Forwarded from Data Analytics
Thank you so much everyone for the awesome response. I have created an entire checklist to learn SQL, Power BI, Excel, Python & Tableau.
You can access Free Checklist here.
Like this post if it helps ๐โค๏ธ
I'll try bringing more resources like these in the future to help you as much as I can.
Share with credits: https://t.iss.one/sqlspecialist
You can access Free Checklist here.
Like this post if it helps ๐โค๏ธ
I'll try bringing more resources like these in the future to help you as much as I can.
Share with credits: https://t.iss.one/sqlspecialist
๐24๐5โค3๐1
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.
๐12โค2
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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).
๐9โค1
What to do and What to avoid!
When sitting in front of an interviewer, your actions and words can make or break your chances.
Itโs 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.
๐Do:
1. Be Prepared.
2. Dress Appropriately.
3. Be Punctual.
4. Maintain Good Posture.
5. Listen Carefully.
6. Ask Thoughtful Questions.
7. Be Honest.
๐Don't:
1. Donโt Fidget.
2. Donโt Speak Negatively About Past Employers.
3. Donโt Interrupt.
4. Donโt Overshare.
5. Donโt Forget to Follow Up.
By keeping these dos and donโts in mind, youโll be better prepared to make a strong impression in your interview.
Good luck!
Hope this helps you ๐
When sitting in front of an interviewer, your actions and words can make or break your chances.
Itโs 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.
๐Do:
1. Be Prepared.
2. Dress Appropriately.
3. Be Punctual.
4. Maintain Good Posture.
5. Listen Carefully.
6. Ask Thoughtful Questions.
7. Be Honest.
๐Don't:
1. Donโt Fidget.
2. Donโt Speak Negatively About Past Employers.
3. Donโt Interrupt.
4. Donโt Overshare.
5. Donโt Forget to Follow Up.
By keeping these dos and donโts in mind, youโll be better prepared to make a strong impression in your interview.
Good luck!
Hope this helps you ๐
๐6โค5
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 ๐โค๏ธ
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 ๐โค๏ธ
Hope it helps :)
๐11โค4๐1
How Data Analytics Helps to Grow Business to Best
๐๐
https://datasimplifier.com/data-analytics-helps-to-grow/
๐๐
https://datasimplifier.com/data-analytics-helps-to-grow/
๐5โค1
Why is Excel Often the Starting Point for SQL ?
Here's how Excel can help you before you dive into SQL:
โ๏ธ ๐๐๐๐๐๐๐ = ๐๐๐ ๐๐๐๐๐
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.
โ๏ธ ๐๐๐ ๐๐ง๐ ๐๐๐๐๐ ๐๐จ๐ซ ๐๐๐ ๐๐ฎ๐๐ซ๐ข๐๐ฌ
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.
โ๏ธ ๐ ๐๐๐๐๐ ๐๐ญ๐๐ญ๐๐ฆ๐๐ง๐ญ๐ฌ & ๐๐๐๐๐ ๐ข๐ง ๐๐๐
Excel's ๐ ๐๐๐๐๐ statements let you make choices with your data. This is similar to using WHERE in SQL to pick specific data.
โ๏ธ ๐๐๐ง๐๐ฅ๐ข๐ง๐ ๐๐๐ญ๐๐ฌ ๐๐ง๐ ๐๐๐ฑ๐ญ
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.
โ๏ธ ๐๐ข๐ฏ๐จ๐ญ ๐๐๐๐ฅ๐๐ฌ & ๐๐๐๐๐ ๐๐ ๐ข๐ง ๐๐๐
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.
โ๏ธ ๐๐๐๐๐๐๐ & ๐๐ฒ๐ฉ๐๐ซ๐ฅ๐ข๐ง๐ค๐ฌ
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://t.iss.one/excel_analyst
Hope this helps you ๐
Here's how Excel can help you before you dive into SQL:
โ๏ธ ๐๐๐๐๐๐๐ = ๐๐๐ ๐๐๐๐๐
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.
โ๏ธ ๐๐๐ ๐๐ง๐ ๐๐๐๐๐ ๐๐จ๐ซ ๐๐๐ ๐๐ฎ๐๐ซ๐ข๐๐ฌ
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.
โ๏ธ ๐ ๐๐๐๐๐ ๐๐ญ๐๐ญ๐๐ฆ๐๐ง๐ญ๐ฌ & ๐๐๐๐๐ ๐ข๐ง ๐๐๐
Excel's ๐ ๐๐๐๐๐ statements let you make choices with your data. This is similar to using WHERE in SQL to pick specific data.
โ๏ธ ๐๐๐ง๐๐ฅ๐ข๐ง๐ ๐๐๐ญ๐๐ฌ ๐๐ง๐ ๐๐๐ฑ๐ญ
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.
โ๏ธ ๐๐ข๐ฏ๐จ๐ญ ๐๐๐๐ฅ๐๐ฌ & ๐๐๐๐๐ ๐๐ ๐ข๐ง ๐๐๐
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.
โ๏ธ ๐๐๐๐๐๐๐ & ๐๐ฒ๐ฉ๐๐ซ๐ฅ๐ข๐ง๐ค๐ฌ
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://t.iss.one/excel_analyst
Hope this helps you ๐
๐11๐ฅฐ2๐2โค1
Free Programming and Data Analytics Resources ๐๐
โ 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://t.iss.one/datasciencefun/1371
๐ 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/
๐ค 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
๐ Useful Resources for the Programmers
Data Analyst Roadmap
https://t.iss.one/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://t.iss.one/datasciencefree/68
Ethical Hacking Bootcamp
https://t.iss.one/ethicalhackingtoday/3
Unity Documentation
https://docs.unity3d.com/Manual/index.html
Advanced Javascript concepts
https://t.iss.one/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://t.iss.one/programming_guide/76
Free PowerBI course by Microsoft
https://docs.microsoft.com/en-us/users/microsoftpowerplatform-5978/collections/k8xidwwnzk1em
Data Structures Interview Preparation
https://t.iss.one/crackingthecodinginterview/309?single
๐ป Free Programming Courses by Microsoft
โฏ JavaScript
https://learn.microsoft.com/training/paths/web-development-101/
โฏ TypeScript
https://learn.microsoft.com/training/paths/build-javascript-applications-typescript/
โฏ C#
https://learn.microsoft.com/users/dotnet/collections/yz26f8y64n7k07
Join @free4unow_backup for more free resources.
ENJOY LEARNING ๐๐
โ 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://t.iss.one/datasciencefun/1371
๐ 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/
๐ค 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
๐ Useful Resources for the Programmers
Data Analyst Roadmap
https://t.iss.one/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://t.iss.one/datasciencefree/68
Ethical Hacking Bootcamp
https://t.iss.one/ethicalhackingtoday/3
Unity Documentation
https://docs.unity3d.com/Manual/index.html
Advanced Javascript concepts
https://t.iss.one/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://t.iss.one/programming_guide/76
Free PowerBI course by Microsoft
https://docs.microsoft.com/en-us/users/microsoftpowerplatform-5978/collections/k8xidwwnzk1em
Data Structures Interview Preparation
https://t.iss.one/crackingthecodinginterview/309?single
๐ป Free Programming Courses by Microsoft
โฏ JavaScript
https://learn.microsoft.com/training/paths/web-development-101/
โฏ TypeScript
https://learn.microsoft.com/training/paths/build-javascript-applications-typescript/
โฏ C#
https://learn.microsoft.com/users/dotnet/collections/yz26f8y64n7k07
Join @free4unow_backup for more free resources.
ENJOY LEARNING ๐๐
๐7๐2
Essential tools and skills required to become a data analyst ๐๐
### 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, scripting, 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:
โฏ SQL โ t.iss.one/sqlanalyst
โฏ Power BI โ @PowerBI_analyst
โฏ Resources โ @learndataanalysis
โฏ Excel โ t.iss.one/excel_analyst
โฏ Data Portfolio โ @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 LEARNING๐๐
### 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, scripting, 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:
โฏ SQL โ t.iss.one/sqlanalyst
โฏ Power BI โ @PowerBI_analyst
โฏ Resources โ @learndataanalysis
โฏ Excel โ t.iss.one/excel_analyst
โฏ Data Portfolio โ @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 LEARNING๐๐
๐8โค2๐1
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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