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
We are now a community of 30000+ members on LinkedIn
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https://www.linkedin.com/company/sql-analysts/
Thank you so much for the love and support ❤️
More quality content to come 😄
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https://www.linkedin.com/company/sql-analysts/
Thank you so much for the love and support ❤️
More quality content to come 😄
👍4
🔟 Project Ideas for a data analyst
Customer Segmentation: Analyze customer data to segment them based on their behaviors, preferences, or demographics, helping businesses tailor their marketing strategies.
Churn Prediction: Build a model to predict customer churn, identifying factors that contribute to churn and proposing strategies to retain customers.
Sales Forecasting: Use historical sales data to create a predictive model that forecasts future sales, aiding inventory management and resource planning.
Market Basket Analysis: Analyze
transaction data to identify associations between products often purchased together, assisting retailers in optimizing product placement and cross-selling.
Sentiment Analysis: Analyze social media or customer reviews to gauge public sentiment about a product or service, providing valuable insights for brand reputation management.
Healthcare Analytics: Examine medical records to identify trends, patterns, or correlations in patient data, aiding in disease prediction, treatment optimization, and resource allocation.
Financial Fraud Detection: Develop algorithms to detect anomalous transactions and patterns in financial data, helping prevent fraud and secure transactions.
A/B Testing Analysis: Evaluate the results of A/B tests to determine the effectiveness of different strategies or changes on websites, apps, or marketing campaigns.
Energy Consumption Analysis: Analyze energy usage data to identify patterns and inefficiencies, suggesting strategies for optimizing energy consumption in buildings or industries.
Real Estate Market Analysis: Study housing market data to identify trends in property prices, rental rates, and demand, assisting buyers, sellers, and investors in making informed decisions.
Remember to choose a project that aligns with your interests and the domain you're passionate about.
Data Analyst Roadmap
👇👇
https://t.iss.one/sqlspecialist/379
ENJOY LEARNING 👍👍
Customer Segmentation: Analyze customer data to segment them based on their behaviors, preferences, or demographics, helping businesses tailor their marketing strategies.
Churn Prediction: Build a model to predict customer churn, identifying factors that contribute to churn and proposing strategies to retain customers.
Sales Forecasting: Use historical sales data to create a predictive model that forecasts future sales, aiding inventory management and resource planning.
Market Basket Analysis: Analyze
transaction data to identify associations between products often purchased together, assisting retailers in optimizing product placement and cross-selling.
Sentiment Analysis: Analyze social media or customer reviews to gauge public sentiment about a product or service, providing valuable insights for brand reputation management.
Healthcare Analytics: Examine medical records to identify trends, patterns, or correlations in patient data, aiding in disease prediction, treatment optimization, and resource allocation.
Financial Fraud Detection: Develop algorithms to detect anomalous transactions and patterns in financial data, helping prevent fraud and secure transactions.
A/B Testing Analysis: Evaluate the results of A/B tests to determine the effectiveness of different strategies or changes on websites, apps, or marketing campaigns.
Energy Consumption Analysis: Analyze energy usage data to identify patterns and inefficiencies, suggesting strategies for optimizing energy consumption in buildings or industries.
Real Estate Market Analysis: Study housing market data to identify trends in property prices, rental rates, and demand, assisting buyers, sellers, and investors in making informed decisions.
Remember to choose a project that aligns with your interests and the domain you're passionate about.
Data Analyst Roadmap
👇👇
https://t.iss.one/sqlspecialist/379
ENJOY LEARNING 👍👍
👍18
✅ Here is the 35 Most Asked EXCEL Interview Questions for Data Analyst/Business Analyst roles
👇👇
https://bit.ly/4aIi4Xb
Save the post for future reference
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https://bit.ly/4aIi4Xb
Save the post for future reference
❤6👍3
Ways to improve the performance of Tableau
👉🏻 Use an Extract to make workbooks run faster.
👉🏻 Reduce the number of marks on the view to avoid information overload.
👉🏻 Hide unused fields.
👉🏻 Use Context filters.
👉🏻 Use indexing in tables and use the same fields for filtering.
👉🏻 Remove unnecessary calculations and sheets.
👉🏻 Use an Extract to make workbooks run faster.
👉🏻 Reduce the number of marks on the view to avoid information overload.
👉🏻 Hide unused fields.
👉🏻 Use Context filters.
👉🏻 Use indexing in tables and use the same fields for filtering.
👉🏻 Remove unnecessary calculations and sheets.
Complete Roadmap to become Data Analyst in 2024
👇👇
https://www.instagram.com/reel/C5ap0uUAXO6/?igsh=djA2YjlxbDNzamMy
👇👇
https://www.instagram.com/reel/C5ap0uUAXO6/?igsh=djA2YjlxbDNzamMy
👍5
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.
👍5👻2❤1
Secret to become a pro data analyst 👇👇
https://www.linkedin.com/posts/sql-analysts_today-lets-unlock-the-secrets-of-successful-activity-7186347720750546944-RXu1?utm_source=share&utm_medium=member_android
https://www.linkedin.com/posts/sql-analysts_today-lets-unlock-the-secrets-of-successful-activity-7186347720750546944-RXu1?utm_source=share&utm_medium=member_android
👍3
Top 10 Excel Concepts for Beginners 👇👇
https://www.linkedin.com/posts/sql-analysts_top-10-excel-concepts-for-beginners-like-activity-7186694979040198658-d9xe?utm_source=share&utm_medium=member_android
https://www.linkedin.com/posts/sql-analysts_top-10-excel-concepts-for-beginners-like-activity-7186694979040198658-d9xe?utm_source=share&utm_medium=member_android
👍3
Free Courses by Cisco 👇👇
🔷Data Analytics.
https://skillsforall.com/course/data-analytics-essentials?courseLang=en-US
🔷Data Science
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🔷JavaScript
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🔷Data Analytics.
https://skillsforall.com/course/data-analytics-essentials?courseLang=en-US
🔷Data Science
https://skillsforall.com/course/introduction-data-science?courseLang=en-US
🔷JavaScript
https://skillsforall.com/course/javascript-essentials-1?courseLang=en-US
🔷Python Essentials
https://skillsforall.com/course/python-essentials-1?courseLang=en-US
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https://skillsforall.com/course/introduction-to-cybersecurity?courseLang=en-US
📌Join our Community
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Do react ❤️ if you want more content like this
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Becoming a Data Analyst in 2024 is more difficult than it was a couple of years ago. The competition has grown but so has the demand for Data Analysts!
There are 5 areas you need to excel at to land a career in data. (so punny...)
1. Skills
2. Experience
3. Networking
4. Job Search
5. Education
Let's dive into the first and most important area, skills.
Skills
Every data analytics job will require a different set of skills for their job description. To cover the majority of entry-level positions, you should focus on the core 3 (or 4 if you have time).
- Excel
- SQL
- Tableau or Power BI
- Python or R(optional)
No need to learn any more than this to get started. Start learning other skills AFTER you land your first job and see what data analytics path you really enjoy.
You might fall into a path that doesn't require Python at all and if you took 3 months to learn it, you wasted 3 months. Your goal should be to get your foot in the door.
Experience
So how do you show that you have experience if you have never worked as a Data Analyst professionally?
It's actually easier than you think!
There are a few ways you can gain experience. volunteer, freelance, or any analytics work at your current job.
First ask your friends, family, or even Reddit if anyone needs help with their data.
Second, you can join Upwork or Fiverr to land some freelance gigs to gain great experience and some extra money.
Thirdly, even if your title isn't "Data Analyst", you might analyze data anyway. Use this as experience!
Networking
I love this section the most. It has been proven by everyone I have mentored that this is one of the most important areas to learn.
Start talking to other Data Analysts, start connecting with the RIGHT people, start posting on LinkedIn, start following people in the field, and start commenting on posts.
All of this, over time, will continue to get "eyes" on your profile. This will lead to more calls, interviews, and like the people I teach, job offers.
Consistency is important here.
Job Search
I believe this is not a skill and is more like a "numbers game". And the ones who excel here, are the ones who are consistent.
I'm not saying you need to apply all day every day but you should spend SOME time applying every day.
This is important because you don't know when exactly a company will be posting their job posting. You also want to be one of the first people to apply so that means you need to check the job boards in multiple small chunks rather than spend all of your time applying in a single chunk of time.
The best way to do this is to open up all of the filters and select the most recent and posted within the last 3 days.
Education
If you have a degree or are currently on your way to getting one, this section doesn't really apply to you since you have a leg up on a lot more job opportunities.
So how else does someone show they are educated enough to become a Data Analyst?
You need to prove it by taking relevant courses in relation to the industry you want to enter. After the course, the actual certificate does not hold much weight unless it's an accredited certificate like a Tableau Professional Certificate.
To counter this, you need to use your project descriptions to explain how you used data to solve a business problem and explain it professionally.
There are so many other areas you could work on but focussing on these to start will definitely get you going in the right direction.
Take time to put these actions to work. Pivot when something isn't working and adapt.
It will take time but these actions will reduce the time it takes you to become a Data Analyst in 2024
There are 5 areas you need to excel at to land a career in data. (so punny...)
1. Skills
2. Experience
3. Networking
4. Job Search
5. Education
Let's dive into the first and most important area, skills.
Skills
Every data analytics job will require a different set of skills for their job description. To cover the majority of entry-level positions, you should focus on the core 3 (or 4 if you have time).
- Excel
- SQL
- Tableau or Power BI
- Python or R(optional)
No need to learn any more than this to get started. Start learning other skills AFTER you land your first job and see what data analytics path you really enjoy.
You might fall into a path that doesn't require Python at all and if you took 3 months to learn it, you wasted 3 months. Your goal should be to get your foot in the door.
Experience
So how do you show that you have experience if you have never worked as a Data Analyst professionally?
It's actually easier than you think!
There are a few ways you can gain experience. volunteer, freelance, or any analytics work at your current job.
First ask your friends, family, or even Reddit if anyone needs help with their data.
Second, you can join Upwork or Fiverr to land some freelance gigs to gain great experience and some extra money.
Thirdly, even if your title isn't "Data Analyst", you might analyze data anyway. Use this as experience!
Networking
I love this section the most. It has been proven by everyone I have mentored that this is one of the most important areas to learn.
Start talking to other Data Analysts, start connecting with the RIGHT people, start posting on LinkedIn, start following people in the field, and start commenting on posts.
All of this, over time, will continue to get "eyes" on your profile. This will lead to more calls, interviews, and like the people I teach, job offers.
Consistency is important here.
Job Search
I believe this is not a skill and is more like a "numbers game". And the ones who excel here, are the ones who are consistent.
I'm not saying you need to apply all day every day but you should spend SOME time applying every day.
This is important because you don't know when exactly a company will be posting their job posting. You also want to be one of the first people to apply so that means you need to check the job boards in multiple small chunks rather than spend all of your time applying in a single chunk of time.
The best way to do this is to open up all of the filters and select the most recent and posted within the last 3 days.
Education
If you have a degree or are currently on your way to getting one, this section doesn't really apply to you since you have a leg up on a lot more job opportunities.
So how else does someone show they are educated enough to become a Data Analyst?
You need to prove it by taking relevant courses in relation to the industry you want to enter. After the course, the actual certificate does not hold much weight unless it's an accredited certificate like a Tableau Professional Certificate.
To counter this, you need to use your project descriptions to explain how you used data to solve a business problem and explain it professionally.
There are so many other areas you could work on but focussing on these to start will definitely get you going in the right direction.
Take time to put these actions to work. Pivot when something isn't working and adapt.
It will take time but these actions will reduce the time it takes you to become a Data Analyst in 2024
👍29❤5🫡4☃1