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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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✅ Survey Data Analysis – Clean, visualize, and interpret survey insights
✅ E-commerce Data Analysis – Funnel analysis, product trends, and revenue mapping
✅ Superstore Sales Analysis – Use public datasets to show time series and cohort trends
✅ Marketing Campaign Effectiveness – SQL + A/B test analysis with statistical methods
✅ Financial Dashboard – Visualize profit, loss, and KPIs using Power BI
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What is the difference between data scientist, data engineer, data analyst and business intelligence?

🧑🔬 Data Scientist
Focus: Using data to build models, make predictions, and solve complex problems.
Cleans and analyzes data
Builds machine learning models
Answers “Why is this happening?” and “What will happen next?”
Works with statistics, algorithms, and coding (Python, R)
Example: Predict which customers are likely to cancel next month

🛠️ Data Engineer
Focus: Building and maintaining the systems that move and store data.
Designs and builds data pipelines (ETL/ELT)
Manages databases, data lakes, and warehouses
Ensures data is clean, reliable, and ready for others to use
Uses tools like SQL, Airflow, Spark, and cloud platforms (AWS, Azure, GCP)
Example: Create a system that collects app data every hour and stores it in a warehouse

📊 Data Analyst
Focus: Exploring data and finding insights to answer business questions.
Pulls and visualizes data (dashboards, reports)
Answers “What happened?” or “What’s going on right now?”
Works with SQL, Excel, and tools like Tableau or Power BI
Less coding and modeling than a data scientist
Example: Analyze monthly sales and show trends by region

📈 Business Intelligence (BI) Professional
Focus: Helping teams and leadership understand data through reports and dashboards.
Designs dashboards and KPIs (key performance indicators)
Translates data into stories for non-technical users
Often overlaps with data analyst role but more focused on reporting
Tools: Power BI, Looker, Tableau, Qlik
Example: Build a dashboard showing company performance by department

🧩 Summary Table
Data Scientist - What will happen? Tools: Python, R, ML tools, predictions & models
Data Engineer - How does the data move and get stored? Tools: SQL, Spark, cloud tools, infrastructure & pipelines
Data Analyst - What happened? Tools: SQL, Excel, BI tools, reports & exploration
BI Professional - How can we see business performance clearly? Tools: Power BI, Tableau, dashboards & insights for decision-makers

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Data Engineers build the roads.
Data Scientists drive smart cars to predict traffic.
Data Analysts look at traffic data to see patterns.
BI Professionals show everyone the traffic report on a screen.