Monetizing Your Data Analytics Skills: Side Hustles & Passive Income Streams

Once you've mastered data analytics, you can leverage your expertise to generate income beyond your 9-to-5 job. Here’s how:

1️⃣ Freelancing & Consulting 💼

Offer data analytics, visualization, or SQL expertise on platforms like Upwork, Fiverr, and Toptal.

Provide business intelligence solutions, dashboard building, or data cleaning services.

Work with startups, small businesses, and enterprises remotely.


2️⃣ Creating & Selling Online Courses 🎥

Teach SQL, Power BI, Python, or Data Visualization on platforms like Udemy, Coursera, and Teachable.

Offer exclusive workshops or bootcamps via LinkedIn, Gumroad, or your website.

Monetize your expertise once and earn passive income forever.


3️⃣ Blogging & Technical Writing ✍️

Write data-related articles on Medium, Towards Data Science, or Substack.

Start a newsletter focused on analytics trends and career growth.

Earn through Medium Partner Program, sponsored posts, or affiliate marketing.


4️⃣ YouTube & Social Media Monetization 📹

Create a YouTube channel sharing tutorials on SQL, Power BI, Python, and real-world analytics projects.

Monetize through ads, sponsorships, and memberships.

Grow a LinkedIn, Twitter, or Instagram audience and collaborate with brands.


5️⃣ Affiliate Marketing in Data Analytics 🔗

Promote courses, books, tools (Tableau, Power BI, Python IDEs) and earn commissions.

Join Udemy, Coursera, or DataCamp affiliate programs.

Recommend data tools, laptops, or online learning resources through blogs or YouTube.


6️⃣ Selling Templates & Dashboards 📊

Create Power BI or Tableau templates and sell them on Gumroad or Etsy.

Offer SQL query libraries, Excel automation scripts, or data storytelling templates.

Provide customized analytics solutions for different industries.


7️⃣ Writing E-books or Guides 📖

Publish an e-book on SQL, Power BI, or breaking into data analytics.

Sell through Amazon Kindle, Gumroad, or your website.

Provide case studies, real-world datasets, and practice problems.


8️⃣ Building a Subscription-Based Community 🌍

Create a private Slack, Discord, or Telegram group for data professionals.

Charge for premium access, mentorship, and exclusive content.

Offer live Q&A sessions, job referrals, and networking opportunities.


9️⃣ Developing & Selling AI-Powered Tools 🤖

Build Python scripts, automation tools, or AI-powered analytics apps.

Sell on GitHub, Gumroad, or AppSumo.

Offer API-based solutions for businesses needing automated insights.


🔟 Landing Paid Speaking Engagements & Workshops 🎤

Speak at data conferences, webinars, and corporate training events.

Offer paid workshops for businesses or universities.

Become a recognized expert in your niche and command high fees.

Start Small, Scale Fast! 🚀

The data analytics field offers endless opportunities to earn beyond a job. Start with freelancing, content creation, or digital products—then scale it into a business!

Hope it helps :)

#dataanalytics

Uber Business Analyst Interview: 1-3 Years Experience

SQL Queries:

1.  Develop an SQL query to retrieve the third transaction for each user, including user ID, transaction amount, and date.
2.  Compute the average driver rating for each city using data from the rides and ratings tables.
3.  Construct an SQL query to identify users registered with Gmail addresses from the 'users' database.
4.  Define database denormalization.
5.  Analyze click-through conversion rates using data from the ad_clicks and cab_bookings tables.
6.  Define a self-join and provide a practical application example.

Scenario-Based Question:

1.  Determine the probability that at least two of three recommended driver routes are the fastest, assuming a 70% success rate for each route.

Guesstimate Questions:

1.  Estimate the number of Uber drivers operating in Delhi.
2.  Estimate the daily departure volume of Uber vehicles from Bengaluru Airport.

Hope it is helpful 🤍

Key Power BI Functions Every Analyst Should Master

DAX Functions:

1. CALCULATE():

Purpose: Modify context or filter data for calculations.

Example: CALCULATE(SUM(Sales[Amount]), Sales[Region] = "East")



2. SUM():

Purpose: Adds up column values.

Example: SUM(Sales[Amount])



3. AVERAGE():

Purpose: Calculates the mean of column values.

Example: AVERAGE(Sales[Amount])



4. RELATED():

Purpose: Fetch values from a related table.

Example: RELATED(Customers[Name])



5. FILTER():

Purpose: Create a subset of data for calculations.

Example: FILTER(Sales, Sales[Amount] > 100)



6. IF():

Purpose: Apply conditional logic.

Example: IF(Sales[Amount] > 1000, "High", "Low")



7. ALL():

Purpose: Removes filters to calculate totals.

Example: ALL(Sales[Region])



8. DISTINCT():

Purpose: Return unique values in a column.

Example: DISTINCT(Sales[Product])



9. RANKX():

Purpose: Rank values in a column.

Example: RANKX(ALL(Sales[Region]), SUM(Sales[Amount]))



10. FORMAT():

Purpose: Format numbers or dates as text.

Example: FORMAT(TODAY(), "MM/DD/YYYY")

You can refer these Power BI Interview Resources to learn more: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

Like this post if you want me to continue this Power BI series 👍♥️

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

Quick Recap of Tableau Concepts

1️⃣ Data Source: Connects to various data sources like Excel, databases, or cloud services to pull in data for analysis.

2️⃣ Dimensions & Measures: Dimensions are qualitative fields (e.g., names, dates), while measures are quantitative fields (e.g., sales, profit).

3️⃣ Filters: Used to narrow down the data displayed on your visualizations based on specific conditions.

4️⃣ Marks Card: Controls the visual details of charts, such as color, size, text, and tooltip.

5️⃣ Calculated Fields: Custom calculations created using formulas to add new insights to your data.

6️⃣ Aggregations: Functions like SUM, AVG, and COUNT that summarize large sets of data.

7️⃣ Dashboards: Collections of visualizations combined into a single view to tell a more comprehensive story.

8️⃣ Actions: Interactive elements that allow users to filter, highlight, or navigate between sheets in a dashboard.

9️⃣ Parameters: Dynamic values that allow you to adjust the content of your visualizations or calculations.

🔟 Tableau Server / Tableau Online: Platforms for publishing, sharing, and collaborating on Tableau workbooks and dashboards with others.

Best Resources to learn Tableau: https://t.iss.one/DataSimplifier

Hope you'll like it

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Here is a powerful 𝗜𝗡𝗧𝗘𝗥𝗩𝗜𝗘𝗪 𝗧𝗜𝗣 to help you land a job!

Most people who are skilled enough would be able to clear technical rounds with ease.

But when it comes to 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿𝗮𝗹/𝗰𝘂𝗹𝘁𝘂𝗿𝗲 𝗳𝗶𝘁 rounds, some folks may falter and lose the potential offer.

Many companies schedule a behavioral round with a top-level manager in the organization to understand the culture fit (except for freshers).

One needs to clear this round to reach the salary negotiation round.

Here are some tips to clear such rounds:

1️⃣ Once the HR schedules the interview, try to find the LinkedIn profile of the interviewer using the name in their email ID.

2️⃣ Learn more about his/her past experiences and try to strike up a conversation on that during the interview.

3️⃣ This shows that you have done good research and also helps strike a personal connection.

4️⃣ Also, this is the round not just to evaluate if you're a fit for the company, but also to assess if the company is a right fit for you.

5️⃣ Hence, feel free to ask many questions about your role and company to get a clear understanding before taking the offer. This shows that you really care about the role you're getting into.

💡 𝗕𝗼𝗻𝘂𝘀 𝘁𝗶𝗽 - Be polite yet assertive in such interviews. It impresses a lot of senior folks.

10 Advanced Excel Concepts for Data Analysts

1. VLOOKUP & XLOOKUP for Fast Data Retrieval:
Quickly find data from different sheets with VLOOKUP or XLOOKUP for flexible lookups and defaults when no match is found.

2. Pivot Tables for Summarizing Data:
Quickly summarize, explore, and analyze large datasets with drag-and-drop ease.

3. Conditional Formatting for Key Insights:
Highlight trends and outliers automatically with conditional formatting, like Color Scales for instant data visualization.

4. Data Validation for Consistent Entries:
Use dropdowns and set criteria to avoid entry errors and maintain data consistency.

5. IFERROR for Clean Formulas:
Replace errors with default values like "N/A" for cleaner, more professional sheets.

6. INDEX-MATCH for Advanced Lookups:
INDEX-MATCH is more flexible than VLOOKUP, allowing lookups in any direction and handling large datasets effectively.

7. TEXT Functions for Data Cleaning:
Use LEFT, RIGHT, and TEXT functions to clean up inconsistent data formats or extract specific data elements.

8. Sparklines for Mini Data Visuals:
Insert mini line or bar charts directly in cells to show trends at a glance without taking up space.

9. Array Formulas (UNIQUE, FILTER, SORT):
Create dynamic lists and automatically update data with array formulas, perfect for unique values or filtered results.

10. Power Query for Efficient Data Transformation:
Use Power Query to clean and reshape data from multiple sources effortlessly, making data prep faster.

Read this blog for more details

I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://t.iss.one/DataSimplifier

Like for more ♥️

Share with credits: https://t.iss.one/sqlspecialist

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📈 Roadmap to Become a Data Analyst — What to Learn in Each Month (6 Months Plan)

🗓️ Month 1: Foundations
- Excel (formulas, pivot tables, charts)
- Basic Statistics (mean, median, variance, correlation)
- Data types & distributions

🗓️ Month 2: SQL Mastery
- SELECT, WHERE, GROUP BY, JOINs
- Subqueries, CTEs, window functions
- Practice on real datasets (e.g. MySQL + Kaggle)

🗓️ Month 3: Python for Analysis
- Pandas, NumPy for data manipulation
- Matplotlib & Seaborn for visualization
- Jupyter Notebooks for presentation

🗓️ Month 4: Dashboarding Tools
- Power BI or Tableau
- Build interactive dashboards
- Learn storytelling with visuals

🗓️ Month 5: Real Projects & Case Studies
- Analyze sales, marketing, HR, or finance data
- Create full reports with insights & visuals
- Document projects for your portfolio

🗓️ Month 6: Interview Prep & Applications
- Mock interviews
- Revise common questions (SQL, case studies, scenario-based)
- Polish resume, LinkedIn, and GitHub

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Data Analyst Interview Questions 👇

1.How to create filters in Power BI?

Filters are an integral part of Power BI reports. They are used to slice and dice the data as per the dimensions we want. Filters are created in a couple of ways.

Using Slicers: A slicer is a visual under Visualization Pane. This can be added to the design view to filter our reports. When a slicer is added to the design view, it requires a field to be added to it. For example- Slicer can be added for Country fields. Then the data can be filtered based on countries.
Using Filter Pane: The Power BI team has added a filter pane to the reports, which is a single space where we can add different fields as filters. And these fields can be added depending on whether you want to filter only one visual(Visual level filter), or all the visuals in the report page(Page level filters), or applicable to all the pages of the report(report level filters)


2.How to sort data in Power BI?

Sorting is available in multiple formats. In the data view, a common sorting option of alphabetical order is there. Apart from that, we have the option of Sort by column, where one can sort a column based on another column. The sorting option is available in visuals as well. Sort by ascending and descending option by the fields and measure present in the visual is also available.


3.How to convert pdf to excel?

Open the PDF document you want to convert in XLSX format in Acrobat DC.
Go to the right pane and click on the “Export PDF” option.
Choose spreadsheet as the Export format.
Select “Microsoft Excel Workbook.”
Now click “Export.”
Download the converted file or share it.


4. How to enable macros in excel?

Click the file tab and then click “Options.”
A dialog box will appear. In the “Excel Options” dialog box, click on the “Trust Center” and then “Trust Center Settings.”
Go to the “Macro Settings” and select “enable all macros.”
Click OK to apply the macro settings.

SQL Data Analytics Roadmap 👆

Essential Skills Excel for Data Analysts 🚀

1️⃣ Data Cleaning & Transformation

Remove Duplicates – Ensure unique records.
Find & Replace – Quick data modifications.
Text Functions – TRIM, LEN, LEFT, RIGHT, MID, PROPER.
Data Validation – Restrict input values.

2️⃣ Data Analysis & Manipulation

Sorting & Filtering – Organize and extract key insights.
Conditional Formatting – Highlight trends, outliers.
Pivot Tables – Summarize large datasets efficiently.
Power Query – Automate data transformation.

3️⃣ Essential Formulas & Functions

Lookup Functions – VLOOKUP, HLOOKUP, XLOOKUP, INDEX-MATCH.
Logical Functions – IF, AND, OR, IFERROR, IFS.
Aggregation Functions – SUM, AVERAGE, MIN, MAX, COUNT, COUNTA.
Text Functions – CONCATENATE, TEXTJOIN, SUBSTITUTE.

4️⃣ Data Visualization
Charts & Graphs – Bar, Line, Pie, Scatter, Histogram.

Sparklines – Miniature charts inside cells.
Conditional Formatting – Color scales, data bars.
Dashboard Creation – Interactive and dynamic reports.

5️⃣ Advanced Excel Techniques
Array Formulas – Dynamic calculations with multiple values.
Power Pivot & DAX – Advanced data modeling.
What-If Analysis – Goal Seek, Scenario Manager.
Macros & VBA – Automate repetitive tasks.

6️⃣ Data Import & Export
CSV & TXT Files – Import and clean raw data.
Power Query – Connect to databases, web sources.
Exporting Reports – PDF, CSV, Excel formats.

Here you can find some free Excel books & useful resources: https://t.iss.one/excel_data

Hope it helps :)

#dataanalyst

Step-by-step guide to become a Data Analyst in 2025—📊

1. Learn the Fundamentals:
Start with Excel, basic statistics, and data visualization concepts.

2. Pick Up Key Tools & Languages:
Master SQL, Python (or R), and data visualization tools like Tableau or Power BI.

3. Get Formal Education or Certification:
A bachelor’s degree in a relevant field (like Computer Science, Math, or Economics) helps, but you can also do online courses or certifications in data analytics.

4. Build Hands-on Experience:
Work on real-world projects—use Kaggle datasets, internships, or freelance gigs to practice data cleaning, analysis, and visualization.

5. Create a Portfolio:
Showcase your projects on GitHub or a personal website. Include dashboards, reports, and code samples.

6. Develop Soft Skills:
Focus on communication, problem-solving, teamwork, and attention to detail—these are just as important as technical skills.

7. Apply for Entry-Level Jobs:
Look for roles like “Junior Data Analyst” or “Business Analyst.” Tailor your resume to highlight your skills and portfolio.

8. Keep Learning:
Stay updated with new tools (like AI-driven analytics), trends, and advanced topics such as machine learning or domain-specific analytics.

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If you are targeting your first Data Analyst job then this is why you should avoid guided projects

The common thing nowadays is "Coffee Sales Analysis" and "Pizza Sales Analysis"

I don't see these projects as PROJECTS

But as big RED flags

We are showing our SKILLS through projects, RIGHT?

Then what's WRONG with these projects?

Don't think from YOUR side

Think from the HIRING team's side

These projects have more than a MILLION views on YouTube

Even if you consider 50% of this NUMBER

Then just IMAGINE how many aspiring Data Analysts would have created this same project

Hiring teams see hundreds of resumes and portfolios on a DAILY basis

Just imagine how many times they would have seen the SAME titles of projects again and again

They would know that these projects are PUBLICLY available for EVERYONE

You have simply copied pasted the ENTIRE project from YouTube

So now if I want to hire a Data Analyst then how would I JUDGE you or your technical skills?

What is the USE of Pizza or Coffee sales analysis projects for MY company?

By doing such guided projects, you are involving yourself in a big circle of COMPETITION

I repeat, there were more than a MILLION views

So please AVOID guided projects at all costs

Guided projects are good for your personal PRACTICE and LinkedIn CONTENT

But try not to involve them in your PORTFOLIO or RESUME

Python Cheatsheet ✅

Python Iterators 👆

Data Analyst Interview QnA

1. Find avg of salaries department wise from table.

Answer-

SELECT department_id, AVG(salary) AS avg_salary
FROM employees
GROUP BY department_id;

2. What does Filter context in DAX mean?

Answer - Filter context in DAX refers to the subset of data that is actively being used in the calculation of a measure or in the evaluation of an expression. This context is determined by filters on the dashboard items like slicers, visuals, and filters pane which restrict the data being processed.

3. Explain how to implement Row-Level Security (RLS) in Power BI.

Answer - Row-Level Security (RLS) in Power BI can be implemented by:

- Creating roles within the Power BI service.
- Defining DAX expressions that specify the data each role can access.
- Assigning users to these roles either in Power BI or dynamically through AD group membership.

4. Create a dictionary, add elements to it, modify an element, and then print the dictionary in alphabetical order of keys.

Answer -

d = {'apple': 2, 'banana': 5}
d['orange'] = 3  # Add element
d['apple'] = 4   # Modify element
sorted_d = dict(sorted(d.items()))  # Sort dictionary
print(sorted_d)

5. Find and print duplicate values in a list of assorted numbers, along with the number of times each value is repeated.

Answer -

from collections import Counter

numbers = [1, 2, 2, 3, 4, 5, 1, 6, 7, 3, 8, 1]
count = Counter(numbers)
duplicates = {k: v for k, v in count.items() if v > 1}
print(duplicates)

Few ways to optimise SQL Queries 👇👇

Use Indexing: Properly indexing your database tables can significantly speed up query performance by allowing the database to quickly locate the rows needed for a query.

Optimize Joins: Minimize the number of joins and use appropriate join types (e.g., INNER JOIN, LEFT JOIN) to ensure efficient data retrieval.

Avoid SELECT * : Instead of selecting all columns using SELECT *, explicitly specify only the columns needed for the query to reduce unnecessary data transfer and processing overhead.

Use WHERE Clause Wisely: Filter rows early in the query using WHERE clause to reduce the dataset size before joining or aggregating data.

Avoid Subqueries: Whenever possible, rewrite subqueries as JOINs or use Common Table Expressions (CTEs) for better performance.

Limit the Use of DISTINCT: Minimize the use of DISTINCT as it requires sorting and duplicate removal, which can be resource-intensive for large datasets.

Optimize GROUP BY and ORDER BY: Use GROUP BY and ORDER BY clauses judiciously, and ensure that they are using indexed columns whenever possible to avoid unnecessary sorting.

Consider Partitioning: Partition large tables to distribute data across multiple nodes, which can improve query performance by reducing I/O operations.

Monitor Query Performance: Regularly monitor query performance using tools like query execution plans, database profiler, and performance monitoring tools to identify and address bottlenecks.

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