Essential Topics to Master Data Science Interviews: 🚀
SQL:
1. Foundations
- Craft SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Embrace Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Navigate through simple databases and tables
2. Intermediate SQL
- Utilize Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Embrace Subqueries and nested queries
- Master Common Table Expressions (WITH clause)
- Implement CASE statements for logical queries
3. Advanced SQL
- Explore Advanced JOIN techniques (self-join, non-equi join)
- Dive into Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- Optimize queries with indexing
- Execute Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Python Basics
- Grasp Syntax, variables, and data types
- Command Control structures (if-else, for and while loops)
- Understand Basic data structures (lists, dictionaries, sets, tuples)
- Master Functions, lambda functions, and error handling (try-except)
- Explore Modules and packages
2. Pandas & Numpy
- Create and manipulate DataFrames and Series
- Perfect Indexing, selecting, and filtering data
- Handle missing data (fillna, dropna)
- Aggregate data with groupby, summarizing data
- Merge, join, and concatenate datasets
3. Data Visualization with Python
- Plot with Matplotlib (line plots, bar plots, histograms)
- Visualize with Seaborn (scatter plots, box plots, pair plots)
- Customize plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Excel Essentials
- Conduct Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Dive into charts and basic data visualization
- Sort and filter data, use Conditional formatting
2. Intermediate Excel
- Master Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- Leverage PivotTables and PivotCharts for summarizing data
- Utilize data validation tools
- Employ What-if analysis tools (Data Tables, Goal Seek)
3. Advanced Excel
- Harness Array formulas and advanced functions
- Dive into Data Model & Power Pivot
- Explore Advanced Filter, Slicers, and Timelines in Pivot Tables
- Create dynamic charts and interactive dashboards
Power BI:
1. Data Modeling in Power BI
- Import data from various sources
- Establish and manage relationships between datasets
- Grasp Data modeling basics (star schema, snowflake schema)
2. Data Transformation in Power BI
- Use Power Query for data cleaning and transformation
- Apply advanced data shaping techniques
- Create Calculated columns and measures using DAX
3. Data Visualization and Reporting in Power BI
- Craft interactive reports and dashboards
- Utilize Visualizations (bar, line, pie charts, maps)
- Publish and share reports, schedule 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.
Show some ❤️ if you're ready to elevate your data science game! 📊
ENJOY LEARNING 👍👍
SQL:
1. Foundations
- Craft SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Embrace Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Navigate through simple databases and tables
2. Intermediate SQL
- Utilize Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Embrace Subqueries and nested queries
- Master Common Table Expressions (WITH clause)
- Implement CASE statements for logical queries
3. Advanced SQL
- Explore Advanced JOIN techniques (self-join, non-equi join)
- Dive into Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- Optimize queries with indexing
- Execute Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Python Basics
- Grasp Syntax, variables, and data types
- Command Control structures (if-else, for and while loops)
- Understand Basic data structures (lists, dictionaries, sets, tuples)
- Master Functions, lambda functions, and error handling (try-except)
- Explore Modules and packages
2. Pandas & Numpy
- Create and manipulate DataFrames and Series
- Perfect Indexing, selecting, and filtering data
- Handle missing data (fillna, dropna)
- Aggregate data with groupby, summarizing data
- Merge, join, and concatenate datasets
3. Data Visualization with Python
- Plot with Matplotlib (line plots, bar plots, histograms)
- Visualize with Seaborn (scatter plots, box plots, pair plots)
- Customize plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Excel Essentials
- Conduct Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Dive into charts and basic data visualization
- Sort and filter data, use Conditional formatting
2. Intermediate Excel
- Master Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- Leverage PivotTables and PivotCharts for summarizing data
- Utilize data validation tools
- Employ What-if analysis tools (Data Tables, Goal Seek)
3. Advanced Excel
- Harness Array formulas and advanced functions
- Dive into Data Model & Power Pivot
- Explore Advanced Filter, Slicers, and Timelines in Pivot Tables
- Create dynamic charts and interactive dashboards
Power BI:
1. Data Modeling in Power BI
- Import data from various sources
- Establish and manage relationships between datasets
- Grasp Data modeling basics (star schema, snowflake schema)
2. Data Transformation in Power BI
- Use Power Query for data cleaning and transformation
- Apply advanced data shaping techniques
- Create Calculated columns and measures using DAX
3. Data Visualization and Reporting in Power BI
- Craft interactive reports and dashboards
- Utilize Visualizations (bar, line, pie charts, maps)
- Publish and share reports, schedule 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.
Show some ❤️ if you're ready to elevate your data science game! 📊
ENJOY LEARNING 👍👍
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Questions & Answers for Data Analyst Interview
Question 1: Describe a time when you used data analysis to solve a business problem.
Ideal answer: This is your opportunity to showcase your data analysis skills in a real-world context. Be specific and provide examples of your work. For example, you could talk about a time when you used data analysis to identify customer churn, improve marketing campaigns, or optimize product development.
Question 2: What are some of the challenges you have faced in previous data analysis projects, and how did you overcome them?
Ideal answer: This question is designed to assess your problem-solving skills and your ability to learn from your experiences. Be honest and upfront about the challenges you have faced, but also focus on how you overcame them. For example, you could talk about a time when you had to deal with a large and messy dataset, or a time when you had to work with a tight deadline.
Question 3: How do you handle missing values in a dataset?
Ideal answer: Missing values are a common problem in data analysis, so it is important to know how to handle them properly. There are a variety of different methods that you can use, depending on the specific situation. For example, you could delete the rows with missing values, impute the missing values using a statistical method, or assign a default value to the missing values.
Question 4: How do you identify and remove outliers?
Ideal answer: Outliers are data points that are significantly different from the rest of the data. They can be caused by data errors or by natural variation in the data. It is important to identify and remove outliers before performing data analysis, as they can skew the results. There are a variety of different methods that you can use to identify outliers, such as the interquartile range (IQR) method or the standard deviation method.
Question 5: How do you interpret and communicate the results of your data analysis to non-technical audiences?
Ideal answer: It is important to be able to communicate your data analysis findings to both technical and non-technical audiences. When communicating to non-technical audiences, it is important to avoid using jargon and to focus on the key takeaways from your analysis. You can use data visualization tools to help you communicate your findings in a clear and concise way.
In addition to providing specific examples and answers to the questions, it is also important to be enthusiastic and demonstrate your passion for data analysis. Show the interviewer that you are excited about the opportunity to use your skills to solve real-world problems.
Question 1: Describe a time when you used data analysis to solve a business problem.
Ideal answer: This is your opportunity to showcase your data analysis skills in a real-world context. Be specific and provide examples of your work. For example, you could talk about a time when you used data analysis to identify customer churn, improve marketing campaigns, or optimize product development.
Question 2: What are some of the challenges you have faced in previous data analysis projects, and how did you overcome them?
Ideal answer: This question is designed to assess your problem-solving skills and your ability to learn from your experiences. Be honest and upfront about the challenges you have faced, but also focus on how you overcame them. For example, you could talk about a time when you had to deal with a large and messy dataset, or a time when you had to work with a tight deadline.
Question 3: How do you handle missing values in a dataset?
Ideal answer: Missing values are a common problem in data analysis, so it is important to know how to handle them properly. There are a variety of different methods that you can use, depending on the specific situation. For example, you could delete the rows with missing values, impute the missing values using a statistical method, or assign a default value to the missing values.
Question 4: How do you identify and remove outliers?
Ideal answer: Outliers are data points that are significantly different from the rest of the data. They can be caused by data errors or by natural variation in the data. It is important to identify and remove outliers before performing data analysis, as they can skew the results. There are a variety of different methods that you can use to identify outliers, such as the interquartile range (IQR) method or the standard deviation method.
Question 5: How do you interpret and communicate the results of your data analysis to non-technical audiences?
Ideal answer: It is important to be able to communicate your data analysis findings to both technical and non-technical audiences. When communicating to non-technical audiences, it is important to avoid using jargon and to focus on the key takeaways from your analysis. You can use data visualization tools to help you communicate your findings in a clear and concise way.
In addition to providing specific examples and answers to the questions, it is also important to be enthusiastic and demonstrate your passion for data analysis. Show the interviewer that you are excited about the opportunity to use your skills to solve real-world problems.
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Everyone thinks being a great data analyst is about advanced algorithms and complex dashboards.
But real data excellence comes from methodical habits that build trust and deliver real insights.
Here are 20 signs of a truly effective analyst 👇
✅ They document every step of their analysis
➝ Clear notes make their work reproducible and trustworthy.
✅ They check data quality before the analysis begins
➝ Garbage in = garbage out. Always validate first.
✅ They use version control religiously
➝ Every code change is tracked. Nothing gets lost.
✅ They explore data thoroughly before diving in
➝ Understanding context prevents costly misinterpretations.
✅ They create automated noscripts for repetitive tasks
➝ Efficiency isn’t a luxury—it’s a necessity.
✅ They maintain a reusable code library
➝ Smart analysts never solve the same problem twice.
✅ They test assumptions with multiple validation methods
➝ One test isn’t enough; they triangulate confidence.
✅ They organize project files logically
➝ Their work is navigable by anyone, not just themselves.
✅ They seek peer reviews on critical work
➝ Fresh eyes catch blind spots.
✅ They continuously absorb industry knowledge
➝ Learning never stops. Trends change too quickly.
✅ They prioritize business-impacting projects
➝ Every analysis must drive real decisions.
✅ They explain complex findings simply
➝ Technical brilliance is useless without clarity.
✅ They write readable, well-commented code
➝ Their work is accessible to others, long after they're gone.
✅ They maintain robust backup systems
➝ Data loss is never an option.
✅ They learn from analytical mistakes
➝ Errors become stepping stones, not roadblocks.
✅ They build strong stakeholder relationships
➝ Data is only valuable when people use it.
✅ They break complex projects into manageable chunks
➝ Progress happens through disciplined, incremental work.
✅ They handle sensitive data with proper security
➝ Compliance isn’t optional—it’s foundational.
✅ They create visualizations that tell clear stories
➝ A chart without a narrative is just decoration.
✅ They actively seek evidence against their conclusions
➝ Confirmation bias is their biggest enemy.
The best analysts aren’t the ones with the most tools—they’re the ones with the most rigorous practices.
Which of these habits could transform your data work today? 🚀
Join biggest telegram channel to master data analytics: https://news.1rj.ru/str/sqlspecialist
But real data excellence comes from methodical habits that build trust and deliver real insights.
Here are 20 signs of a truly effective analyst 👇
✅ They document every step of their analysis
➝ Clear notes make their work reproducible and trustworthy.
✅ They check data quality before the analysis begins
➝ Garbage in = garbage out. Always validate first.
✅ They use version control religiously
➝ Every code change is tracked. Nothing gets lost.
✅ They explore data thoroughly before diving in
➝ Understanding context prevents costly misinterpretations.
✅ They create automated noscripts for repetitive tasks
➝ Efficiency isn’t a luxury—it’s a necessity.
✅ They maintain a reusable code library
➝ Smart analysts never solve the same problem twice.
✅ They test assumptions with multiple validation methods
➝ One test isn’t enough; they triangulate confidence.
✅ They organize project files logically
➝ Their work is navigable by anyone, not just themselves.
✅ They seek peer reviews on critical work
➝ Fresh eyes catch blind spots.
✅ They continuously absorb industry knowledge
➝ Learning never stops. Trends change too quickly.
✅ They prioritize business-impacting projects
➝ Every analysis must drive real decisions.
✅ They explain complex findings simply
➝ Technical brilliance is useless without clarity.
✅ They write readable, well-commented code
➝ Their work is accessible to others, long after they're gone.
✅ They maintain robust backup systems
➝ Data loss is never an option.
✅ They learn from analytical mistakes
➝ Errors become stepping stones, not roadblocks.
✅ They build strong stakeholder relationships
➝ Data is only valuable when people use it.
✅ They break complex projects into manageable chunks
➝ Progress happens through disciplined, incremental work.
✅ They handle sensitive data with proper security
➝ Compliance isn’t optional—it’s foundational.
✅ They create visualizations that tell clear stories
➝ A chart without a narrative is just decoration.
✅ They actively seek evidence against their conclusions
➝ Confirmation bias is their biggest enemy.
The best analysts aren’t the ones with the most tools—they’re the ones with the most rigorous practices.
Which of these habits could transform your data work today? 🚀
Join biggest telegram channel to master data analytics: https://news.1rj.ru/str/sqlspecialist
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Excel Interview Question & Anwers for Data Analytics Interview✅
[Part-1]
What is Excel and what are its primary uses?
Excel is a software program created by Microsoft that uses spreadsheets to organize numbers and data with formulas and functions. Its primary uses include creating spreadsheets, performing calculations, and making charts.
How do you open a new workbook in Excel?
To open a new workbook in Excel, you can either click on "File" in the menu, then select "New" and "Blank Workbook," or you can press "Ctrl + N" on your keyboard.
Explain the difference between a workbook and a worksheet.
A workbook is like a file that contains all your data and is made up of one or more worksheets. Worksheets are the individual pages within a workbook where you enter and manipulate data.
How do you navigate between different worksheets in Excel?
To move between different worksheets in Excel, you can click on the tabs at the bottom of the Excel window. Each tab represents a different worksheet.
Like for more 😄
Join: https://news.1rj.ru/str/excel_analyst
[Part-1]
What is Excel and what are its primary uses?
Excel is a software program created by Microsoft that uses spreadsheets to organize numbers and data with formulas and functions. Its primary uses include creating spreadsheets, performing calculations, and making charts.
How do you open a new workbook in Excel?
To open a new workbook in Excel, you can either click on "File" in the menu, then select "New" and "Blank Workbook," or you can press "Ctrl + N" on your keyboard.
Explain the difference between a workbook and a worksheet.
A workbook is like a file that contains all your data and is made up of one or more worksheets. Worksheets are the individual pages within a workbook where you enter and manipulate data.
How do you navigate between different worksheets in Excel?
To move between different worksheets in Excel, you can click on the tabs at the bottom of the Excel window. Each tab represents a different worksheet.
Like for more 😄
Join: https://news.1rj.ru/str/excel_analyst
👍4
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.
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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Walmart Data Analyst Interview Questions: Python & SQL ✅
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Data visualization tools
Data visualization tools provide you with an easier way to create visual representations of large data sets. When dealing with data sets that include bunches of data points, automating the process of creating a visualization, at least in part, makes your job significantly easier.
The best data visualization tools on the market should have one important thing in common. It is their ease of use. The best tools can also handle huge sets of data. And the last but not least, they can output an array of different chart, graph, and map types.
There are hundreds, of applications, tools, and noscripts available to create visualizations of large data sets. Many are very basic and have a lot of overlapping features.
- Tableau (and Tableau Public
Hundreds of data import options. Mapping capability. Free public version available. Lots of video tutorials to walk you through how to use Tableau.
- Infogram
Tiered pricing, including a free plan with basic features. Includes 35+ chart types and 550+ map types. Drag and drop editor. API for importing additional data sources.
- ChartBlocks
Free and reasonably priced paid plans are available. Easy to use wizard for importing the necessary data.
- Datawrapper
Specifically designed for newsroom data visualization. Free plan is a good fit for smaller sites. Tool includes a built-in color blindness checker.
- D3.js
A JavaScript library for manipulating documents using data. Very powerful and customizable. Huge number of chart types possible. A focus on web standards. Tools available to let non-programmers create visualizations. Free and open source.
- Looker Studio (Google Data Studio)
Free data visualization tool that is specifically for creating interactive charts for embedding online. Easily access a wide variety of data.
- FusionCharts
A JavaScript-based option for creating web and mobile dashboards. Huge number of chart and map format options. More features than most other visualization tools. Integrates with a number of different frameworks and programming languages.
- Chart.js
A simple but flexible JavaScript charting library. Free and open source. Responsive and cross-browser compatible output.
- Grafana
Open source, with free and paid options available. Large selection of data sources available. Variety of chart types available. Makes creating dynamic dashboards simple. Can work with mixed data feeds.
Data visualization tools provide you with an easier way to create visual representations of large data sets. When dealing with data sets that include bunches of data points, automating the process of creating a visualization, at least in part, makes your job significantly easier.
The best data visualization tools on the market should have one important thing in common. It is their ease of use. The best tools can also handle huge sets of data. And the last but not least, they can output an array of different chart, graph, and map types.
There are hundreds, of applications, tools, and noscripts available to create visualizations of large data sets. Many are very basic and have a lot of overlapping features.
- Tableau (and Tableau Public
Hundreds of data import options. Mapping capability. Free public version available. Lots of video tutorials to walk you through how to use Tableau.
- Infogram
Tiered pricing, including a free plan with basic features. Includes 35+ chart types and 550+ map types. Drag and drop editor. API for importing additional data sources.
- ChartBlocks
Free and reasonably priced paid plans are available. Easy to use wizard for importing the necessary data.
- Datawrapper
Specifically designed for newsroom data visualization. Free plan is a good fit for smaller sites. Tool includes a built-in color blindness checker.
- D3.js
A JavaScript library for manipulating documents using data. Very powerful and customizable. Huge number of chart types possible. A focus on web standards. Tools available to let non-programmers create visualizations. Free and open source.
- Looker Studio (Google Data Studio)
Free data visualization tool that is specifically for creating interactive charts for embedding online. Easily access a wide variety of data.
- FusionCharts
A JavaScript-based option for creating web and mobile dashboards. Huge number of chart and map format options. More features than most other visualization tools. Integrates with a number of different frameworks and programming languages.
- Chart.js
A simple but flexible JavaScript charting library. Free and open source. Responsive and cross-browser compatible output.
- Grafana
Open source, with free and paid options available. Large selection of data sources available. Variety of chart types available. Makes creating dynamic dashboards simple. Can work with mixed data feeds.
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