Excel Formulas every data analyst should know
❤7
📊 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!
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!
❤14
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Whether you're preparing for placements or starting your coding journey — we’ve got you covered!
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📌 Master Data Structures & Algorithms
– Perfect for SDE interviews & competitive coding
– Covers Arrays, Trees, Graphs, DP & 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
📌 Master Data Structures & Algorithms
– Perfect for SDE interviews & competitive coding
– Covers Arrays, Trees, Graphs, DP & more
🔗 Join now: https://gfgcdn.com/tu/W84/
✅ 2. Python Beginner to Advanced
📌 Learn Python from scratch to expert level
– Covers basics, OOPs, file handling, projects & 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 ❤️
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 ❤️
❤3
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 👍👍
👇👇
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 👍👍
❤9
PowerBI Interview Questions 🚀🔥
📊 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!
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!
❤17
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.
The GigaChat Ultra-Preview, a new MoE model featuring 702 billion parameters, has been compiled specifically with the Russian language in mind and trained entirely from scratch. Read a detailed post from the team here.
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.
The GigaChat Ultra-Preview, a new MoE model featuring 702 billion parameters, has been compiled specifically with the Russian language in mind and trained entirely from scratch. Read a detailed post from the team here.
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.
AI Journey
AI Journey Conference on 19-21 November 2025. Key speakers in the area of artificial intelligence technology
AI Journey Conference on 19-21 November 2025. Key speakers in the area of artificial intelligence technology.
❤4
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
ENJOY LEARNING 👍👍
👇👇
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
ENJOY LEARNING 👍👍
❤10
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.
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.
❤5👍1