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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Data Analyst Checklist to review before your next interview in 2024 👇👇
https://www.linkedin.com/posts/sqlspecialist_dataanalyst-dataanalytics-dataanalysis-activity-7161585338870710272--VGI?utm_source=share&utm_medium=member_android
https://www.linkedin.com/posts/sqlspecialist_dataanalyst-dataanalytics-dataanalysis-activity-7161585338870710272--VGI?utm_source=share&utm_medium=member_android
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Learning Excel for data analytics can be a valuable skill. Here are some steps you can take to learn Excel topics for data analytics:
1. Take an online course: There are many online courses available that specifically focus on Excel for data analytics. Look for courses on platforms like Coursera, Udemy, or LinkedIn Learning.
2. Practice with datasets: The best way to learn Excel is by practicing with real-world datasets. You can find datasets online on websites like Kaggle or data.gov. Practice manipulating and analyzing the data using Excel functions and tools.
3. Learn important functions: Familiarize yourself with important Excel functions for data analysis such as VLOOKUP, INDEX-MATCH, SUMIFS, AVERAGEIFS, COUNTIFS, and PivotTables.
4. Master data visualization: Excel offers powerful tools for data visualization such as charts and graphs. Learn how to create visually appealing and informative charts to present your data effectively.
5. Explore advanced features: Excel has many advanced features that can be useful for data analytics, such as Power Query, Power Pivot, and macros. Take the time to explore these features and understand how they can enhance your data analysis capabilities.
6. Join online communities: Join online forums and communities dedicated to Excel and data analytics. This can be a great way to ask questions, share knowledge, and learn from others who are also interested in data analytics.
7. Practice regularly: Like any skill, learning Excel for data analytics requires regular practice. Set aside time each week to practice your Excel skills and work on different data analysis projects.
By following these steps and staying consistent in your practice, you can become proficient in using Excel for data analytics.
1. Take an online course: There are many online courses available that specifically focus on Excel for data analytics. Look for courses on platforms like Coursera, Udemy, or LinkedIn Learning.
2. Practice with datasets: The best way to learn Excel is by practicing with real-world datasets. You can find datasets online on websites like Kaggle or data.gov. Practice manipulating and analyzing the data using Excel functions and tools.
3. Learn important functions: Familiarize yourself with important Excel functions for data analysis such as VLOOKUP, INDEX-MATCH, SUMIFS, AVERAGEIFS, COUNTIFS, and PivotTables.
4. Master data visualization: Excel offers powerful tools for data visualization such as charts and graphs. Learn how to create visually appealing and informative charts to present your data effectively.
5. Explore advanced features: Excel has many advanced features that can be useful for data analytics, such as Power Query, Power Pivot, and macros. Take the time to explore these features and understand how they can enhance your data analysis capabilities.
6. Join online communities: Join online forums and communities dedicated to Excel and data analytics. This can be a great way to ask questions, share knowledge, and learn from others who are also interested in data analytics.
7. Practice regularly: Like any skill, learning Excel for data analytics requires regular practice. Set aside time each week to practice your Excel skills and work on different data analysis projects.
By following these steps and staying consistent in your practice, you can become proficient in using Excel for data analytics.
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Important questions for data analyst interview👇👇
1. Can you walk me through a project where you had to analyze a large dataset and draw meaningful insights from it?
2. How do you ensure the accuracy and reliability of your analysis results?
3. What programming languages and tools are you proficient in for data analysis?
4. How do you approach data cleaning and preprocessing before conducting analysis?
5. Can you give an example of a time when you had to communicate complex data analysis results to non-technical stakeholders?
6. How do you stay current with industry trends and best practices in data analysis?
7. Have you ever worked with machine learning algorithms or predictive modeling? If so, can you provide an example of a project where you applied these techniques?
8. How do you handle missing or incomplete data in your analysis process?
9. Can you discuss a challenging problem you encountered during a data analysis project and how you overcame it?
10. How do you prioritize and manage multiple projects or tasks simultaneously as a data analyst?
1. Can you walk me through a project where you had to analyze a large dataset and draw meaningful insights from it?
2. How do you ensure the accuracy and reliability of your analysis results?
3. What programming languages and tools are you proficient in for data analysis?
4. How do you approach data cleaning and preprocessing before conducting analysis?
5. Can you give an example of a time when you had to communicate complex data analysis results to non-technical stakeholders?
6. How do you stay current with industry trends and best practices in data analysis?
7. Have you ever worked with machine learning algorithms or predictive modeling? If so, can you provide an example of a project where you applied these techniques?
8. How do you handle missing or incomplete data in your analysis process?
9. Can you discuss a challenging problem you encountered during a data analysis project and how you overcame it?
10. How do you prioritize and manage multiple projects or tasks simultaneously as a data analyst?
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Forwarded from Free Online Courses with Certificate | Udacity Free Courses | Eduonix | IP Cybersecurity | Coursera | Premium Certified Courses
Hey 👋
Here you can access Data Analytics Interview Preparation Resources ❤️🔥👇
https://dataanalysts.gumroad.com/l/krhnsk/DataSimplifier?a=363448787
◾How to get it:
1. Click on the link
2. Enter the amount you like [Can be 0 as well :) ]
3. Click the 'I Want This' Button
4. Enter your email and get it delivered!
I'd appreciate it if you could give it a 5 star when you download it.
Join for more: https://t.iss.one/sqlspecialist
Thanks 😊
Here you can access Data Analytics Interview Preparation Resources ❤️🔥👇
https://dataanalysts.gumroad.com/l/krhnsk/DataSimplifier?a=363448787
◾How to get it:
1. Click on the link
2. Enter the amount you like [Can be 0 as well :) ]
3. Click the 'I Want This' Button
4. Enter your email and get it delivered!
I'd appreciate it if you could give it a 5 star when you download it.
Join for more: https://t.iss.one/sqlspecialist
Thanks 😊
👍13❤2🥰2
Websites to practice SQL and ace your data interview 👇
https://www.linkedin.com/posts/sql-analysts_sql-activity-7167002035056320512-BNsj?utm_source=share&utm_medium=member_android
https://www.linkedin.com/posts/sql-analysts_sql-activity-7167002035056320512-BNsj?utm_source=share&utm_medium=member_android
📊🚀A beginner's roadmap for learning SQL:
🔹Understand Basics:
Learn what SQL is and its purpose in managing relational databases.
Understand basic database concepts like tables, rows, columns, and relationships.
🔹Learn SQL Syntax:
Familiarize yourself with SQL syntax for common commands like SELECT, INSERT, UPDATE, DELETE.
Understand clauses like WHERE, ORDER BY, GROUP BY, and JOIN.
🔹Setup a Database:
Install a relational database management system (RDBMS) like MySQL, SQLite, or PostgreSQL.
Practice creating databases, tables, and inserting data.
🔹Retrieve Data (SELECT):
Learn to retrieve data from a database using SELECT statements.
Practice filtering data using WHERE clause and sorting using ORDER BY.
🔹Modify Data (INSERT, UPDATE, DELETE):
Understand how to insert new records, update existing ones, and delete data.
Be cautious with DELETE to avoid unintentional data loss.
🔹Working with Functions:
Explore SQL functions like COUNT, AVG, SUM, MAX, MIN for data analysis.
Understand string functions, date functions, and mathematical functions.
🔹Data Filtering and Sorting:
Learn advanced filtering techniques using AND, OR, and IN operators.
Practice sorting data using multiple columns.
🔹Table Relationships (JOIN):
Understand the concept of joining tables to retrieve data from multiple tables.
Learn about INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.
🔹Grouping and Aggregation:
Explore GROUP BY clause to group data based on specific columns.
Understand aggregate functions for summarizing data (SUM, AVG, COUNT).
🔹Subqueries:
Learn to use subqueries to perform complex queries.
Understand how to use subqueries in SELECT, WHERE, and FROM clauses.
🔹Indexes and Optimization:
Gain knowledge about indexes and their role in optimizing queries.
Understand how to optimize SQL queries for better performance.
🔹Transactions and ACID Properties:
Learn about transactions and the ACID properties (Atomicity, Consistency, Isolation, Durability).
Understand how to use transactions to maintain data integrity.
🔹Normalization:
Understand the basics of database normalization to design efficient databases.
Learn about 1NF, 2NF, 3NF, and BCNF.
🔹Backup and Recovery:
Understand the importance of database backups.
Learn how to perform backups and recovery operations.
🔹Practice and Projects:
Apply your knowledge through hands-on projects.
Practice on platforms like LeetCode, HackerRank, or build your own small database-driven projects.
👀👍Remember to practice regularly and build real-world projects to reinforce your learning. Happy coding!
🔹Understand Basics:
Learn what SQL is and its purpose in managing relational databases.
Understand basic database concepts like tables, rows, columns, and relationships.
🔹Learn SQL Syntax:
Familiarize yourself with SQL syntax for common commands like SELECT, INSERT, UPDATE, DELETE.
Understand clauses like WHERE, ORDER BY, GROUP BY, and JOIN.
🔹Setup a Database:
Install a relational database management system (RDBMS) like MySQL, SQLite, or PostgreSQL.
Practice creating databases, tables, and inserting data.
🔹Retrieve Data (SELECT):
Learn to retrieve data from a database using SELECT statements.
Practice filtering data using WHERE clause and sorting using ORDER BY.
🔹Modify Data (INSERT, UPDATE, DELETE):
Understand how to insert new records, update existing ones, and delete data.
Be cautious with DELETE to avoid unintentional data loss.
🔹Working with Functions:
Explore SQL functions like COUNT, AVG, SUM, MAX, MIN for data analysis.
Understand string functions, date functions, and mathematical functions.
🔹Data Filtering and Sorting:
Learn advanced filtering techniques using AND, OR, and IN operators.
Practice sorting data using multiple columns.
🔹Table Relationships (JOIN):
Understand the concept of joining tables to retrieve data from multiple tables.
Learn about INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.
🔹Grouping and Aggregation:
Explore GROUP BY clause to group data based on specific columns.
Understand aggregate functions for summarizing data (SUM, AVG, COUNT).
🔹Subqueries:
Learn to use subqueries to perform complex queries.
Understand how to use subqueries in SELECT, WHERE, and FROM clauses.
🔹Indexes and Optimization:
Gain knowledge about indexes and their role in optimizing queries.
Understand how to optimize SQL queries for better performance.
🔹Transactions and ACID Properties:
Learn about transactions and the ACID properties (Atomicity, Consistency, Isolation, Durability).
Understand how to use transactions to maintain data integrity.
🔹Normalization:
Understand the basics of database normalization to design efficient databases.
Learn about 1NF, 2NF, 3NF, and BCNF.
🔹Backup and Recovery:
Understand the importance of database backups.
Learn how to perform backups and recovery operations.
🔹Practice and Projects:
Apply your knowledge through hands-on projects.
Practice on platforms like LeetCode, HackerRank, or build your own small database-driven projects.
👀👍Remember to practice regularly and build real-world projects to reinforce your learning. Happy coding!
👍9❤5🔥1
1. Explain some cases where k-Means clustering fails to give good results
k-means has trouble clustering data where clusters are of various sizes and densities.Outliers will cause the centroids to be dragged, or the outliers might get their own cluster instead of being ignored. Outliers should be clipped or removed before clustering.If the number of dimensions increase, a distance-based similarity measure converges to a constant value between any given examples. Dimensions should be reduced before clustering them.
2. If your Time-Series Dataset is very long, what architecture would you use?
If the dataset for time-series is very long, LSTMs are ideal for it because it can not only process single data points, but also entire sequences of data. A time-series being a sequence of data makes LSTM ideal for it.For an even stronger representational capacity, making the LSTM's multi-layered is better.Another method for long time-series dataset is to use CNNs to extract information.
3. What are some common Data Preparation Operations you would use for Time Series Data?
Parsing time series information from various sources and formats.
Generating sequences of fixed-frequency dates and time spans.
Manipulating and converting date times with time zone information.
Resampling or converting a time series to a particular frequency.
4. Describe the Difference Between Window Functions and Aggregate Functions in SQL.
The main difference between window functions and aggregate functions is that aggregate functions group multiple rows into a single result row; all the individual rows in the group are collapsed and their individual data is not shown. On the other hand, window functions produce a result for each individual row. This result is usually shown as a new column value in every row within the window.
5. What is Ribbon in Excel and where does it appear?
The Ribbon is basically your key interface with Excel and it appears at the top of the Excel window. It allows users to access many of the most important commands directly. It consists of many tabs such as File, Home, View, Insert, etc. You can also customize the ribbon to suit your preferences. To customize the Ribbon, right-click on it and select the “Customize the Ribbon” option.
6. Can you mention some advantages and disadvantages of decision trees?
The advantages of decision trees are that they are easier to interpret, are nonparametric and hence robust to outliers, and have relatively few parameters to tune.
On the other hand, the disadvantage is that they are prone to overfitting.
For more, join: https://t.iss.one/DataAnalystInterview
k-means has trouble clustering data where clusters are of various sizes and densities.Outliers will cause the centroids to be dragged, or the outliers might get their own cluster instead of being ignored. Outliers should be clipped or removed before clustering.If the number of dimensions increase, a distance-based similarity measure converges to a constant value between any given examples. Dimensions should be reduced before clustering them.
2. If your Time-Series Dataset is very long, what architecture would you use?
If the dataset for time-series is very long, LSTMs are ideal for it because it can not only process single data points, but also entire sequences of data. A time-series being a sequence of data makes LSTM ideal for it.For an even stronger representational capacity, making the LSTM's multi-layered is better.Another method for long time-series dataset is to use CNNs to extract information.
3. What are some common Data Preparation Operations you would use for Time Series Data?
Parsing time series information from various sources and formats.
Generating sequences of fixed-frequency dates and time spans.
Manipulating and converting date times with time zone information.
Resampling or converting a time series to a particular frequency.
4. Describe the Difference Between Window Functions and Aggregate Functions in SQL.
The main difference between window functions and aggregate functions is that aggregate functions group multiple rows into a single result row; all the individual rows in the group are collapsed and their individual data is not shown. On the other hand, window functions produce a result for each individual row. This result is usually shown as a new column value in every row within the window.
5. What is Ribbon in Excel and where does it appear?
The Ribbon is basically your key interface with Excel and it appears at the top of the Excel window. It allows users to access many of the most important commands directly. It consists of many tabs such as File, Home, View, Insert, etc. You can also customize the ribbon to suit your preferences. To customize the Ribbon, right-click on it and select the “Customize the Ribbon” option.
6. Can you mention some advantages and disadvantages of decision trees?
The advantages of decision trees are that they are easier to interpret, are nonparametric and hence robust to outliers, and have relatively few parameters to tune.
On the other hand, the disadvantage is that they are prone to overfitting.
For more, join: https://t.iss.one/DataAnalystInterview
👍9❤7
Avoid directly copying YouTube projects onto your resume because if everyone looks the same, recruiters might discard resumes.
Instead, for eg, let's say you are working on a SQL case study, download a dataset from Kaggle (usually a CSV file), set up a Postgre/MySQL database, connect it with the data, and prompt ChatGPT with questions ranging from basic to advanced SQL.
Solve the questions step by step. When using PowerBI, connect to the database and create a compelling dashboard. Don't just upload the dataset; employ DAX queries, statistical functions, and avoid relying solely on drag-and-drop features. Use Formatting section to do creative stuff and add your unique element in the project.
ENJOY LEARNING 👍👍
Instead, for eg, let's say you are working on a SQL case study, download a dataset from Kaggle (usually a CSV file), set up a Postgre/MySQL database, connect it with the data, and prompt ChatGPT with questions ranging from basic to advanced SQL.
Solve the questions step by step. When using PowerBI, connect to the database and create a compelling dashboard. Don't just upload the dataset; employ DAX queries, statistical functions, and avoid relying solely on drag-and-drop features. Use Formatting section to do creative stuff and add your unique element in the project.
ENJOY LEARNING 👍👍
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Best practices for writing SQL queries:
Join for more: https://t.iss.one/learndataanalysis
1- Write SQL keywords in capital letters.
2- Use table aliases with columns when you are joining multiple tables.
3- Never use select *, always mention list of columns in select clause.
4- Add useful comments wherever you write complex logic. Avoid too many comments.
5- Use joins instead of subqueries when possible for better performance.
6- Create CTEs instead of multiple sub queries , it will make your query easy to read.
7- Join tables using JOIN keywords instead of writing join condition in where clause for better readability.
8- Never use order by in sub queries , It will unnecessary increase runtime.
9- If you know there are no duplicates in 2 tables, use UNION ALL instead of UNION for better performance.
SQL Basics: https://t.iss.one/sqlanalyst/105
Join for more: https://t.iss.one/learndataanalysis
1- Write SQL keywords in capital letters.
2- Use table aliases with columns when you are joining multiple tables.
3- Never use select *, always mention list of columns in select clause.
4- Add useful comments wherever you write complex logic. Avoid too many comments.
5- Use joins instead of subqueries when possible for better performance.
6- Create CTEs instead of multiple sub queries , it will make your query easy to read.
7- Join tables using JOIN keywords instead of writing join condition in where clause for better readability.
8- Never use order by in sub queries , It will unnecessary increase runtime.
9- If you know there are no duplicates in 2 tables, use UNION ALL instead of UNION for better performance.
SQL Basics: https://t.iss.one/sqlanalyst/105
👍11
🚀👉Data Analytics skills and projects to add in a resume to get shortlisted
1. Technical Skills:
Proficiency in data analysis tools (e.g., Python, R, SQL).
Data visualization skills using tools like Tableau or Power BI.
Experience with statistical analysis and modeling techniques.
2. Data Cleaning and Preprocessing:
Showcase skills in cleaning and preprocessing raw data for analysis.
Highlight expertise in handling missing data and outliers effectively.
3. Database Management:
Mention experience with databases (e.g., MySQL, PostgreSQL) for data retrieval and manipulation.
4. Machine Learning:
If applicable, include knowledge of machine learning algorithms and their application in data analytics projects.
5. Data Storytelling:
Emphasize your ability to communicate insights effectively through data storytelling.
6. Big Data Technologies:
If relevant, mention experience with big data technologies such as Hadoop or Spark.
7. Business Acumen:
Showcase an understanding of the business context and how your analytics work contributes to organizational goals.
8. Problem-Solving:
Highlight instances where you solved business problems through data-driven insights.
9. Collaboration and Communication:
Demonstrate your ability to work in a team and communicate complex findings to non-technical stakeholders.
10. Projects:
List specific data analytics projects you've worked on, detailing the problem, methodology, tools used, and the impact on decision-making.
11. Certifications:
Include relevant certifications such as those from platforms like Coursera, edX, or industry-recognized certifications in data analytics.
12. Continuous Learning:
Showcase any ongoing education, workshops, or courses to display your commitment to staying updated in the field.
💼Tailor your resume to the specific job description, emphasizing the skills and experiences that align with the requirements of the position you're applying for.
1. Technical Skills:
Proficiency in data analysis tools (e.g., Python, R, SQL).
Data visualization skills using tools like Tableau or Power BI.
Experience with statistical analysis and modeling techniques.
2. Data Cleaning and Preprocessing:
Showcase skills in cleaning and preprocessing raw data for analysis.
Highlight expertise in handling missing data and outliers effectively.
3. Database Management:
Mention experience with databases (e.g., MySQL, PostgreSQL) for data retrieval and manipulation.
4. Machine Learning:
If applicable, include knowledge of machine learning algorithms and their application in data analytics projects.
5. Data Storytelling:
Emphasize your ability to communicate insights effectively through data storytelling.
6. Big Data Technologies:
If relevant, mention experience with big data technologies such as Hadoop or Spark.
7. Business Acumen:
Showcase an understanding of the business context and how your analytics work contributes to organizational goals.
8. Problem-Solving:
Highlight instances where you solved business problems through data-driven insights.
9. Collaboration and Communication:
Demonstrate your ability to work in a team and communicate complex findings to non-technical stakeholders.
10. Projects:
List specific data analytics projects you've worked on, detailing the problem, methodology, tools used, and the impact on decision-making.
11. Certifications:
Include relevant certifications such as those from platforms like Coursera, edX, or industry-recognized certifications in data analytics.
12. Continuous Learning:
Showcase any ongoing education, workshops, or courses to display your commitment to staying updated in the field.
💼Tailor your resume to the specific job description, emphasizing the skills and experiences that align with the requirements of the position you're applying for.
👍13❤2🥰2👨💻1
FREE Resources to Learn & Practice Python 👇👇
https://www.linkedin.com/posts/sql-analysts_free-resources-to-learn-practice-python-activity-7169171544026275842-lSPD?utm_source=share&utm_medium=member_android
https://www.linkedin.com/posts/sql-analysts_free-resources-to-learn-practice-python-activity-7169171544026275842-lSPD?utm_source=share&utm_medium=member_android
👍1
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Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources
Do you want to know my journey as a data analyst so far?
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 experience 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 experience 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 ❤️
❤8👍7👨💻1💅1
Here are some Excel shortcuts that are commonly used by data analysts:
1. Ctrl + C: Copy
2. Ctrl + V: Paste
3. Ctrl + X: Cut
4. Ctrl + Z: Undo
5. Ctrl + Y: Redo
6. Ctrl + S: Save
7. Ctrl + F: Find
8. Ctrl + H: Replace
9. Ctrl + Arrow Keys: Navigate to the edge of data
10. Ctrl + Shift + Arrow Keys: Select data range
11. Ctrl + Home: Go to cell A1
12. Ctrl + End: Go to last cell with data
13. Ctrl + Shift + L: Toggle filters
14. Alt + ; : Select visible cells only
15. F2: Edit active cell
16. Ctrl + Shift + Enter: Enter an array formula
17. Ctrl + D: Fill down
18. Ctrl + R: Fill right
19. Ctrl + 1: Format cells dialog box
20. Ctrl + Shift + 1, 2, 3, etc.: Apply different number formats
These shortcuts can significantly increase your efficiency when working with Excel as a data analyst.
One of the very important tool that a data analyst must be aware of is Excel
1. Ctrl + C: Copy
2. Ctrl + V: Paste
3. Ctrl + X: Cut
4. Ctrl + Z: Undo
5. Ctrl + Y: Redo
6. Ctrl + S: Save
7. Ctrl + F: Find
8. Ctrl + H: Replace
9. Ctrl + Arrow Keys: Navigate to the edge of data
10. Ctrl + Shift + Arrow Keys: Select data range
11. Ctrl + Home: Go to cell A1
12. Ctrl + End: Go to last cell with data
13. Ctrl + Shift + L: Toggle filters
14. Alt + ; : Select visible cells only
15. F2: Edit active cell
16. Ctrl + Shift + Enter: Enter an array formula
17. Ctrl + D: Fill down
18. Ctrl + R: Fill right
19. Ctrl + 1: Format cells dialog box
20. Ctrl + Shift + 1, 2, 3, etc.: Apply different number formats
These shortcuts can significantly increase your efficiency when working with Excel as a data analyst.
One of the very important tool that a data analyst must be aware of is Excel
❤10👍10
Best practices for writing SQL queries:
Join for more: https://t.iss.one/learndataanalysis
1- Write SQL keywords in capital letters.
2- Use table aliases with columns when you are joining multiple tables.
3- Never use select *, always mention list of columns in select clause.
4- Add useful comments wherever you write complex logic. Avoid too many comments.
5- Use joins instead of subqueries when possible for better performance.
6- Create CTEs instead of multiple sub queries , it will make your query easy to read.
7- Join tables using JOIN keywords instead of writing join condition in where clause for better readability.
8- Never use order by in sub queries , It will unnecessary increase runtime.
9- If you know there are no duplicates in 2 tables, use UNION ALL instead of UNION for better performance.
SQL Basics: https://t.iss.one/sqlanalyst/105
Join for more: https://t.iss.one/learndataanalysis
1- Write SQL keywords in capital letters.
2- Use table aliases with columns when you are joining multiple tables.
3- Never use select *, always mention list of columns in select clause.
4- Add useful comments wherever you write complex logic. Avoid too many comments.
5- Use joins instead of subqueries when possible for better performance.
6- Create CTEs instead of multiple sub queries , it will make your query easy to read.
7- Join tables using JOIN keywords instead of writing join condition in where clause for better readability.
8- Never use order by in sub queries , It will unnecessary increase runtime.
9- If you know there are no duplicates in 2 tables, use UNION ALL instead of UNION for better performance.
SQL Basics: https://t.iss.one/sqlanalyst/105
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