The Real Truth About Junior Data Analytics Interviews DataAnalytics
(From someone who's interviewed 50+ analysts)

Let me save you hours of interview prep...

SQL Round

WHAT THEY SAY:
"Complex SQL knowledge"

WHAT THEY ACTUALLY TEST:

Can you clean messy data
Do you check for NULL values
How do you handle duplicates
Can you explain your logic
Do you verify results

REAL QUESTIONS:

"Find duplicate transactions"
"Calculate monthly sales"
"Show top customers"
That's it. Really. ⤵️

Excel Interview

WHAT THEY SAY:
"Advanced Excel skills"

WHAT THEY ACTUALLY TEST:

VLOOKUP/XLOOKUP usage
Pivot Table comfort
Basic formulas
Data cleaning approach
Problem-solving process

Business Case

WHAT THEY SAY:
"Data analysis presentation"

WHAT THEY REALLY WANT:

Can you explain simply
Do you ask good questions
Can you structure analysis
Do you focus on impact
Are you confident with data ⤵️

Common Scenarios

The "Messy Data" Test
They give you:

Inconsistent formats
Missing values
Duplicate records

They watch:

How you spot issues
What questions you ask
Your cleaning approach

The "Explain It" Challenge

They ask:
"Walk me through your analysis"

They assess:

Communication clarity
Technical understanding
Business thinking
Confidence level ⤵️

How to Actually Prepare

Practice Basics:

Simple SQL queries
Excel fundamentals
Clear explanation

Business Understanding:

Read company metrics
Understand industry
Know basic KPIs
Prepare good questions

Real Scenarios to Practice:

Monthly sales analysis
Customer segmentation
Product performance
Marketing campaign results

Reality Check:

They care more about:

How you think
How you communicate
How you solve problems

Than:
Perfect technical knowledge
Complex code
Advanced statistics

SQL Basics for Beginners: Must-Know Concepts

1. What is SQL? 
   SQL (Structured Query Language) is a standard language used to communicate with databases. It allows you to query, update, and manage relational databases by writing simple or complex queries.

2. SQL Syntax 
   SQL is written using statements, which consist of keywords like SELECT, FROM, WHERE, etc., to perform operations on the data.
   - SQL keywords are not case-sensitive, but it's common to write them in uppercase (e.g., SELECT, FROM).

3. SQL Data Types 
   Databases store data in different formats. The most common data types are:
   - INT (Integer): For whole numbers.
   - VARCHAR(n) or TEXT: For storing text data.
   - DATE: For dates.
   - DECIMAL: For precise decimal values, often used in financial calculations.

4. Basic SQL Queries 
   Here are some fundamental SQL operations:

   - SELECT Statement: Used to retrieve data from a database.
   

     SELECT column1, column2 FROM table_name;
     

   - WHERE Clause: Filters data based on conditions.
   

     SELECT * FROM table_name WHERE condition;
     

   - ORDER BY: Sorts data in ascending (ASC) or descending (DESC) order.
   

     SELECT column1, column2 FROM table_name ORDER BY column1 ASC;
     

   - LIMIT: Limits the number of rows returned.
   

     SELECT * FROM table_name LIMIT 5;
     

5. Filtering Data with WHERE Clause 
   The WHERE clause helps you filter data based on a condition:
 

   SELECT * FROM employees WHERE salary > 50000;
   

   You can use comparison operators like:
   - =: Equal to
   - >: Greater than
   - <: Less than
   - LIKE: For pattern matching

6. Aggregating Data 
   SQL provides functions to summarize or aggregate data:
   - COUNT(): Counts the number of rows.
   

     SELECT COUNT(*) FROM table_name;
     

   - SUM(): Adds up values in a column.
   

     SELECT SUM(salary) FROM employees;
     

   - AVG(): Calculates the average value.
   

     SELECT AVG(salary) FROM employees;
     

   - GROUP BY: Groups rows that have the same values into summary rows.
   

     SELECT department, AVG(salary) FROM employees GROUP BY department;
     

7. Joins in SQL 
   Joins combine data from two or more tables:
   - INNER JOIN: Retrieves records with matching values in both tables.
   

     SELECT employees.name, departments.department
     FROM employees
     INNER JOIN departments
     ON employees.department_id = departments.id;
     

   - LEFT JOIN: Retrieves all records from the left table and matched records from the right table.
   

     SELECT employees.name, departments.department
     FROM employees
     LEFT JOIN departments
     ON employees.department_id = departments.id;
     

8. Inserting Data
   To add new data to a table, you use the INSERT INTO statement:
 

   INSERT INTO employees (name, position, salary) VALUES ('John Doe', 'Analyst', 60000);
   

9. Updating Data
   You can update existing data in a table using the UPDATE statement:
 

   UPDATE employees SET salary = 65000 WHERE name = 'John Doe';
   

10. Deleting Data
    To remove data from a table, use the DELETE statement:
  

    DELETE FROM employees WHERE name = 'John Doe';
   

Starting your journey as a data analyst is an amazing start for your career. As you progress, you might find new areas that pique your interest:

• Data Science: If you enjoy diving deep into statistics, predictive modeling, and machine learning, this could be your next challenge.

• Data Engineering: If building and optimizing data pipelines excites you, this might be the path for you.

• Business Analysis: If you're passionate about translating data into strategic business insights, consider transitioning to a business analyst role.

But remember, even if you stick with data analysis, there's always room for growth, especially with the evolving landscape of AI.

No matter where your path leads, the key is to start now.

Essential Skills for Data Analysis ☝️

CHOOSING THE RIGHT DATA ANALYTICS TOOLS

With so many data analytics tools available,
how do you pick the right one?

The truth is—there’s no one-size-fits-all answer.
The best tool depends on your needs, your data, and your goals.

Here’s how to decide:

🔹 For Data Exploration & Cleaning → SQL, Python (Pandas), Excel
🔹 For Dashboarding & Reporting → Tableau, Power BI, Looker
🔹 For Big Data Processing → Spark, Snowflake, Google BigQuery
🔹 For Statistical Analysis → R, Python (Statsmodels, SciPy)
🔹 For Machine Learning → Python (Scikit-learn, TensorFlow)

Ask yourself:
✅ What type of data am I working with?
✅ Do I need interactive dashboards?
✅ Is coding necessary, or do I need a no-code tool?
✅ What does my team/stakeholder prefer?

The best tool is the one that helps you solve problems efficiently.

Underrated Telegram Channel for Data Analysts 👇👇
https://t.iss.one/sqlspecialist

Here, you will get free tutorials to learn SQL, Python, Power BI, Excel and many more

Hope you guys will like it 😄

How do you handle null, 0, and blank values in your data during the cleaning process?

Sometimes interview questions are also based on this topic. Many data aspirants or even some professionals sometimes make the mistake of simply deleting missing values or trying to fill them without proper analysis.This can damage the integrity of the analysis. It’s essential to ask or find out the reason behind missing values in the data
whether from the project head, client, or through own investigation.

𝘼𝙣𝙨𝙬𝙚𝙧:

Handling null, 0, and blank values is crucial for ensuring the accuracy and reliability of data analysis. Here’s how to approach it:

1. 𝙄𝙙𝙚𝙣𝙩𝙞𝙛𝙮𝙞𝙣𝙜 𝙖𝙣𝙙 𝙐𝙣𝙙𝙚𝙧𝙨𝙩𝙖𝙣𝙙𝙞𝙣𝙜 𝙩𝙝𝙚 𝘾𝙤𝙣𝙩𝙚𝙭𝙩:
   - 𝙉𝙪𝙡𝙡 𝙑𝙖𝙡𝙪𝙚𝙨: These represent missing or undefined data. Identify them using functions like 'ISNULL' or filters in Power Query.
   - 0 𝙑𝙖𝙡𝙪𝙚𝙨: These can be legitimate data points but may also indicate missing data in some contexts. Understanding the context is important.
   - 𝘽𝙡𝙖𝙣𝙠 𝙑𝙖𝙡𝙪𝙚𝙨: These can be spaces or empty strings. Identify them using 'LEN', 'TRIM', or filters.

2. 𝙃𝙖𝙣𝙙𝙡𝙞𝙣𝙜 𝙏𝙝𝙚𝙨𝙚 𝙑𝙖𝙡𝙪𝙚𝙨 𝙐𝙨𝙞𝙣𝙜 𝙋𝙧𝙤𝙥𝙚𝙧 𝙏𝙚𝙘𝙝𝙣𝙞𝙦𝙪𝙚𝙨:
   - 𝙉𝙪𝙡𝙡 𝙑𝙖𝙡𝙪𝙚𝙨: Typically decide whether to impute, remove, or leave them based on the dataset’s context and the analysis requirements. Common imputation methods include using mean, median, or a placeholder.
   - 0 𝙑𝙖𝙡𝙪𝙚𝙨: If 0s are valid data, leave them as is. If they indicate missing data, treat them similarly to null values.

   - 𝘽𝙡𝙖𝙣𝙠 𝙑𝙖𝙡𝙪𝙚𝙨: Convert blanks to nulls or handle them as needed. This involves using 'IF' statements or Power Query transformations.

3. 𝙐𝙨𝙞𝙣𝙜 𝙀𝙭𝙘𝙚𝙡 𝙖𝙣𝙙 𝙋𝙤𝙬𝙚𝙧 𝙌𝙪𝙚𝙧𝙮:
   - 𝙀𝙭𝙘𝙚𝙡: Use formulas like 'IFERROR', 'IF', and 'VLOOKUP' to handle these values.
   - 𝙋𝙤𝙬𝙚𝙧 𝙌𝙪𝙚𝙧𝙮: Use transformations to filter, replace, or fill null and blank values. Steps like 'Fill Down', 'Replace Values', and custom columns help automate the process.

By carefully considering the context and using appropriate methods, the data cleaning process maintains the integrity and quality of the data.

Hope it helps :)

Data Analyst Interview Questions
[Python, SQL, PowerBI]

1. Is indentation required in python?
Ans: Indentation is necessary for Python. It specifies a block of code. All code within loops, classes, functions, etc is specified within an indented block. It is usually done using four space characters. If your code is not indented necessarily, it will not execute accurately and will throw errors as well.

2. What are Entities and Relationships?
Ans:
Entity: An entity can be a real-world object that can be easily identifiable. For example, in a college database, students, professors, workers, departments, and projects can be referred to as entities.

Relationships: Relations or links between entities that have something to do with each other. For example – The employee’s table in a company’s database can be associated with the salary table in the same database.

3. What are Aggregate and Scalar functions?
Ans: An aggregate function performs operations on a collection of values to return a single scalar value. Aggregate functions are often used with the GROUP BY and HAVING clauses of the SELECT statement. A scalar function returns a single value based on the input value.

4. What are Custom Visuals in Power BI?
Ans: Custom Visuals are like any other visualizations, generated using Power BI. The only difference is that it develops the custom visuals using a custom SDK. The languages like JQuery and JavaScript are used to create custom visuals in Power BI

ENJOY LEARNING 👍👍

Finance is one of the highest paid domains for Data Science jobs.

Here’s a complete step by step roadmap to learn Data Science for Finance 👇👇

Step 1: Understand the fundamentals of finance

Step 2: Learn essential programming languages and tools

Step 3: Learn the fundamentals of statistics for Data Science

Step 4: Learn Data Manipulation, Analysis, and Visualization

Step 5: Dive deep into Data Science and Machine Learning Algorithms

Step 6: Learn to work with Financial Data

BECOMING A DATA ANALYST IN 2025

Becoming a data analyst doesn’t have to be expensive in 2025.

With the right free resources and a structured approach,
you can become a skilled data analyst.

Here’s a roadmap with free resources to guide your journey:

1️⃣ Learn the Basics of Data Analytics
Start with foundational concepts like:
↳ What is data analytics?
↳ Types of analytics (descriptive, predictive, prescriptive).
↳ Basics of data types and statistics.

📘 Free Resources:
1. Intro to Statistics : https://www.khanacademy.org/math/statistics-probability
2. Introduction to Data Analytics by IBM (audit for free) :
https://imp.i384100.net/WyNqoM


2️⃣ Master Excel for Data Analysis
Excel is an essential tool for data cleaning, analysis, and visualization.

📘 Free Resources:
1. Excel Is Fun (YouTube): https://www.youtube.com/user/ExcelIsFun
2. Chandoo.org: https://chandoo.org/

🎯 Practice: Learn how to create pivot tables and use functions like VLOOKUP, SUMIF, and IF.


3️⃣ Learn SQL for Data Queries
SQL is the language of data—used to retrieve and manipulate datasets.

📘 Free Resources:
1. W3Schools SQL Tutorial : https://www.w3schools.com/sql/
2. Mode Analytics SQL Tutorial : https://mode.com/sql-tutorial/

🎯 Practice: Write SELECT, WHERE, and JOIN queries on free datasets.


4️⃣ Get Hands-On with Data Visualization
Learn to communicate insights visually with tools like Tableau or Power BI.

📘 Free Resources:
1. Tableau Public: https://www.tableau.com/learn/training
2. Power BI Community Blog: https://community.fabric.microsoft.com/t5/Power-BI-Community-Blog/bg-p/community_blog

🎯 Practice: Create dashboards to tell stories using real datasets.

5️⃣ Dive into Python or R for Analytics
Coding isn’t mandatory, but Python or R can open up advanced analytics.

📘 Free Resources:
1. Google’s Python Course https://developers.google.com/edu/python
2. R for Data Science (free book) r4ds.had.co.nz

🎯 Practice: Use libraries like Pandas (Python) or dplyr (R) to clean and analyze data.


6️⃣ Work on Real Projects
Apply your skills to real-world datasets to build your portfolio.

📘 Free Resources:
Kaggle: Datasets and beginner-friendly competitions.
Google Dataset Search: Access datasets on any topic.

🎯 Project Ideas:
Analyze sales data and create a dashboard.
Predict customer churn using a public dataset.


7️⃣ Build Your Portfolio and Network
Showcase your projects and connect with others in the field.

📘 Tips:
→ Use GitHub to share your work.
→ Create LinkedIn posts about your learning journey.
→ Join forums like r/DataScience on Reddit or LinkedIn groups.

💡 Start small, use free resources, and keep building.
💡 Remember: Every small step adds up to big progress.

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𝗪𝗮𝗻𝘁 𝘁𝗼 𝗸𝗻𝗼𝘄 𝘄𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝗶𝗻 𝗮 𝗿𝗲𝗮𝗹 𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘀𝘁 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄?

𝗕𝗮𝘀𝗶𝗰 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻

-Brief introduction about yourself.

-Explanation of how you developed an interest in learning Power BI despite having a chemical background.


𝗧𝗼𝗼𝗹𝘀 𝗣𝗿𝗼𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆

-Discussion about the tools you are proficient in.

-Detailed explanation of a project that demonstrated your proficiency in these tools.

𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗘𝘅𝗽𝗹𝗮𝗻𝗮𝘁𝗶𝗼𝗻

Explain about any Data Analytics Project you did, below are some follow-up questions for sales related data analysis project

Follow-up Question:

Was there any improvement in sales after building the report?

Provide a clear before and after scenario in sales post-report creation.

What areas did you identify where the company was losing sales, and what were your recommendations?

- How do you check the quality of data when it's given to you?
Explain your methods for ensuring data quality.

- How do you handle null values? Describe your approach to managing null values in datasets.


𝗦𝗤𝗟 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀

-Explain the order in which SQL clauses are executed.

-Write a query to find the percentage of the 18-year-old population.
Details: You are given two tables:
Table 1: Contains states and their respective populations.
Table 2: Contains three columns (state, gender, and population of 18-year-olds).

-Explain window functions and how to rank values in SQL.

- Difference between JOIN and UNION.

-How to return unique values in SQL.

𝗕𝗲𝗵𝗮𝘃𝗶𝗼𝗿𝗮𝗹 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀

-Solve a puzzle involving 3 gallons of water in one jar and 2 gallons in another to get exactly 4 gallons.
Step-by-step solution for the water puzzle.

- What skills have you learned on your own? Discuss the skills you self-taught and their impact on your career.

-Describe cases when you showcased team spirit.

-⭐ 𝗦𝗼𝗰𝗶𝗮𝗹 𝗠𝗲𝗱𝗶𝗮 𝗔𝗽𝗽 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻
Scenario: Choose any social media app (I choose Discord).
Question: What function/feature would you add to the Discord app, and how would you track its success?

- Rate yourself on Excel, SQL, and Python out of 10.

- What are your strengths in data analytics?

Like if it helps :)