If I need to teach someone data analytics from the basics, here is my strategy:
1. I will first remove the fear of tools from that person
2. i will start with the excel because it looks familiar and easy to use
3. I put more emphasis on projects like at least 5 to 6 with the excel. because in industry you learn by doing things
4. I will release the person from the tutorial hell and move into a more action oriented person
5. Then I move to the sql because every job wants it , even with the ai tools you need strong understanding for it if you are going to use it daily
6. After strong understanding, I will push the person to solve 100 to 150 Sql problems from basic to advance
7. It helps the person to develop the analytical thinking
8. Then I push the person to solve 3 case studies as it helps how we pull the data in the real life
9. Then I move the person to power bi to do again 5 projects by using either sql or excel files
10. Now the fear is removed.
11. Now I push the person to solve unguided challenges and present them by video recording as it increases the problem solving, communication and data story telling skills
12. Further it helps you to clear case study round given by most of the companies
13. Now i help the person how to present them in resume and also how these tools are used in real world.
14. You know the interesting fact, all of above is present free in youtube and I also mentor the people through existing youtube videos.
15. But people stuck in the tutorial hell, loose motivation , stay confused that they are either in the right direction or not.
16. As a personal mentor , I help them to get of the tutorial hell, set them in the right direction and they stay motivated when they start to see the difference before amd after mentorship
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://topmate.io/analyst/861634
Hope this helps you ๐
1. I will first remove the fear of tools from that person
2. i will start with the excel because it looks familiar and easy to use
3. I put more emphasis on projects like at least 5 to 6 with the excel. because in industry you learn by doing things
4. I will release the person from the tutorial hell and move into a more action oriented person
5. Then I move to the sql because every job wants it , even with the ai tools you need strong understanding for it if you are going to use it daily
6. After strong understanding, I will push the person to solve 100 to 150 Sql problems from basic to advance
7. It helps the person to develop the analytical thinking
8. Then I push the person to solve 3 case studies as it helps how we pull the data in the real life
9. Then I move the person to power bi to do again 5 projects by using either sql or excel files
10. Now the fear is removed.
11. Now I push the person to solve unguided challenges and present them by video recording as it increases the problem solving, communication and data story telling skills
12. Further it helps you to clear case study round given by most of the companies
13. Now i help the person how to present them in resume and also how these tools are used in real world.
14. You know the interesting fact, all of above is present free in youtube and I also mentor the people through existing youtube videos.
15. But people stuck in the tutorial hell, loose motivation , stay confused that they are either in the right direction or not.
16. As a personal mentor , I help them to get of the tutorial hell, set them in the right direction and they stay motivated when they start to see the difference before amd after mentorship
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://topmate.io/analyst/861634
Hope this helps you ๐
โค2
๐ฆ๐๐ฒ๐ฝ ๐๐ป๐๐ผ ๐ฎ ๐๐๐ ๐๐ป๐ฎ๐น๐๐๐โ๐ ๐ฆ๐ต๐ผ๐ฒ๐: ๐๐ฟ๐ฒ๐ฒ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐ฆ๐ถ๐บ๐๐น๐ฎ๐๐ถ๐ผ๐ป + ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ฒ๐
๐ผ Ever Wondered How Data Shapes Real Business Decisions at a Top Consulting Firm?๐งโ๐ปโจ๏ธ
Now you can experience it firsthand with this interactive simulation from BCG (Boston Consulting Group)๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/45HWKRP
This is a powerful resume booster and a unique way to prove your analytical skillsโ ๏ธ
๐ผ Ever Wondered How Data Shapes Real Business Decisions at a Top Consulting Firm?๐งโ๐ปโจ๏ธ
Now you can experience it firsthand with this interactive simulation from BCG (Boston Consulting Group)๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/45HWKRP
This is a powerful resume booster and a unique way to prove your analytical skillsโ ๏ธ
โค1
Building Your Personal Brand as a Data Analyst ๐
A strong personal brand can help you land better job opportunities, attract freelance clients, and position you as a thought leader in data analytics.
Hereโs how to build and grow your brand effectively:
1๏ธโฃ Optimize Your LinkedIn Profile ๐
Use a clear, professional profile picture and a compelling headline (e.g., Data Analyst | SQL | Power BI | Python Enthusiast).
Write an engaging "About" section showcasing your skills, experience, and passion for data analytics.
Share projects, case studies, and insights to demonstrate expertise.
Engage with industry leaders, recruiters, and fellow analysts.
2๏ธโฃ Share Valuable Content Consistently โ๏ธ
Post insightful LinkedIn posts, Medium articles, or Twitter threads on SQL, Power BI, Python, and industry trends.
Write about real-world case studies, common mistakes, and career advice.
Share data visualization tips, SQL tricks, or step-by-step tutorials.
3๏ธโฃ Contribute to Open-Source & GitHub ๐ป
Publish SQL queries, Python scripts, Jupyter notebooks, and dashboards.
Share projects with real datasets to showcase your hands-on skills.
Collaborate on open-source data analytics projects to gain exposure.
4๏ธโฃ Engage in Online Data Analytics Communities ๐
Join and contribute to Reddit (r/dataanalysis, r/SQL), Stack Overflow, and Data Science Discord groups.
Participate in Kaggle competitions to gain practical experience.
Answer questions on Quora, LinkedIn, or Twitter to establish credibility.
5๏ธโฃ Speak at Webinars & Meetups ๐ค
Host or participate in webinars on LinkedIn, YouTube, or data conferences.
Join local meetups or online communities like DataCamp and Tableau User Groups.
Share insights on career growth, best practices, and analytics trends.
6๏ธโฃ Create a Portfolio Website ๐
Build a personal website showcasing your projects, resume, and blog.
Include interactive dashboards, case studies, and problem-solving examples.
Use Wix, WordPress, or GitHub Pages to get started.
7๏ธโฃ Network & Collaborate ๐ค
Connect with hiring managers, recruiters, and senior analysts.
Collaborate on guest blog posts, podcasts, or YouTube interviews.
Attend data science and analytics conferences to expand your reach.
8๏ธโฃ Start a YouTube Channel or Podcast ๐ฅ
Share short tutorials on SQL, Power BI, Python, and Excel.
Interview industry experts and discuss data analytics career paths.
Offer career guidance, resume tips, and interview prep content.
9๏ธโฃ Offer Free Value Before Monetizing ๐ก
Give away free e-books, templates, or mini-courses to attract an audience.
Provide LinkedIn Live Q&A sessions, career guidance, or free tutorials.
Once you build trust, you can monetize through consulting, courses, and coaching.
๐ Stay Consistent & Keep Learning
Building a brand takes timeโstay consistent with content creation and engagement.
Keep learning new skills and sharing your journey to stay relevant.
Follow industry leaders, subscribe to analytics blogs, and attend workshops.
A strong personal brand in data analytics can open unlimited opportunitiesโfrom job offers to freelance gigs and consulting projects.
Start small, be consistent, and showcase your expertise! ๐ฅ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
#dataanalyst
A strong personal brand can help you land better job opportunities, attract freelance clients, and position you as a thought leader in data analytics.
Hereโs how to build and grow your brand effectively:
1๏ธโฃ Optimize Your LinkedIn Profile ๐
Use a clear, professional profile picture and a compelling headline (e.g., Data Analyst | SQL | Power BI | Python Enthusiast).
Write an engaging "About" section showcasing your skills, experience, and passion for data analytics.
Share projects, case studies, and insights to demonstrate expertise.
Engage with industry leaders, recruiters, and fellow analysts.
2๏ธโฃ Share Valuable Content Consistently โ๏ธ
Post insightful LinkedIn posts, Medium articles, or Twitter threads on SQL, Power BI, Python, and industry trends.
Write about real-world case studies, common mistakes, and career advice.
Share data visualization tips, SQL tricks, or step-by-step tutorials.
3๏ธโฃ Contribute to Open-Source & GitHub ๐ป
Publish SQL queries, Python scripts, Jupyter notebooks, and dashboards.
Share projects with real datasets to showcase your hands-on skills.
Collaborate on open-source data analytics projects to gain exposure.
4๏ธโฃ Engage in Online Data Analytics Communities ๐
Join and contribute to Reddit (r/dataanalysis, r/SQL), Stack Overflow, and Data Science Discord groups.
Participate in Kaggle competitions to gain practical experience.
Answer questions on Quora, LinkedIn, or Twitter to establish credibility.
5๏ธโฃ Speak at Webinars & Meetups ๐ค
Host or participate in webinars on LinkedIn, YouTube, or data conferences.
Join local meetups or online communities like DataCamp and Tableau User Groups.
Share insights on career growth, best practices, and analytics trends.
6๏ธโฃ Create a Portfolio Website ๐
Build a personal website showcasing your projects, resume, and blog.
Include interactive dashboards, case studies, and problem-solving examples.
Use Wix, WordPress, or GitHub Pages to get started.
7๏ธโฃ Network & Collaborate ๐ค
Connect with hiring managers, recruiters, and senior analysts.
Collaborate on guest blog posts, podcasts, or YouTube interviews.
Attend data science and analytics conferences to expand your reach.
8๏ธโฃ Start a YouTube Channel or Podcast ๐ฅ
Share short tutorials on SQL, Power BI, Python, and Excel.
Interview industry experts and discuss data analytics career paths.
Offer career guidance, resume tips, and interview prep content.
9๏ธโฃ Offer Free Value Before Monetizing ๐ก
Give away free e-books, templates, or mini-courses to attract an audience.
Provide LinkedIn Live Q&A sessions, career guidance, or free tutorials.
Once you build trust, you can monetize through consulting, courses, and coaching.
๐ Stay Consistent & Keep Learning
Building a brand takes timeโstay consistent with content creation and engagement.
Keep learning new skills and sharing your journey to stay relevant.
Follow industry leaders, subscribe to analytics blogs, and attend workshops.
A strong personal brand in data analytics can open unlimited opportunitiesโfrom job offers to freelance gigs and consulting projects.
Start small, be consistent, and showcase your expertise! ๐ฅ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
#dataanalyst
โค1
๐๐ญ๐๐ซ๐ญ ๐๐จ๐ฎ๐ซ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ญ๐ข๐๐ฌ ๐๐จ๐ฎ๐ซ๐ง๐๐ฒ โ ๐๐๐% ๐
๐ซ๐๐ & ๐๐๐ ๐ข๐ง๐ง๐๐ซ-๐
๐ซ๐ข๐๐ง๐๐ฅ๐ฒ๐
Want to dive into data analytics but donโt know where to start?๐งโ๐ปโจ๏ธ
These free Microsoft learning paths take you from analytics basics to creating dashboards, AI insights with Copilot, and end-to-end analytics with Microsoft Fabric.๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/47oQD6f
No prior experience needed โ just curiosityโ ๏ธ
Want to dive into data analytics but donโt know where to start?๐งโ๐ปโจ๏ธ
These free Microsoft learning paths take you from analytics basics to creating dashboards, AI insights with Copilot, and end-to-end analytics with Microsoft Fabric.๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/47oQD6f
No prior experience needed โ just curiosityโ ๏ธ
โค2
Most popular Python libraries for data visualization:
Matplotlib โ The most fundamental library for static charts. Best for basic visualizations like line, bar, and scatter plots. Highly customizable but requires more coding.
Seaborn โ Built on Matplotlib, it simplifies statistical data visualization with beautiful defaults. Ideal for correlation heatmaps, categorical plots, and distribution analysis.
Plotly โ Best for interactive visualizations with zooming, hovering, and real-time updates. Great for dashboards, web applications, and 3D plotting.
Bokeh โ Designed for interactive and web-based visualizations. Excellent for handling large datasets, streaming data, and integrating with Flask/Django.
Altair โ A declarative library that makes complex statistical plots easy with minimal code. Best for quick and clean data exploration.
For static charts, start with Matplotlib or Seaborn. If you need interactivity, use Plotly or Bokeh. For quick EDA, Altair is a great choice.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
#python
Matplotlib โ The most fundamental library for static charts. Best for basic visualizations like line, bar, and scatter plots. Highly customizable but requires more coding.
Seaborn โ Built on Matplotlib, it simplifies statistical data visualization with beautiful defaults. Ideal for correlation heatmaps, categorical plots, and distribution analysis.
Plotly โ Best for interactive visualizations with zooming, hovering, and real-time updates. Great for dashboards, web applications, and 3D plotting.
Bokeh โ Designed for interactive and web-based visualizations. Excellent for handling large datasets, streaming data, and integrating with Flask/Django.
Altair โ A declarative library that makes complex statistical plots easy with minimal code. Best for quick and clean data exploration.
For static charts, start with Matplotlib or Seaborn. If you need interactivity, use Plotly or Bokeh. For quick EDA, Altair is a great choice.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
#python
โค4
Join our WhatsApp channel for free learning lessons
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https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
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https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
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Python Programming WhatsApp Channel. Perfect channel to learn Python Programming ๐จโ๐ป๐ฉโ๐ป
Download Free Books & Courses to master Python Programming
- โ Free Courses
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Download Free Books & Courses to master Python Programming
- โ Free Courses
- โ Coding Projects
- โ Important Pdfs
- โ Artificial Intelligence Bootcamps
- โ Data Scienceโฆ
โค1
๐ฎ๐ฑ+ ๐ ๐๐๐-๐๐ป๐ผ๐ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ค๐๐ฒ๐๐๐ถ๐ผ๐ป๐ ๐๐ผ ๐๐ฎ๐ป๐ฑ ๐ฌ๐ผ๐๐ฟ ๐๐ฟ๐ฒ๐ฎ๐บ ๐๐ผ๐ฏ ๐
Breaking into Data Analytics isnโt just about knowing the tools โ itโs about answering the right questions with confidence๐งโ๐ปโจ๏ธ
Whether youโre aiming for your first role or looking to level up your career, these real interview questions will test your skills๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3JumloI
Donโt just learn โ prepare smartโ ๏ธ
Breaking into Data Analytics isnโt just about knowing the tools โ itโs about answering the right questions with confidence๐งโ๐ปโจ๏ธ
Whether youโre aiming for your first role or looking to level up your career, these real interview questions will test your skills๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3JumloI
Donโt just learn โ prepare smartโ ๏ธ
โค1
Essential Data Analysis Techniques Every Analyst Should Know
1. Descriptive Statistics: Understanding measures of central tendency (mean, median, mode) and measures of spread (variance, standard deviation) to summarize data.
2. Data Cleaning: Techniques to handle missing values, outliers, and inconsistencies in data, ensuring that the data is accurate and reliable for analysis.
3. Exploratory Data Analysis (EDA): Using visualization tools like histograms, scatter plots, and box plots to uncover patterns, trends, and relationships in the data.
4. Hypothesis Testing: The process of making inferences about a population based on sample data, including understanding p-values, confidence intervals, and statistical significance.
5. Correlation and Regression Analysis: Techniques to measure the strength of relationships between variables and predict future outcomes based on existing data.
6. Time Series Analysis: Analyzing data collected over time to identify trends, seasonality, and cyclical patterns for forecasting purposes.
7. Clustering: Grouping similar data points together based on characteristics, useful in customer segmentation and market analysis.
8. Dimensionality Reduction: Techniques like PCA (Principal Component Analysis) to reduce the number of variables in a dataset while preserving as much information as possible.
9. ANOVA (Analysis of Variance): A statistical method used to compare the means of three or more samples, determining if at least one mean is different.
10. Machine Learning Integration: Applying machine learning algorithms to enhance data analysis, enabling predictions, and automation of tasks.
Like this post if you need more ๐โค๏ธ
Hope it helps :)
1. Descriptive Statistics: Understanding measures of central tendency (mean, median, mode) and measures of spread (variance, standard deviation) to summarize data.
2. Data Cleaning: Techniques to handle missing values, outliers, and inconsistencies in data, ensuring that the data is accurate and reliable for analysis.
3. Exploratory Data Analysis (EDA): Using visualization tools like histograms, scatter plots, and box plots to uncover patterns, trends, and relationships in the data.
4. Hypothesis Testing: The process of making inferences about a population based on sample data, including understanding p-values, confidence intervals, and statistical significance.
5. Correlation and Regression Analysis: Techniques to measure the strength of relationships between variables and predict future outcomes based on existing data.
6. Time Series Analysis: Analyzing data collected over time to identify trends, seasonality, and cyclical patterns for forecasting purposes.
7. Clustering: Grouping similar data points together based on characteristics, useful in customer segmentation and market analysis.
8. Dimensionality Reduction: Techniques like PCA (Principal Component Analysis) to reduce the number of variables in a dataset while preserving as much information as possible.
9. ANOVA (Analysis of Variance): A statistical method used to compare the means of three or more samples, determining if at least one mean is different.
10. Machine Learning Integration: Applying machine learning algorithms to enhance data analysis, enabling predictions, and automation of tasks.
Like this post if you need more ๐โค๏ธ
Hope it helps :)
โค2๐1