in/eYUth5XB
• Tools: GitHub, Notion, Streamlit

Steps 1–5 give you skills.

But Step 6 builds proof.

Don’t just learn, showcase what you know. That’s how you land your first data job.

♻️ Save it for later or share it with someone who might find it helpful!

6 months ago | [YT] | 1

The Job Ladder

You don’t need a $10,000 bootcamp to learn Data Analytics.

(Start with these free resources and go from zero to job-ready.)

1. Programming Essentials
→ freecodecamp.org: Python for data
→ learnpython.org: Hands-on Python practice
→ sqlzoo.net: Learn SQL by doing
→ mode.com/sql-tutorial: SQL for data analysis

2. Statistics & Probability
→ khanacademy.org: Interactive stat lessons
→ seeing-theory.brown.edu: Visual intuition of stats
→ statquest.org: YouTube explanations made simple

3. Exploratory Data Analysis (EDA)
→ datacamp.com: Free intro to data viz (first chapter free)
→ towardsdatascience.com: Blog posts with real datasets
→ kaggle.com/learn/pandas: Hands-on Pandas tutorials

4. Data Visualization
→ lnkd.in/dCRWgPTb: Learn Matplotlib
→ seaborn.pydata.org: High-level visualization
→ public.tableau.com: Tableau Public (free tool)
→ lnkd.in/djXY4TGF: Power BI beginner path

5. Machine Learning Basics
→ scikit-learn.org: ML for beginners
→ lnkd.in/debeAaR5: Learn by doing
→ mlcourse.ai: Open course with notebooks & competitions
→ lnkd.in/dUMJwsMP: Google’s ML Crash Course

6. GitHub Repos for Practice
→ lnkd.in/dpPpNrKS: Pandas exercises
→ lnkd.in/dC3Vw877: Interview prep
→ lnkd.in/deZUW8fD: Project guide
→ lnkd.in/dbC3Gvz4: Real-world use cases

7. Real-World Projects
→ kaggle.com/datasets: Download & analyze datasets
→ data.gov: Public US data
→ ourworldindata.org: Clean, curated datasets
→ awesome-datascience.com: Project ideas & datasets

8. Business Intelligence & Dashboards
→ powerbi.microsoft.com: Power BI learning path
→ lookerstudio.google.com: Google’s free dashboarding tool
→ microsoft.com/learn: Interactive Power BI modules
→ tableau.com/learn/training: Tableau beginner workshops

9. Cloud for Data
→ aws.amazon.com/training: Free cloud data courses
→ cloud.google.com/training: GCP analytics & BigQuery
→ lnkd.in/dHrwqXMC: Azure data learning paths
→ cloudskillsboost.google: GCP labs & sandboxes

Once you’re familiar with the stack, do this:

↳ Join a Data Community (Slack, Discord, LinkedIn groups)
↳ Follow Blogs/Newsletters (e.g., Towards Data Science, The Data Hustle 😉)
↳ Build Portfolio Projects (host on GitHub & share on LinkedIn)
↳ Share Learnings Publicly (start writing or teaching!)
↳ Go for a Certification (Google DA, IBM DS, or Microsoft PL-300)

♻️ Save it for later or share it with someone who might find it helpful!

𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 12,000+ readers here → lnkd.in/dUfe4Ac6

6 months ago | [YT] | 0

The Job Ladder

SQL Questions

1. Write a SQL query to find the second-highest salary from an employee table.
2. How do you optimize a slow-running SQL query?
3. Explain the difference between JOIN and UNION .
4. Write a query to find duplicate records in a table.
5. What are window functions in SQL? Give an example.
6. How would you handle missing or null values in SQL?
7. Explain the difference between HAVING and WHERE clauses.
8. What is a Common Table Expression (CTE) ? How is it different from a subquery?
9. Write a SQL query to calculate the Customer Lifetime Value (CLV) .
10. What are indexes , and how do they improve query performance?

Python Questions

11. How do you handle missing values in Pandas?
12. Explain the difference between a list and a tuple.
13. What are lambda functions in Python? Give an example.
14. How do you read a CSV file using Pandas?
15. Explain the difference between apply() , map() , and vectorization in Pandas.
16. How would you perform data transformation using Python?
17. What is the difference between NumPy and Pandas?
18. How do you merge two DataFrames in Pandas?
19. What is the purpose of the groupby() function in Pandas?
20. Write a Python function to find the factorial of a number.

Power BI Questions

21. What are calculated columns and measures in Power BI?
22. Explain Power Query and how it is used for data transformation.
23. What are the different types of filters in Power BI?
24. How do you optimize a Power BI report for performance?
25. What is the difference between a Star Schema and a Snowflake Schema ?
26. Explain row-level security (RLS) in Power BI.
27. What is the purpose of DAX ? Give an example of a DAX function.
28. How do you create a relationship between tables in Power BI?
29. Explain how to handle large datasets in Power BI efficiently.
30. How do you create a dynamic dashboard in Power BI?

Subscribe..Please.....

6 months ago | [YT] | 0

The Job Ladder

🚀 Want to Stand Out in Data Analytics Interviews?

Here’s your unfair advantage , 6 powerful portfolio projects that will actually get you noticed.

💼 These aren’t just any projects , each one is designed to sharpen your real world data skills and boost your confidence like never before:

𝟭. 𝗥𝗲𝘁𝗮𝗶𝗹 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝘄𝗶𝘁𝗵 𝗦𝘂𝗽𝗲𝗿𝘀𝘁𝗼𝗿𝗲 𝗗𝗮𝘁𝗮

Learn to build a cloud-based analytics pipeline from scratch.
Tech stack: AWS S3, Glue, Athena, SQL, QuickSight
lnkd.in/ebypRkxz

𝟮. 𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗘𝗧𝗟 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗣𝗿𝗼𝗷𝗲𝗰𝘁

Master the full data journey: extract, transform, analyze.
Tech stack: SQL, Python
lnkd.in/eJy8WNGz

𝟯. 𝗡𝗲𝘁𝗳𝗹𝗶𝘅 𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 & 𝗘𝗟𝗧 𝗣𝗿𝗼𝗷𝗲𝗰𝘁

Improve data quality and prepare datasets for deep insights.
Tech stack: Python, SQL
lnkd.in/eiP3SKCn

𝟰. 𝗦𝗤𝗟 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗣𝗿𝗼𝗷𝗲𝗰𝘁

Build a rock-solid foundation in advanced query writing.
Tech stack: SQL
lnkd.in/eXn82pEd

𝟱. 𝗬𝗲𝗹𝗽 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗥𝗲𝘃𝗶𝗲𝘄 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀

Dive into sentiment analysis and cloud data warehousing.
Tech stack: S3, Python, Snowflake, SQL
lnkd.in/eex2k9aR

𝟲. 𝗙𝗼𝗼𝗱 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝘆 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗣𝗿𝗼𝗷𝗲𝗰𝘁

Solve real business problems using complex SQL techniques.
Tech stack: Advanced SQL
lnkd.in/ev4pDVU9

🎯 By the end of these projects, you won’t just know data analytics , you’ll own it. 😇

6 months ago | [YT] | 0

The Job Ladder

Commonly Asked 30 Power BI Interview Questions with Answers

1. What is the Power BI Gateway, and when should it be used?
A tool enabling secure data transfer between on-premises data sources and Power BI Service; used for DirectQuery or scheduled refresh.

2. How to publish a report from Power BI Desktop to Power BI Service?
Save the report → Click "Publish" → Choose workspace.

3. How can data refresh be scheduled in Power BI Service?
Set refresh schedule in dataset settings after configuring gateway.

4. Key differences between DirectQuery and Import modes?
DirectQuery: Real-time data, slower visuals.
Import: Cached data, faster performance.

5. How do you approach data modeling in Power BI?
Use star schema, create relationships, optimize measures, and ensure data integrity.

6. Explain the star and snowflake schema.
Star Schema: Central fact table connected to dimension tables.
Snowflake Schema: Dimension tables are normalized.

7. Types of visuals in Power BI and selecting the best one?
Bar, line, pie, map, table, etc.; choose based on data type and insights needed.

8. Role of drill through and drilldown in reports?
Drill Through: Navigate to detailed pages.
Drilldown: View hierarchical data within the same visual.

10. What is the Q&A feature in Power BI?
AI-powered tool to query data using natural language.

11. Sharing reports with non-licensed users?
Use Power BI Premium or export reports to PDF/PPT.

12. Function of Power BI REST API?
Automates workflows, manages reports/dashboards programmatically.

13. How are bookmarks used?
Save report views for navigation or storytelling.

14. Strategies to optimize report performance?
Use aggregations, reduce visuals, minimize calculated columns.

15. Handling large datasets and optimization?
Enable incremental refresh, use DirectQuery, optimize DAX.

16. What are DAX functions, and how are they used?
Expressions for calculations and queries in Power BI.

20. How to create a calculated column?
Use DAX in Power BI Desktop's data view.

21. Explain relationships and their creation.
Define table connections using primary and foreign keys.

23. How is the Filter Pane used?
Apply filters at visual, page, or report level.

24. Purpose of Power BI Service App Workspaces?
Collaborate, manage reports/dashboards, and share content.

25. Row-level security (RLS) implementation?
Define roles and DAX filters to restrict data access.

26. Difference between a Power BI Dashboard and Report?
Dashboard: Single-page summary.
Report: Multi-page interactive insights.

27. Best practices for designing dashboards?
Keep visuals simple, focus on KPIs, use themes.

29. Role of dataflows?
Reusable ETL processes in Power BI Service.

30. Parameters in Power BI and their benefits?
Dynamic inputs for queries, enhance flexibility.

6 months ago | [YT] | 0

The Job Ladder

You’re allowed to treat “Data Analyst” as a starting point, not your final job.

Your company.

Your pay band.

Even that job title on LinkedIn.

None of it HAS to dictate your next role.

Plenty of analysts go on to become product managers, data engineers, ML engineers, and many more roles.

Here's why:
Skills compound
↳ Every small skill you learn today can be used in the future. Data Analysts make great PMs because they are already analytical
Curiosity scales
↳ Change the industry, keep the questions: What happened? Why did it happen? What should we do now?
Titles expire
↳ Job labels have a shelf life. Your skills aren't boun to a single resume point.

----
If you found this helpful, repost ♻️ to help another analyst out

6 months ago | [YT] | 0

The Job Ladder

Nobody enjoys delivering bad news.

But after interviews, providing candidates feedback is a must, not a maybe.

Giving candidates feedback is not just professional, it's humane.

It also shows respect for the effort they've put in and helps them grow for future opportunities.

6 months ago | [YT] | 0

The Job Ladder

Why Many Data Analyst aspirants Struggle to Crack Data Analyst Interviews?

Being a fresher and trying to land a Data Analyst job isn’t easy and it’s not always because you’re not smart or hardworking.

Here are a few common reasons:

1. Only learning tools, not applying them-
Knowing SQL, Excel or Power BI isn’t enough. You need to show how you’ve used them through projects, case studies or internships.


2. No portfolio or GitHub/Power BI public profile-
Recruiters want to see your work. If you haven’t already, start building and sharing dashboards or data projects.


3. Lack of communication skills-
It’s not just about analyzing data. You must explain your insights clearly, especially in interviews.


4. Focusing only on jobs, not on building skills-
Don’t just keep applying. Keep learning. Practice real-world problems. Follow data communities.


5. Generic resume
Customize your resume for every job. Highlight tools, skills and projects clearly.



Tip for Freshers:
Start with small projects using simple datasets (like government data, IPL stats or businesses like Zomato/Swiggy).
Share your learnings on LinkedIn and you’ll stand out more than you think.

Stay consistent, keep building and don’t give up after a few rejections.
Every Data Analyst was once a fresher.

6 months ago | [YT] | 0

The Job Ladder

Give me a worst data analysis practice.

I’ll go first...

❌ Jumping straight into charting without understanding the why behind the data.
I've seen it too often, dashboards filled with colorful visuals but no real direction. Numbers are moving, but no one knows what decisions to make from them.

Good analysis starts before the spreadsheet opens:
What question are we trying to answer?
Who needs this insight?
What decision will this drive?

Earlier in my journey, I spent days building reports that looked sleek but didn’t move the needle.
Now? Every dashboard I touch starts with a conversation, not a chart.

7 months ago | [YT] | 0

The Job Ladder

5 mistakes every data analyst will make early in their career:

1. Not validating the analytics/reporting they deliver

2. Accidentally tweeting highly confidential data

3. Forwarding a video of you rapping the entirety of Hamilton the Musical (then getting offended when they respond, “You’re still into this in 2025?”)

4. Leaving open a Word document on your computer titled “How I am going to ruin my coworkers’ lives” that goes into amazingly specific detail of how you’re going to ruin each one of your coworkers lives, only for those scoundrels to come across it while you’re at lunch.

5. Forgetting a comma in your SQL code (oops lol)

7 months ago | [YT] | 0