To become a successful data analyst, you need a combination of technical skills, analytical skills, and soft skills. Here are some key skills required to excel in a data analyst role:
1. Statistical Analysis: Understanding statistical concepts and being able to apply them to analyze data sets is essential for a data analyst. Knowledge of probability, hypothesis testing, regression analysis, and other statistical techniques is important.
2. Data Manipulation: Proficiency in tools like SQL for querying databases and manipulating data is crucial. Knowledge of data cleaning, transformation, and preparation techniques is also important.
3. Data Visualization: Being able to create meaningful visualizations using tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn is essential for effectively communicating insights from data.
4. Programming: Strong programming skills in languages like Python or R are often required for data analysis tasks. Knowledge of libraries like Pandas, NumPy, and scikit-learn in Python can be beneficial.
5. Machine Learning(optional): Understanding machine learning concepts and being able to apply algorithms for predictive modeling, clustering, and classification tasks is becoming increasingly important for data analysts.
6. Database Management: Knowledge of database systems like MySQL, PostgreSQL, or MongoDB is useful for working with large datasets and understanding how data is stored and retrieved.
7. Critical Thinking: Data analysts need to be able to think critically and approach problems analytically. Being able to identify patterns, trends, and outliers in data is important for drawing meaningful insights.
8. Business Acumen: Understanding the business context and objectives behind the data analysis is crucial. Data analysts should be able to translate data insights into actionable recommendations for business decision-making.
9. Communication Skills: Data analysts need to effectively communicate their findings to non-technical stakeholders. Strong written and verbal communication skills are essential for presenting complex data analysis results in a clear and understandable manner.
10. Continuous Learning: The field of data analysis is constantly evolving, so a willingness to learn new tools, techniques, and technologies is important for staying current and adapting to changes in the industry.
By developing these skills and gaining practical experience through projects or internships, you can build a strong portfolio for a successful career as a data analyst.
1. Statistical Analysis: Understanding statistical concepts and being able to apply them to analyze data sets is essential for a data analyst. Knowledge of probability, hypothesis testing, regression analysis, and other statistical techniques is important.
2. Data Manipulation: Proficiency in tools like SQL for querying databases and manipulating data is crucial. Knowledge of data cleaning, transformation, and preparation techniques is also important.
3. Data Visualization: Being able to create meaningful visualizations using tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn is essential for effectively communicating insights from data.
4. Programming: Strong programming skills in languages like Python or R are often required for data analysis tasks. Knowledge of libraries like Pandas, NumPy, and scikit-learn in Python can be beneficial.
5. Machine Learning(optional): Understanding machine learning concepts and being able to apply algorithms for predictive modeling, clustering, and classification tasks is becoming increasingly important for data analysts.
6. Database Management: Knowledge of database systems like MySQL, PostgreSQL, or MongoDB is useful for working with large datasets and understanding how data is stored and retrieved.
7. Critical Thinking: Data analysts need to be able to think critically and approach problems analytically. Being able to identify patterns, trends, and outliers in data is important for drawing meaningful insights.
8. Business Acumen: Understanding the business context and objectives behind the data analysis is crucial. Data analysts should be able to translate data insights into actionable recommendations for business decision-making.
9. Communication Skills: Data analysts need to effectively communicate their findings to non-technical stakeholders. Strong written and verbal communication skills are essential for presenting complex data analysis results in a clear and understandable manner.
10. Continuous Learning: The field of data analysis is constantly evolving, so a willingness to learn new tools, techniques, and technologies is important for staying current and adapting to changes in the industry.
By developing these skills and gaining practical experience through projects or internships, you can build a strong portfolio for a successful career as a data analyst.
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NoSQL vs SQL
NoSQL databases provide flexible data models ideal for diverse data structures and scalability.
1. Key-Value: Simple, uses key-value pairs (e.g., Redis).
2. Document: Stores data in JSON/BSON documents (e.g., MongoDB).
3. Graph: Manages complex relationships with nodes and edges (e.g., Neo4j).
4. Column Store: Optimized for analytics, organizes data by columns (e.g., Cassandra).
SQL databases, like RDBMS and OLAP, provide structured, relational storage for traditional and analytical needs
1. RDBMS: Traditional relational databases with tables (e.g., PostgreSQL & MySQL).
2. OLAP: Designed for complex analysis and multidimensional data (e.g., SQL Server Analysis Services).
NoSQL databases provide flexible data models ideal for diverse data structures and scalability.
1. Key-Value: Simple, uses key-value pairs (e.g., Redis).
2. Document: Stores data in JSON/BSON documents (e.g., MongoDB).
3. Graph: Manages complex relationships with nodes and edges (e.g., Neo4j).
4. Column Store: Optimized for analytics, organizes data by columns (e.g., Cassandra).
SQL databases, like RDBMS and OLAP, provide structured, relational storage for traditional and analytical needs
1. RDBMS: Traditional relational databases with tables (e.g., PostgreSQL & MySQL).
2. OLAP: Designed for complex analysis and multidimensional data (e.g., SQL Server Analysis Services).
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What to do and What to avoid!
When sitting in front of an interviewer, your actions and words can make or break your chances.
It’s more than just answering questions, it's about presenting yourself as the ideal candidate.
Here are some clear do's and don'ts to keep in mind.
📌Do:
1. Be Prepared.
2. Dress Appropriately.
3. Be Punctual.
4. Maintain Good Posture.
5. Listen Carefully.
6. Ask Thoughtful Questions.
7. Be Honest.
📌Don't:
1. Don’t Fidget.
2. Don’t Speak Negatively About Past Employers.
3. Don’t Interrupt.
4. Don’t Overshare.
5. Don’t Forget to Follow Up.
By keeping these dos and don’ts in mind, you’ll be better prepared to make a strong impression in your interview.
Good luck!
Hope this helps you 😊
When sitting in front of an interviewer, your actions and words can make or break your chances.
It’s more than just answering questions, it's about presenting yourself as the ideal candidate.
Here are some clear do's and don'ts to keep in mind.
📌Do:
1. Be Prepared.
2. Dress Appropriately.
3. Be Punctual.
4. Maintain Good Posture.
5. Listen Carefully.
6. Ask Thoughtful Questions.
7. Be Honest.
📌Don't:
1. Don’t Fidget.
2. Don’t Speak Negatively About Past Employers.
3. Don’t Interrupt.
4. Don’t Overshare.
5. Don’t Forget to Follow Up.
By keeping these dos and don’ts in mind, you’ll be better prepared to make a strong impression in your interview.
Good luck!
Hope this helps you 😊
👍7❤5
Here are few Important SQL interview questions with topics
Basic SQL Concepts:
Explain the difference between SQL and NoSQL databases.
What are the common data types in SQL?
Querying:
How do you retrieve all records from a table named "Customers"?
What is the difference between SELECT and SELECT DISTINCT in a query?
Explain the purpose of the WHERE clause in SQL queries.
Joins:
Describe the types of joins in SQL (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN).
How would you retrieve data from two tables using an INNER JOIN?
Aggregate Functions:
What are aggregate functions in SQL? Can you name a few?
How do you calculate the average, sum, and count of a column in a SQL query?
Grouping and Filtering:
Explain the GROUP BY clause and its use in SQL.
How would you filter the results of an SQL query using the HAVING clause?
Subqueries:
What is a subquery, and when would you use one in SQL?
Provide an example of a subquery in an SQL statement.
Indexes and Optimization:
Why are indexes important in a database?
How would you optimize a slow-running SQL query?
Normalization and Data Integrity:
What is database normalization, and why is it important?
How can you enforce data integrity in a SQL database?
Transactions:
What is a SQL transaction, and why would you use it?
Explain the concepts of ACID properties in database transactions.
Views and Stored Procedures:
What is a database view, and when would you create one?
What is a stored procedure, and how does it differ from a regular SQL query?
Advanced SQL:
Can you write a recursive SQL query, and when would you use recursion?
Explain the concept of window functions in SQL.
These questions cover a range of SQL topics, from basic concepts to more advanced techniques, and can help assess a candidate's knowledge and skills in SQL :)
Like this post if you need more 👍❤️
Hope it helps :)
Basic SQL Concepts:
Explain the difference between SQL and NoSQL databases.
What are the common data types in SQL?
Querying:
How do you retrieve all records from a table named "Customers"?
What is the difference between SELECT and SELECT DISTINCT in a query?
Explain the purpose of the WHERE clause in SQL queries.
Joins:
Describe the types of joins in SQL (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN).
How would you retrieve data from two tables using an INNER JOIN?
Aggregate Functions:
What are aggregate functions in SQL? Can you name a few?
How do you calculate the average, sum, and count of a column in a SQL query?
Grouping and Filtering:
Explain the GROUP BY clause and its use in SQL.
How would you filter the results of an SQL query using the HAVING clause?
Subqueries:
What is a subquery, and when would you use one in SQL?
Provide an example of a subquery in an SQL statement.
Indexes and Optimization:
Why are indexes important in a database?
How would you optimize a slow-running SQL query?
Normalization and Data Integrity:
What is database normalization, and why is it important?
How can you enforce data integrity in a SQL database?
Transactions:
What is a SQL transaction, and why would you use it?
Explain the concepts of ACID properties in database transactions.
Views and Stored Procedures:
What is a database view, and when would you create one?
What is a stored procedure, and how does it differ from a regular SQL query?
Advanced SQL:
Can you write a recursive SQL query, and when would you use recursion?
Explain the concept of window functions in SQL.
These questions cover a range of SQL topics, from basic concepts to more advanced techniques, and can help assess a candidate's knowledge and skills in SQL :)
Like this post if you need more 👍❤️
Hope it helps :)
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How Data Analytics Helps to Grow Business to Best
👇👇
https://datasimplifier.com/data-analytics-helps-to-grow/
👇👇
https://datasimplifier.com/data-analytics-helps-to-grow/
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Why is Excel Often the Starting Point for SQL ?
Here's how Excel can help you before you dive into SQL:
✔️ 𝐕𝐋𝐎𝐎𝐊𝐔𝐏 = 𝐒𝐐𝐋 𝐉𝐎𝐈𝐍𝐒
In Excel, we use VLOOKUP to bring together data from different sheets. It's just like using JOINS in SQL to get data from more than one table.
✔️ 𝐒𝐔𝐌 𝐚𝐧𝐝 𝐂𝐎𝐔𝐍𝐓 𝐟𝐨𝐫 𝐒𝐐𝐋 𝐐𝐮𝐞𝐫𝐢𝐞𝐬
Excel's SUM and COUNT functions are like practice for SQL queries. They help you add up and count things, which is what you often do in SQL.
✔️ 𝐅𝐈𝐋𝐓𝐄𝐑 𝐒𝐭𝐚𝐭𝐞𝐦𝐞𝐧𝐭𝐬 & 𝐖𝐇𝐄𝐑𝐄 𝐢𝐧 𝐒𝐐𝐋
Excel's 𝐅𝐈𝐋𝐓𝐄𝐑 statements let you make choices with your data. This is similar to using WHERE in SQL to pick specific data.
✔️ 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐃𝐚𝐭𝐞𝐬 𝐚𝐧𝐝 𝐓𝐞𝐱𝐭
Both Excel and SQL have ways to work with dates and text. Learning these in Excel first can make it easier when you switch to SQL.
✔️ 𝐏𝐢𝐯𝐨𝐭 𝐓𝐚𝐛𝐥𝐞𝐬 & 𝐆𝐑𝐎𝐔𝐏 𝐁𝐘 𝐢𝐧 𝐒𝐐𝐋
Ever used pivot tables in Excel? They're a good start for understanding the GROUP BY function in SQL, which helps you organize and summarize data.
✔️ 𝐗𝐋𝐎𝐎𝐊𝐔𝐏 & 𝐇𝐲𝐩𝐞𝐫𝐥𝐢𝐧𝐤𝐬
Excel's XLOOKUP and hyperlinks are like SQL's ways of finding and linking data. They give you a peek into how SQL finds and connects information.
Learning Excel first makes SQL easier to understand. It's not just about learning a tool, it's about getting ready for the bigger world of data!
You will be asked questions on SQL in interviews for sure! Make sure to practice 2-3 questions daily, it can't be mastered overnight!
Share our channel link with your true friends: https://news.1rj.ru/str/excel_analyst
Hope this helps you 😊
Here's how Excel can help you before you dive into SQL:
✔️ 𝐕𝐋𝐎𝐎𝐊𝐔𝐏 = 𝐒𝐐𝐋 𝐉𝐎𝐈𝐍𝐒
In Excel, we use VLOOKUP to bring together data from different sheets. It's just like using JOINS in SQL to get data from more than one table.
✔️ 𝐒𝐔𝐌 𝐚𝐧𝐝 𝐂𝐎𝐔𝐍𝐓 𝐟𝐨𝐫 𝐒𝐐𝐋 𝐐𝐮𝐞𝐫𝐢𝐞𝐬
Excel's SUM and COUNT functions are like practice for SQL queries. They help you add up and count things, which is what you often do in SQL.
✔️ 𝐅𝐈𝐋𝐓𝐄𝐑 𝐒𝐭𝐚𝐭𝐞𝐦𝐞𝐧𝐭𝐬 & 𝐖𝐇𝐄𝐑𝐄 𝐢𝐧 𝐒𝐐𝐋
Excel's 𝐅𝐈𝐋𝐓𝐄𝐑 statements let you make choices with your data. This is similar to using WHERE in SQL to pick specific data.
✔️ 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐃𝐚𝐭𝐞𝐬 𝐚𝐧𝐝 𝐓𝐞𝐱𝐭
Both Excel and SQL have ways to work with dates and text. Learning these in Excel first can make it easier when you switch to SQL.
✔️ 𝐏𝐢𝐯𝐨𝐭 𝐓𝐚𝐛𝐥𝐞𝐬 & 𝐆𝐑𝐎𝐔𝐏 𝐁𝐘 𝐢𝐧 𝐒𝐐𝐋
Ever used pivot tables in Excel? They're a good start for understanding the GROUP BY function in SQL, which helps you organize and summarize data.
✔️ 𝐗𝐋𝐎𝐎𝐊𝐔𝐏 & 𝐇𝐲𝐩𝐞𝐫𝐥𝐢𝐧𝐤𝐬
Excel's XLOOKUP and hyperlinks are like SQL's ways of finding and linking data. They give you a peek into how SQL finds and connects information.
Learning Excel first makes SQL easier to understand. It's not just about learning a tool, it's about getting ready for the bigger world of data!
You will be asked questions on SQL in interviews for sure! Make sure to practice 2-3 questions daily, it can't be mastered overnight!
Share our channel link with your true friends: https://news.1rj.ru/str/excel_analyst
Hope this helps you 😊
👍11🥰2👌2❤1
Free Programming and Data Analytics Resources 👇👇
✅ Data science and Data Analytics Free Courses by Google
https://developers.google.com/edu/python/introduction
https://grow.google/intl/en_in/data-analytics-course/?tab=get-started-in-the-field
https://cloud.google.com/data-science?hl=en
https://developers.google.com/machine-learning/crash-course
https://news.1rj.ru/str/datasciencefun/1371
🔍 Free Data Analytics Courses by Microsoft
1. Get started with microsoft dataanalytics
https://learn.microsoft.com/en-us/training/paths/data-analytics-microsoft/
2. Introduction to version control with git
https://learn.microsoft.com/en-us/training/paths/intro-to-vc-git/
3. Microsoft azure ai fundamentals
https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/
🤖 Free AI Courses by Microsoft
1. Fundamentals of AI by Microsoft
https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/
2. Introduction to AI with python by Harvard.
https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python
📚 Useful Resources for the Programmers
Data Analyst Roadmap
https://news.1rj.ru/str/sqlspecialist/94
Free C course from Microsoft
https://docs.microsoft.com/en-us/cpp/c-language/?view=msvc-170&viewFallbackFrom=vs-2019
Interactive React Native Resources
https://fullstackopen.com/en/part10
Python for Data Science and ML
https://news.1rj.ru/str/datasciencefree/68
Ethical Hacking Bootcamp
https://news.1rj.ru/str/ethicalhackingtoday/3
Unity Documentation
https://docs.unity3d.com/Manual/index.html
Advanced Javanoscript concepts
https://news.1rj.ru/str/Programming_experts/72
Oops in Java
https://nptel.ac.in/courses/106105224
Intro to Version control with Git
https://docs.microsoft.com/en-us/learn/modules/intro-to-git/0-introduction
Python Data Structure and Algorithms
https://news.1rj.ru/str/programming_guide/76
Free PowerBI course by Microsoft
https://docs.microsoft.com/en-us/users/microsoftpowerplatform-5978/collections/k8xidwwnzk1em
Data Structures Interview Preparation
https://news.1rj.ru/str/crackingthecodinginterview/309
🍻 Free Programming Courses by Microsoft
❯ JavaScript
http://learn.microsoft.com/training/paths/web-development-101/
❯ TypeScript
http://learn.microsoft.com/training/paths/build-javanoscript-applications-typenoscript/
❯ C#
http://learn.microsoft.com/users/dotnet/collections/yz26f8y64n7k07
Join @free4unow_backup for more free resources.
ENJOY LEARNING 👍👍
✅ Data science and Data Analytics Free Courses by Google
https://developers.google.com/edu/python/introduction
https://grow.google/intl/en_in/data-analytics-course/?tab=get-started-in-the-field
https://cloud.google.com/data-science?hl=en
https://developers.google.com/machine-learning/crash-course
https://news.1rj.ru/str/datasciencefun/1371
🔍 Free Data Analytics Courses by Microsoft
1. Get started with microsoft dataanalytics
https://learn.microsoft.com/en-us/training/paths/data-analytics-microsoft/
2. Introduction to version control with git
https://learn.microsoft.com/en-us/training/paths/intro-to-vc-git/
3. Microsoft azure ai fundamentals
https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/
🤖 Free AI Courses by Microsoft
1. Fundamentals of AI by Microsoft
https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/
2. Introduction to AI with python by Harvard.
https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python
📚 Useful Resources for the Programmers
Data Analyst Roadmap
https://news.1rj.ru/str/sqlspecialist/94
Free C course from Microsoft
https://docs.microsoft.com/en-us/cpp/c-language/?view=msvc-170&viewFallbackFrom=vs-2019
Interactive React Native Resources
https://fullstackopen.com/en/part10
Python for Data Science and ML
https://news.1rj.ru/str/datasciencefree/68
Ethical Hacking Bootcamp
https://news.1rj.ru/str/ethicalhackingtoday/3
Unity Documentation
https://docs.unity3d.com/Manual/index.html
Advanced Javanoscript concepts
https://news.1rj.ru/str/Programming_experts/72
Oops in Java
https://nptel.ac.in/courses/106105224
Intro to Version control with Git
https://docs.microsoft.com/en-us/learn/modules/intro-to-git/0-introduction
Python Data Structure and Algorithms
https://news.1rj.ru/str/programming_guide/76
Free PowerBI course by Microsoft
https://docs.microsoft.com/en-us/users/microsoftpowerplatform-5978/collections/k8xidwwnzk1em
Data Structures Interview Preparation
https://news.1rj.ru/str/crackingthecodinginterview/309
🍻 Free Programming Courses by Microsoft
❯ JavaScript
http://learn.microsoft.com/training/paths/web-development-101/
❯ TypeScript
http://learn.microsoft.com/training/paths/build-javanoscript-applications-typenoscript/
❯ C#
http://learn.microsoft.com/users/dotnet/collections/yz26f8y64n7k07
Join @free4unow_backup for more free resources.
ENJOY LEARNING 👍👍
👍7👌2
Essential tools and skills required to become a data analyst 👇👇
### Data Analysis and Visualization:
1. Microsoft Excel: Essential for data manipulation, analysis, and basic modeling.
2. SQL (Structured Query Language): Crucial for querying databases and extracting data for analysis.
3. Tableau or Power BI: Powerful tools for creating interactive dashboards and visualizing data.
### Programming and Data Manipulation:(Optional)
4. Python: Used for data manipulation, noscripting, and automation.
5. R: Useful for statistical computing, data visualization, and basic analytics.
### Statistical Analysis:
6. Statistical Software (SPSS, SAS): Tools for advanced statistical analysis and modeling.(Optional)
7. Advanced Excel Functions: Proficiency in pivot tables, VLOOKUP, statistical functions, and data cleaning techniques.
### Project Management and Collaboration:(Optional)
8. Jira or Trello: Tools for project management, task tracking, and collaboration.
9. Confluence or SharePoint: Platforms for documentation, collaboration, and knowledge sharing.
### Business Process Management:(Optional)
10. Business Process Modeling Tools (Visio, Lucidchart): Used for modeling, analyzing, and optimizing business processes.
### Additional Skills:
11. Google Analytics: Important for understanding website traffic and user behavior. (Optional)
12. CRM Systems (Salesforce, HubSpot): Knowledge of these systems aids in analyzing sales data and customer interactions.(Optional)
13. Version Control (Git): Helps manage changes in analytical projects and ensures versioning control. (Optional)
### Data Warehousing and Database Management:
14. Data Warehousing (Amazon Redshift, Google BigQuery): Knowledge of these platforms for handling large-scale datasets and optimizing queries. (Optional)
### Soft Skills:
15. Communication: Clear and concise communication of findings and recommendations.
16. Problem-Solving & Critical Thinking: Ability to analyze complex problems and derive actionable insights.
I know this list might seem extensive, so it's best to begin with mastering Excel, Power BI, and SQL. As you progress, you can gradually add other tools from the list based on specific project needs and requirements.
Here are some essential telegram channels with important resources:
❯ SQL ➟ t.me/sqlanalyst
❯ Power BI ➟ @PowerBI_analyst
❯ Resources ➟ @learndataanalysis
❯ Excel ➟ t.me/excel_analyst
❯ Data Portfolio ➟ @DataPortfolio
Also, try building projects & data portfolio while learning these skills. Creating data analytics projects will help you in showcasing the skills while giving job interviews.
Join @free4unow_backup for more resources
ENJOY LEARNING👍👍
### Data Analysis and Visualization:
1. Microsoft Excel: Essential for data manipulation, analysis, and basic modeling.
2. SQL (Structured Query Language): Crucial for querying databases and extracting data for analysis.
3. Tableau or Power BI: Powerful tools for creating interactive dashboards and visualizing data.
### Programming and Data Manipulation:(Optional)
4. Python: Used for data manipulation, noscripting, and automation.
5. R: Useful for statistical computing, data visualization, and basic analytics.
### Statistical Analysis:
6. Statistical Software (SPSS, SAS): Tools for advanced statistical analysis and modeling.(Optional)
7. Advanced Excel Functions: Proficiency in pivot tables, VLOOKUP, statistical functions, and data cleaning techniques.
### Project Management and Collaboration:(Optional)
8. Jira or Trello: Tools for project management, task tracking, and collaboration.
9. Confluence or SharePoint: Platforms for documentation, collaboration, and knowledge sharing.
### Business Process Management:(Optional)
10. Business Process Modeling Tools (Visio, Lucidchart): Used for modeling, analyzing, and optimizing business processes.
### Additional Skills:
11. Google Analytics: Important for understanding website traffic and user behavior. (Optional)
12. CRM Systems (Salesforce, HubSpot): Knowledge of these systems aids in analyzing sales data and customer interactions.(Optional)
13. Version Control (Git): Helps manage changes in analytical projects and ensures versioning control. (Optional)
### Data Warehousing and Database Management:
14. Data Warehousing (Amazon Redshift, Google BigQuery): Knowledge of these platforms for handling large-scale datasets and optimizing queries. (Optional)
### Soft Skills:
15. Communication: Clear and concise communication of findings and recommendations.
16. Problem-Solving & Critical Thinking: Ability to analyze complex problems and derive actionable insights.
I know this list might seem extensive, so it's best to begin with mastering Excel, Power BI, and SQL. As you progress, you can gradually add other tools from the list based on specific project needs and requirements.
Here are some essential telegram channels with important resources:
❯ SQL ➟ t.me/sqlanalyst
❯ Power BI ➟ @PowerBI_analyst
❯ Resources ➟ @learndataanalysis
❯ Excel ➟ t.me/excel_analyst
❯ Data Portfolio ➟ @DataPortfolio
Also, try building projects & data portfolio while learning these skills. Creating data analytics projects will help you in showcasing the skills while giving job interviews.
Join @free4unow_backup for more resources
ENJOY LEARNING👍👍
👍8❤2👏1
Career Path for a Data Analyst
Education: Start by earning a bachelor's degree in fields like math, stats, economics, or computer science.
Skills Growth: Learn programming (Python/R), data tools (SQL/Excel), and visualization. Master data analysis basics.
Entry-Level Role: Begin as a Junior Data Analyst. Learn data cleaning, organization, and basic analysis.
Specialization: Deepen your expertise in a specific industry. Explore advanced analytics and visualization tools.
Advanced Analytics: Move up to Senior Data Analyst. Tackle complex projects and predictive modeling.
Machine Learning: Explore machine learning and data modeling techniques. Familiarize yourself with algorithms, and learn how to implement predictive and classification models.
Domain Expertise: Develop expertise in a particular industry, such as healthcare, finance, e-commerce, etc. This knowledge will enable you to provide more valuable insights from data.
Leadership Roles: As you gain experience, you can move into roles like Data Analytics Manager or Data Science Manager, where you'll oversee teams and projects.
Continuous Learning: Stay updated with the latest tools, techniques, and industry trends. Attend workshops, conferences, and online courses to keep your skills relevant.
Networking: Build a strong professional network within the data analytics community. This can open up opportunities and help you stay informed about industry developments.
Remember, your career path can be personalized based on your interests and strengths. Continuous learning and adaptability are key in the ever-evolving field of data analysis :)
Education: Start by earning a bachelor's degree in fields like math, stats, economics, or computer science.
Skills Growth: Learn programming (Python/R), data tools (SQL/Excel), and visualization. Master data analysis basics.
Entry-Level Role: Begin as a Junior Data Analyst. Learn data cleaning, organization, and basic analysis.
Specialization: Deepen your expertise in a specific industry. Explore advanced analytics and visualization tools.
Advanced Analytics: Move up to Senior Data Analyst. Tackle complex projects and predictive modeling.
Machine Learning: Explore machine learning and data modeling techniques. Familiarize yourself with algorithms, and learn how to implement predictive and classification models.
Domain Expertise: Develop expertise in a particular industry, such as healthcare, finance, e-commerce, etc. This knowledge will enable you to provide more valuable insights from data.
Leadership Roles: As you gain experience, you can move into roles like Data Analytics Manager or Data Science Manager, where you'll oversee teams and projects.
Continuous Learning: Stay updated with the latest tools, techniques, and industry trends. Attend workshops, conferences, and online courses to keep your skills relevant.
Networking: Build a strong professional network within the data analytics community. This can open up opportunities and help you stay informed about industry developments.
Remember, your career path can be personalized based on your interests and strengths. Continuous learning and adaptability are key in the ever-evolving field of data analysis :)
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1. Does SQL support programming language features?
It is true that SQL is a language, but it does not support programming as it is not a programming language, it is a command language. We do not have some programming concepts in SQL like for loops or while loop, we only have commands which we can use to query, update, delete, etc. data in the database. SQL allows us to manipulate data in a database.
2. What is a trigger?
Trigger is a statement that a system executes automatically when there is any modification to the database. In a trigger, we first specify when the trigger is to be executed and then the action to be performed when the trigger executes. Triggers are used to specify certain integrity constraints and referential constraints that cannot be specified using the constraint mechanism of SQL.
3. What are aggregate and scalar functions?
For doing operations on data SQL has many built-in functions, they are categorized into two categories and further sub-categorized into seven different functions under each category. The categories are:
Aggregate functions:
These functions are used to do operations from the values of the column and a single value is returned.
Scalar functions:
These functions are based on user input, these too return a single value.
4. Define SQL Order by the statement?
The ORDER BY statement in SQL is used to sort the fetched data in either ascending or descending according to one or more columns.
By default ORDER BY sorts the data in ascending order.
We can use the keyword DESC to sort the data in descending order and the keyword ASC to sort in ascending order.
5. What is the difference between primary key and unique constraints?
The primary key cannot have NULL values, the unique constraints can have NULL values. There is only one primary key in a table, but there can be multiple unique constraints. The primary key creates the clustered index automatically but the unique key does not.
It is true that SQL is a language, but it does not support programming as it is not a programming language, it is a command language. We do not have some programming concepts in SQL like for loops or while loop, we only have commands which we can use to query, update, delete, etc. data in the database. SQL allows us to manipulate data in a database.
2. What is a trigger?
Trigger is a statement that a system executes automatically when there is any modification to the database. In a trigger, we first specify when the trigger is to be executed and then the action to be performed when the trigger executes. Triggers are used to specify certain integrity constraints and referential constraints that cannot be specified using the constraint mechanism of SQL.
3. What are aggregate and scalar functions?
For doing operations on data SQL has many built-in functions, they are categorized into two categories and further sub-categorized into seven different functions under each category. The categories are:
Aggregate functions:
These functions are used to do operations from the values of the column and a single value is returned.
Scalar functions:
These functions are based on user input, these too return a single value.
4. Define SQL Order by the statement?
The ORDER BY statement in SQL is used to sort the fetched data in either ascending or descending according to one or more columns.
By default ORDER BY sorts the data in ascending order.
We can use the keyword DESC to sort the data in descending order and the keyword ASC to sort in ascending order.
5. What is the difference between primary key and unique constraints?
The primary key cannot have NULL values, the unique constraints can have NULL values. There is only one primary key in a table, but there can be multiple unique constraints. The primary key creates the clustered index automatically but the unique key does not.
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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.
[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.
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Data Analyst INTERVIEW QUESTIONS AND ANSWERS
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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 👍👍
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