If I had to start learning data analyst all over again, I'd follow this:

1- Learn SQL:

---- Joins (Inner, Left, Full outer and Self)
---- Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
---- Group by and Having clause
---- CTE and Subquery
---- Windows Function (Rank, Dense Rank, Row number, Lead, Lag etc)

2- Learn Excel:

---- Mathematical (COUNT, SUM, AVG, MIN, MAX, etc)
---- Logical Functions (IF, AND, OR, NOT)
---- Lookup and Reference (VLookup, INDEX, MATCH etc)
---- Pivot Table, Filters, Slicers

3- Learn BI Tools:

---- Data Integration and ETL (Extract, Transform, Load)
---- Report Generation
---- Data Exploration and Ad-hoc Analysis
---- Dashboard Creation

4- Learn Python (Pandas) Optional:

---- Data Structures, Data Cleaning and Preparation
---- Data Manipulation
---- Merging and Joining Data (Merging and joining DataFrames -similar to SQL joins)
---- Data Visualization (Basic plotting using Matplotlib and Seaborn)

Hope this helps you 😊

You're STILL a data analyst even if...

- you only use Excel
- you forgot the SQL syntax
- you bombed the big interview
- you don't know how to program
- you did an analysis completely wrong
- you can't remember the right function name
- you have to Google how to do something easy you've done before

You're NOT a data analyst when...
- you give up

SO DON'T GIVE UP! KEEP GOING!

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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15 SQL interview questions for freshers

1) What is SQL and what is it used for?
Answer: SQL is a language for managing and querying relational databases. It’s used to retrieve, insert, update, delete data and to manage schema and permissions.

2) What are the different types of SQL statements?
Answer: DDL (Data Definition Language), DML (Data Manipulation Language), DCL (Data Control Language), and DTL (Transaction Control Language).

3) What is a primary key?
Answer: A unique identifier for each row in a table; cannot be NULL.

4) What is a foreign key?
Answer: A field that creates a link between two tables, enforcing referential integrity.

5) What is the difference between INNER JOIN and LEFT JOIN?
Answer: INNER JOIN returns matching rows from both tables; LEFT JOIN returns all rows from the left table and matched rows from the right table (NULL if no match).

6) What is normalization?
Answer: Organizing data to reduce redundancy by dividing into related tables and defining relationships.

7) What is a database index?
Answer: A data structure that improves the speed of data retrieval; can be on one or more columns.

8) What is GROUP BY and HAVING?
Answer: GROUP BY aggregates rows by column(s); HAVING filters groups after aggregation (unlike WHERE which filters rows before aggregation).

9) What is a subquery?
Answer: A query nested inside another query, used to perform operations that depend on another query’s result.

10) What is a view?
Answer: A saved query that presents data as a virtual table; does not store data itself.

11) What is transaction management?
Answer: Ensuring data integrity using ACID properties; COMMIT to save, ROLLBACK to undo, and SAVEPOINT to set a point to roll back to.

12) What are SQL constraints?
Answer: Rules like PRIMARY KEY, FOREIGN KEY, NOT NULL, UNIQUE, CHECK, and DEFAULT to enforce data integrity.

13) What is the difference between WHERE and HAVING?
Answer: WHERE filters rows before grouping; HAVING filters groups after aggregation.

14) What is a stored procedure?
Answer: A precompiled set of SQL statements stored in the database, can be executed with parameters.

15) What is the difference between UNION and UNION ALL?
Answer: UNION removes duplicates between results; UNION ALL keeps all rows, including duplicates.

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Hi Everyone!

Just a reminder - Don’t pay a single rupee to anyone asking you for anything during your job search journey.

No company asks for a single rupee neither during an interview process, not even during visa sponsorship.

So don’t pay a single rupee to anyone asking you for any interview or any verification process.

Save yourself from getting scammed!

Save your and your parents hard earned money.

You already have the skills and expertise in Data Analytics tools like SQL, Power BI, Tableau, and Python. 𝐍𝐨𝐰, 𝐡𝐨𝐰 𝐝𝐨 𝐲𝐨𝐮 𝐟𝐢𝐧𝐝 𝐚 𝐣𝐨𝐛?

1. Tailor your LinkedIn profile to highlight your Data Analyst skills and experience.

2. Make a list of companies that hire Data Analysts and follow them on LinkedIn to stay updated on job openings. (Ex- McKinsey & Company, BCG, Bain & Company, Google, Amazon, Microsoft, IBM, Goldman Sachs, JPMorgan Chase, Walmart, Target)

3. Follow HRs from your target companies on LinkedIn and reach out to them for job openings or whenever they post about job openings, send your resume to them within 2-3 hours via LinkedIn or email if available.

4. Connect with Managers or Senior Managers in Data Analyst roles at your target companies on LinkedIn and ask if they are hiring for their team or would be willing to refer you for any relevant Data analyst role.

5. Apply for jobs on LinkedIn, Naukri, and directly on the company's website.

📊 Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)

Aggregate functions are used to perform calculations on multiple rows of a table and return a single value. They're mostly used with GROUP BY, but also work standalone.

1. COUNT()
Returns the number of rows.

Example:

SELECT COUNT(*) FROM employees;

Counts all employees in the table.

You can also count only non-null values in a column:

SELECT COUNT(email) FROM customers;


2. SUM()
Adds up all the values in a numeric column.

Example:

SELECT SUM(salary) FROM employees;

Gives you the total salary payout.


3. AVG()
Calculates the average value of a numeric column.

Example:

SELECT AVG(price) FROM products;

Finds the average product price.


4. MIN()
Returns the lowest value.

Example:

SELECT MIN(salary) FROM employees;

Finds the smallest salary.


5. MAX()
Returns the highest value.

Example:

SELECT MAX(salary) FROM employees;

Finds the highest salary in the table.


Bonus Example:

SELECT
COUNT(*) AS total_orders,
SUM(amount) AS total_revenue,
AVG(amount) AS avg_order_value
FROM orders;

This gives you a quick business summary: number of orders, total revenue, and average order value.


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Key SQL Concepts for Data Analyst Interviews

1. Joins: Understand how to use INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN to combine data from different tables, ensuring you can retrieve the needed information from relational databases.

2. Group By and Aggregate Functions: Master GROUP BY along with aggregate functions like COUNT(), SUM(), AVG(), MAX(), and MIN() to summarize data and generate meaningful reports.

3. Data Filtering: Use WHERE, HAVING, and CASE statements to filter and manipulate data effectively, enabling precise data extraction based on specific conditions.

4. Subqueries: Employ subqueries to retrieve data nested within other queries, allowing for more complex data retrieval and analysis scenarios.

5. Window Functions: Leverage window functions such as ROW_NUMBER(), RANK(), DENSE_RANK(), and LAG() to perform calculations across a set of table rows, returning result sets with contextual calculations.

6. Data Types: Ensure proficiency in choosing and handling various SQL data types (VARCHAR, INT, DATE, etc.) to store and query data accurately.

7. Indexes: Learn how to create and manage indexes to speed up the retrieval of data from databases, particularly in tables with large volumes of records.

8. Normalization: Apply normalization principles to organize database tables efficiently, reducing redundancy and improving data integrity.

9. CTEs and Views: Utilize Common Table Expressions (CTEs) and Views to write modular, reusable, and readable queries, making complex data analysis tasks more manageable.

10. Data Import/Export: Know how to import and export data between SQL databases and other tools like BI tools to facilitate comprehensive data analysis workflows.

Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/DataSimplifier

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4 Career Paths In Data Analytics

1) Data Analyst:

Role: Data Analysts interpret data and provide actionable insights through reports and visualizations.

They focus on querying databases, analyzing trends, and creating dashboards to help businesses make data-driven decisions.

Skills: Proficiency in SQL, Excel, data visualization tools (like Tableau or Power BI), and a good grasp of statistics.

Typical Tasks: Generating reports, creating visualizations, identifying trends and patterns, and presenting findings to stakeholders.


2)Data Scientist:

Role: Data Scientists use advanced statistical techniques, machine learning algorithms, and programming to analyze and interpret complex data.

They develop models to predict future trends and solve intricate problems.
Skills: Strong programming skills (Python, R), knowledge of machine learning, statistical analysis, data manipulation, and data visualization.

Typical Tasks: Building predictive models, performing complex data analyses, developing machine learning algorithms, and working with big data technologies.


3)Business Intelligence (BI) Analyst:

Role: BI Analysts focus on leveraging data to help businesses make strategic decisions.

They create and manage BI tools and systems, analyze business performance, and provide strategic recommendations.

Skills: Experience with BI tools (such as Power BI, Tableau, or Qlik), strong analytical skills, and knowledge of business operations and strategy.

Typical Tasks: Designing and maintaining dashboards and reports, analyzing business performance metrics, and providing insights for strategic planning.

4)Data Engineer:

Role: Data Engineers build and maintain the infrastructure required for data generation, storage, and processing. They ensure that data pipelines are efficient and reliable, and they prepare data for analysis.

Skills: Proficiency in programming languages (such as Python, Java, or Scala), experience with database management systems (SQL and NoSQL), and knowledge of data warehousing and ETL (Extract, Transform, Load) processes.

Typical Tasks: Designing and building data pipelines, managing and optimizing databases, ensuring data quality, and collaborating with data scientists and analysts.

Hope this helps you 😊

📁 Data Analyst Resume Tips 💼📊

1️⃣ Start with a Strong Summary
– 2-3 lines summarizing your experience, tools, and impact.
– Example: “Detail-oriented Data Analyst with 2+ years of experience in SQL, Excel, and Power BI. Passionate about turning data into actionable insights.”

2️⃣ Highlight Technical Skills Clearly
– Tools: SQL, Excel, Python, Power BI, Tableau
– Concepts: Data Cleaning, EDA, Dashboarding, A/B Testing

3️⃣ Use Impact-Driven Bullet Points
✅ “Improved reporting speed by 40% using Power BI”
✅ “Analyzed customer churn with Python, improving retention by 12%”
❌ “Worked with data daily”

4️⃣ Include Projects with Business Context
– Name the project, tool used, and business outcome.
– Add GitHub or portfolio links.

5️⃣ Quantify Your Work
– Numbers catch attention!
– Use KPIs: revenue impact, time saved, accuracy improved

6️⃣ Education & Certifications
– Mention degrees, online courses, bootcamps
– Highlight certificates like Google Data Analytics, Excel for Business, etc.

7️⃣ Tailor Your Resume to the Job
– Use keywords from the job denoscription
– Rearrange skills/projects based on the role focus

📝 Keep it 1-page (for 0–3 yrs exp), well-formatted, and typo-free!

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📌 7 Steps to Ace Your Data Analyst Job Interview 💼✅

1️⃣ Know the Role Inside-Out
– Read the JD carefully
– Understand tools, responsibilities, and business domain
– Align your experience with their requirements

2️⃣ Revise Core Skills
– SQL: Joins, Window functions, Aggregations
– Excel: VLOOKUP, Pivot Tables, Charts
– Power BI/Tableau: Dashboarding, Filters, Slicers
– Python: Pandas, NumPy, basic EDA (if required)

3️⃣ Prepare for Case Studies & Scenarios
– Practice questions like:
“How would you find top-selling products?”
“How would you reduce churn?”
– Focus on business thinking + technical approach

4️⃣ Show Your Projects Smartly
– Pick 2-3 relevant projects
– Explain goal, tools, insights, and impact
– Keep it concise and results-driven

5️⃣ Practice Behavioral Questions
– “Tell me about a time you handled a data error”
– “Describe a challenging project”
– Use STAR method: Situation, Task, Action, Result

6️⃣ Ask Insightful Questions
– “How does your team use data to make decisions?”
– “What tools/tech stack do analysts use here?”

7️⃣ Follow-Up Professionally
– Send a short thank-you message/email
– Reaffirm your interest and value

🧠 Confidence = Preparation + Practice!

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Power BI DAX Cheatsheet 🚀

1️⃣ Basics of DAX (Data Analysis Expressions)

DAX is used to create custom calculations in Power BI.

It works with tables and columns, not individual cells.

Functions in DAX are similar to Excel but optimized for relational data.


2️⃣ Aggregation Functions

SUM(ColumnName): Adds all values in a column.

AVERAGE(ColumnName): Finds the mean of values.

MIN(ColumnName): Returns the smallest value.

MAX(ColumnName): Returns the largest value.

COUNT(ColumnName): Counts non-empty values.

COUNTROWS(TableName): Counts rows in a table.


3️⃣ Logical Functions

IF(condition, result_if_true, result_if_false): Conditional statement.

SWITCH(expression, value1, result1, value2, result2, default): Alternative to nested IF.

AND(condition1, condition2): Returns TRUE if both conditions are met.

OR(condition1, condition2): Returns TRUE if either condition is met.


4️⃣ Time Intelligence Functions

TODAY(): Returns the current date.

YEAR(TODAY()): Extracts the year from a date.

TOTALYTD(SUM(Sales[Amount]), Date[Date]): Year-to-date total.

SAMEPERIODLASTYEAR(Date[Date]): Returns values from the same period last year.

DATEADD(Date[Date], -1, MONTH): Shifts dates by a specified interval.


5️⃣ Filtering Functions

FILTER(Table, Condition): Returns a filtered table.

ALL(TableName): Removes all filters from a table.

ALLEXCEPT(TableName, Column1, Column2): Removes all filters except specified columns.

KEEPFILTERS(FilterExpression): Keeps filters applied while using other functions.


6️⃣ Ranking & Row Context Functions

RANKX(Table, Expression, [Value], [Order]): Ranks values in a column.

TOPN(N, Table, OrderByExpression): Returns the top N rows based on an expression.


7️⃣ Iterators (Row-by-Row Calculations)

SUMX(Table, Expression): Iterates over a table and sums calculated values.

AVERAGEX(Table, Expression): Iterates over a table and finds the average.

MAXX(Table, Expression): Finds the maximum value based on an expression.


8️⃣ Relationships & Lookup Functions

RELATED(ColumnName): Fetches a related column from another table.

LOOKUPVALUE(ColumnName, SearchColumn, SearchValue): Returns a value from a column where another column matches a value.


9️⃣ Variables in DAX

VAR variableName = Expression RETURN variableName

Improves performance by reducing redundant calculations.


🔟 Advanced DAX Concepts

Calculated Columns: Created at the column level, stored in the data model.

Measures: Dynamic calculations based on user interactions in Power BI visuals.

Row Context vs. Filter Context: Understanding how DAX applies calculations at different levels.

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

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