𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 & 𝗙𝘂𝗹𝗹𝘀𝘁𝗮𝗰𝗸 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗔𝗿𝗲 𝗛𝗶𝗴𝗵𝗹𝘆 𝗗𝗲𝗺𝗮𝗻𝗱𝗶𝗻𝗴 𝗜𝗻 𝟮𝟬𝟮𝟲😍

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1. What are the ways to detect outliers?

Outliers are detected using two methods:

Box Plot Method: According to this method, the value is considered an outlier if it exceeds or falls below 1.5*IQR (interquartile range), that is, if it lies above the top quartile (Q3) or below the bottom quartile (Q1).

Standard Deviation Method: According to this method, an outlier is defined as a value that is greater or lower than the mean ± (3*standard deviation).


2. What is a Recursive Stored Procedure?

A stored procedure that calls itself until a boundary condition is reached, is called a recursive stored procedure. This recursive function helps the programmers to deploy the same set of code several times as and when required.


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.

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✅ Data Analytics Essentials

TECH SKILLS (NON-NEGOTIABLE)

1️⃣ SQL
• Joins, Group by, Window functions
• Handle NULLs and duplicates
Example: LEFT JOIN fits a churn query to include non-churned users

2️⃣ Excel
• Pivot tables, Lookups, IF logic
• Clean raw data fast
Example: Reconcile 50k rows in minutes using Pivot tables

3️⃣ Power BI or Tableau
• Data modeling, Measures, Filters
• One dashboard, One question
Example: Sales drop by region and month dashboard

4️⃣ Python
• pandas for cleaning and analysis
• matplotlib or seaborn for quick visuals
Example: Groupby revenue by cohort

5️⃣ Statistics Basics
• Mean vs median, Variance, Correlation
• Know when averages lie
Example: Median salary explains skewed data

 

SOFT SKILLS (DEAL BREAKERS)

1️⃣ Business Thinking
• Ask why before how
• Tie insights to decisions
Example: High churn points to onboarding gaps

2️⃣ Communication
• Explain insights without jargon
• One slide, One takeaway
Example: Revenue fell due to fewer repeat users

3️⃣ Problem Framing
• Convert vague asks into clear questions
• Define metrics early
Example: What defines an active user?

4️⃣ Attention to Detail
• Validate numbers
• Double check logic
• Small errors kill trust

5️⃣ Stakeholder Handling
• Listen first
• Clarify scope
• Push back with data

🎯 Balance both tech and soft skills to grow faster as an analyst

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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.

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🧠 SQL Interview Question (Commonly Asked)
📌

orders(order_id, customer_id, order_date, order_amount)

❓ Ques :

👉 Find customers whose order amount strictly increased compared to their previous order.

🧩 How Interviewers Expect You to Think

• Order data correctly using dates
• Compare current row with previous row
• Use window functions for self-comparison
• Avoid self-joins when window functions fit better
• Filter only after comparison logic

💡 SQL Solution

WITH ranked_orders AS (
SELECT
customer_id,
order_date,
order_amount,
LAG(order_amount) OVER (
PARTITION BY customer_id
ORDER BY order_date
) AS prev_order_amount
FROM orders
)
SELECT DISTINCT
customer_id
FROM ranked_orders
WHERE order_amount > prev_order_amount;

🔥 React ♥️ if you want more moderate-to-advanced SQL interview questions

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🧠 SQL Interview Question (Commonly Asked)
📌

products(product_id, product_name, category_id, price)

❓ Ques :

👉 Find the second highest priced product in each category.

🧩 How Interviewers Expect You to Think

• Partition data by category
• Rank products based on price (descending)
• Understand difference between RANK, DENSE_RANK, and ROW_NUMBER
• Handle ties properly
• Filter after ranking logic

💡 SQL Solution

WITH ranked_products AS (
SELECT
product_id,
product_name,
category_id,
price,
DENSE_RANK() OVER (
PARTITION BY category_id
ORDER BY price DESC
) AS price_rank
FROM products
)

SELECT
product_id,
product_name,
category_id,
price
FROM ranked_products
WHERE price_rank = 2;

🔥 Why this question is powerful:

• Tests window functions deeply
• Checks ranking logic understanding
• Very common in Data Analyst interviews

❤️ React if you want more scenario-based SQL questions

🧠 SQL Interview Question (Tricky & Logic-Based)
📌

logins(user_id, login_date)

❓ Ques :

👉 Find users who logged in for 3 or more consecutive days.

🧩 How Interviewers Expect You to Think

• Understand consecutive date logic
• Use date arithmetic smartly
• Create groups using row-number difference trick
• Avoid complex self-joins
• Aggregate after forming streak groups

💡 SQL Solution

WITH numbered_logins AS (
SELECT
user_id,
login_date,
ROW_NUMBER() OVER (
PARTITION BY user_id
ORDER BY login_date
) AS rn
FROM logins
),
grouped_logins AS (
SELECT
user_id,
login_date,
DATE_SUB(login_date, INTERVAL rn DAY) AS grp
FROM numbered_logins
)

SELECT
user_id
FROM grouped_logins
GROUP BY user_id, grp
HAVING COUNT(*) >= 3;

🔥 Why this question is powerful:

• Tests advanced window function usage
• Checks understanding of gaps & islands concept
• Evaluates real-world product analytics thinking
• Very common in growth / engagement analytics interviews

❤️ React if you want more scenario-based SQL questions

🧠 SQL Interview Question (Moderate & Analytical)
📌

events(user_id, event_name, event_date)

-- event_name values: 'Visited', 'Added_to_Cart', 'Purchased'

❓ Ques :

👉 Find users who added a product to cart but never completed the purchase.

🧩 How Interviewers Expect You to Think

• Understand funnel stage logic
• Apply conditional aggregation correctly
• Ensure absence of a specific event
• Avoid double counting users

💡 SQL Solution

SELECT
user_id
FROM events
GROUP BY user_id
HAVING
SUM(CASE WHEN event_name = 'Added_to_Cart' THEN 1 ELSE 0 END) > 0
AND SUM(CASE WHEN event_name = 'Purchased' THEN 1 ELSE 0 END) = 0;

🔥 Why This Question Is Powerful

• Tests real business thinking (conversion funnel analysis)
• Checks ability to detect missing conditions
• Common in product & e-commerce analytics interviews
• Evaluates aggregation + logical filtering skills together

❤️ React if you want more real interview-level SQL questions

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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. Explain the Difference Between Tableau Worksheet, Dashboard, Story, and Workbook in Tableau?

Tableau uses a workbook and sheet file structure, much like Microsoft Excel.
A workbook contains sheets, which can be a worksheet, dashboard, or a story.
A worksheet contains a single view along with shelves, legends, and the Data pane.
A dashboard is a collection of views from multiple worksheets.
A story contains a sequence of worksheets or dashboards that work together to convey information.

4. How can you split a column into 2 or more columns?

You can split a column into 2 or more columns by following the below steps:
1. Select the cell that you want to split. Then, navigate to the Data tab, after that, select Text to Columns. 2. Select the delimiter. 3. Choose the column data format and select the destination you want to display the split. 4. The final output will look like below where the text is split into multiple columns.

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🧠 SQL Interview Question (Moderate & Revenue Analysis)
📌

orders(order_id, customer_id, order_amount)

❓ Ques :

👉 Find customers who contribute more than 30% of the total company revenue.

🧩 How Interviewers Expect You to Think

• Calculate overall total revenue
• Aggregate revenue at customer level
• Compare individual contribution against total
• Avoid recalculating total multiple times inefficiently

💡 SQL Solution

WITH total_revenue AS (
SELECT SUM(order_amount) AS total_rev
FROM orders
),
customer_revenue AS (
SELECT
customer_id,
SUM(order_amount) AS cust_rev
FROM orders
GROUP BY customer_id
)
SELECT c.customer_id
FROM customer_revenue c
CROSS JOIN total_revenue t
WHERE c.cust_rev > 0.30 * t.total_rev;

🔥 Why This Question Is Powerful

• Tests percentage-based business logic
• Evaluates ability to combine multiple aggregations
• Reflects real-world Pareto (80/20) analysis scenarios
• Common in product, growth & revenue analytics interviews

❤️ React if you want more real interview-level SQL questions