Python Detailed Roadmap 🚀
📌 1. Basics
◼ Data Types & Variables
◼ Operators & Expressions
◼ Control Flow (if, loops)
📌 2. Functions & Modules
◼ Defining Functions
◼ Lambda Functions
◼ Importing & Creating Modules
📌 3. File Handling
◼ Reading & Writing Files
◼ Working with CSV & JSON
📌 4. Object-Oriented Programming (OOP)
◼ Classes & Objects
◼ Inheritance & Polymorphism
◼ Encapsulation
📌 5. Exception Handling
◼ Try-Except Blocks
◼ Custom Exceptions
📌 6. Advanced Python Concepts
◼ List & Dictionary Comprehensions
◼ Generators & Iterators
◼ Decorators
📌 7. Essential Libraries
◼ NumPy (Arrays & Computations)
◼ Pandas (Data Analysis)
◼ Matplotlib & Seaborn (Visualization)
📌 8. Web Development & APIs
◼ Web Scraping (BeautifulSoup, Scrapy)
◼ API Integration (Requests)
◼ Flask & Django (Backend Development)
📌 9. Automation & Scripting
◼ Automating Tasks with Python
◼ Working with Selenium & PyAutoGUI
📌 10. Data Science & Machine Learning
◼ Data Cleaning & Preprocessing
◼ Scikit-Learn (ML Algorithms)
◼ TensorFlow & PyTorch (Deep Learning)
📌 11. Projects
◼ Build Real-World Applications
◼ Showcase on GitHub
📌 12. ✅ Apply for Jobs
◼ Strengthen Resume & Portfolio
◼ Prepare for Technical Interviews
Like for more ❤️💪
📌 1. Basics
◼ Data Types & Variables
◼ Operators & Expressions
◼ Control Flow (if, loops)
📌 2. Functions & Modules
◼ Defining Functions
◼ Lambda Functions
◼ Importing & Creating Modules
📌 3. File Handling
◼ Reading & Writing Files
◼ Working with CSV & JSON
📌 4. Object-Oriented Programming (OOP)
◼ Classes & Objects
◼ Inheritance & Polymorphism
◼ Encapsulation
📌 5. Exception Handling
◼ Try-Except Blocks
◼ Custom Exceptions
📌 6. Advanced Python Concepts
◼ List & Dictionary Comprehensions
◼ Generators & Iterators
◼ Decorators
📌 7. Essential Libraries
◼ NumPy (Arrays & Computations)
◼ Pandas (Data Analysis)
◼ Matplotlib & Seaborn (Visualization)
📌 8. Web Development & APIs
◼ Web Scraping (BeautifulSoup, Scrapy)
◼ API Integration (Requests)
◼ Flask & Django (Backend Development)
📌 9. Automation & Scripting
◼ Automating Tasks with Python
◼ Working with Selenium & PyAutoGUI
📌 10. Data Science & Machine Learning
◼ Data Cleaning & Preprocessing
◼ Scikit-Learn (ML Algorithms)
◼ TensorFlow & PyTorch (Deep Learning)
📌 11. Projects
◼ Build Real-World Applications
◼ Showcase on GitHub
📌 12. ✅ Apply for Jobs
◼ Strengthen Resume & Portfolio
◼ Prepare for Technical Interviews
Like for more ❤️💪
❤2
SQL Interview Questions with Answers
1. How to change a table name in SQL?
This is the command to change a table name in SQL:
ALTER TABLE table_name
RENAME TO new_table_name;
We will start off by giving the keywords ALTER TABLE, then we will follow it up by giving the original name of the table, after that, we will give in the keywords RENAME TO and finally, we will give the new table name.
2. How to use LIKE in SQL?
The LIKE operator checks if an attribute value matches a given string pattern. Here is an example of LIKE operator
SELECT * FROM employees WHERE first_name like ‘Steven’;
With this command, we will be able to extract all the records where the first name is like “Steven”.
3. If we drop a table, does it also drop related objects like constraints, indexes, columns, default, views and sorted procedures?
Yes, SQL server drops all related objects, which exists inside a table like constraints, indexes, columns, defaults etc. But dropping a table will not drop views and sorted procedures as they exist outside the table.
4. Explain SQL Constraints.
SQL Constraints are used to specify the rules of data type in a table. They can be specified while creating and altering the table. The following are the constraints in SQL: NOT NULL CHECK DEFAULT UNIQUE PRIMARY KEY FOREIGN KEY
React ❤️ for more
1. How to change a table name in SQL?
This is the command to change a table name in SQL:
ALTER TABLE table_name
RENAME TO new_table_name;
We will start off by giving the keywords ALTER TABLE, then we will follow it up by giving the original name of the table, after that, we will give in the keywords RENAME TO and finally, we will give the new table name.
2. How to use LIKE in SQL?
The LIKE operator checks if an attribute value matches a given string pattern. Here is an example of LIKE operator
SELECT * FROM employees WHERE first_name like ‘Steven’;
With this command, we will be able to extract all the records where the first name is like “Steven”.
3. If we drop a table, does it also drop related objects like constraints, indexes, columns, default, views and sorted procedures?
Yes, SQL server drops all related objects, which exists inside a table like constraints, indexes, columns, defaults etc. But dropping a table will not drop views and sorted procedures as they exist outside the table.
4. Explain SQL Constraints.
SQL Constraints are used to specify the rules of data type in a table. They can be specified while creating and altering the table. The following are the constraints in SQL: NOT NULL CHECK DEFAULT UNIQUE PRIMARY KEY FOREIGN KEY
React ❤️ for more
❤2
Data Analyst Interview Questions
Q1: How would you analyze data to understand user connection patterns on a professional network?
Ans: I'd use graph databases like Neo4j for social network analysis. By analyzing connection patterns, I can identify influencers or isolated communities.
Q2: Describe a challenging data visualization you created to represent user engagement metrics.
Ans: I visualized multi-dimensional data showing user engagement across features, regions, and time using tools like D3.js, creating an interactive dashboard with drill-down capabilities.
Q3: How would you identify and target passive job seekers on LinkedIn?
Ans: I'd analyze user behavior patterns, like increased profile updates, frequent visits to job postings, or engagement with career-related content, to identify potential passive job seekers.
Q4: How do you measure the effectiveness of a new feature launched on LinkedIn?
Ans: I'd set up A/B tests, comparing user engagement metrics between those who have access to the new feature and a control group. I'd then analyze metrics like time spent, feature usage frequency, and overall platform engagement to measure effectiveness.
Q1: How would you analyze data to understand user connection patterns on a professional network?
Ans: I'd use graph databases like Neo4j for social network analysis. By analyzing connection patterns, I can identify influencers or isolated communities.
Q2: Describe a challenging data visualization you created to represent user engagement metrics.
Ans: I visualized multi-dimensional data showing user engagement across features, regions, and time using tools like D3.js, creating an interactive dashboard with drill-down capabilities.
Q3: How would you identify and target passive job seekers on LinkedIn?
Ans: I'd analyze user behavior patterns, like increased profile updates, frequent visits to job postings, or engagement with career-related content, to identify potential passive job seekers.
Q4: How do you measure the effectiveness of a new feature launched on LinkedIn?
Ans: I'd set up A/B tests, comparing user engagement metrics between those who have access to the new feature and a control group. I'd then analyze metrics like time spent, feature usage frequency, and overall platform engagement to measure effectiveness.
❤3
The Only SQL You Actually Need For Your First Job (Data Analytics)
The Learning Trap: What Most Beginners Fall Into
When starting out, it's common to feel like you need to master every possible SQL concept. You binge YouTube videos, tutorials, and courses, yet still feel lost in interviews or when given a real dataset.
Common traps:
- Complex subqueries
- Advanced CTEs
- Recursive queries
- 100+ tutorials watched
- 0 practical experience
Reality Check: What You'll Actually Use 75% of the Time
Most data analytics roles (especially entry-level) require clarity, speed, and confidence with core SQL operations. Here’s what covers most daily work:
1. SELECT, FROM, WHERE — The Foundation
SELECT name, age
FROM employees
WHERE department = 'Finance';
This is how almost every query begins. Whether exploring a dataset or building a dashboard, these are always in use.
2. JOINs — Combining Data From Multiple Tables
SELECT e.name, d.department_name
FROM employees e
JOIN departments d ON e.department_id = d.id;
You’ll often join tables like employee data with department, customer orders with payments, etc.
3. GROUP BY — Summarizing Data
SELECT department, COUNT(*) AS employee_count
FROM employees
GROUP BY department;
Used to get summaries by categories like sales per region or users by plan.
4. ORDER BY — Sorting Results
SELECT name, salary
FROM employees
ORDER BY salary DESC;
Helps sort output for dashboards or reports.
5. Aggregations — Simple But Powerful
Common functions: COUNT(), SUM(), AVG(), MIN(), MAX()
SELECT AVG(salary)
FROM employees
WHERE department = 'IT';
Gives quick insights like average deal size or total revenue.
6. ROW_NUMBER() — Adding Row Logic
SELECT *
FROM (
SELECT *, ROW_NUMBER() OVER(PARTITION BY customer_id ORDER BY order_date DESC) as rn
FROM orders
) sub
WHERE rn = 1;
Used for deduplication, rankings, or selecting the latest record per group.
Credits: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
React ❤️ for more
The Learning Trap: What Most Beginners Fall Into
When starting out, it's common to feel like you need to master every possible SQL concept. You binge YouTube videos, tutorials, and courses, yet still feel lost in interviews or when given a real dataset.
Common traps:
- Complex subqueries
- Advanced CTEs
- Recursive queries
- 100+ tutorials watched
- 0 practical experience
Reality Check: What You'll Actually Use 75% of the Time
Most data analytics roles (especially entry-level) require clarity, speed, and confidence with core SQL operations. Here’s what covers most daily work:
1. SELECT, FROM, WHERE — The Foundation
SELECT name, age
FROM employees
WHERE department = 'Finance';
This is how almost every query begins. Whether exploring a dataset or building a dashboard, these are always in use.
2. JOINs — Combining Data From Multiple Tables
SELECT e.name, d.department_name
FROM employees e
JOIN departments d ON e.department_id = d.id;
You’ll often join tables like employee data with department, customer orders with payments, etc.
3. GROUP BY — Summarizing Data
SELECT department, COUNT(*) AS employee_count
FROM employees
GROUP BY department;
Used to get summaries by categories like sales per region or users by plan.
4. ORDER BY — Sorting Results
SELECT name, salary
FROM employees
ORDER BY salary DESC;
Helps sort output for dashboards or reports.
5. Aggregations — Simple But Powerful
Common functions: COUNT(), SUM(), AVG(), MIN(), MAX()
SELECT AVG(salary)
FROM employees
WHERE department = 'IT';
Gives quick insights like average deal size or total revenue.
6. ROW_NUMBER() — Adding Row Logic
SELECT *
FROM (
SELECT *, ROW_NUMBER() OVER(PARTITION BY customer_id ORDER BY order_date DESC) as rn
FROM orders
) sub
WHERE rn = 1;
Used for deduplication, rankings, or selecting the latest record per group.
Credits: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
React ❤️ for more
❤5
Data Analyst Interview Questions & Preparation Tips
Be prepared with a mix of technical, analytical, and business-oriented interview questions.
1. Technical Questions (Data Analysis & Reporting)
SQL Questions:
How do you write a query to fetch the top 5 highest revenue-generating customers?
Explain the difference between INNER JOIN, LEFT JOIN, and FULL OUTER JOIN.
How would you optimize a slow-running query?
What are CTEs and when would you use them?
Data Visualization (Power BI / Tableau / Excel)
How would you create a dashboard to track key performance metrics?
Explain the difference between measures and calculated columns in Power BI.
How do you handle missing data in Tableau?
What are DAX functions, and can you give an example?
ETL & Data Processing (Alteryx, Power BI, Excel)
What is ETL, and how does it relate to BI?
Have you used Alteryx for data transformation? Explain a complex workflow you built.
How do you automate reporting using Power Query in Excel?
2. Business and Analytical Questions
How do you define KPIs for a business process?
Give an example of how you used data to drive a business decision.
How would you identify cost-saving opportunities in a reporting process?
Explain a time when your report uncovered a hidden business insight.
3. Scenario-Based & Behavioral Questions
Stakeholder Management:
How do you handle a situation where different business units have conflicting reporting requirements?
How do you explain complex data insights to non-technical stakeholders?
Problem-Solving & Debugging:
What would you do if your report is showing incorrect numbers?
How do you ensure the accuracy of a new KPI you introduced?
Project Management & Process Improvement:
Have you led a project to automate or improve a reporting process?
What steps do you take to ensure the timely delivery of reports?
4. Industry-Specific Questions (Credit Reporting & Financial Services)
What are some key credit risk metrics used in financial services?
How would you analyze trends in customer credit behavior?
How do you ensure compliance and data security in reporting?
5. General HR Questions
Why do you want to work at this company?
Tell me about a challenging project and how you handled it.
What are your strengths and weaknesses?
Where do you see yourself in five years?
How to Prepare?
Brush up on SQL, Power BI, and ETL tools (especially Alteryx).
Learn about key financial and credit reporting metrics.(varies company to company)
Practice explaining data-driven insights in a business-friendly manner.
Be ready to showcase problem-solving skills with real-world examples.
React with ❤️ if you want me to also post sample answer for the above questions
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
Be prepared with a mix of technical, analytical, and business-oriented interview questions.
1. Technical Questions (Data Analysis & Reporting)
SQL Questions:
How do you write a query to fetch the top 5 highest revenue-generating customers?
Explain the difference between INNER JOIN, LEFT JOIN, and FULL OUTER JOIN.
How would you optimize a slow-running query?
What are CTEs and when would you use them?
Data Visualization (Power BI / Tableau / Excel)
How would you create a dashboard to track key performance metrics?
Explain the difference between measures and calculated columns in Power BI.
How do you handle missing data in Tableau?
What are DAX functions, and can you give an example?
ETL & Data Processing (Alteryx, Power BI, Excel)
What is ETL, and how does it relate to BI?
Have you used Alteryx for data transformation? Explain a complex workflow you built.
How do you automate reporting using Power Query in Excel?
2. Business and Analytical Questions
How do you define KPIs for a business process?
Give an example of how you used data to drive a business decision.
How would you identify cost-saving opportunities in a reporting process?
Explain a time when your report uncovered a hidden business insight.
3. Scenario-Based & Behavioral Questions
Stakeholder Management:
How do you handle a situation where different business units have conflicting reporting requirements?
How do you explain complex data insights to non-technical stakeholders?
Problem-Solving & Debugging:
What would you do if your report is showing incorrect numbers?
How do you ensure the accuracy of a new KPI you introduced?
Project Management & Process Improvement:
Have you led a project to automate or improve a reporting process?
What steps do you take to ensure the timely delivery of reports?
4. Industry-Specific Questions (Credit Reporting & Financial Services)
What are some key credit risk metrics used in financial services?
How would you analyze trends in customer credit behavior?
How do you ensure compliance and data security in reporting?
5. General HR Questions
Why do you want to work at this company?
Tell me about a challenging project and how you handled it.
What are your strengths and weaknesses?
Where do you see yourself in five years?
How to Prepare?
Brush up on SQL, Power BI, and ETL tools (especially Alteryx).
Learn about key financial and credit reporting metrics.(varies company to company)
Practice explaining data-driven insights in a business-friendly manner.
Be ready to showcase problem-solving skills with real-world examples.
React with ❤️ if you want me to also post sample answer for the above questions
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤1
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 👍👍
👇👇
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 👍👍
❤2
To effectively learn SQL for a Data Analyst role, follow these steps:
1. Start with a basic course: Begin by taking a basic course on YouTube to familiarize yourself with SQL syntax and terminologies. I recommend the "Learn Complete SQL" playlist from the "techTFQ" YouTube channel.
2. Practice syntax and commands: As you learn new terminologies from the course, practice their syntax on the "w3schools" website. This site provides clear examples of SQL syntax, commands, and functions.
3. Solve practice questions: After completing the initial steps, start solving easy-level SQL practice questions on platforms like "Hackerrank," "Leetcode," "Datalemur," and "Stratascratch." If you get stuck, use the discussion forums on these platforms or ask ChatGPT for help. You can paste the problem into ChatGPT and use a prompt like:
- "Explain the step-by-step solution to the above problem as I am new to SQL, also explain the solution as per the order of execution of SQL."
4. Gradually increase difficulty: Gradually move on to more difficult practice questions. If you encounter new SQL concepts, watch YouTube videos on those topics or ask ChatGPT for explanations.
5. Consistent practice: The most crucial aspect of learning SQL is consistent practice. Regular practice will help you build and solidify your skills.
By following these steps and maintaining regular practice, you'll be well on your way to mastering SQL for a Data Analyst role.
1. Start with a basic course: Begin by taking a basic course on YouTube to familiarize yourself with SQL syntax and terminologies. I recommend the "Learn Complete SQL" playlist from the "techTFQ" YouTube channel.
2. Practice syntax and commands: As you learn new terminologies from the course, practice their syntax on the "w3schools" website. This site provides clear examples of SQL syntax, commands, and functions.
3. Solve practice questions: After completing the initial steps, start solving easy-level SQL practice questions on platforms like "Hackerrank," "Leetcode," "Datalemur," and "Stratascratch." If you get stuck, use the discussion forums on these platforms or ask ChatGPT for help. You can paste the problem into ChatGPT and use a prompt like:
- "Explain the step-by-step solution to the above problem as I am new to SQL, also explain the solution as per the order of execution of SQL."
4. Gradually increase difficulty: Gradually move on to more difficult practice questions. If you encounter new SQL concepts, watch YouTube videos on those topics or ask ChatGPT for explanations.
5. Consistent practice: The most crucial aspect of learning SQL is consistent practice. Regular practice will help you build and solidify your skills.
By following these steps and maintaining regular practice, you'll be well on your way to mastering SQL for a Data Analyst role.
❤2
Important Excel, Tableau, Statistics, SQL related Questions with answers
1. What are the common problems that data analysts encounter during analysis?
The common problems steps involved in any analytics project are:
Handling duplicate data
Collecting the meaningful right data at the right time
Handling data purging and storage problems
Making data secure and dealing with compliance issues
2. Explain the Type I and Type II errors in Statistics?
In Hypothesis testing, a Type I error occurs when the null hypothesis is rejected even if it is true. It is also known as a false positive.
A Type II error occurs when the null hypothesis is not rejected, even if it is false. It is also known as a false negative.
3. How do you make a dropdown list in MS Excel?
First, click on the Data tab that is present in the ribbon.
Under the Data Tools group, select Data Validation.
Then navigate to Settings > Allow > List.
Select the source you want to provide as a list array.
4. How do you subset or filter data in SQL?
To subset or filter data in SQL, we use WHERE and HAVING clauses which give us an option of including only the data matching certain conditions.
5. What is a Gantt Chart in Tableau?
A Gantt chart in Tableau depicts the progress of value over the period, i.e., it shows the duration of events. It consists of bars along with the time axis. The Gantt chart is mostly used as a project management tool where each bar is a measure of a task in the project
1. What are the common problems that data analysts encounter during analysis?
The common problems steps involved in any analytics project are:
Handling duplicate data
Collecting the meaningful right data at the right time
Handling data purging and storage problems
Making data secure and dealing with compliance issues
2. Explain the Type I and Type II errors in Statistics?
In Hypothesis testing, a Type I error occurs when the null hypothesis is rejected even if it is true. It is also known as a false positive.
A Type II error occurs when the null hypothesis is not rejected, even if it is false. It is also known as a false negative.
3. How do you make a dropdown list in MS Excel?
First, click on the Data tab that is present in the ribbon.
Under the Data Tools group, select Data Validation.
Then navigate to Settings > Allow > List.
Select the source you want to provide as a list array.
4. How do you subset or filter data in SQL?
To subset or filter data in SQL, we use WHERE and HAVING clauses which give us an option of including only the data matching certain conditions.
5. What is a Gantt Chart in Tableau?
A Gantt chart in Tableau depicts the progress of value over the period, i.e., it shows the duration of events. It consists of bars along with the time axis. The Gantt chart is mostly used as a project management tool where each bar is a measure of a task in the project
❤1
🔟 Project Ideas for a data analyst
Customer Segmentation: Analyze customer data to segment them based on their behaviors, preferences, or demographics, helping businesses tailor their marketing strategies.
Churn Prediction: Build a model to predict customer churn, identifying factors that contribute to churn and proposing strategies to retain customers.
Sales Forecasting: Use historical sales data to create a predictive model that forecasts future sales, aiding inventory management and resource planning.
Market Basket Analysis: Analyze
transaction data to identify associations between products often purchased together, assisting retailers in optimizing product placement and cross-selling.
Sentiment Analysis: Analyze social media or customer reviews to gauge public sentiment about a product or service, providing valuable insights for brand reputation management.
Healthcare Analytics: Examine medical records to identify trends, patterns, or correlations in patient data, aiding in disease prediction, treatment optimization, and resource allocation.
Financial Fraud Detection: Develop algorithms to detect anomalous transactions and patterns in financial data, helping prevent fraud and secure transactions.
A/B Testing Analysis: Evaluate the results of A/B tests to determine the effectiveness of different strategies or changes on websites, apps, or marketing campaigns.
Energy Consumption Analysis: Analyze energy usage data to identify patterns and inefficiencies, suggesting strategies for optimizing energy consumption in buildings or industries.
Real Estate Market Analysis: Study housing market data to identify trends in property prices, rental rates, and demand, assisting buyers, sellers, and investors in making informed decisions.
Remember to choose a project that aligns with your interests and the domain you're passionate about.
Data Analyst Roadmap
👇👇
https://news.1rj.ru/str/sqlspecialist/379
ENJOY LEARNING 👍👍
Customer Segmentation: Analyze customer data to segment them based on their behaviors, preferences, or demographics, helping businesses tailor their marketing strategies.
Churn Prediction: Build a model to predict customer churn, identifying factors that contribute to churn and proposing strategies to retain customers.
Sales Forecasting: Use historical sales data to create a predictive model that forecasts future sales, aiding inventory management and resource planning.
Market Basket Analysis: Analyze
transaction data to identify associations between products often purchased together, assisting retailers in optimizing product placement and cross-selling.
Sentiment Analysis: Analyze social media or customer reviews to gauge public sentiment about a product or service, providing valuable insights for brand reputation management.
Healthcare Analytics: Examine medical records to identify trends, patterns, or correlations in patient data, aiding in disease prediction, treatment optimization, and resource allocation.
Financial Fraud Detection: Develop algorithms to detect anomalous transactions and patterns in financial data, helping prevent fraud and secure transactions.
A/B Testing Analysis: Evaluate the results of A/B tests to determine the effectiveness of different strategies or changes on websites, apps, or marketing campaigns.
Energy Consumption Analysis: Analyze energy usage data to identify patterns and inefficiencies, suggesting strategies for optimizing energy consumption in buildings or industries.
Real Estate Market Analysis: Study housing market data to identify trends in property prices, rental rates, and demand, assisting buyers, sellers, and investors in making informed decisions.
Remember to choose a project that aligns with your interests and the domain you're passionate about.
Data Analyst Roadmap
👇👇
https://news.1rj.ru/str/sqlspecialist/379
ENJOY LEARNING 👍👍
❤4
Data Analyst Interview Questions 👇
1.How to create filters in Power BI?
Filters are an integral part of Power BI reports. They are used to slice and dice the data as per the dimensions we want. Filters are created in a couple of ways.
Using Slicers: A slicer is a visual under Visualization Pane. This can be added to the design view to filter our reports. When a slicer is added to the design view, it requires a field to be added to it. For example- Slicer can be added for Country fields. Then the data can be filtered based on countries.
Using Filter Pane: The Power BI team has added a filter pane to the reports, which is a single space where we can add different fields as filters. And these fields can be added depending on whether you want to filter only one visual(Visual level filter), or all the visuals in the report page(Page level filters), or applicable to all the pages of the report(report level filters)
2.How to sort data in Power BI?
Sorting is available in multiple formats. In the data view, a common sorting option of alphabetical order is there. Apart from that, we have the option of Sort by column, where one can sort a column based on another column. The sorting option is available in visuals as well. Sort by ascending and descending option by the fields and measure present in the visual is also available.
3.How to convert pdf to excel?
Open the PDF document you want to convert in XLSX format in Acrobat DC.
Go to the right pane and click on the “Export PDF” option.
Choose spreadsheet as the Export format.
Select “Microsoft Excel Workbook.”
Now click “Export.”
Download the converted file or share it.
4. How to enable macros in excel?
Click the file tab and then click “Options.”
A dialog box will appear. In the “Excel Options” dialog box, click on the “Trust Center” and then “Trust Center Settings.”
Go to the “Macro Settings” and select “enable all macros.”
Click OK to apply the macro settings.
1.How to create filters in Power BI?
Filters are an integral part of Power BI reports. They are used to slice and dice the data as per the dimensions we want. Filters are created in a couple of ways.
Using Slicers: A slicer is a visual under Visualization Pane. This can be added to the design view to filter our reports. When a slicer is added to the design view, it requires a field to be added to it. For example- Slicer can be added for Country fields. Then the data can be filtered based on countries.
Using Filter Pane: The Power BI team has added a filter pane to the reports, which is a single space where we can add different fields as filters. And these fields can be added depending on whether you want to filter only one visual(Visual level filter), or all the visuals in the report page(Page level filters), or applicable to all the pages of the report(report level filters)
2.How to sort data in Power BI?
Sorting is available in multiple formats. In the data view, a common sorting option of alphabetical order is there. Apart from that, we have the option of Sort by column, where one can sort a column based on another column. The sorting option is available in visuals as well. Sort by ascending and descending option by the fields and measure present in the visual is also available.
3.How to convert pdf to excel?
Open the PDF document you want to convert in XLSX format in Acrobat DC.
Go to the right pane and click on the “Export PDF” option.
Choose spreadsheet as the Export format.
Select “Microsoft Excel Workbook.”
Now click “Export.”
Download the converted file or share it.
4. How to enable macros in excel?
Click the file tab and then click “Options.”
A dialog box will appear. In the “Excel Options” dialog box, click on the “Trust Center” and then “Trust Center Settings.”
Go to the “Macro Settings” and select “enable all macros.”
Click OK to apply the macro settings.
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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
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
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