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

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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

Hey guys,

Today, I curated a list of essential Power BI interview questions that every aspiring data analyst should be prepared to answer 👇👇

1. What is Power BI?

Power BI is a business analytics service developed by Microsoft. It provides tools for aggregating, analyzing, visualizing, and sharing data. With Power BI, users can create dynamic dashboards and interactive reports from multiple data sources.

Key Features:
- Data transformation using Power Query
- Powerful visualizations and reporting tools
- DAX (Data Analysis Expressions) for complex calculations

2. What are the building blocks of Power BI?

The main building blocks of Power BI include:
- Visualizations: Graphical representations of data (charts, graphs, etc.).
- Datasets: A collection of data used to create visualizations.
- Reports: A collection of visualizations on one or more pages.
- Dashboards: A single page that combines multiple visualizations from reports.
- Tiles: Single visualization found on a report or dashboard.

3. What is DAX, and why is it important in Power BI?

DAX (Data Analysis Expressions) is a formula language used in Power BI for creating custom calculations and aggregations. DAX is similar to Excel formulas but offers much more powerful data manipulation capabilities.

Tip: Be ready to explain not just the syntax, but scenarios where DAX is essential, such as calculating year-over-year growth or creating dynamic measures.

4. How does Power BI differ from Excel in data visualization?

While Excel is great for individual analysis and data manipulation, Power BI excels in handling large datasets, creating interactive dashboards, and sharing insights across the organization. Power BI also integrates better and allows for real-time data streaming.

5. What are the types of filters in Power BI, and how are they used?

Power BI offers several types of filters to refine data and display only what’s relevant:

- Visual-level filters: Apply filters to individual visuals.
- Page-level filters: Apply filters to all the visuals on a report page.
- Report-level filters: Apply filters to all pages in the report.

Filters help to create more customized and targeted reports by narrowing down the data view based on specific conditions.

6. What are Power BI Desktop, Power BI Service, and Power BI Mobile? How do they interact?

- Power BI Desktop: A desktop-based application used for data modeling, creating reports, and building dashboards.
- Power BI Service: A cloud-based platform that allows users to publish and share reports created in Power BI Desktop.
- Power BI Mobile: Allows users to view reports and dashboards on mobile devices for on-the-go access.

These components work together in a typical workflow:
1. Build reports and dashboards in Power BI Desktop.
2. Publish them to the Power BI Service for sharing and collaboration.
3. View and interact with reports on Power BI Mobile for easy access anywhere.

7. Explain the difference between calculated columns and measures.

- Calculated columns are added to a table using DAX and are calculated row by row.
- Measures are calculations used in aggregations, such as sums, averages, and ratios. Unlike calculated columns, measures are dynamic and evaluated based on the filter context of a report.

8. How would you perform data cleaning and transformation in Power BI?

Data cleaning and transformation in Power BI are mainly done using Power Query Editor. Here, you can:
- Remove duplicates or empty rows
- Split columns (e.g., text into multiple parts)
- Change data types (e.g., text to numbers)
- Merge and append queries from different data sources

Power BI isn’t just about visuals; it’s about turning raw data into actionable insights. So, keep honing your skills, try building dashboards, and soon enough, you’ll be impressing your interviewers too!

Hope it helps :)

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1. What is the difference between the RANK() and DENSE_RANK() functions?

The RANK() function in the result set defines the rank of each row within your ordered partition. If both rows have the same rank, the next number in the ranking will be the previous rank plus a number of duplicates. If we have three records at rank 4, for example, the next level indicated is 7. The DENSE_RANK() function assigns a distinct rank to each row within a partition based on the provided column value, with no gaps. If we have three records at rank 4, for example, the next level indicated is 5.

2. Explain One-hot encoding and Label Encoding. How do they affect the dimensionality of the given dataset?

One-hot encoding is the representation of categorical variables as binary vectors. Label Encoding is converting labels/words into numeric form. Using one-hot encoding increases the dimensionality of the data set. Label encoding doesn’t affect the dimensionality of the data set. One-hot encoding creates a new variable for each level in the variable whereas, in Label encoding, the levels of a variable get encoded as 1 and 0.

3. What is the shortcut to add a filter to a table in EXCEL?

The filter mechanism is used when you want to display only specific data from the entire dataset. By doing so, there is no change being made to the data. The shortcut to add a filter to a table is Ctrl+Shift+L.

4. What is DAX in Power BI?

DAX stands for Data Analysis Expressions. It's a collection of functions, operators, and constants used in formulas to calculate and return values. In other words, it helps you create new info from data you already have.

5. Define shelves and sets in Tableau?

Shelves: Every worksheet in Tableau will have shelves such as columns, rows, marks, filters, pages, and more. By placing filters on shelves we can build our own visualization structure. We can control the marks by including or excluding data.
Sets: The sets are used to compute a condition on which the dataset will be prepared. Data will be grouped together based on a condition. Fields which is responsible for grouping are known assets. For example – students having grades of more than 70%.

Q. Explain the data preprocessing steps in data analysis.

Ans. Data preprocessing transforms the data into a format that is more easily and effectively processed in data mining, machine learning and other data science tasks.
1. Data profiling.
2. Data cleansing.
3. Data reduction.
4. Data transformation.
5. Data enrichment.
6. Data validation.

Q. What Are the Three Stages of Building a Model in Machine Learning?

Ans. The three stages of building a machine learning model are:

Model Building: Choosing a suitable algorithm for the model and train it according to the requirement

Model Testing: Checking the accuracy of the model through the test data

Applying the Model: Making the required changes after testing and use the final model for real-time projects


Q. What are the subsets of SQL?

Ans. The following are the four significant subsets of the SQL:

Data definition language (DDL): It defines the data structure that consists of commands like CREATE, ALTER, DROP, etc.

Data manipulation language (DML): It is used to manipulate existing data in the database. The commands in this category are SELECT, UPDATE, INSERT, etc.

Data control language (DCL): It controls access to the data stored in the database. The commands in this category include GRANT and REVOKE.

Transaction Control Language (TCL): It is used to deal with the transaction operations in the database. The commands in this category are COMMIT, ROLLBACK, SET TRANSACTION, SAVEPOINT, etc.


Q. What is a Parameter in Tableau? Give an Example.

Ans. A parameter is a dynamic value that a customer could select, and you can use it to replace constant values in calculations, filters, and reference lines.
For example, when creating a filter to show the top 10 products based on total profit instead of the fixed value, you can update the filter to show the top 10, 20, or 30 products using a parameter.

🔥 Recent Data Analyst Interview Q&A at Deloitte 🔥

Question:
👉 Write an SQL query to extract the third highest salary from an employee table with columns EID and ESalary.

Solution:

SELECT ESalary  
FROM (  
  SELECT ESalary,  
         DENSE_RANK() OVER (ORDER BY ESalary DESC) AS salary_rank  
  FROM employee  
) AS ranked_salaries  
WHERE salary_rank = 3;

Explanation of the Query:

1️⃣ Step 1: Create a Subquery

The subquery ranks all salaries in descending order using DENSE_RANK().

2️⃣ Step 2: Rank the Salaries

Assigns ranks: 1 for the highest salary, 2 for the second-highest, and so on.

3️⃣ Step 3: Assign an Alias

The subquery is given an alias (ranked_salaries) to use in the main query.

4️⃣ Step 4: Filter for the Third Highest Salary

The WHERE clause filters the results to include only the salary with rank 3.

5️⃣ Step 5: Display the Third Highest Salary

The main query selects and displays the third-highest salary.

By following these steps, you can easily extract the third-highest salary from the table.



#DataAnalyst #SQL #InterviewTips

Data Analytics project ideas to build your portfolio in 2025:

1. Sales Data Analysis Dashboard

Analyze sales trends, seasonal patterns, and product performance.

Use Power BI, Tableau, or Python (Dash/Plotly) for visualization.



2. Customer Segmentation

Use clustering (K-means, hierarchical) on customer data to identify groups.

Provide actionable marketing insights.



3. Social Media Sentiment Analysis

Analyze tweets or reviews using NLP to gauge public sentiment.

Visualize positive, negative, and neutral trends over time.



4. Churn Prediction Model

Analyze customer data to predict who might leave a service.

Use logistic regression, decision trees, or random forest.



5. Financial Data Analysis

Study stock prices, moving averages, and volatility.

Create an interactive dashboard with key metrics.



6. Healthcare Analytics

Analyze patient data for disease trends or hospital resource usage.

Use visualization to highlight key findings.



7. Website Traffic Analysis

Use Google Analytics data to identify user behavior patterns.

Suggest improvements for user engagement and conversion.



8. Employee Attrition Analysis

Analyze HR data to find factors leading to employee turnover.

Use statistical tests and visualization.


React ❤️ for more

Resume Template for Data Analyst Fresher

Roadmap for Becoming a Data Analyst 📈 📖

1. Prerequisites
- Learn basic Excel/Google Sheets for data handling
- Learn Python or R for data manipulation
- Study Mathematics & Statistics:
1️⃣ Mean, median, mode, standard deviation
2️⃣ Probability, hypothesis testing, distributions

2. Learn Essential Tools & Libraries
- Python libraries: Pandas, NumPy, Matplotlib, Seaborn
- SQL: For querying databases
- Excel: Pivot tables, VLOOKUP, charts
- Power BI / Tableau: For data visualization

3. Data Handling & Preprocessing
- Understand data types, missing values
- Data cleaning techniques
- Data transformation & feature engineering

4. Exploratory Data Analysis (EDA)
- Identify patterns, trends, and outliers
- Use visualizations (bar charts, histograms, heatmaps)
- Summarize findings effectively

5. Basic Analytics & Business Insights
- Understand KPIs, metrics, dashboards
- Build analytical reports
- Translate data into actionable business insights

6. Real Projects & Practice
- Analyze sales, customer, or marketing data
- Perform churn analysis or product performance reviews
- Use platforms like Kaggle or Google Dataset Search

7. Communication & Storytelling
- Present insights with compelling visuals
- Create clear, concise reports for stakeholders

8. Advanced Skills (Optional)
- Learn Predictive Modeling (basic ML)
- Understand A/B Testing, time-series analysis
- Explore Big Data Tools: Spark, Hadoop (if needed)

9. Career Prep
- Build a strong portfolio on GitHub
- Create a LinkedIn profile with projects
- Prepare for SQL, Excel, and scenario-based interviews

💡 Consistent practice + curiosity = great data analyst!

💬 Double Tap ♥️ for more

If you want to be a data analyst, you should work to become as good at SQL as possible. 📱

1. SELECT

What a surprise! I need to choose what data I want to return.

2. FROM

Again, no shock here. I gotta choose what table I am pulling my data from.

3. WHERE

This is also pretty basic, but I almost always filter the data to whatever range I need and filter the data to whatever condition I’m looking for.

4. JOIN

This may surprise you that the next one isn’t one of the other core SQL clauses, but at least for my work, I utilize some kind of join in almost every query I write.

5. Calculations

This isn’t necessarily a function of SQL, but I write a lot of calculations in my queries. Common examples include finding the time between two dates and multiplying and dividing values to get what I need.

Add operators and a couple data cleaning functions and that’s 80%+ of the SQL I write on the job.

React ♥️ for more

✅ Top 10 Power BI Interview Questions & Answers 📊💼

1️⃣ What is Power BI and why is it used?
Power BI is Microsoft’s business analytics tool for creating interactive dashboards and reports. It helps visualize data for better decision-making.

2️⃣ Key components of Power BI?
- Power BI Desktop: For building reports
- Power BI Service: Cloud sharing & collaboration
- Power BI Mobile: Access on mobile
- Power BI Gateway: Connect on-premise data
- Power BI Report Server: On-premise reporting

3️⃣ What is DAX?
DAX (Data Analysis Expressions) is the formula language used to create custom measures, calculated columns, and tables.

4️⃣ Calculated Column vs Measure?
- Calculated Column: Row-by-row calculation, adds new column
- Measure: Aggregates data, used in visuals

5️⃣ DirectQuery vs Import Mode?
- Import: Faster, data stored in Power BI
- DirectQuery: Real-time queries, slower, connects live to DB

6️⃣ What are Relationships in Power BI?
They define how tables connect using keys, allowing cross-table filtering and data modeling.

7️⃣ How to optimize performance?
- Use Import mode
- Follow Star Schema
- Limit visuals & slicers
- Use aggregated tables
- Optimize DAX

8️⃣ What is a Slicer?
A visual filter users can interact with to filter data on the report page.

9️⃣ Handling null values?
- Use Replace Values in Power Query
- Use DAX like: IF(ISBLANK([Column]), 0, [Column])
- Use COALESCE for defaults

🔟 What are Bookmarks?
They save the report's state (filters, visuals) to create guided views or navigation buttons.

👍 React ❤️ if you found this helpful!

✅ Top 7 Must-Prepare Topics for Data Analyst Interviews (2025 Edition) 📊🕵️‍♂️

1️⃣ SQL Mastery
⦁ Joins, subqueries, window functions
⦁ Aggregations & groupings
⦁ Query optimization & data manipulation

2️⃣ Excel Skills
⦁ Pivot tables & charts
⦁ VLOOKUP, INDEX-MATCH
⦁ Data cleaning & conditional formatting

3️⃣ Data Visualization
⦁ Tools: Power BI, Tableau basics
⦁ Creating dashboards & reports
⦁ Storytelling with data

4️⃣ Statistics & Probability
⦁ Denoscriptive stats (mean, median, mode)
⦁ Probability concepts & distributions
⦁ Hypothesis testing & confidence intervals

5️⃣ Data Cleaning & Wrangling
⦁ Handling missing values & outliers
⦁ Data validation & transformation
⦁ Working with messy datasets

6️⃣ Basic Programming (Python/R)
⦁ Data manipulation with Pandas/R tidyverse
⦁ Writing functions & automation noscripts
⦁ Simple EDA (exploratory data analysis)

7️⃣ Business Acumen & Problem Solving
⦁ KPIs & metrics understanding
⦁ Translating business questions to analysis
⦁ Communicating insights effectively

💬 Tap ❤️ for more!

How to Become a Data Analyst from Scratch! 🚀

Whether you're starting fresh or upskilling, here's your roadmap:

➜ Master Excel and SQL - solve SQL problems from leetcode & hackerank
➜ Get the hang of either Power BI or Tableau - do some hands-on projects
➜ learn what the heck ATS is and how to get around it
➜ learn to be ready for any interview question
➜ Build projects for a data portfolio
➜ And you don't need to do it all at once!
➜ Fail and learn to pick yourself up whenever required

Whether it's acing interviews or building an impressive portfolio, give yourself the space to learn, fail, and grow. Good things take time ✅

Like if it helps ❤️

I have curated best 80+ top-notch Data Analytics Resources 👇👇
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Hope it helps :)

Data Analyst interview questions

1) What joins are mostly used in SQL?
2) Use cases of Cross and Self Joins?
3) Write a query to exclude weekends from a table?
4) What are Window Functions?
5) What is the difference between CTEs and Subqueries?
6) How can you optimize SQL queries?
7) How can you convert data from rows into columns?
8) If there are 10 different KPIs calculated from different tables on a daily basis, how would you compile them into a single report?

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