🥳🚀When delving into data analytics and initiating your SQL journey, prioritize mastering the fundamental concepts that address the majority of problems before delving into other topics.
👉🏻 Basic Aggregation function:
1️⃣ AVG
2️⃣ COUNT
3️⃣ SUM
4️⃣ MIN
5️⃣ MAX
👉🏻 JOINS
1️⃣ Left
2️⃣ Inner
3️⃣ Self (Important, Practice questions on self join)
👉🏻 Windows Function (Important)
1️⃣ Learn how partitioning works
2️⃣ Learn the different use cases where Ranking/Numbering Functions are used? ( ROW_NUMBER,RANK, DENSE_RANK, NTILE)
3️⃣ Use Cases of LEAD & LAG functions
4️⃣ Use cases of Aggregate window functions
👉🏻 GROUP BY
👉🏻 WHERE vs HAVING
👉🏻 CASE STATEMENT
👉🏻 UNION vs Union ALL
👉🏻 LOGICAL OPERATORS
Other Commonly used functions:
👉🏻 IFNULL
👉🏻 COALESCE
👉🏻 ROUND
👉🏻 Working with Date Functions
1️⃣ EXTRACTING YEAR/MONTH/WEEK/DAY
2️⃣ Calculating date differences
👉🏻CTE
👉🏻Views & Triggers (optional)
Here is an amazing resources to learn & practice SQL: https://t.iss.one/sqlanalyst/195
Hope it helps in your SQL learning 📚
👉🏻 Basic Aggregation function:
1️⃣ AVG
2️⃣ COUNT
3️⃣ SUM
4️⃣ MIN
5️⃣ MAX
👉🏻 JOINS
1️⃣ Left
2️⃣ Inner
3️⃣ Self (Important, Practice questions on self join)
👉🏻 Windows Function (Important)
1️⃣ Learn how partitioning works
2️⃣ Learn the different use cases where Ranking/Numbering Functions are used? ( ROW_NUMBER,RANK, DENSE_RANK, NTILE)
3️⃣ Use Cases of LEAD & LAG functions
4️⃣ Use cases of Aggregate window functions
👉🏻 GROUP BY
👉🏻 WHERE vs HAVING
👉🏻 CASE STATEMENT
👉🏻 UNION vs Union ALL
👉🏻 LOGICAL OPERATORS
Other Commonly used functions:
👉🏻 IFNULL
👉🏻 COALESCE
👉🏻 ROUND
👉🏻 Working with Date Functions
1️⃣ EXTRACTING YEAR/MONTH/WEEK/DAY
2️⃣ Calculating date differences
👉🏻CTE
👉🏻Views & Triggers (optional)
Here is an amazing resources to learn & practice SQL: https://t.iss.one/sqlanalyst/195
Hope it helps in your SQL learning 📚
👍14😁2❤1
Data Analysis vs Data Science
Data analysis often focuses on interpreting and summarizing existing data, requiring skills like statistical analysis, SQL, and data visualization.
On the other hand, data science involves a broader set of skills, including machine learning, predictive modeling, and advanced programming.
In essence, data analysis is a subset of data science, with data scientists often having a more extensive toolkit for handling complex and unstructured data.
Free Resources to become data analyst -> https://www.linkedin.com/posts/sql-analysts_freecertificates-dataanalysts-python-activity-7113004712412524545-Uw4k
Steps to become data scientist -> https://t.iss.one/learndataanalysis/559
Data analysis often focuses on interpreting and summarizing existing data, requiring skills like statistical analysis, SQL, and data visualization.
On the other hand, data science involves a broader set of skills, including machine learning, predictive modeling, and advanced programming.
In essence, data analysis is a subset of data science, with data scientists often having a more extensive toolkit for handling complex and unstructured data.
Free Resources to become data analyst -> https://www.linkedin.com/posts/sql-analysts_freecertificates-dataanalysts-python-activity-7113004712412524545-Uw4k
Steps to become data scientist -> https://t.iss.one/learndataanalysis/559
👍8
Data visualization is one of the steps of the data science process, which states that after data has been collected, processed and modeled, it must be visualized for conclusions to be made.
When a data scientist is writing advanced predictive analytics or machine learning (ML) algorithms, it becomes important to visualize the outputs to monitor results and ensure that models are performing as intended.
This is because visualizations of complex algorithms are generally easier to interpret than numerical outputs.
When a data scientist is writing advanced predictive analytics or machine learning (ML) algorithms, it becomes important to visualize the outputs to monitor results and ensure that models are performing as intended.
This is because visualizations of complex algorithms are generally easier to interpret than numerical outputs.
👍6❤3🥰1
Hello everyone here is everything that you need to know if you are planning to learn Business Analytics
👇👇
https://t.iss.one/analystcommunity/3
👇👇
https://t.iss.one/analystcommunity/3
Telegram
Business Analysts
Hello everyone here is everything that you need to know if you are planning to learn Business Analytics
1. What is the difference between Business Analytics and Data Analytics
1. Focus:
- Data Analytics: Analyzing data for patterns and insights.
…
1. What is the difference between Business Analytics and Data Analytics
1. Focus:
- Data Analytics: Analyzing data for patterns and insights.
…
👍1
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.
Join for more excel resources: https://t.iss.one/excel_analyst
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.
Join for more excel resources: https://t.iss.one/excel_analyst
👍11❤1
If you’re trying to get a job in data analytics, simplify your roadmap through SPN(skills, portfolio, network) Method:
1. Learn the Skills :-
What to Learn: Focus on mastering SQL, Excel, and a data visualization tool like Tableau or Power BI.
How to Learn: Utilize online resources, tutorials, and practice exercises to hone your skills.
2. Build Your Portfolio :-
Why it's Important: A portfolio showcases your abilities to potential employers.
How to Build: Create a free website using platforms like Wix or Wordpress.
What to Include: Write-ups of your projects, detailing the business problems you've tackled and the methods you've used. Provide links to your code and dashboards.
3. Expand Your Network :-
Why Network: Building connections increases your chances of landing a job.
Where to Network: Connect with professionals on LinkedIn, attend local data meetups, and engage in industry-related events.
How to Network: Interact genuinely with others, avoiding spammy or impersonal outreach tactics.
4. Stay Positive and Persistent:-
Why it Matters: Job hunting can be challenging, but maintaining a positive attitude and persevering is key.
How to Stay Motivated: Believe in your abilities and keep pushing forward despite obstacles.
Conclusion: Keep Going!
Final Encouragement: You've got what it takes. Keep learning, networking, and persevering. You'll reach your goals!
If it's useful give us 👍
1. Learn the Skills :-
What to Learn: Focus on mastering SQL, Excel, and a data visualization tool like Tableau or Power BI.
How to Learn: Utilize online resources, tutorials, and practice exercises to hone your skills.
2. Build Your Portfolio :-
Why it's Important: A portfolio showcases your abilities to potential employers.
How to Build: Create a free website using platforms like Wix or Wordpress.
What to Include: Write-ups of your projects, detailing the business problems you've tackled and the methods you've used. Provide links to your code and dashboards.
3. Expand Your Network :-
Why Network: Building connections increases your chances of landing a job.
Where to Network: Connect with professionals on LinkedIn, attend local data meetups, and engage in industry-related events.
How to Network: Interact genuinely with others, avoiding spammy or impersonal outreach tactics.
4. Stay Positive and Persistent:-
Why it Matters: Job hunting can be challenging, but maintaining a positive attitude and persevering is key.
How to Stay Motivated: Believe in your abilities and keep pushing forward despite obstacles.
Conclusion: Keep Going!
Final Encouragement: You've got what it takes. Keep learning, networking, and persevering. You'll reach your goals!
If it's useful give us 👍
👍18❤5
Top 10 Difficult SQL Interview Questions 👇👇
https://www.linkedin.com/posts/sql-analysts_top-10-difficult-sql-interview-questions-activity-7176816116885774339-rqrt?utm_source=share&utm_medium=member_android
Let me know in comments if you want to continue the series or need answers as well. Also ping your answers if you know any.
These are bit advanced questions, so I don't expect everyone to solve it but some of you can try 😄
https://www.linkedin.com/posts/sql-analysts_top-10-difficult-sql-interview-questions-activity-7176816116885774339-rqrt?utm_source=share&utm_medium=member_android
Let me know in comments if you want to continue the series or need answers as well. Also ping your answers if you know any.
These are bit advanced questions, so I don't expect everyone to solve it but some of you can try 😄
👍4
👍4
Learning data analytics in 2024 can be an exciting and rewarding journey. Here are some steps you can take to start learning data analytics:
1. Understand the Basics: Begin by familiarizing yourself with the basic concepts of data analytics, such as data types, data visualization, statistical analysis, and machine learning.
2. Take Online Courses: There are many online platforms that offer courses in data analytics, such as Coursera, Udemy, and edX. Look for courses that cover topics like data manipulation, data visualization, and predictive modeling.
3. Practice with Real Data: To truly understand data analytics, you need to practice with real datasets. You can find datasets on websites like Kaggle or UCI Machine Learning Repository to work on real-world projects.
4. Learn Tools and Software: Familiarize yourself with popular data analytics tools and software like Python, R, SQL, Tableau, and Power BI. These tools are commonly used in the industry for data analysis.
5. Join Data Analytics Communities: Join online communities like Reddit, LinkedIn groups, or local meetups to connect with other data analysts and learn from their experiences.
6. Build a Portfolio: Create a portfolio of your data analytics projects to showcase your skills to potential employers. Include detailed descriptions of the problem you solved, the data analysis techniques you used, and the results you achieved.
7. Stay Updated: Data analytics is a rapidly evolving field, so it's important to stay updated on the latest trends and technologies. Follow industry blogs, attend webinars, and participate in online forums to stay informed.
Give credits while sharing: https://t.iss.one/learndataanalysis
1. Understand the Basics: Begin by familiarizing yourself with the basic concepts of data analytics, such as data types, data visualization, statistical analysis, and machine learning.
2. Take Online Courses: There are many online platforms that offer courses in data analytics, such as Coursera, Udemy, and edX. Look for courses that cover topics like data manipulation, data visualization, and predictive modeling.
3. Practice with Real Data: To truly understand data analytics, you need to practice with real datasets. You can find datasets on websites like Kaggle or UCI Machine Learning Repository to work on real-world projects.
4. Learn Tools and Software: Familiarize yourself with popular data analytics tools and software like Python, R, SQL, Tableau, and Power BI. These tools are commonly used in the industry for data analysis.
5. Join Data Analytics Communities: Join online communities like Reddit, LinkedIn groups, or local meetups to connect with other data analysts and learn from their experiences.
6. Build a Portfolio: Create a portfolio of your data analytics projects to showcase your skills to potential employers. Include detailed descriptions of the problem you solved, the data analysis techniques you used, and the results you achieved.
7. Stay Updated: Data analytics is a rapidly evolving field, so it's important to stay updated on the latest trends and technologies. Follow industry blogs, attend webinars, and participate in online forums to stay informed.
Give credits while sharing: https://t.iss.one/learndataanalysis
👍11❤1
Some basic concepts regarding data and database
Data is representation of the facts, measurements, figures, or concepts in a formalized manner having no
specific meaning.
Database is an organized collection of the data stored and can be accessed electronically in a computer system.
DBMS are software systems that enable users to store, retrieve, define and manage data in a database easily.
RDBMS is a type of DBMS that stores data in a row-based table structure which connects related data elements.
SQL is a database query language used for storing and managing data in RDBMS.
Data is representation of the facts, measurements, figures, or concepts in a formalized manner having no
specific meaning.
Database is an organized collection of the data stored and can be accessed electronically in a computer system.
DBMS are software systems that enable users to store, retrieve, define and manage data in a database easily.
RDBMS is a type of DBMS that stores data in a row-based table structure which connects related data elements.
SQL is a database query language used for storing and managing data in RDBMS.
👍7
Best Resources to learn data analytics 👇👇
https://www.linkedin.com/posts/sql-analysts_best-youtube-channels-to-learn-data-analytics-activity-7178615970716917760-aNRB?utm_source=share&utm_medium=member_android
https://www.linkedin.com/posts/sql-analysts_best-youtube-channels-to-learn-data-analytics-activity-7178615970716917760-aNRB?utm_source=share&utm_medium=member_android
👍2
Unlock the ultimate roadmap to Data Analyst mastery in 2024: Your crystal-clear path to success awaits!🚀🥳
1. Understand the Basics:
• Fundamentals of Data Analysis
• Statistics
• Probability
• Basic Mathematics
• data types
• data structures
• data manipulation techniques
2. Learn Tools and Technologies:
• Microsoft Excel
• SQL (Structured Query Language)
• Python or R for Data Manipulation
• Libraries such as Pandas, NumPy, Matplotlib, Seaborn (Python) or dplyr, ggplot2 (R)
3. Database Knowledge:
• Understanding Databases
• Querying Databases Efficiently
• Writing Complex SQL Queries
4. Data Visualization:
• Principles of Effective Visualization
• Graphs and Charts Creation
• Tools like Tableau, Power BI, Matplotlib, Seaborn
5. Statistical Analysis:
• Hypothesis Testing
• Regression Analysis
• Clustering
• Other Statistical Methods
6. Data Cleaning and Preprocessing:
• Handling Missing Values
• Outlier Detection and Treatment
• Data Normalization and Scaling
• Feature Engineering
7. Machine Learning Basics:
• Introduction to Machine
Learning
• Common Algorithms (e.g., Linear Regression, Logistic Regression, Decision Trees, k-Nearest Neighbors)
• Application of Algorithms in Data Analysis
Hope this helps 👍❤️
1. Understand the Basics:
• Fundamentals of Data Analysis
• Statistics
• Probability
• Basic Mathematics
• data types
• data structures
• data manipulation techniques
2. Learn Tools and Technologies:
• Microsoft Excel
• SQL (Structured Query Language)
• Python or R for Data Manipulation
• Libraries such as Pandas, NumPy, Matplotlib, Seaborn (Python) or dplyr, ggplot2 (R)
3. Database Knowledge:
• Understanding Databases
• Querying Databases Efficiently
• Writing Complex SQL Queries
4. Data Visualization:
• Principles of Effective Visualization
• Graphs and Charts Creation
• Tools like Tableau, Power BI, Matplotlib, Seaborn
5. Statistical Analysis:
• Hypothesis Testing
• Regression Analysis
• Clustering
• Other Statistical Methods
6. Data Cleaning and Preprocessing:
• Handling Missing Values
• Outlier Detection and Treatment
• Data Normalization and Scaling
• Feature Engineering
7. Machine Learning Basics:
• Introduction to Machine
Learning
• Common Algorithms (e.g., Linear Regression, Logistic Regression, Decision Trees, k-Nearest Neighbors)
• Application of Algorithms in Data Analysis
Hope this helps 👍❤️
👍19❤8
11 Quick tips to improve your data interpretation skills
Hands-On Projects: Work on real-world projects that involve analyzing data. This could be personal projects or participating in online competitions like Kaggle. Practical experience will enhance your skills.
Data Visualization: Practice creating various types of charts and graphs to visually represent data. Tools like Tableau or Python's matplotlib/seaborn libraries can help.
Storytelling with Data: Practice presenting your findings in a clear and compelling manner. Communicating insights effectively is crucial in data interpretation.
Data Challenges: Engage in data challenges or puzzles that require you to manipulate and interpret data. Websites like Project Euler or DataCamp offer such challenges.
Case Studies: Study existing data analysis case studies to understand how experts approach and interpret data. This can provide insights into different methodologies.
Mentorship: Seek guidance from experienced data analysts or scientists. Learning from their experiences and feedback can accelerate your growth.
Critical Thinking: Practice questioning the data and assumptions underlying your analysis. Developing a critical mindset will help you identify potential errors or biases.
Domain Expertise: Choose a specific field of interest and delve deep into its data. Becoming knowledgeable about the domain will enhance your ability to extract meaningful insights.
Experimentation: Try different analysis techniques, algorithms, and approaches to see what works best for different types of data and questions.
Peer Collaboration: Join or create study groups with peers who share your interest in data analysis. Discussing different approaches and sharing insights can be invaluable.
Feedback Loop: Continuously seek feedback on your work. Constructive criticism can help you refine your skills and identify areas for improvement.
Remember that improving data interpretation skills is an ongoing process. Be patient, persistent, and open to learning from your experiences and mistakes :)
Hands-On Projects: Work on real-world projects that involve analyzing data. This could be personal projects or participating in online competitions like Kaggle. Practical experience will enhance your skills.
Data Visualization: Practice creating various types of charts and graphs to visually represent data. Tools like Tableau or Python's matplotlib/seaborn libraries can help.
Storytelling with Data: Practice presenting your findings in a clear and compelling manner. Communicating insights effectively is crucial in data interpretation.
Data Challenges: Engage in data challenges or puzzles that require you to manipulate and interpret data. Websites like Project Euler or DataCamp offer such challenges.
Case Studies: Study existing data analysis case studies to understand how experts approach and interpret data. This can provide insights into different methodologies.
Mentorship: Seek guidance from experienced data analysts or scientists. Learning from their experiences and feedback can accelerate your growth.
Critical Thinking: Practice questioning the data and assumptions underlying your analysis. Developing a critical mindset will help you identify potential errors or biases.
Domain Expertise: Choose a specific field of interest and delve deep into its data. Becoming knowledgeable about the domain will enhance your ability to extract meaningful insights.
Experimentation: Try different analysis techniques, algorithms, and approaches to see what works best for different types of data and questions.
Peer Collaboration: Join or create study groups with peers who share your interest in data analysis. Discussing different approaches and sharing insights can be invaluable.
Feedback Loop: Continuously seek feedback on your work. Constructive criticism can help you refine your skills and identify areas for improvement.
Remember that improving data interpretation skills is an ongoing process. Be patient, persistent, and open to learning from your experiences and mistakes :)
👍4
Do you want to answer interesting easy to moderate level MCQs for data analysts?
Anonymous Poll
99%
Yes
1%
No