1. What is a Self-Join?
A self-join is a type of join that can be used to connect two tables. As a result, it is a unary relationship. Each row of the table is attached to itself and all other rows of the same table in a self-join. As a result, a self-join is mostly used to combine and compare rows from the same database table.
2. What is OLTP?
OLTP, or online transactional processing, allows huge groups of people to execute massive amounts of database transactions in real time, usually via the internet. A database transaction occurs when data in a database is changed, inserted, deleted, or queried.
3. What is the difference between joining and blending in Tableau?
Joining term is used when you are combining data from the same source, for example, worksheet in an Excel file or tables in Oracle databaseWhile blending requires two completely defined data sources in your report.
4. How to prevent someone from copying the cell from your worksheet in excel?
If you want to protect your worksheet from being copied, go into Menu bar > Review > Protect sheet > Password.
By entering password you can prevent your worksheet from getting copied.
5. What is the difference between the RANK() and DENSE_RANK() functions?
The RANK() function in the result set defines the rank of each row within your ordered partition. If both rows have the same rank, the next number in the ranking will be the previous rank plus a number of duplicates. If we have three records at rank 4, for example, the next level indicated is 7. The DENSE_RANK() function assigns a distinct rank to each row within a partition based on the provided column value, with no gaps. If we have three records at rank 4, for example, the next level indicated is 5.
A self-join is a type of join that can be used to connect two tables. As a result, it is a unary relationship. Each row of the table is attached to itself and all other rows of the same table in a self-join. As a result, a self-join is mostly used to combine and compare rows from the same database table.
2. What is OLTP?
OLTP, or online transactional processing, allows huge groups of people to execute massive amounts of database transactions in real time, usually via the internet. A database transaction occurs when data in a database is changed, inserted, deleted, or queried.
3. What is the difference between joining and blending in Tableau?
Joining term is used when you are combining data from the same source, for example, worksheet in an Excel file or tables in Oracle databaseWhile blending requires two completely defined data sources in your report.
4. How to prevent someone from copying the cell from your worksheet in excel?
If you want to protect your worksheet from being copied, go into Menu bar > Review > Protect sheet > Password.
By entering password you can prevent your worksheet from getting copied.
5. What is the difference between the RANK() and DENSE_RANK() functions?
The RANK() function in the result set defines the rank of each row within your ordered partition. If both rows have the same rank, the next number in the ranking will be the previous rank plus a number of duplicates. If we have three records at rank 4, for example, the next level indicated is 7. The DENSE_RANK() function assigns a distinct rank to each row within a partition based on the provided column value, with no gaps. If we have three records at rank 4, for example, the next level indicated is 5.
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My journey as a data analyst so far: https://www.linkedin.com/posts/sql-analysts_today-i-decided-to-share-my-journey-in-data-activity-7169341885914853376-GWnd?utm_source=share&utm_medium=member_android
Feel free to write down your opinion in the comments.
I usually don't share my personal journey online but sometimes even small things like this help others.
I hope you guys can relate with me ❤️
Feel free to write down your opinion in the comments.
I usually don't share my personal journey online but sometimes even small things like this help others.
I hope you guys can relate with me ❤️
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Data Analyst Interview Resources
My journey as a data analyst so far: https://www.linkedin.com/posts/sql-analysts_today-i-decided-to-share-my-journey-in-data-activity-7169341885914853376-GWnd?utm_source=share&utm_medium=member_android Feel free to write down your opinion in the comments.…
Let me know in comments if you want to view my data analytics portfolio 😄
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10 steps to crack your next interview 👇
https://www.linkedin.com/posts/sql-analysts_10-quick-steps-to-crack-your-next-job-interview-activity-7172238670891102208-Vvd4?utm_source=share&utm_medium=member_android
https://www.linkedin.com/posts/sql-analysts_10-quick-steps-to-crack-your-next-job-interview-activity-7172238670891102208-Vvd4?utm_source=share&utm_medium=member_android
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Data Analyst Interview Resources
Top 20 SQL Interview Questions 👇👇 https://www.linkedin.com/posts/sql-analysts_sqlinterview-dataanalytics-techinterviews-activity-7172454208644878336-pA_8?utm_source=share&utm_medium=member_android
Like this post if you want me to continue posting interview questions on linkedin
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📚👀🚀Preparing for a Data science/ Data Analytics interview can be challenging, but with the right strategy, you can enhance your chances of success. Here are some key tips to assist you in getting ready:
Review Fundamental Concepts: Ensure you have a strong grasp of statistics, probability, linear algebra, data structures, algorithms, and programming languages like Python, R, and SQL.
Refresh Machine Learning Knowledge: Familiarize yourself with various machine learning algorithms, including supervised, unsupervised, and reinforcement learning.
Practice Coding: Sharpen your coding skills by solving data science-related problems on platforms like HackerRank, LeetCode, and Kaggle.
Build a Project Portfolio: Showcase your proficiency by creating a portfolio highlighting projects covering data cleaning, wrangling, exploratory data analysis, and machine learning.
Hone Communication Skills: Practice articulating complex technical ideas in simple terms, as effective communication is vital for data scientists when interacting with non-technical stakeholders.
Research the Company: Gain insights into the company's operations, industry, and how they leverage data to solve challenges.
🧠👍By adhering to these guidelines, you'll be well-prepared for your upcoming data science interview. Best of luck!
Hope this helps 👍❤️:-)
👍👀Be the first one to know the latest Job openings
https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
Review Fundamental Concepts: Ensure you have a strong grasp of statistics, probability, linear algebra, data structures, algorithms, and programming languages like Python, R, and SQL.
Refresh Machine Learning Knowledge: Familiarize yourself with various machine learning algorithms, including supervised, unsupervised, and reinforcement learning.
Practice Coding: Sharpen your coding skills by solving data science-related problems on platforms like HackerRank, LeetCode, and Kaggle.
Build a Project Portfolio: Showcase your proficiency by creating a portfolio highlighting projects covering data cleaning, wrangling, exploratory data analysis, and machine learning.
Hone Communication Skills: Practice articulating complex technical ideas in simple terms, as effective communication is vital for data scientists when interacting with non-technical stakeholders.
Research the Company: Gain insights into the company's operations, industry, and how they leverage data to solve challenges.
🧠👍By adhering to these guidelines, you'll be well-prepared for your upcoming data science interview. Best of luck!
Hope this helps 👍❤️:-)
👍👀Be the first one to know the latest Job openings
https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
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Power BI Interview Questions
👇👇
https://www.linkedin.com/posts/sql-analysts_%3F%3F%3F%3F%3F-%3F%3F-%3F%3F%3F%3F%3F%3F%3F%3F%3F-%3F%3F%3F-activity-7173573504162750464-PBI_
👇👇
https://www.linkedin.com/posts/sql-analysts_%3F%3F%3F%3F%3F-%3F%3F-%3F%3F%3F%3F%3F%3F%3F%3F%3F-%3F%3F%3F-activity-7173573504162750464-PBI_
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Essential Data Analytics Tools you should know 👇👇
https://www.linkedin.com/posts/sql-analysts_you-dont-need-to-know-each-and-every-data-activity-7174271852712779776-zPlS?utm_source=share&utm_medium=member_android
https://www.linkedin.com/posts/sql-analysts_you-dont-need-to-know-each-and-every-data-activity-7174271852712779776-zPlS?utm_source=share&utm_medium=member_android
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Data Analyst Interview Resources
Power BI Interview Questions 👇👇 https://www.linkedin.com/posts/sql-analysts_%3F%3F%3F%3F%3F-%3F%3F-%3F%3F%3F%3F%3F%3F%3F%3F%3F-%3F%3F%3F-activity-7173573504162750464-PBI_
Do you guys want more posts on interview questions for other tools like SQL, Tableau, Alteryx, Power BI & Excel?
Anonymous Poll
98%
Yes
2%
No
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📚🚀Becoming a successful data analyst requires a blend of technical, analytical, and soft skills. Key competencies for excelling in this role include:
Statistical Analysis: Mastery of statistical concepts such as probability, hypothesis testing, and regression analysis is essential.
Data Manipulation: Proficiency in SQL for data querying and manipulation, along with skills in data cleaning and transformation techniques.
Data Visualization: Ability to create insightful visualizations using tools like Tableau, Power BI, or Python libraries such as Matplotlib and Seaborn.
Programming: Strong programming skills in languages like Python or R, along with knowledge of relevant libraries like Pandas and NumPy.
Machine Learning (optional): Understanding of machine learning principles for predictive modeling and classification tasks.
Database Management: Familiarity with database systems such as MySQL, PostgreSQL, or MongoDB for handling large datasets.
Critical Thinking: Ability to analyze data critically, identify patterns, trends, and outliers.
Business Acumen: Understanding the business context and translating data insights into actionable recommendations.
Communication Skills: Effective communication of findings to non-technical stakeholders through both written and verbal means.
Continuous Learning: Commitment to ongoing learning and staying abreast of new tools, techniques, and industry trends to remain competitive.
By honing these skills and gaining practical experience through projects or internships, individuals can build a robust portfolio for a thriving career in data analysis.
React 👍❤️ to this it is very helpful...
Statistical Analysis: Mastery of statistical concepts such as probability, hypothesis testing, and regression analysis is essential.
Data Manipulation: Proficiency in SQL for data querying and manipulation, along with skills in data cleaning and transformation techniques.
Data Visualization: Ability to create insightful visualizations using tools like Tableau, Power BI, or Python libraries such as Matplotlib and Seaborn.
Programming: Strong programming skills in languages like Python or R, along with knowledge of relevant libraries like Pandas and NumPy.
Machine Learning (optional): Understanding of machine learning principles for predictive modeling and classification tasks.
Database Management: Familiarity with database systems such as MySQL, PostgreSQL, or MongoDB for handling large datasets.
Critical Thinking: Ability to analyze data critically, identify patterns, trends, and outliers.
Business Acumen: Understanding the business context and translating data insights into actionable recommendations.
Communication Skills: Effective communication of findings to non-technical stakeholders through both written and verbal means.
Continuous Learning: Commitment to ongoing learning and staying abreast of new tools, techniques, and industry trends to remain competitive.
By honing these skills and gaining practical experience through projects or internships, individuals can build a robust portfolio for a thriving career in data analysis.
React 👍❤️ to this it is very helpful...
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Top 20 Excel Interview Questions for Data Analysts 👇
https://www.linkedin.com/posts/sql-analysts_excel-dataanalytics-activity-7175072975526739968-dx5r
Like the post if you need more similar content
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1. What are Query and Query language?
A query is nothing but a request sent to a database to retrieve data or information. The required data can be retrieved from a table or many tables in the database.
Query languages use various types of queries to retrieve data from databases. SQL, Datalog, and AQL are a few examples of query languages; however, SQL is known to be the widely used query language.
2. What are Superkey and candidate key?
A super key may be a single or a combination of keys that help to identify a record in a table. Know that Super keys can have one or more attributes, even though all the attributes are not necessary to identify the records.
A candidate key is the subset of Superkey, which can have one or more than one attributes to identify records in a table. Unlike Superkey, all the attributes of the candidate key must be helpful to identify the records.
3. What do you mean by buffer pool and mention its benefits?
A buffer pool in SQL is also known as a buffer cache. All the resources can store their cached data pages in a buffer pool. The size of the buffer pool can be defined during the configuration of an instance of SQL Server.
The following are the benefits of a buffer pool:
Increase in I/O performance
Reduction in I/O latency
Increase in transaction throughput
Increase in reading performance
4. What is the difference between Zero and NULL values in SQL?
When a field in a column doesn’t have any value, it is said to be having a NULL value. Simply put, NULL is the blank field in a table. It can cancel be considered as an unassigned, unknown, or unavailable value. On the contrary, zero is a number, and it is an available, assigned, and known value.
A query is nothing but a request sent to a database to retrieve data or information. The required data can be retrieved from a table or many tables in the database.
Query languages use various types of queries to retrieve data from databases. SQL, Datalog, and AQL are a few examples of query languages; however, SQL is known to be the widely used query language.
2. What are Superkey and candidate key?
A super key may be a single or a combination of keys that help to identify a record in a table. Know that Super keys can have one or more attributes, even though all the attributes are not necessary to identify the records.
A candidate key is the subset of Superkey, which can have one or more than one attributes to identify records in a table. Unlike Superkey, all the attributes of the candidate key must be helpful to identify the records.
3. What do you mean by buffer pool and mention its benefits?
A buffer pool in SQL is also known as a buffer cache. All the resources can store their cached data pages in a buffer pool. The size of the buffer pool can be defined during the configuration of an instance of SQL Server.
The following are the benefits of a buffer pool:
Increase in I/O performance
Reduction in I/O latency
Increase in transaction throughput
Increase in reading performance
4. What is the difference between Zero and NULL values in SQL?
When a field in a column doesn’t have any value, it is said to be having a NULL value. Simply put, NULL is the blank field in a table. It can cancel be considered as an unassigned, unknown, or unavailable value. On the contrary, zero is a number, and it is an available, assigned, and known value.
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How to answer tell me about yourself questions in data analyst interview 👇👇
https://news.1rj.ru/str/learndataanalysis/844
https://news.1rj.ru/str/learndataanalysis/844
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1. List the different types of relationships in SQL.
One-to-One - This can be defined as the relationship between two tables where each record in one table is associated with the maximum of one record in the other table.
One-to-Many & Many-to-One - This is the most commonly used relationship where a record in a table is associated with multiple records in the other table.
Many-to-Many - This is used in cases when multiple instances on both sides are needed for defining a relationship.
Self-Referencing Relationships - This is used when a table needs to define a relationship with itself.
2. What are the different views available in Power BI Desktop?
There are three different views in Power BI, each of which serves another purpose:
Report View - In this view, users can add visualizations and additional report pages and publish the same on the portal.
Data View - In this view, data shaping can be performed using Query Editor tools.
Model View - In this view, users can manage relationships between complex datasets.
3. What are macros in Excel?
Excel allows you to automate the tasks you do regularly by recording them into macros. So, a macro is an action or a set of them that you can perform n number of times. For example, if you have to record the sales of each item at the end of the day, you can create a macro that will automatically calculate the sales, profits, loss, etc and use the same for the future instead of manually calculating it every day.
One-to-One - This can be defined as the relationship between two tables where each record in one table is associated with the maximum of one record in the other table.
One-to-Many & Many-to-One - This is the most commonly used relationship where a record in a table is associated with multiple records in the other table.
Many-to-Many - This is used in cases when multiple instances on both sides are needed for defining a relationship.
Self-Referencing Relationships - This is used when a table needs to define a relationship with itself.
2. What are the different views available in Power BI Desktop?
There are three different views in Power BI, each of which serves another purpose:
Report View - In this view, users can add visualizations and additional report pages and publish the same on the portal.
Data View - In this view, data shaping can be performed using Query Editor tools.
Model View - In this view, users can manage relationships between complex datasets.
3. What are macros in Excel?
Excel allows you to automate the tasks you do regularly by recording them into macros. So, a macro is an action or a set of them that you can perform n number of times. For example, if you have to record the sales of each item at the end of the day, you can create a macro that will automatically calculate the sales, profits, loss, etc and use the same for the future instead of manually calculating it every day.
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1. What is the Difference Between a Shallow Copy and Deep Copy in python?
Deep copy creates a different object and populates it with the child objects of the original object. Therefore, changes in the original object are not reflected in the copy. copy.deepcopy() creates a Deep Copy. Shallow copy creates a different object and populates it with the references of the child objects within the original object. Therefore, changes in the original object are reflected in the copy. copy.copy creates a Shallow Copy.
2. How can you remove duplicate values in a range of cells?
1. To delete duplicate values in a column, select the highlighted cells, and press the delete button. After deleting the values, go to the ‘Conditional Formatting’ option present in the Home tab. Choose ‘Clear Rules’ to remove the rules from the sheet.
2. You can also delete duplicate values by selecting the ‘Remove Duplicates’ option under Data Tools present in the Data tab.
3. Define shelves and sets in Tableau?
Shelves: Every worksheet in Tableau will have shelves such as columns, rows, marks, filters, pages, and more. By placing filters on shelves we can build our own visualization structure. We can control the marks by including or excluding data.
Sets: The sets are used to compute a condition on which the dataset will be prepared. Data will be grouped together based on a condition. Fields which is responsible for grouping are known assets. For example – students having grades of more than 70%.
4. Define Entity, Entity type, and Entity set.
Entity can be anything, be it a place, class or object which has an independent existence in the real world.
Entity Type represents a set of entities that have similar attributes.
Entity Set in the database represents a collection of entities having a particular entity type.
Deep copy creates a different object and populates it with the child objects of the original object. Therefore, changes in the original object are not reflected in the copy. copy.deepcopy() creates a Deep Copy. Shallow copy creates a different object and populates it with the references of the child objects within the original object. Therefore, changes in the original object are reflected in the copy. copy.copy creates a Shallow Copy.
2. How can you remove duplicate values in a range of cells?
1. To delete duplicate values in a column, select the highlighted cells, and press the delete button. After deleting the values, go to the ‘Conditional Formatting’ option present in the Home tab. Choose ‘Clear Rules’ to remove the rules from the sheet.
2. You can also delete duplicate values by selecting the ‘Remove Duplicates’ option under Data Tools present in the Data tab.
3. Define shelves and sets in Tableau?
Shelves: Every worksheet in Tableau will have shelves such as columns, rows, marks, filters, pages, and more. By placing filters on shelves we can build our own visualization structure. We can control the marks by including or excluding data.
Sets: The sets are used to compute a condition on which the dataset will be prepared. Data will be grouped together based on a condition. Fields which is responsible for grouping are known assets. For example – students having grades of more than 70%.
4. Define Entity, Entity type, and Entity set.
Entity can be anything, be it a place, class or object which has an independent existence in the real world.
Entity Type represents a set of entities that have similar attributes.
Entity Set in the database represents a collection of entities having a particular entity type.
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1. What is the Difference Between a Shallow Copy and Deep Copy in python?
Deepcopy creates a different object and populates it with the child objects of the original object. Therefore, changes in the original object are not reflected in the copy. copy.deepcopy() creates a Deep Copy. Shallow copy creates a different object and populates it with the references of the child objects within the original object. Therefore, changes in the original object are reflected in the copy. copy.copy creates a Shallow Copy.
2. How can you remove duplicate values in a range of cells?
1. To delete duplicate values in a column, select the highlighted cells, and press the delete button. After deleting the values, go to the ‘Conditional Formatting’ option present in the Home tab. Choose ‘Clear Rules’ to remove the rules from the sheet.
2. You can also delete duplicate values by selecting the ‘Remove Duplicates’ option under Data Tools present in the Data tab.
3. Define shelves and sets in Tableau?
Shelves: Every worksheet in Tableau will have shelves such as columns, rows, marks, filters, pages, and more. By placing filters on shelves we can build our own visualization structure. We can control the marks by including or excluding data.
Sets: The sets are used to compute a condition on which the dataset will be prepared. Data will be grouped together based on a condition. Fields which is responsible for grouping are known assets. For example – students having grades of more than 70%.
Deepcopy creates a different object and populates it with the child objects of the original object. Therefore, changes in the original object are not reflected in the copy. copy.deepcopy() creates a Deep Copy. Shallow copy creates a different object and populates it with the references of the child objects within the original object. Therefore, changes in the original object are reflected in the copy. copy.copy creates a Shallow Copy.
2. How can you remove duplicate values in a range of cells?
1. To delete duplicate values in a column, select the highlighted cells, and press the delete button. After deleting the values, go to the ‘Conditional Formatting’ option present in the Home tab. Choose ‘Clear Rules’ to remove the rules from the sheet.
2. You can also delete duplicate values by selecting the ‘Remove Duplicates’ option under Data Tools present in the Data tab.
3. Define shelves and sets in Tableau?
Shelves: Every worksheet in Tableau will have shelves such as columns, rows, marks, filters, pages, and more. By placing filters on shelves we can build our own visualization structure. We can control the marks by including or excluding data.
Sets: The sets are used to compute a condition on which the dataset will be prepared. Data will be grouped together based on a condition. Fields which is responsible for grouping are known assets. For example – students having grades of more than 70%.
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Data Analyst Interview Questions
1. What do Tableau's sets and groups mean?
Data is grouped using sets and groups according to predefined criteria. The primary distinction between the two is that although a set can have only two options—either in or out—a group can divide the dataset into several groups. A user should decide which group or sets to apply based on the conditions.
2.What in Excel is a macro?
An Excel macro is an algorithm or a group of steps that helps automate an operation by capturing and replaying the steps needed to finish it. Once the steps have been saved, you may construct a Macro that the user can alter and replay as often as they like.
Macro is excellent for routine work because it also gets rid of mistakes. Consider the scenario when an account manager needs to share reports about staff members who owe the company money. If so, it can be automated by utilising a macro and making small adjustments each month as necessary.
3.Gantt chart in Tableau
A Tableau Gantt chart illustrates the duration of events as well as the progression of value across the period. Along with the time axis, it has bars. The Gantt chart is primarily used as a project management tool, with each bar representing a project job.
4.In Microsoft Excel, how do you create a drop-down list?
Start by selecting the Data tab from the ribbon.
Select Data Validation from the Data Tools group.
Go to Settings > Allow > List next.
Choose the source you want to offer in the form of a list array.
1. What do Tableau's sets and groups mean?
Data is grouped using sets and groups according to predefined criteria. The primary distinction between the two is that although a set can have only two options—either in or out—a group can divide the dataset into several groups. A user should decide which group or sets to apply based on the conditions.
2.What in Excel is a macro?
An Excel macro is an algorithm or a group of steps that helps automate an operation by capturing and replaying the steps needed to finish it. Once the steps have been saved, you may construct a Macro that the user can alter and replay as often as they like.
Macro is excellent for routine work because it also gets rid of mistakes. Consider the scenario when an account manager needs to share reports about staff members who owe the company money. If so, it can be automated by utilising a macro and making small adjustments each month as necessary.
3.Gantt chart in Tableau
A Tableau Gantt chart illustrates the duration of events as well as the progression of value across the period. Along with the time axis, it has bars. The Gantt chart is primarily used as a project management tool, with each bar representing a project job.
4.In Microsoft Excel, how do you create a drop-down list?
Start by selecting the Data tab from the ribbon.
Select Data Validation from the Data Tools group.
Go to Settings > Allow > List next.
Choose the source you want to offer in the form of a list array.
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Data Analytics Interview Preparation Series Part-1
[Questions with Answers]
Why did you want your job?
I was originally studying physics but didn't want to do a PhD. So, after my master’s I decided I would try a job working with data. I noticed that it was quite common for people studying science to go into data after. I had several friends who went on to become data scientists directly after their undergrad.
I noticed that given my background in maths and some noscripting in Python (thanks to computational physics classes), it wouldn't be too hard to make the jump.
I went into data science because I wanted a more mathematical role with a research component (model design, experimentation, metric design etc.)
This was instead of a more practical role like data analysis or data engineering.
It turned out to be a cool choice and I'm enjoying my time as a data scientist right now!
Why did you choose the industry that you work in?
I work in a music-tech start up. I love it because I make music on the side. Being able to work in
music and be surrounded by people who are also passionate about music is very cool!
The company organizes concerts with artists that we work with etc. It's really cool! This makes the job more interesting for me, given that it's so tightly related to what I love to do.
Like if want me to continue the series 😄❤️
[Questions with Answers]
Why did you want your job?
I was originally studying physics but didn't want to do a PhD. So, after my master’s I decided I would try a job working with data. I noticed that it was quite common for people studying science to go into data after. I had several friends who went on to become data scientists directly after their undergrad.
I noticed that given my background in maths and some noscripting in Python (thanks to computational physics classes), it wouldn't be too hard to make the jump.
I went into data science because I wanted a more mathematical role with a research component (model design, experimentation, metric design etc.)
This was instead of a more practical role like data analysis or data engineering.
It turned out to be a cool choice and I'm enjoying my time as a data scientist right now!
Why did you choose the industry that you work in?
I work in a music-tech start up. I love it because I make music on the side. Being able to work in
music and be surrounded by people who are also passionate about music is very cool!
The company organizes concerts with artists that we work with etc. It's really cool! This makes the job more interesting for me, given that it's so tightly related to what I love to do.
Like if want me to continue the series 😄❤️
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Time to test your data analytics knowledge
👇👇
https://www.instagram.com/dataanalyticsinterview?utm_source=qr&igsh=MXNkbXM3dmN2Nmhibw==
Let's see who gives the first correct answer 😁
👇👇
https://www.instagram.com/dataanalyticsinterview?utm_source=qr&igsh=MXNkbXM3dmN2Nmhibw==
Let's see who gives the first correct answer 😁
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