📊 Core Data Analyst Interview Topics You Should Know ✅

1️⃣ Excel/Spreadsheet Skills
⦁ VLOOKUP, INDEX-MATCH, XLOOKUP (newer Excel fave)
⦁ Pivot Tables for summarizing data
⦁ Conditional Formatting to highlight trends
⦁ Data Cleaning & Validation with formulas like IFERROR

2️⃣ SQL & Databases
⦁ SELECT, JOINs (INNER, LEFT, RIGHT, FULL)
⦁ GROUP BY, HAVING, ORDER BY for aggregations
⦁ Subqueries & Window Functions (ROW_NUMBER, LAG)
⦁ CTEs for cleaner, reusable queries

3️⃣ Data Visualization
⦁ Tools: Power BI, Tableau, Excel, Google Data Studio
⦁ Best practices: Choose charts wisely (bar for comparisons, line for trends)
⦁ Dashboards & Interactivity with slicers/drill-downs
⦁ Storytelling with Data to make insights pop

4️⃣ Statistics & Probability
⦁ Mean, Median, Mode, Standard Deviation for summaries
⦁ Correlation vs. Causation (correlation doesn't imply cause!)
⦁ Hypothesis Testing (t-test, p-value for significance)
⦁ Confidence Intervals to gauge reliability

5️⃣ Python for Data Analysis
⦁ Libraries: Pandas for dataframes, NumPy for arrays, Matplotlib/Seaborn for plots
⦁ Data wrangling & cleaning (handling nulls, merging)
⦁ Basic EDA: Describe stats, visualizations, correlations

6️⃣ Business Understanding
⦁ KPI identification (e.g., conversion rate, churn)
⦁ Funnel analysis for drop-offs
⦁ A/B Testing basics to validate changes
⦁ Decision-making support with actionable recommendations

7️⃣ Problem Solving & Case Studies
⦁ Product metrics (DAU/MAU, retention)
⦁ Customer segmentation (RFM analysis)
⦁ Market trend analysis with time-series

8️⃣ ETL Concepts
⦁ Extract from sources, Transform (clean/aggregate), Load to warehouses
⦁ Data pipeline basics using tools like Airflow or dbt

9️⃣ Data Cleaning Techniques
⦁ Handling missing values (impute or drop)
⦁ Duplicates, outliers detection/removal
⦁ Data formatting (standardize dates, text)

🔟 Soft Skills & Communication
⦁ Explaining insights to non-technical stakeholders simply
⦁ Clear visualization storytelling (avoid clutter)
⦁ Collaborating with cross-functional teams for context

💬 Tap ❤️ for more!

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Whether you're preparing for placements or starting your coding journey — we’ve got you covered!

✅ 1. DSA Self-Paced Course
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– Covers Arrays, Trees, Graphs, DP & more
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Hey guys 👋

I was working on something big from last few days.

Finally, I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://topmate.io/analyst/861634

If you go on purchasing these books, it will cost you more than 15000 but I kept the minimal price for everyone's benefit.

I hope these resources will help you in data analytics journey.

I will add more resources here in the future without any additional cost.

All the best for your career ❤️

Skills Needed To Become Data Analyst

Useful websites to practice and enhance your data analytics skills
👇👇

1. Python
http://learnpython.org

2. SQL
https://www.sql-practice.com/

3. Excel
https://excel-practice-online.com/

4. Power BI
https://www.workout-wednesday.com/power-bi-challenges/

5. Quiz and Interview Questions
https://news.1rj.ru/str/sqlspecialist

Haven't shared lot of resources to avoid too much distraction

Just focus on the basics, practice learnings and work on building projects to improve your skills. Thats the best way to learn in my opinion 😄

Join @free4unow_backup for more free courses

ENJOY LEARNING 👍👍

PowerBI Interview Questions 🚀🔥

Machine Learning Roadmap

📊 Data Analytics Basics Cheatsheet

1. What is Data Analytics?
Analyzing raw data to find patterns, trends, and insights to support decision-making.

2. Types of Data Analytics:
⦁ Denoscriptive: What happened?
⦁ Diagnostic: Why did it happen?
⦁ Predictive: What might happen next?
⦁ Prenoscriptive: What should be done?

3. Key Tools & Languages:
⦁ Excel – Quick analysis & charts
⦁ SQL – Query and manage databases
⦁ Python (Pandas, NumPy, Matplotlib)
⦁ Power BI / Tableau – Dashboards & visualization

4. Data Cleaning Basics:
⦁ Handle missing values
⦁ Remove duplicates
⦁ Convert data types
⦁ Standardize formats

5. Exploratory Data Analysis (EDA):
⦁ Summary stats (mean, median, mode)
⦁ Data distribution
⦁ Correlation matrix
⦁ Visual tools: bar charts, boxplots, scatter plots

6. Data Visualization:
⦁ Use charts to simplify insights
⦁ Choose chart types based on data (line for trends, bar for comparisons, pie for proportions)

7. SQL Essentials:
⦁ SELECT, WHERE, JOIN, GROUP BY, HAVING, ORDER BY
⦁ Aggregate functions: COUNT, SUM, AVG, MAX, MIN

8. Python for Analysis:
⦁ Pandas for dataframes
⦁ Matplotlib/Seaborn for plotting
⦁ Scikit-learn for basic ML models

*9. Metrics to Know:
⦁ Growth %, Conversion rate, Retention rate
⦁ KPIs specific to domain (finance, marketing, etc.)

*10. Real-World Use Cases:
⦁ Customer segmentation
⦁ Sales trend analysis
⦁ A/B testing
⦁ Forecasting demand

💬 Tap ❤️ for more!

Sber presented Europe’s largest open-source project at AI Journey as it opened access to its flagship models — the GigaChat Ultra-Preview and Lightning, in addition to a new generation of the GigaAM-v3 open-source models for speech recognition and a full range of image and video generation models in the new Kandinsky 5.0 line, including the Video Pro, Video Lite and Image Lite.

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For the first time in Russia, an MoE model of this scale has been trained entirely from scratch — without relying on any foreign weights. Training from scratch, and on such a scale to boot, is a challenge that few teams in the world have taken on.

Our flagship Kandinsky Video Pro model has caught up with Veo 3 in terms of visual quality and surpassed Wan 2.2-A14B. Read a detailed post from the team here.

The code and weights for all models are now available to all users under MIT license, including commercial use.

Complete SQL road map
👇👇

1.Intro to SQL
• Definition
• Purpose
• Relational DBs
• DBMS

2.Basic SQL Syntax
• SELECT
• FROM
• WHERE
• ORDER BY
• GROUP BY

3. Data Types
• Integer
• Floating-Point
• Character
• Date
• VARCHAR
• TEXT
• BLOB
• BOOLEAN

4.Sub languages
• DML
• DDL
• DQL
• DCL
• TCL

5. Data Manipulation
• INSERT
• UPDATE
• DELETE

6. Data Definition
• CREATE
• ALTER
• DROP
• Indexes

7.Query Filtering and Sorting
• WHERE
• AND
• OR Conditions
• Ascending
• Descending

8. Data Aggregation
• SUM
• AVG
• COUNT
• MIN
• MAX

9.Joins and Relationships
• INNER JOIN
• LEFT JOIN
• RIGHT JOIN
• Self-Joins
• Cross Joins
• FULL OUTER JOIN

10.Subqueries
• Subqueries used in
• Filtering data
• Aggregating data
• Joining tables
• Correlated Subqueries

11.Views
• Creating
• Modifying
• Dropping Views

12.Transactions
• ACID Properties
• COMMIT
• ROLLBACK
• SAVEPOINT
• ROLLBACK TO SAVEPOINT

13.Stored Procedures
• CREATE PROCEDURE
• ALTER PROCEDURE
• DROP PROCEDURE
• EXECUTE PROCEDURE
• User-Defined Functions (UDFs)

14.Triggers
• Trigger Events
• Trigger Execution and Syntax

15. Security and Permissions
• CREATE USER
• GRANT
• REVOKE
• ALTER USER
• DROP USER

16.Optimizations
• Indexing Strategies
• Query Optimization

17.Normalization
• 1NF(Normal Form)
• 2NF
• 3NF
• BCNF

18.Backup and Recovery
• Database Backups
• Point-in-Time Recovery

19.NoSQL Databases
• MongoDB
• Cassandra etc...
• Key differences

20. Data Integrity
• Primary Key
• Foreign Key

21.Advanced SQL Queries
• Window Functions
• Common Table Expressions (CTEs)

22.Full-Text Search
• Full-Text Indexes
• Search Optimization

23. Data Import and Export
• Importing Data
• Exporting Data (CSV, JSON)
• Using SQL Dump Files

24.Database Design
• Entity-Relationship Diagrams
• Normalization Techniques

25.Advanced Indexing
• Composite Indexes
• Covering Indexes

26.Database Transactions
• Savepoints
• Nested Transactions
• Two-Phase Commit Protocol

27.Performance Tuning
• Query Profiling and Analysis
• Query Cache Optimization

------------------ END -------------------

Some good resources to learn SQL

1.Tutorial & Courses
• Learn SQL: https://bit.ly/3FxxKPz
• Udacity: imp.i115008.net/AoAg7K

2. YouTube Channel's
• FreeCodeCamp:rb.gy/pprz73
• Programming with Mosh: rb.gy/g62hpe

3. Books
• SQL in a Nutshell: https://news.1rj.ru/str/DataAnalystInterview/158

4. SQL Interview Questions
https://news.1rj.ru/str/sqlanalyst/72

Join @free4unow_backup for more free resourses

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The Shift in Data Analyst Roles: What You Should Apply for in 2025

The traditional “Data Analyst” noscript is gradually declining in demand in 2025 not because data is any less important, but because companies are getting more specific in what they’re looking for.

Today, many roles that were once grouped under “Data Analyst” are now split into more domain-focused noscripts, depending on the team or function they support.

Here are some roles gaining traction:
* Business Analyst
* Product Analyst
* Growth Analyst
* Marketing Analyst
* Financial Analyst
* Operations Analyst
* Risk Analyst
* Fraud Analyst
* Healthcare Analyst
* Technical Analyst
* Business Intelligence Analyst
* Decision Support Analyst
* Power BI Developer
* Tableau Developer

Focus on the skillsets and business context these roles demand.

Whether you're starting out or transitioning, look beyond "Data Analyst" and align your profile with industry-specific roles. It’s not about the noscript—it’s about the value you bring to a team.

🔥 𝗦𝘁𝗼𝗽 𝗪𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝗧𝘂𝘁𝗼𝗿𝗶𝗮𝗹𝘀.

𝗦𝘁𝗮𝗿𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗶𝗻𝗴 𝗟𝗶𝗸𝗲 𝗮 𝗥𝗲𝗮𝗹 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿.

If you want 𝗷𝗼𝗯-𝗿𝗲𝗮𝗱𝘆 𝗦𝗤𝗟, 𝗣𝘆𝘁𝗵𝗼𝗻, 𝗣𝘆𝗦𝗽𝗮𝗿𝗸, 𝗔𝘇𝘂𝗿𝗲 & 𝗦𝗻𝗼𝘄𝗳𝗹𝗮𝗸𝗲 skills,

Here’s where to practice and what exactly to practice because these are mainly expected in all the companies especially in EY, PwC, KPMG & Deloitte 👇

1️⃣ 𝗦𝗤𝗟 — 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝗮𝗹 & 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗟𝗲𝘃𝗲𝗹

LeetCode (SQL): https://lnkd.in/gudFeUbZ
HackerRank (SQL): https://lnkd.in/g9hpE6vQ
SQLZoo: https://sqlzoo.net/
• JOINs (INNER, LEFT, RIGHT)
• GROUP BY & HAVING
• Window functions (ROW_NUMBER, RANK)
• CTEs (WITH clause)
• Query optimization logic

2️⃣ 𝗣𝘆𝘁𝗵𝗼𝗻 — 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗙𝗼𝗰𝘂𝘀

LeetCode (Python): https://lnkd.in/gaEvhsvi
HackerRank (Python): https://lnkd.in/gGHkAE47
Exercism (Python): https://lnkd.in/gAuvZmwZ
• Functions & modules
• File handling (CSV, JSON)
• Data structures (list, dict)
• Error handling & logging
• Clean, readable code

3️⃣ 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 — 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗛𝗮𝗻𝗱𝘀-𝗢𝗻

Databricks Community: https://lnkd.in/gpDTBDpq
SparkByExamples: https://lnkd.in/gfjnQ7Ud
Kaggle Notebooks: https://lnkd.in/gm7YU7Fp
• DataFrames & transformations
• Joins & aggregations
• Partitioning & caching
• Handling large datasets
• Performance tuning basics

4️⃣ 𝗔𝘇𝘂𝗿𝗲 — 𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴

Azure Free Account: https://lnkd.in/gk_Dpb9v
Microsoft Learn: https://lnkd.in/gb8nTnBf
Azure Data Factory: https://lnkd.in/ggpsYk7X
• Data ingestion using ADF
• ADLS Gen2 storage layers
• Parameterized pipelines
• Incremental data loads
• Monitoring & debugging

5️⃣ 𝗦𝗻𝗼𝘄𝗳𝗹𝗮𝗸𝗲 — 𝗥𝗲𝗮𝗹 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗶𝗻𝗴

Snowflake Trial: https://lnkd.in/g2dHRA9f
Sample Data: https://lnkd.in/grsV2X47
Snowflake Learn: https://lnkd.in/gVpiNKHF

• Data Loading and Unloading
• Fact & dimension modeling
• ELT inside Snowflake
• Query Profile analysis
• Cost & performance tuning