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Data Analysis is not just SQL.
Data Analysis is not just PowerBI/Tableau.
Data Analysis is not just Python.
Data Analysis is not just Excel.


𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐢𝐬 𝐚𝐛𝐨𝐮𝐭:

✅𝐈𝐧𝐬𝐢𝐠𝐡𝐭 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲: It's about uncovering the stories hidden within the data.

✅𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐌𝐚𝐤𝐢𝐧𝐠: It's about informing business decisions with data-driven insights.

✅ 𝐓𝐫𝐞𝐧𝐝 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: It's about identifying trends and patterns to forecast future outcomes.

✅ 𝐏𝐫𝐨𝐛𝐥𝐞𝐦-𝐒𝐨𝐥𝐯𝐢𝐧𝐠: It's about addressing business challenges with data-backed solutions.

✅ 𝐂𝐫𝐢𝐭𝐢𝐜𝐚𝐥 𝐓𝐡𝐢𝐧𝐤𝐢𝐧𝐠: It's about evaluating data with an analytical mindset to ensure accurate and reliable conclusions.

✅ 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐈𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭: It's about iterating and refining processes for better outcomes.

Tools like Power BI, Tableau, Excel, and Python are just that—tools. The real value lies in how we use them to transform data into actionable insights.

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✨The STAR method is a powerful technique used to answer behavioral interview questions effectively.

It helps structure responses by focusing on Situation, Task, Action, and Result. For analytics professionals, using the STAR method ensures that you demonstrate your problem-solving abilities, technical skills, and business acumen in a clear and concise way.

Here’s how the STAR method works, tailored for an analytics interview:

📍 1. Situation
Describe the context or challenge you faced. For analysts, this might be related to data challenges, business processes, or system inefficiencies. Be specific about the setting, whether it was a project, a recurring task, or a special initiative.

Example: “At my previous role as a data analyst at XYZ Company, we were experiencing a high churn rate among our subnoscription customers. This was a critical issue because it directly impacted revenue.”*

📍 2. Task
Explain the responsibilities you had or the goals you needed to achieve in that situation. In analytics, this usually revolves around diagnosing the problem, designing experiments, or conducting data analysis.

Example: “I was tasked with identifying the factors contributing to customer churn and providing actionable insights to the marketing team to help them improve retention.”*

📍 3. Action
Detail the specific actions you took to address the problem. Be sure to mention any tools, software, or methodologies you used (e.g., SQL, Python, data #visualization tools, #statistical #models). This is your opportunity to showcase your technical expertise and approach to problem-solving.

Example: “I collected and analyzed customer data using #SQL to extract key trends. I then used #Python for data cleaning and statistical analysis, focusing on engagement metrics, product usage patterns, and customer feedback. I also collaborated with the marketing and product teams to understand business priorities.”*

📍 4. Result
Highlight the outcome of your actions, especially any measurable impact. Quantify your results if possible, as this demonstrates your effectiveness as an analyst. Show how your analysis directly influenced business decisions or outcomes.

Example: “As a result of my analysis, we discovered that customers were disengaging due to a lack of certain product features. My insights led to a targeted marketing campaign and product improvements, reducing churn by 15% over the next quarter.”*

Example STAR Answer for an Analytics Interview Question:
Question: *"Tell me about a time you used data to solve a business problem."*

Answer (STAR format): 
🔻*S*: “At my previous company, our sales team was struggling with inconsistent performance, and management wasn’t sure which factors were driving the variance.” 
🔻*T*: “I was assigned the task of conducting a detailed analysis to identify key drivers of sales performance and propose data-driven recommendations.” 
🔻*A*: “I began by collecting sales data over the past year and segmented it by region, product line, and sales representative. I then used Python for #statistical #analysis and developed a regression model to determine the key factors influencing sales outcomes. I also visualized the data using #Tableau to present the findings to non-technical stakeholders.” 
🔻*R*: “The analysis revealed that product mix and regional seasonality were significant contributors to the variability. Based on my findings, the company adjusted their sales strategy, leading to a 20% increase in sales efficiency in the next quarter.”

Hope this helps you 😊

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10 DAX Functions Every Power BI Learner Should Know!



1. SUM
   Scenario: Calculate the total sales amount.
   DAX Formula: Total Sales = SUM(Sales[SalesAmount])

2. AVERAGE
   Scenario: Find the average sales per transaction.
   DAX Formula: Average Sales = AVERAGE(Sales[SalesAmount])

3. COUNTROWS
   Scenario: Count the number of transactions.
   DAX Formula: Transaction Count = COUNTROWS(Sales)

4. DISTINCTCOUNT
   Scenario: Count the number of unique customers.
   DAX Formula: Unique Customers = DISTINCTCOUNT(Sales[CustomerID])

5. CALCULATE
   Scenario: Calculate the total sales for a specific product category.
   DAX Formula: Total Sales (Category) = CALCULATE(SUM(Sales[SalesAmount]), Products[Category] = "Electronics")

6. FILTER
   Scenario: Calculate the total sales for transactions above a certain amount.
   DAX Formula: High Value Sales = CALCULATE(SUM(Sales[SalesAmount]), FILTER(Sales, Sales[SalesAmount] > 1000))

7. IF
   Scenario: Create a calculated column to categorize transactions as "High" or "Low" based on sales amount.
   DAX Formula: Transaction Category = IF(Sales[SalesAmount] > 500, "High", "Low")

8. RELATED
   Scenario: Fetch product names from the Products table into the Sales table.
   DAX Formula: Product Name = RELATED(Products[ProductName])

9. YEAR
   Scenario: Extract the year from the transaction date.
   DAX Formula: Transaction Year = YEAR(Sales[TransactionDate])

10. DATESYTD
    Scenario: Calculate year-to-date sales.
    DAX Formula: YTD Sales = TOTALYTD(SUM(Sales[SalesAmount]), Sales[TransactionDate])

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Data Science Interview Questions with Answers

What’s the difference between random forest and gradient boosting?

Random Forests builds each tree independently while Gradient Boosting builds one tree at a time.
Random Forests combine results at the end of the process (by averaging or "majority rules") while Gradient Boosting combines results along the way.

What happens to our linear regression model if we have three columns in our data: x, y, z  —  and z is a sum of x and y?

We would not be able to perform the regression. Because z is linearly dependent on x and y so when performing the regression  would be a singular (not invertible) matrix.

Which regularization techniques do you know?

There are mainly two types of regularization,

L1 Regularization (Lasso regularization) - Adds the sum of absolute values of the coefficients to the cost function.
L2 Regularization (Ridge regularization) - Adds the sum of squares of coefficients to the cost function

Here, Lambda determines the amount of regularization.

How does L2 regularization look like in a linear model?

L2 regularization adds a penalty term to our cost function which is equal to the sum of squares of models coefficients multiplied by a lambda hyperparameter.

This technique makes sure that the coefficients are close to zero and is widely used in cases when we have a lot of features that might correlate with each other.

What are the main parameters in the gradient boosting model?

There are many parameters, but below are a few key defaults.

learning_rate=0.1 (shrinkage).
n_estimators=100 (number of trees).
max_depth=3.
min_samples_split=2.
min_samples_leaf=1.
subsample=1.0.

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