Tableau Cheat Sheet ✅

This Tableau cheatsheet is designed to be your quick reference guide for data visualization and analysis using Tableau. Whether you’re a beginner learning the basics or an experienced user looking for a handy resource, this cheatsheet covers essential topics.

1. Connecting to Data
- Use *Connect* pane to connect to various data sources (Excel, SQL Server, Text files, etc.).

2. Data Preparation
- Data Interpreter: Clean data automatically using the Data Interpreter.
- Join Data: Combine data from multiple tables using joins (Inner, Left, Right, Outer).
- Union Data: Stack data from multiple tables with the same structure.

3. Creating Views
- Drag & Drop: Drag fields from the Data pane onto Rows, Columns, or Marks to create visualizations.
- Show Me: Use the *Show Me* panel to select different visualization types.

4. Types of Visualizations
- Bar Chart: Compare values across categories.
- Line Chart: Display trends over time.
- Pie Chart: Show proportions of a whole (use sparingly).
- Map: Visualize geographic data.
- Scatter Plot: Show relationships between two variables.

5. Filters
- Dimension Filters: Filter data based on categorical values.
- Measure Filters: Filter data based on numerical values.
- Context Filters: Set a context for other filters to improve performance.

6. Calculated Fields
- Create calculated fields to derive new data:
- Example: Sales Growth = SUM([Sales]) - SUM([Previous Sales])

7. Parameters
- Use parameters to allow user input and control measures dynamically.

8. Formatting
- Format fonts, colors, borders, and lines using the Format pane for better visual appeal.

9. Dashboards
- Combine multiple sheets into a dashboard using the *Dashboard* tab.
- Use dashboard actions (filter, highlight, URL) to create interactivity.

10. Story Points
- Create a story to guide users through insights with narrative and visualizations.

11. Publishing & Sharing
- Publish dashboards to Tableau Server or Tableau Online for sharing and collaboration.

12. Export Options
- Export to PDF or image for offline use.

13. Keyboard Shortcuts
- Show/Hide Sidebar: Ctrl+Alt+T
- Duplicate Sheet: Ctrl + D
- Undo: Ctrl + Z
- Redo: Ctrl + Y

14. Performance Optimization
- Use extracts instead of live connections for faster performance.
- Optimize calculations and filters to improve dashboard loading times.

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Data Analyst vs Data Scientist: Must-Know Differences

Data Analyst:
- Role: Primarily focuses on interpreting data, identifying trends, and creating reports that inform business decisions.
- Best For: Individuals who enjoy working with existing data to uncover insights and support decision-making in business processes.
- Key Responsibilities:
  - Collecting, cleaning, and organizing data from various sources.
  - Performing denoscriptive analytics to summarize the data (trends, patterns, anomalies).
  - Creating reports and dashboards using tools like Excel, SQL, Power BI, and Tableau.
  - Collaborating with business stakeholders to provide data-driven insights and recommendations.
- Skills Required:
  - Proficiency in data visualization tools (e.g., Power BI, Tableau).
  - Strong analytical and statistical skills, along with expertise in SQL and Excel.
  - Familiarity with business intelligence and basic programming (optional).
- Outcome: Data analysts provide actionable insights to help companies make informed decisions by analyzing and visualizing data, often focusing on current and historical trends.

Data Scientist:
- Role: Combines statistical methods, machine learning, and programming to build predictive models and derive deeper insights from data.
- Best For: Individuals who enjoy working with complex datasets, developing algorithms, and using advanced analytics to solve business problems.
- Key Responsibilities:
  - Designing and developing machine learning models for predictive analytics.
  - Collecting, processing, and analyzing large datasets (structured and unstructured).
  - Using statistical methods, algorithms, and data mining to uncover hidden patterns.
  - Writing and maintaining code in programming languages like Python, R, and SQL.
  - Working with big data technologies and cloud platforms for scalable solutions.
- Skills Required:
  - Proficiency in programming languages like Python, R, and SQL.
  - Strong understanding of machine learning algorithms, statistics, and data modeling.
  - Experience with big data tools (e.g., Hadoop, Spark) and cloud platforms (AWS, Azure).
- Outcome: Data scientists develop models that predict future outcomes and drive innovation through advanced analytics, going beyond what has happened to explain why it happened and what will happen next.

Data analysts focus on analyzing and visualizing existing data to provide insights for current business challenges, while data scientists apply advanced algorithms and machine learning to predict future outcomes and derive deeper insights. Data scientists typically handle more complex problems and require a stronger background in statistics, programming, and machine learning.

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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.

Hope it helps :)

Amazon Data Analyst Interview Questions for 1-3 years of experience role :-

A. SQL:

1. You have two tables: Employee and Department.
- Employee Table Columns: Employee_id, Employee_Name, Department_id, Salary
- Department Table Columns: Department_id, Department_Name, Location

Write an SQL query to find the name of the employee with the highest salary in each location.

2. You have two tables: Orders and Customers.
- Orders Table Columns: Order_id, Customer_id, Order_Date, Amount
- Customers Table Columns: Customer_id, Customer_Name, Join_Date

Write an SQL query to calculate the total order amount for each customer who joined in the current year. The output should contain Customer_Name and the total amount.

B. Python:

1. Basic oral questions on NumPy (e.g., array creation, slicing, broadcasting) and Matplotlib (e.g., plot types, customization).

2. Basic oral questions on pandas (like: groupby, loc/iloc, merge & join, etc.)

2. Write the code in NumPy and Pandas to replicate the functionality of your answer to the second SQL question.

C. Leadership or Situational Questions:

(Based on the leadership principle of Bias for Action)

- Describe a situation where you had to make a quick decision with limited information. How did you proceed, and what was the outcome?

(Based on the leadership principle of Dive Deep)

- Can you share an example of a project where you had to delve deeply into the data to uncover insights or solve a problem? What steps did you take, and what were the results?

(Based on the leadership principle of Customer Obsession)

- Tell us about a time when you went above and beyond to meet a customer's needs or expectations. How did you identify their requirements, and what actions did you take to deliver exceptional service?

D. Excel:

Questions on advanced functions like VLOOKUP, XLookup, SUMPRODUCT, INDIRECT, TEXT functions, SUMIFS, COUNTIFS, LOOKUPS, INDEX & MATCH, AVERAGEIFS. Plus, some basic questions on pivot tables, conditional formatting, data validation, and charts.

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If I had to start learning data analyst all over again, I'd follow this:

1- Learn SQL:

---- Joins (Inner, Left, Full outer and Self)
---- Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
---- Group by and Having clause
---- CTE and Subquery
---- Windows Function (Rank, Dense Rank, Row number, Lead, Lag etc)

2- Learn Excel:

---- Mathematical (COUNT, SUM, AVG, MIN, MAX, etc)
---- Logical Functions (IF, AND, OR, NOT)
---- Lookup and Reference (VLookup, INDEX, MATCH etc)
---- Pivot Table, Filters, Slicers

3- Learn BI Tools:

---- Data Integration and ETL (Extract, Transform, Load)
---- Report Generation
---- Data Exploration and Ad-hoc Analysis
---- Dashboard Creation

4- Learn Python (Pandas) Optional:

---- Data Structures, Data Cleaning and Preparation
---- Data Manipulation
---- Merging and Joining Data (Merging and joining DataFrames -similar to SQL joins)
---- Data Visualization (Basic plotting using Matplotlib and Seaborn)

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Essential Pandas Functions for Data Analysis

Data Loading:

pd.read_csv() - Load data from a CSV file.

pd.read_excel() - Load data from an Excel file.


Data Inspection:

df.head(n) - View the first n rows.

df.info() - Get a summary of the dataset.

df.describe() - Generate summary statistics.


Data Manipulation:

df.drop(columns=['col1', 'col2']) - Remove specific columns.

df.rename(columns={'old_name': 'new_name'}) - Rename columns.

df['col'] = df['col'].apply(func) - Apply a function to a column.


Filtering and Sorting:

df[df['col'] > value] - Filter rows based on a condition.

df.sort_values(by='col', ascending=True) - Sort rows by a column.


Aggregation:

df.groupby('col').sum() - Group data and compute the sum.

df['col'].value_counts() - Count unique values in a column.


Merging and Joining:

pd.merge(df1, df2, on='key') - Merge two DataFrames.

pd.concat([df1, df2]) - Concatenate

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SQL Interview Questions which can be asked in a Data Analyst Interview.

1️⃣ What is difference between Primary key and Unique key?

◼Primary key- A column or set of columns which uniquely identifies each record in a table. It can't contain null values and only one primary key
can exist in a table.

◼Unique key-Similar to primary key it also uniquely identifies each record in a table and can contain null values.Multiple Unique key can exist in a table.

2️⃣ What is a Candidate key?

◼A key or set of keys that uniquely identifies each record in a table.It is a combination of Primary and Alternate key.

3️⃣ What is a Constraint?

◼Specific rule or limit that we define in our table. E.g - NOT NULL,AUTO INCREMENT

4️⃣ Can you differentiate between TRUNCATE and DELETE?

◼TRUNCATE is a DDL command. It deletes the entire data from a table but preserves the structure of table.It doesn't deletes the data row by row hence faster than DELETE command, while DELETE is a DML command and it deletes the entire data based on specified condition else deletes the entire data,also it deletes the data row by row hence slower than TRUNCATE command.

5️⃣ What is difference between 'View' and 'Stored Procedure'?

◼A View is a virtual table that gets data from the base table .It is basically a Select statement,while Stored Procedure is a sql statement or set of sql statement stored on database server.

6️⃣ What is difference between a Common Table Expression and temporary table?

◼CTE is a temporary result set that is defined within execution scope of a single SELECT ,DELETE,UPDATE statement while temporary table is stored in TempDB and gets deleted once the session expires.

7️⃣ Differentiate between a clustered index and a non-clustered index?

◼ A clustered index determines physical ordering of data in a table and a table can have only one clustered index while a non-clustered index is analogous to index of a book where index is stored at one place and data at other place and index will have pointers to storage location of the data,a table can have more than one non-clustered index.

8️⃣ Explain triggers ?

◼They are sql codes which are automatically executed in response to certain events on a table.They are used to maintain integrity of data.

20 Must-Know Statistics Questions for Data Analyst and Business Analyst Role:

1️⃣ What is the difference between denoscriptive and inferential statistics?
2️⃣ Explain mean, median, and mode and when to use each.
3️⃣ What is standard deviation, and why is it important?
4️⃣ Define correlation vs. causation with examples.
5️⃣ What is a p-value, and how do you interpret it?
6️⃣ Explain the concept of confidence intervals.
7️⃣ What are outliers, and how can you handle them?
8️⃣ When would you use a t-test vs. a z-test?
9️⃣ What is the Central Limit Theorem (CLT), and why is it important?
🔟 Explain the difference between population and sample.
1️⃣1️⃣ What is regression analysis, and what are its key assumptions?
1️⃣2️⃣ How do you calculate probability, and why does it matter in analytics?
1️⃣3️⃣ Explain the concept of Bayes’ Theorem with a practical example.
1️⃣4️⃣ What is an ANOVA test, and when should it be used?
1️⃣5️⃣ Define skewness and kurtosis in a dataset.
1️⃣6️⃣ What is the difference between parametric and non-parametric tests?
1️⃣7️⃣ What are Type I and Type II errors in hypothesis testing?
1️⃣8️⃣ How do you handle missing data in a dataset?
1️⃣9️⃣ What is A/B testing, and how do you analyze the results?
2️⃣0️⃣ What is a Chi-square test, and when is it used?

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20 Must-Know Statistics Questions for Data Analyst and Business Analyst Roles (With Detailed Answers)

1. What is the difference between denoscriptive and inferential statistics?

Denoscriptive statistics summarize and organize data (e.g., mean, median, mode).

Inferential statistics make predictions or inferences about a population based on a sample (e.g., hypothesis testing, confidence intervals).


2. Explain mean, median, and mode and when to use each.

Mean is the average; use when data is symmetrically distributed.

Median is the middle value; best when data has outliers.

Mode is the most frequent value; useful for categorical data.


3. What is standard deviation, and why is it important?

It measures data spread around the mean. A low value = less variability; high value = more spread. Important for understanding consistency and risk.


4. Define correlation vs. causation with examples.

Correlation: Two variables move together but don't cause each other (e.g., ice cream sales and drowning).

Causation: One variable directly affects another (e.g., smoking causes lung cancer).


5. What is a p-value, and how do you interpret it?

P-value measures the probability of observing results given that the null hypothesis is true. A small p-value (typically < 0.05) suggests rejecting the null.


6. Explain the concept of confidence intervals.

A range of values used to estimate a population parameter. A 95% CI means there's a 95% chance the true value falls within the range.


7. What are outliers, and how can you handle them?

Outliers are extreme values differing significantly from others. Handle using:

Removal (if due to error)

Transformation

Capping (e.g., winsorizing)



8. When would you use a t-test vs. a z-test?

T-test: Small samples (n < 30) and unknown population standard deviation.

Z-test: Large samples and known standard deviation.


9. What is the Central Limit Theorem (CLT), and why is it important?

CLT states that the sampling distribution of the sample mean approaches a normal distribution as sample size grows, regardless of population distribution. Essential for inference.


10. Explain the difference between population and sample.

Population: Entire group of interest.

Sample: Subset used for analysis. Inference is made from the sample to the population.


11. What is regression analysis, and what are its key assumptions?

Predicts a dependent variable using one or more independent variables.

Assumptions: Linearity, independence, homoscedasticity, no multicollinearity, normality of residuals.


12. How do you calculate probability, and why does it matter in analytics?

Probability = (Favorable outcomes) / (Total outcomes).

Critical for risk estimation, decision-making, and predictions.


13. Explain the concept of Bayes’ Theorem with a practical example.

Bayes’ updates the probability of an event based on new evidence:

P(A|B) = [P(B|A) * P(A)] / P(B)


Example: Calculating disease probability given a positive test result.


14. What is an ANOVA test, and when should it be used?

ANOVA (Analysis of Variance) compares means across 3+ groups to see if at least one differs.

Use when comparing more than two groups.


15. Define skewness and kurtosis in a dataset.

Skewness: Measure of asymmetry (positive = right-skewed, negative = left).

Kurtosis: Measure of tail thickness (high kurtosis = heavy tails, outliers).


16. What is the difference between parametric and non-parametric tests?

Parametric: Assumes data follows a distribution (e.g., t-test).

Non-parametric: No assumptions; use with skewed or ordinal data (e.g., Mann-Whitney U).


17. What are Type I and Type II errors in hypothesis testing?

Type I error: False positive (rejecting a true null).

Type II error: False negative (failing to reject a false null).


18. How do you handle missing data in a dataset?

Methods:

Deletion (listwise or pairwise)

Imputation (mean, median, mode, regression)

Advanced: KNN, MICE

19. What is A/B testing, and how do you analyze the results?

Comparing two versions (A & B) to see which performs better.

Use t-tests or proportions test, check for statistical significance.


20. What is a Chi-square test, and when is it used?

Tests independence between categorical variables.

Used in contingency tables (e.g., is gender associated with purchase behavior?).

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I see so many people jump into data analytics, excited by its popularity, only to feel lost or uninterested soon after. I get it, data isn’t for everyone, and that’s okay.

Data analytics requires a certain spark or say curiosity. You need that drive to dig deeper, to understand why things happen, to explore how data pieces connect to reveal a bigger picture. Without that spark, it’s easy to feel overwhelmed or even bored.

Before diving in, ask yourself, Do I really enjoy solving puzzles? Am I genuinely excited about numbers, patterns, and insights? If you’re curious and love learning, data can be incredibly rewarding. But if it’s just about following a trend, it might not be a fulfilling path for you.

Be honest with yourself. Find your passion, whether it’s in data or somewhere else and invest in something that truly excites you.

Hope this helps you 😊

Tableau Cheat Sheet ✅

This Tableau cheatsheet is designed to be your quick reference guide for data visualization and analysis using Tableau. Whether you’re a beginner learning the basics or an experienced user looking for a handy resource, this cheatsheet covers essential topics.

1. Connecting to Data
   - Use *Connect* pane to connect to various data sources (Excel, SQL Server, Text files, etc.).

2. Data Preparation
   - Data Interpreter: Clean data automatically using the Data Interpreter.
   - Join Data: Combine data from multiple tables using joins (Inner, Left, Right, Outer).
   - Union Data: Stack data from multiple tables with the same structure.

3. Creating Views
   - Drag & Drop: Drag fields from the Data pane onto Rows, Columns, or Marks to create visualizations.
   - Show Me: Use the *Show Me* panel to select different visualization types.

4. Types of Visualizations
   - Bar Chart: Compare values across categories.
   - Line Chart: Display trends over time.
   - Pie Chart: Show proportions of a whole (use sparingly).
   - Map: Visualize geographic data.
   - Scatter Plot: Show relationships between two variables.

5. Filters
   - Dimension Filters: Filter data based on categorical values.
   - Measure Filters: Filter data based on numerical values.
   - Context Filters: Set a context for other filters to improve performance.

6. Calculated Fields
   - Create calculated fields to derive new data:
     - Example: Sales Growth = SUM([Sales]) - SUM([Previous Sales])

7. Parameters
   - Use parameters to allow user input and control measures dynamically.

8. Formatting
   - Format fonts, colors, borders, and lines using the Format pane for better visual appeal.

9. Dashboards
   - Combine multiple sheets into a dashboard using the *Dashboard* tab.
   - Use dashboard actions (filter, highlight, URL) to create interactivity.

10. Story Points
    - Create a story to guide users through insights with narrative and visualizations.

11. Publishing & Sharing
    - Publish dashboards to Tableau Server or Tableau Online for sharing and collaboration.

12. Export Options
    - Export to PDF or image for offline use.

13. Keyboard Shortcuts
    - Show/Hide Sidebar: Ctrl+Alt+T
    - Duplicate Sheet: Ctrl + D
    - Undo: Ctrl + Z
    - Redo: Ctrl + Y

14. Performance Optimization
    - Use extracts instead of live connections for faster performance.
    - Optimize calculations and filters to improve dashboard loading times.

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Myntra interview questions for Data Analyst 2024.

1. You have a dataset with missing values. How would you use a combination of Pandas and NumPy to fill missing values based on the mean of the column?
2. How would you create a new column in a Pandas DataFrame by normalizing an existing numeric column using NumPy’s np.min() and np.max()?
3. Explain how to group a Pandas DataFrame by one column and apply a NumPy function, like np.std() (standard deviation), to each group.
4. How can you convert a time-series column in a Pandas DataFrame to NumPy’s datetime format for faster time-based calculations?
5. How would you identify and remove outliers from a Pandas DataFrame using NumPy’s Z-score method (scipy.stats.zscore)?
6. How would you use NumPy’s percentile() function to calculate specific quantiles for a numeric column in a Pandas DataFrame?
7. How would you use NumPy's polyfit() function to perform linear regression on a dataset stored in a Pandas DataFrame?
8. How can you use a combination of Pandas and NumPy to transform categorical data into dummy variables (one-hot encoding)?
9. How would you use both Pandas and NumPy to split a dataset into training and testing sets based on a random seed?
10. How can you apply NumPy's vectorize() function on a Pandas Series for better performance?
11. How would you optimize a Pandas DataFrame containing millions of rows by converting columns to NumPy arrays? Explain the benefits in terms of memory and speed.
12. How can you perform complex mathematical operations, such as matrix multiplication, using NumPy on a subset of a Pandas DataFrame?
13. Explain how you can use np.select() to perform conditional column operations in a Pandas DataFrame.
14. How can you handle time series data in Pandas and use NumPy to perform statistical analysis like rolling variance or covariance?
15. How can you integrate NumPy's random module (np.random) to generate random numbers and add them as a new column in a Pandas DataFrame?
16. Explain how you would use Pandas' applymap() function combined with NumPy’s vectorized operations to transform all elements in a DataFrame.
17. How can you apply mathematical transformations (e.g., square root, logarithm) from NumPy to specific columns in a Pandas DataFrame?
18. How would you efficiently perform element-wise operations between a Pandas DataFrame and a NumPy array of different dimensions?
19. How can you use NumPy functions like np.linalg.inv() or np.linalg.det() for linear algebra operations on numeric columns of a Pandas DataFrame?
20. Explain how you would compute the covariance matrix between multiple numeric columns of a DataFrame using NumPy.
21. What are the key differences between a Pandas DataFrame and a NumPy array? When would you use one over the other?
22. How can you convert a NumPy array into a Pandas DataFrame, and vice versa? Provide an example.

You can find the answers here

Hope this helps you 😊

Data Analyst INTERVIEW QUESTIONS AND ANSWERS
👇👇

1.Can you name the wildcards in Excel?

Ans: There are 3 wildcards in Excel that can ve used in formulas.

Asterisk (*) – 0 or more characters. For example, Ex* could mean Excel, Extra, Expertise, etc.

Question mark (?) – Represents any 1 character. For example, R?ain may mean Rain or Ruin.

Tilde (~) – Used to identify a wildcard character (~, *, ?). For example, If you need to find the exact phrase India* in a list. If you use India* as the search string, you may get any word with India at the beginning followed by different characters (such as Indian, Indiana). If you have to look for India” exclusively, use ~.

Hence, the search string will be india~*. ~ is used to ensure that the spreadsheet reads the following character as is, and not as a wildcard.


2.What is cascading filter in tableau?

Ans: Cascading filters can also be understood as giving preference to a particular filter and then applying other filters on previously filtered data source. Right-click on the filter you want to use as a main filter and make sure it is set as all values in dashboard then select the subsequent filter and select only relevant values to cascade the filters. This will improve the performance of the dashboard as you have decreased the time wasted in running all the filters over complete data source.


3.What is the difference between .twb and .twbx extension?

Ans:
A .twb file contains information on all the sheets, dashboards and stories, but it won’t contain any information regarding data source. Whereas .twbx file contains all the sheets, dashboards, stories and also compressed data sources. For saving a .twbx extract needs to be performed on the data source. If we forward .twb file to someone else than they will be able to see the worksheets and dashboards but won’t be able to look into the dataset.


4.What are the various Power BI versions?

Power BI Premium capacity-based license, for example, allows users with a free license to act on content in workspaces with Premium capacity. A user with a free license can only use the Power BI service to connect to data and produce reports and dashboards in My Workspace outside of Premium capacity. They are unable to exchange material or publish it in other workspaces. To process material, a Power BI license with a free or Pro per-user license only uses a shared and restricted capacity. Users with a Power BI Pro license can only work with other Power BI Pro users if the material is stored in that shared capacity. They may consume user-generated information, post material to app workspaces, share dashboards, and subscribe to dashboards and reports. Pro users can share material with users who don’t have a Power BI Pro subnoscription while workspaces are at Premium capacity.

ENJOY LEARNING 👍👍

🔍 Best Data Analytics Roles Based on Your Graduation Background!

🚀 For Mathematics/Statistics Graduates:
🔹 Data Analyst
🔹 Statistical Analyst
🔹 Quantitative Analyst
🔹 Risk Analyst

🚀 For Computer Science/IT Graduates:
🔹 Data Scientist
🔹 Business Intelligence Developer
🔹 Data Engineer
🔹 Data Architect

🚀 For Economics/Finance Graduates:
🔹 Financial Analyst
🔹 Market Research Analyst
🔹 Economic Consultant
🔹 Data Journalist

🚀 For Business/Management Graduates:
🔹 Business Analyst
🔹 Operations Research Analyst
🔹 Marketing Analytics Manager
🔹 Supply Chain Analyst

🚀 For Engineering Graduates:
🔹 Data Scientist
🔹 Industrial Engineer
🔹 Operations Research Analyst
🔹 Quality Engineer

🚀 For Social Science Graduates:
🔹 Data Analyst
🔹 Research Assistant
🔹 Social Media Analyst
🔹 Public Health Analyst

🚀 For Biology/Healthcare Graduates:
🔹 Clinical Data Analyst
🔹 Biostatistician
🔹 Research Coordinator
🔹 Healthcare Consultant

Some of these roles may require additional certifications or upskilling in SQL, Python, Power BI, Tableau, or Machine Learning to stand out in the job market.

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Quick recap of essential SQL basics 😄👇

SQL is a domain-specific language used for managing and querying relational databases. It's crucial for interacting with databases, retrieving, storing, updating, and deleting data. Here are some fundamental SQL concepts:

1. Database
   - A database is a structured collection of data. It's organized into tables, and SQL is used to manage these tables.

2. Table
   - Tables are the core of a database. They consist of rows and columns, and each row represents a record, while each column represents a data attribute.

3. Query
   - A query is a request for data from a database. SQL queries are used to retrieve information from tables. The SELECT statement is commonly used for this purpose.

4. Data Types
   - SQL supports various data types (e.g., INTEGER, TEXT, DATE) to specify the kind of data that can be stored in a column.

5. Primary Key
   - A primary key is a unique identifier for each row in a table. It ensures that each row is distinct and can be used to establish relationships between tables.

6. Foreign Key
   - A foreign key is a column in one table that links to the primary key in another table. It creates relationships between tables in a database.

7. CRUD Operations
   - SQL provides four primary operations for data manipulation:
     - Create (INSERT) - Add new records to a table.
     - Read (SELECT) - Retrieve data from one or more tables.
     - Update (UPDATE) - Modify existing data.
     - Delete (DELETE) - Remove records from a table.

8. WHERE Clause
   - The WHERE clause is used in SELECT, UPDATE, and DELETE statements to filter and conditionally manipulate data.

9. JOIN
   - JOIN operations are used to combine data from two or more tables based on a related column. Common types include INNER JOIN, LEFT JOIN, and RIGHT JOIN.

10. Index
   - An index is a database structure that improves the speed of data retrieval operations. It's created on one or more columns in a table.

11. Aggregate Functions
   - SQL provides functions like SUM, AVG, COUNT, MAX, and MIN for performing calculations on groups of data.

12. Transactions
   - Transactions are sequences of one or more SQL statements treated as a single unit. They ensure data consistency by either applying all changes or none.

13. Normalization
   - Normalization is the process of organizing data in a database to minimize data redundancy and improve data integrity.

14. Constraints
   - Constraints (e.g., NOT NULL, UNIQUE, CHECK) are rules that define what data is allowed in a table, ensuring data quality and consistency.

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Quick Power BI Dax Revision

1. Measures: Measures in DAX are calculations that are used in Power BI to perform aggregations, calculations, and comparisons on data. They are defined using the DEFINE MEASURE or CALCULATE functions.

2. Calculated Columns: Calculated columns are columns that are created in a table by using DAX expressions. They are calculated row by row when the data is loaded into the model.

3. DAX Functions: DAX provides a wide range of functions for data manipulation and calculation. Some common functions include SUM, AVERAGE, COUNT, FILTER, CALCULATE, RELATED, ALL, ALLEXCEPT, and many more.

4. Context: DAX calculations are performed within a context, which can be row context or filter context. Understanding how context works is crucial for writing accurate DAX expressions.

5. Relationships: Power BI data models are built on relationships between tables. DAX expressions can leverage these relationships to perform calculations across related tables.

6. Time Intelligence Functions: DAX includes a set of time intelligence functions that enable you to perform calculations based on dates and time periods. Examples include TOTALYTD, SAMEPERIODLASTYEAR, DATESBETWEEN, etc.

7. Variables: DAX allows you to declare and use variables within expressions to improve readability and performance of complex calculations.

8. Aggregation Functions: DAX provides aggregation functions like SUMX, AVERAGEX, COUNTX that allow you to iterate over a table and perform aggregations based on specified conditions.

9. Logical Functions: DAX includes logical functions such as IF, AND, OR, SWITCH that help in implementing conditional logic within calculations.

10. Error Handling: DAX provides functions like ISBLANK, IFERROR, BLANK, etc., for handling errors and missing data in calculations.

React ❤️ for more quick recaps

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When preparing for a Power BI interview, you should be ready to answer questions that assess your practical experience, understanding of Power BI’s features, and ability to solve real-world business problems using Power BI. Here are some key questions you might encounter, along with tips on how to answer them:

1. Can you describe a Power BI project you worked on? What was your role?
   - Tip: Provide a detailed overview of the project, including the business problem, your role in the project, the data sources used, key metrics tracked, and the overall impact of the project. Focus on how you contributed to the project’s success.

2. How do you approach designing a dashboard in Power BI?
   - Tip: Explain your process, from understanding the user’s requirements to planning the layout, choosing appropriate visuals, ensuring data accuracy, and focusing on user experience. Mention how you ensure the dashboard is both insightful and easy to use.

3. What are the challenges you’ve faced while working on Power BI projects, and how did you overcome them?
   - Tip: Discuss specific challenges like data integration issues, performance optimization, or dealing with complex DAX calculations. Emphasize how you identified the issue and the steps you took to resolve it.

4. How do you manage large datasets in Power BI to ensure optimal performance?
   - Tip: Talk about techniques like using DirectQuery, aggregations, optimizing data models, using measures instead of calculated columns, and leveraging Power BI’s performance analyzer to optimize the performance of reports.

5. How do you handle data security in Power BI?
   - Tip: Discuss your experience with implementing row-level security (RLS), managing permissions, and ensuring sensitive data is protected. Mention any experience you have with setting up role-based access controls.

6. Can you explain how you use DAX in Power BI to create complex calculations?
   - Tip: Provide examples of DAX formulas you’ve written to solve specific business problems. Discuss the logic behind the calculations and how they were used in your reports or dashboards.

7. How do you integrate Power BI with other tools or systems?
   - Tip: Talk about your experience integrating Power BI with databases (like SQL Server), Excel, SharePoint, or using APIs to pull in data. Also, mention how you might export data or reports to other tools like Excel or PowerPoint.

8. Describe a situation where you used Power BI to provide insights that led to a significant business decision.
   - Tip: Share a specific example where your Power BI report or dashboard uncovered insights that impacted the business. Focus on the outcome and how your analysis influenced the decision-making process.

9. How do you stay updated with new features and updates in Power BI?
   - Tip: Mention resources you use like Microsoft’s Power BI blog, community forums, attending webinars, or taking courses. Emphasize the importance of continuous learning in your role.

10. What is your approach to troubleshooting a Power BI report that isn’t working as expected?
   - Tip: Describe a systematic approach to identifying the root cause, whether it’s related to data refresh issues, incorrect DAX formulas, or visualization problems.

11. Can you walk us through how you set up and manage Power BI dataflows?
   - Tip: Explain the process of creating dataflows, how you configure them to transform and clean data, and how they help in centralizing and reusing data across multiple reports.

13. How do you handle version control and collaboration in Power BI?
   - Tip: Discuss how you use tools like OneDrive, SharePoint, or Power BI Service for version control, and how you collaborate with other team members on reports and dashboards.

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Data Analytics Project Ideas 💡

Data Analyst Interview Questions with Answers

Q1: How would you handle real-time data streaming for analyzing user listening patterns?

Ans:  I'd use platforms like Apache Kafka for real-time data ingestion. Using Python, I'd process this stream to identify real-time patterns and store aggregated data for further analysis.

Q2: Describe a situation where you had to use time series analysis to forecast a trend. 

Ans:  I analyzed monthly active users to forecast future growth. Using Python's statsmodels, I applied ARIMA modeling to the time series data and provided a forecast for the next six months.

Q3: How would you segment and analyze user behavior based on their music preferences? 

Ans: I'd cluster users based on their listening history using unsupervised machine learning techniques like K-means clustering. This would help in creating personalized playlists or recommendations.

Q4: How do you handle missing or incomplete data in user listening logs? 

Ans: I'd use imputation methods based on the nature of the missing data. For instance, if a user's listening time is missing, I might impute it based on their average listening time or use collaborative filtering methods to estimate it based on similar users.