Let's now understand the above Data Analyst Roadmap in detail: 🧠↗️

1️⃣ Learn Excel ⭐️

The foundation of data analysis. Learn formulas, pivot tables, charts, VLOOKUP/XLOOKUP, and conditional formatting. It helps in quick data cleaning and presenting insights.

Excel Resources: https://whatsapp.com/channel/0029VaifY548qIzv0u1AHz3i

2️⃣ Learn SQL 💻

Essential for working with databases. Focus on SELECT, JOIN, GROUP BY, WHERE, and subqueries to extract and manipulate data from relational databases.

SQL Resources: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v

3️⃣ Learn Python 📱

A powerful tool for data manipulation and automation. Master libraries like pandas, numpy, matplotlib, and seaborn for data cleaning and visualization.

Python Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L

4️⃣ Learn Power BI / Tableau 📈

These tools help create interactive dashboards and visual reports. Learn how to import data, create filters, use DAX (Power BI), and design clear visualizations.

Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c

5️⃣ Learn Statistics & Probability 🛍

Know about denoscriptive stats (mean, median, mode), inferential stats, distributions, hypothesis testing, and correlation. Vital for making sense of data trends.

Statistics Resources: https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O

6️⃣ Learn Data Transformation 📈

Learn how to clean, shape, and prepare data for analysis. Use Python (pandas) or Power Query in Power BI, and understand ETL (Extract, Transform, Load) processes.

Data Cleaning: https://whatsapp.com/channel/0029VarxgFqATRSpdUeHUA27

7️⃣ Learn Machine Learning 🧠

Understand basic concepts like regression, classification, clustering, and decision trees. You don’t need to be an ML expert, just grasp how models work and when to use them.

Machine Learning: https://whatsapp.com/channel/0029VawtYcJ1iUxcMQoEuP0O

8️⃣ Build Projects & Portfolio 🏹

Apply what you’ve learned to real datasets—like sales analysis, churn prediction, or dashboard creation. Showcase your work on GitHub or a personal website.

Data Analytics Projects: https://whatsapp.com/channel/0029VbAbnvPLSmbeFYNdNA29

9️⃣ Apply for Jobs 💼

With your skills and portfolio in place, start applying for data analyst roles. Tailor your resume using keywords from job denoscriptions and prepare to answer SQL and Excel tasks in interviews.

Jobs & Internship Opportunities: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226

Share with credits: https://news.1rj.ru/str/sqlspecialist

Double Tap ♥️ for more

Your first SQL noscript will confuse even yourself.

Your first Power BI dashboard will look like it's your first dashboard.

Stop trying to perfect your first handful of projects.

Start pumping out projects left and right.

While learning, it's more important to create than to focus on optimizing.

Quantity > Quality

Once you start getting faster, you'll have more time to swap it to.

Quality > Quantity

You'll improve rapidly this way.

Complete Data Analyst Interview Guide (0–2 Years of Experience)

🔹 Round 1: SQL + Scenario-Based Questions

Q1. Get top 3 products by revenue within each category

SELECT *
FROM (
SELECT p.product_id, p.category, SUM(o.revenue) AS total_revenue,
RANK() OVER(PARTITION BY p.category ORDER BY SUM(o.revenue) DESC) AS rnk
FROM products p
JOIN orders o ON p.product_id = o.product_id
GROUP BY p.product_id, p.category
) ranked
WHERE rnk <= 3;

Q2. Find users who purchased in January but not in February

SELECT DISTINCT user_id
FROM orders
WHERE MONTH(order_date) = 1
AND user_id NOT IN (
SELECT user_id FROM orders WHERE MONTH(order_date) = 2
);

Q3. Avg. ride time by city + peak hours

SELECT city, AVG(DATEDIFF(MINUTE, start_time, end_time)) AS avg_ride_mins
FROM trips
GROUP BY city;

-- For peak hour detection (example logic)
SELECT DATEPART(HOUR, start_time) AS ride_hour, COUNT(*) AS ride_count
FROM trips
GROUP BY DATEPART(HOUR, start_time)
ORDER BY ride_count DESC;


⸻

🔹 Round 2: Python + Data Cleaning

Q1. Clean messy CSV with pandas

import pandas as pd

df = pd.read_csv('data.csv')
df.columns = df.columns.str.strip().str.lower()
df.drop_duplicates(inplace=True)
df['date'] = pd.to_datetime(df['date'], errors='coerce')
df.fillna(method='ffill', inplace=True)

Q2. Extract domain names from email IDs

emails = ['abc@gmail.com', 'xyz@outlook.com']
domains = [email.split('@')[1] for email in emails]

Q3. Difference: .loc[] vs .iloc[]
• .loc[] → label-based selection
• .iloc[] → index-based selection

Q4. Handle outliers using IQR

Q1 = df['column'].quantile(0.25)
Q3 = df['column'].quantile(0.75)
IQR = Q3 - Q1
filtered_df = df[(df['column'] >= Q1 - 1.5*IQR) & (df['column'] <= Q3 + 1.5*IQR)]


⸻

🔹 Round 3: Power BI / Dashboarding

Tasks you should know:
• Create a dashboard with weekly trends, margins, churn %
• Use bookmarks/slicers for KPI toggles
• Apply filters to show top 5 items dynamically
• Exclude visuals from slicer using “Edit Interactions” → turn off filter icon on card visual

🔗 Try replicating dashboards from Power BI Gallery

⸻

🔹 Round 4: Business Case + Logic-Based Thinking

Q1. Sales dropped last quarter — what to check?
• Compare YoY/QoQ data
• Identify categories/geos with the biggest drop
• Analyze order volume vs. avg. order value
• Check marketing spend, discounts, stockouts

Q2. App downloads ⬆️, activity ⬇️ — what’s wrong?
• Check Day 1/7/30 retention
• Is onboarding working?
• UI bugs or crashes?
• Compare install → sign-up → usage funnel

Q3. Returns increasing — how to investigate?
• Analyze return % by brand, category, SKU
• Check return reasons (defects, sizing, etc.)
• Compare returners’ order history
• Seasonal impact?

⸻

🔰 Free Practice Tools:
• 🔹 SQL on LeetCode
• 🔹 Python on Hackerrank
• 🔹 Power BI Gallery

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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.

Steps to become a data analyst

Learn the Basics of Data Analysis:
Familiarize yourself with foundational concepts in data analysis, statistics, and data visualization. Online courses and textbooks can help.
Free books & other useful data analysis resources - https://news.1rj.ru/str/learndataanalysis

Develop Technical Skills:
Gain proficiency in essential tools and technologies such as:

SQL: Learn how to query and manipulate data in relational databases.
Free Resources- @sqlanalyst

Excel: Master data manipulation, basic analysis, and visualization.
Free Resources- @excel_analyst

Data Visualization Tools: Become skilled in tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn.
Free Resources- @PowerBI_analyst

Programming: Learn a programming language like Python or R for data analysis and manipulation.
Free Resources- @pythonanalyst

Statistical Packages: Familiarize yourself with packages like Pandas, NumPy, and SciPy (for Python) or ggplot2 (for R).

Hands-On Practice:
Apply your knowledge to real datasets. You can find publicly available datasets on platforms like Kaggle or create your datasets for analysis.

Build a Portfolio:
Create data analysis projects to showcase your skills. Share them on platforms like GitHub, where potential employers can see your work.

Networking:
Attend data-related meetups, conferences, and online communities. Networking can lead to job opportunities and valuable insights.

Data Analysis Projects:
Work on personal or freelance data analysis projects to gain experience and demonstrate your abilities.

Job Search:
Start applying for entry-level data analyst positions or internships. Look for job listings on company websites, job boards, and LinkedIn.
Jobs & Internship opportunities: @getjobss

Prepare for Interviews:
Practice common data analyst interview questions and be ready to discuss your past projects and experiences.

Continual Learning:
The field of data analysis is constantly evolving. Stay updated with new tools, techniques, and industry trends.

Soft Skills:
Develop soft skills like critical thinking, problem-solving, communication, and attention to detail, as they are crucial for data analysts.

Never ever give up:
The journey to becoming a data analyst can be challenging, with complex concepts and technical skills to learn. There may be moments of frustration and self-doubt, but remember that these are normal parts of the learning process. Keep pushing through setbacks, keep learning, and stay committed to your goal.

ENJOY LEARNING 👍👍

If you want to Excel at Power BI and become a data visualization pro, master these essential features:

• DAX Functions – SUMX(), CALCULATE(), FILTER(), ALL()
• Power Query – Clean & transform data efficiently
• Data Modeling – Relationships, star & snowflake schemas
• Measures vs. Calculated Columns – When & how to use them
• Time Intelligence – TOTALYTD(), DATESINPERIOD(), PREVIOUSMONTH()
• Custom Visuals – Go beyond default charts
• Drill-Through & Drill-Down – Interactive insights
• Row-Level Security (RLS) – Control data access
• Bookmarks & Tooltips – Enhance dashboard storytelling
• Performance Optimization – Speed up slow reports

Like it if you need a complete tutorial on all these topics! 👍❤️

Free Power BI Resources: 👇 https://news.1rj.ru/str/PowerBI_analyst

Share with credits: https://news.1rj.ru/str/sqlspecialist

Hope it helps :)

10 Must-Have Habits for Data Analysts 📊🧠

1️⃣ Develop strong Excel & SQL skills
2️⃣ Master data cleaning — it’s 80% of the job
3️⃣ Always validate your data sources
4️⃣ Visualize data clearly (use Power BI/Tableau)
5️⃣ Ask the right business questions
6️⃣ Stay curious — dig deeper into patterns
7️⃣ Document your analysis & assumptions
8️⃣ Communicate insights, not just numbers
9️⃣ Learn basic Python or R for automation
🔟 Keep learning: analytics is always evolving

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SQL table interview questions:

1. What is a DUAL table and why do we need it?
- it is a special table which gets created automatically when we install Oracle database. It can be used to select pseudo columns, perform calculations and also as sequence generator etc.

2. How many columns and rows are present in DUAL table?
- one column & one row by default.

3. Can we insert more rows in to DUAL table?
- Yes.

4. What's the easiest way to backup a table / how can we create a table based on existing table?
- CREATE TABLE SALES_COPY AS SELECT * FROM SALES.

5. Can we drop all the columns from a table?
- No.

6. What is the difference between count(1) and count(*)?
- Both are same. Both consume same amount of resources, Both perform same operation

Complete Data Analyst Interview Guide (0–2 Years of Experience)

🔹 Round 1: SQL + Scenario-Based Questions

Q1. Get top 3 products by revenue within each category

SELECT *
FROM (
SELECT p.product_id, p.category, SUM(o.revenue) AS total_revenue,
RANK() OVER(PARTITION BY p.category ORDER BY SUM(o.revenue) DESC) AS rnk
FROM products p
JOIN orders o ON p.product_id = o.product_id
GROUP BY p.product_id, p.category
) ranked
WHERE rnk <= 3;

Q2. Find users who purchased in January but not in February

SELECT DISTINCT user_id
FROM orders
WHERE MONTH(order_date) = 1
AND user_id NOT IN (
SELECT user_id FROM orders WHERE MONTH(order_date) = 2
);

Q3. Avg. ride time by city + peak hours

SELECT city, AVG(DATEDIFF(MINUTE, start_time, end_time)) AS avg_ride_mins
FROM trips
GROUP BY city;

-- For peak hour detection (example logic)
SELECT DATEPART(HOUR, start_time) AS ride_hour, COUNT(*) AS ride_count
FROM trips
GROUP BY DATEPART(HOUR, start_time)
ORDER BY ride_count DESC;


⸻

🔹 Round 2: Python + Data Cleaning

Q1. Clean messy CSV with pandas

import pandas as pd

df = pd.read_csv('data.csv')
df.columns = df.columns.str.strip().str.lower()
df.drop_duplicates(inplace=True)
df['date'] = pd.to_datetime(df['date'], errors='coerce')
df.fillna(method='ffill', inplace=True)

Q2. Extract domain names from email IDs

emails = ['abc@gmail.com', 'xyz@outlook.com']
domains = [email.split('@')[1] for email in emails]

Q3. Difference: .loc[] vs .iloc[]
• .loc[] → label-based selection
• .iloc[] → index-based selection

Q4. Handle outliers using IQR

Q1 = df['column'].quantile(0.25)
Q3 = df['column'].quantile(0.75)
IQR = Q3 - Q1
filtered_df = df[(df['column'] >= Q1 - 1.5*IQR) & (df['column'] <= Q3 + 1.5*IQR)]


⸻

🔹 Round 3: Power BI / Dashboarding

Tasks you should know:
• Create a dashboard with weekly trends, margins, churn %
• Use bookmarks/slicers for KPI toggles
• Apply filters to show top 5 items dynamically
• Exclude visuals from slicer using “Edit Interactions” → turn off filter icon on card visual

🔗 Try replicating dashboards from Power BI Gallery

⸻

🔹 Round 4: Business Case + Logic-Based Thinking

Q1. Sales dropped last quarter — what to check?
• Compare YoY/QoQ data
• Identify categories/geos with the biggest drop
• Analyze order volume vs. avg. order value
• Check marketing spend, discounts, stockouts

Q2. App downloads ⬆️, activity ⬇️ — what’s wrong?
• Check Day 1/7/30 retention
• Is onboarding working?
• UI bugs or crashes?
• Compare install → sign-up → usage funnel

Q3. Returns increasing — how to investigate?
• Analyze return % by brand, category, SKU
• Check return reasons (defects, sizing, etc.)
• Compare returners’ order history
• Seasonal impact?

⸻

🔰 Free Practice Tools:
• 🔹 SQL on LeetCode
• 🔹 Python on Hackerrank
• 🔹 Power BI Gallery

Complete Syllabus for Data Analytics interview:

SQL:
1. Basic
  - SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
  - Basic JOINS (INNER, LEFT, RIGHT, FULL)
  - Creating and using simple databases and tables

2. Intermediate
  - Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
  - Subqueries and nested queries
  - Common Table Expressions (WITH clause)
  - CASE statements for conditional logic in queries

3. Advanced
  - Advanced JOIN techniques (self-join, non-equi join)
  - Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
  - optimization with indexing
  - Data manipulation (INSERT, UPDATE, DELETE)

Python:
1. Basic
  - Syntax, variables, data types (integers, floats, strings, booleans)
  - Control structures (if-else, for and while loops)
  - Basic data structures (lists, dictionaries, sets, tuples)
  - Functions, lambda functions, error handling (try-except)
  - Modules and packages

2. Pandas & Numpy
  - Creating and manipulating DataFrames and Series
  - Indexing, selecting, and filtering data
  - Handling missing data (fillna, dropna)
  - Data aggregation with groupby, summarizing data
  - Merging, joining, and concatenating datasets

3. Basic Visualization
  - Basic plotting with Matplotlib (line plots, bar plots, histograms)
  - Visualization with Seaborn (scatter plots, box plots, pair plots)
  - Customizing plots (sizes, labels, legends, color palettes)
  - Introduction to interactive visualizations (e.g., Plotly)

Excel:
1. Basic
  - Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
  - Introduction to charts and basic data visualization
  - Data sorting and filtering
  - Conditional formatting

2. Intermediate
  - Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
  - PivotTables and PivotCharts for summarizing data
  - Data validation tools
  - What-if analysis tools (Data Tables, Goal Seek)

3. Advanced
  - Array formulas and advanced functions
  - Data Model & Power Pivot
- Advanced Filter
- Slicers and Timelines in Pivot Tables
  - Dynamic charts and interactive dashboards

Power BI:
1. Data Modeling
  - Importing data from various sources
  - Creating and managing relationships between different datasets
  - Data modeling basics (star schema, snowflake schema)

2. Data Transformation
  - Using Power Query for data cleaning and transformation
  - Advanced data shaping techniques
  - Calculated columns and measures using DAX

3. Data Visualization and Reporting
  - Creating interactive reports and dashboards
  - Visualizations (bar, line, pie charts, maps)
  - Publishing and sharing reports, scheduling data refreshes

Statistics Fundamentals:
Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution.

Top 50 Data Analytics Interview Questions (2025)

1. What is the difference between data analysis and data analytics?
2. Explain the data cleaning process you follow.
3. How do you handle missing or duplicate data?
4. What is a primary key in a database?
5. Write a SQL query to find the second highest salary in a table.
6. Explain INNER JOIN vs LEFT JOIN with examples.
7. What are outliers? How do you detect and treat them?
8. Describe what a pivot table is and how you use it.
9. How do you validate a data model’s performance?
10. What is hypothesis testing? Explain t-test and z-test.
11. How do you explain complex data insights to non-technical stakeholders?
12. What tools do you use for data visualization?
13. How do you optimize a slow SQL query?
14. Describe a time when your analysis impacted a business decision.
15. What is the difference between clustered and non-clustered indexes?
16. Explain the bias-variance tradeoff.
17. What is collaborative filtering?
18. How do you handle large datasets?
19. What Python libraries do you use for data analysis?
20. Describe data profiling and its importance.
21. How do you detect and handle multicollinearity?
22. Can you explain the concept of data partitioning?
23. What is data normalization? Why is it important?
24. Describe your experience with A/B testing.
25. What’s the difference between supervised and unsupervised learning?
26. How do you keep yourself updated with new tools and techniques?
27. What’s a use case for a LEFT JOIN over an INNER JOIN?
28. Explain the curse of dimensionality.
29. What are the key metrics you track in your analyses?
30. Describe a situation when you had conflicting priorities in a project.
31. What is ETL? Have you worked with any ETL tools?
32. How do you ensure data quality?
33. What’s your approach to storytelling with data?
34. How would you improve an existing dashboard?
35. What’s the role of machine learning in data analytics?
36. Explain a time when you automated a repetitive data task.
37. What’s your experience with cloud platforms for data analytics?
38. How do you approach exploratory data analysis (EDA)?
39. What’s the difference between outlier detection and anomaly detection?
40. Describe a challenging data problem you solved.
41. Explain the concept of data aggregation.
42. What’s your favorite data visualization technique and why?
43. How do you handle unstructured data?
44. What’s the difference between R and Python for data analytics?
45. Describe your process for preparing a dataset for analysis.
46. What is a data lake vs a data warehouse?
47. How do you manage version control of your analysis noscripts?
48. What are your strategies for effective teamwork in analytics projects?
49. How do you handle feedback on your analysis?
50. Can you share an example where you turned data into actionable insights?

Double tap ❤️ for detailed answers