To effectively learn SQL for a Data Analyst role, follow these steps:
1. Start with a basic course: Begin by taking a basic course on YouTube to familiarize yourself with SQL syntax and terminologies. I recommend the "Learn Complete SQL" playlist from the "techTFQ" YouTube channel.
2. Practice syntax and commands: As you learn new terminologies from the course, practice their syntax on the "w3schools" website. This site provides clear examples of SQL syntax, commands, and functions.
3. Solve practice questions: After completing the initial steps, start solving easy-level SQL practice questions on platforms like "Hackerrank," "Leetcode," "Datalemur," and "Stratascratch." If you get stuck, use the discussion forums on these platforms or ask ChatGPT for help. You can paste the problem into ChatGPT and use a prompt like:
- "Explain the step-by-step solution to the above problem as I am new to SQL, also explain the solution as per the order of execution of SQL."
4. Gradually increase difficulty: Gradually move on to more difficult practice questions. If you encounter new SQL concepts, watch YouTube videos on those topics or ask ChatGPT for explanations.
5. Consistent practice: The most crucial aspect of learning SQL is consistent practice. Regular practice will help you build and solidify your skills.
By following these steps and maintaining regular practice, you'll be well on your way to mastering SQL for a Data Analyst role.
1. Start with a basic course: Begin by taking a basic course on YouTube to familiarize yourself with SQL syntax and terminologies. I recommend the "Learn Complete SQL" playlist from the "techTFQ" YouTube channel.
2. Practice syntax and commands: As you learn new terminologies from the course, practice their syntax on the "w3schools" website. This site provides clear examples of SQL syntax, commands, and functions.
3. Solve practice questions: After completing the initial steps, start solving easy-level SQL practice questions on platforms like "Hackerrank," "Leetcode," "Datalemur," and "Stratascratch." If you get stuck, use the discussion forums on these platforms or ask ChatGPT for help. You can paste the problem into ChatGPT and use a prompt like:
- "Explain the step-by-step solution to the above problem as I am new to SQL, also explain the solution as per the order of execution of SQL."
4. Gradually increase difficulty: Gradually move on to more difficult practice questions. If you encounter new SQL concepts, watch YouTube videos on those topics or ask ChatGPT for explanations.
5. Consistent practice: The most crucial aspect of learning SQL is consistent practice. Regular practice will help you build and solidify your skills.
By following these steps and maintaining regular practice, you'll be well on your way to mastering SQL for a Data Analyst role.
โค5
Start your career in data analysis for freshers ๐๐
1. Learn the Basics: Begin with understanding the fundamental concepts of statistics, mathematics, and programming languages like Python or R.
Free Resources: https://t.iss.one/pythonanalyst/103
2. Acquire Technical Skills: Develop proficiency in data analysis tools such as Excel, SQL, and data visualization tools like Tableau or Power BI.
Free Data Analysis Books: https://t.iss.one/learndataanalysis
3. Gain Knowledge in Statistics: A solid foundation in statistical concepts is crucial for data analysis. Learn about probability, hypothesis testing, and regression analysis.
Free course by Khan Academy will help you to enhance these skills.
4. Programming Proficiency: Enhance your programming skills, especially in languages commonly used in data analysis like Python or R. Familiarity with libraries such as Pandas and NumPy in Python is beneficial. Kaggle has amazing content to learn these skills.
5. Data Cleaning and Preprocessing: Understand the importance of cleaning and preprocessing data. Learn techniques to handle missing values, outliers, and transform data for analysis.
6. Database Knowledge: Acquire knowledge about databases and SQL for efficient data retrieval and manipulation.
SQL for data analytics: https://t.iss.one/sqlanalyst
7. Data Visualization: Master the art of presenting insights through visualizations. Learn tools like Matplotlib, Seaborn, or ggplot2 for creating meaningful charts and graphs. If you are from non-technical background, learn Tableau or Power BI.
FREE Resources to learn data visualization: https://t.iss.one/PowerBI_analyst
8. Machine Learning Basics: Familiarize yourself with basic machine learning concepts. This knowledge can be beneficial for advanced analytics tasks.
ML Basics: https://t.iss.one/datasciencefun/1476
9. Build a Portfolio: Work on projects that showcase your skills. This could be personal projects, contributions to open-source projects, or challenges from platforms like Kaggle.
Data Analytics Portfolio Projects: https://t.iss.one/DataPortfolio
10. Networking and Continuous Learning: Engage with the data science community, attend meetups, webinars, and conferences. Build your strong Linkedin profile and enhance your network.
11. Apply for Internships or Entry-Level Positions: Gain practical experience by applying for internships or entry-level positions in data analysis. Real-world projects contribute significantly to your learning.
Data Analyst Jobs & Internship opportunities: https://t.iss.one/jobs_SQL
12. Effective Communication: Develop strong communication skills. Being able to convey your findings and insights in a clear and understandable manner is crucial.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
1. Learn the Basics: Begin with understanding the fundamental concepts of statistics, mathematics, and programming languages like Python or R.
Free Resources: https://t.iss.one/pythonanalyst/103
2. Acquire Technical Skills: Develop proficiency in data analysis tools such as Excel, SQL, and data visualization tools like Tableau or Power BI.
Free Data Analysis Books: https://t.iss.one/learndataanalysis
3. Gain Knowledge in Statistics: A solid foundation in statistical concepts is crucial for data analysis. Learn about probability, hypothesis testing, and regression analysis.
Free course by Khan Academy will help you to enhance these skills.
4. Programming Proficiency: Enhance your programming skills, especially in languages commonly used in data analysis like Python or R. Familiarity with libraries such as Pandas and NumPy in Python is beneficial. Kaggle has amazing content to learn these skills.
5. Data Cleaning and Preprocessing: Understand the importance of cleaning and preprocessing data. Learn techniques to handle missing values, outliers, and transform data for analysis.
6. Database Knowledge: Acquire knowledge about databases and SQL for efficient data retrieval and manipulation.
SQL for data analytics: https://t.iss.one/sqlanalyst
7. Data Visualization: Master the art of presenting insights through visualizations. Learn tools like Matplotlib, Seaborn, or ggplot2 for creating meaningful charts and graphs. If you are from non-technical background, learn Tableau or Power BI.
FREE Resources to learn data visualization: https://t.iss.one/PowerBI_analyst
8. Machine Learning Basics: Familiarize yourself with basic machine learning concepts. This knowledge can be beneficial for advanced analytics tasks.
ML Basics: https://t.iss.one/datasciencefun/1476
9. Build a Portfolio: Work on projects that showcase your skills. This could be personal projects, contributions to open-source projects, or challenges from platforms like Kaggle.
Data Analytics Portfolio Projects: https://t.iss.one/DataPortfolio
10. Networking and Continuous Learning: Engage with the data science community, attend meetups, webinars, and conferences. Build your strong Linkedin profile and enhance your network.
11. Apply for Internships or Entry-Level Positions: Gain practical experience by applying for internships or entry-level positions in data analysis. Real-world projects contribute significantly to your learning.
Data Analyst Jobs & Internship opportunities: https://t.iss.one/jobs_SQL
12. Effective Communication: Develop strong communication skills. Being able to convey your findings and insights in a clear and understandable manner is crucial.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค2
Start your career in data analysis for freshers ๐๐
1. Learn the Basics: Begin with understanding the fundamental concepts of statistics, mathematics, and programming languages like Python or R.
Free Resources: https://t.iss.one/pythonanalyst/103
2. Acquire Technical Skills: Develop proficiency in data analysis tools such as Excel, SQL, and data visualization tools like Tableau or Power BI.
Free Data Analysis Books: https://t.iss.one/learndataanalysis
3. Gain Knowledge in Statistics: A solid foundation in statistical concepts is crucial for data analysis. Learn about probability, hypothesis testing, and regression analysis.
Free course by Khan Academy will help you to enhance these skills.
4. Programming Proficiency: Enhance your programming skills, especially in languages commonly used in data analysis like Python or R. Familiarity with libraries such as Pandas and NumPy in Python is beneficial. Kaggle has amazing content to learn these skills.
5. Data Cleaning and Preprocessing: Understand the importance of cleaning and preprocessing data. Learn techniques to handle missing values, outliers, and transform data for analysis.
6. Database Knowledge: Acquire knowledge about databases and SQL for efficient data retrieval and manipulation.
SQL for data analytics: https://t.iss.one/sqlanalyst
7. Data Visualization: Master the art of presenting insights through visualizations. Learn tools like Matplotlib, Seaborn, or ggplot2 for creating meaningful charts and graphs. If you are from non-technical background, learn Tableau or Power BI.
FREE Resources to learn data visualization: https://t.iss.one/PowerBI_analyst
8. Machine Learning Basics: Familiarize yourself with basic machine learning concepts. This knowledge can be beneficial for advanced analytics tasks.
ML Basics: https://t.iss.one/datasciencefun/1476
9. Build a Portfolio: Work on projects that showcase your skills. This could be personal projects, contributions to open-source projects, or challenges from platforms like Kaggle.
Data Analytics Portfolio Projects: https://t.iss.one/DataPortfolio
10. Networking and Continuous Learning: Engage with the data science community, attend meetups, webinars, and conferences. Build your strong Linkedin profile and enhance your network.
11. Apply for Internships or Entry-Level Positions: Gain practical experience by applying for internships or entry-level positions in data analysis. Real-world projects contribute significantly to your learning.
Data Analyst Jobs & Internship opportunities: https://t.iss.one/jobs_SQL
12. Effective Communication: Develop strong communication skills. Being able to convey your findings and insights in a clear and understandable manner is crucial.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
1. Learn the Basics: Begin with understanding the fundamental concepts of statistics, mathematics, and programming languages like Python or R.
Free Resources: https://t.iss.one/pythonanalyst/103
2. Acquire Technical Skills: Develop proficiency in data analysis tools such as Excel, SQL, and data visualization tools like Tableau or Power BI.
Free Data Analysis Books: https://t.iss.one/learndataanalysis
3. Gain Knowledge in Statistics: A solid foundation in statistical concepts is crucial for data analysis. Learn about probability, hypothesis testing, and regression analysis.
Free course by Khan Academy will help you to enhance these skills.
4. Programming Proficiency: Enhance your programming skills, especially in languages commonly used in data analysis like Python or R. Familiarity with libraries such as Pandas and NumPy in Python is beneficial. Kaggle has amazing content to learn these skills.
5. Data Cleaning and Preprocessing: Understand the importance of cleaning and preprocessing data. Learn techniques to handle missing values, outliers, and transform data for analysis.
6. Database Knowledge: Acquire knowledge about databases and SQL for efficient data retrieval and manipulation.
SQL for data analytics: https://t.iss.one/sqlanalyst
7. Data Visualization: Master the art of presenting insights through visualizations. Learn tools like Matplotlib, Seaborn, or ggplot2 for creating meaningful charts and graphs. If you are from non-technical background, learn Tableau or Power BI.
FREE Resources to learn data visualization: https://t.iss.one/PowerBI_analyst
8. Machine Learning Basics: Familiarize yourself with basic machine learning concepts. This knowledge can be beneficial for advanced analytics tasks.
ML Basics: https://t.iss.one/datasciencefun/1476
9. Build a Portfolio: Work on projects that showcase your skills. This could be personal projects, contributions to open-source projects, or challenges from platforms like Kaggle.
Data Analytics Portfolio Projects: https://t.iss.one/DataPortfolio
10. Networking and Continuous Learning: Engage with the data science community, attend meetups, webinars, and conferences. Build your strong Linkedin profile and enhance your network.
11. Apply for Internships or Entry-Level Positions: Gain practical experience by applying for internships or entry-level positions in data analysis. Real-world projects contribute significantly to your learning.
Data Analyst Jobs & Internship opportunities: https://t.iss.one/jobs_SQL
12. Effective Communication: Develop strong communication skills. Being able to convey your findings and insights in a clear and understandable manner is crucial.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค2
๐๐ฎ๐ง๐ข๐จ๐ซ ๐ฏ๐ฌ. ๐๐๐ง๐ข๐จ๐ซ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ญ
Whatโs the real difference between Junior and Senior Data Analyst?
Itโs not just SQL skills or years on the job โ itโs how they think.
๐Juniors code right away
๐ง Seniors figure out the problem first
Example: Juniors query without asking, Seniors check the goal.
๐Juniors follow orders
๐ง Seniors ask questions
Example: Juniors build blindly, Seniors confirm metrics.
๐Juniors patch data
๐ง Seniors fix the source
Example: Juniors fill gaps, Seniors debug the ETL.
๐Juniors stall in chaos
๐ง Seniors make a plan
Example: Juniors wait, Seniors step up.
๐Juniors focus on tasks
๐ง Seniors see the big picture
Example: Juniors report, Seniors connect to goals.
๐Juniors guess
๐ง Seniors clarify
Example: Juniors assume, Seniors ask the team.
๐Juniors stick to old tools
๐ง Seniors try new ones
Example: Juniors love Excel, Seniors code in Python.
๐Juniors give data
๐ง Seniors give insights
Example: Juniors share stats, Seniors spot trends.
Seniority is about mindset, not just time.
Whatโs the real difference between Junior and Senior Data Analyst?
Itโs not just SQL skills or years on the job โ itโs how they think.
๐Juniors code right away
๐ง Seniors figure out the problem first
Example: Juniors query without asking, Seniors check the goal.
๐Juniors follow orders
๐ง Seniors ask questions
Example: Juniors build blindly, Seniors confirm metrics.
๐Juniors patch data
๐ง Seniors fix the source
Example: Juniors fill gaps, Seniors debug the ETL.
๐Juniors stall in chaos
๐ง Seniors make a plan
Example: Juniors wait, Seniors step up.
๐Juniors focus on tasks
๐ง Seniors see the big picture
Example: Juniors report, Seniors connect to goals.
๐Juniors guess
๐ง Seniors clarify
Example: Juniors assume, Seniors ask the team.
๐Juniors stick to old tools
๐ง Seniors try new ones
Example: Juniors love Excel, Seniors code in Python.
๐Juniors give data
๐ง Seniors give insights
Example: Juniors share stats, Seniors spot trends.
Seniority is about mindset, not just time.
โค3
๐ 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
๐๐
https://t.iss.one/sqlspecialist/379
ENJOY LEARNING ๐๐
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
๐๐
https://t.iss.one/sqlspecialist/379
ENJOY LEARNING ๐๐
โค1
Data Analyst Interview Questions & Preparation Tips
Be prepared with a mix of technical, analytical, and business-oriented interview questions.
1. Technical Questions (Data Analysis & Reporting)
SQL Questions:
How do you write a query to fetch the top 5 highest revenue-generating customers?
Explain the difference between INNER JOIN, LEFT JOIN, and FULL OUTER JOIN.
How would you optimize a slow-running query?
What are CTEs and when would you use them?
Data Visualization (Power BI / Tableau / Excel)
How would you create a dashboard to track key performance metrics?
Explain the difference between measures and calculated columns in Power BI.
How do you handle missing data in Tableau?
What are DAX functions, and can you give an example?
ETL & Data Processing (Alteryx, Power BI, Excel)
What is ETL, and how does it relate to BI?
Have you used Alteryx for data transformation? Explain a complex workflow you built.
How do you automate reporting using Power Query in Excel?
2. Business and Analytical Questions
How do you define KPIs for a business process?
Give an example of how you used data to drive a business decision.
How would you identify cost-saving opportunities in a reporting process?
Explain a time when your report uncovered a hidden business insight.
3. Scenario-Based & Behavioral Questions
Stakeholder Management:
How do you handle a situation where different business units have conflicting reporting requirements?
How do you explain complex data insights to non-technical stakeholders?
Problem-Solving & Debugging:
What would you do if your report is showing incorrect numbers?
How do you ensure the accuracy of a new KPI you introduced?
Project Management & Process Improvement:
Have you led a project to automate or improve a reporting process?
What steps do you take to ensure the timely delivery of reports?
4. Industry-Specific Questions (Credit Reporting & Financial Services)
What are some key credit risk metrics used in financial services?
How would you analyze trends in customer credit behavior?
How do you ensure compliance and data security in reporting?
5. General HR Questions
Why do you want to work at this company?
Tell me about a challenging project and how you handled it.
What are your strengths and weaknesses?
Where do you see yourself in five years?
How to Prepare?
Brush up on SQL, Power BI, and ETL tools (especially Alteryx).
Learn about key financial and credit reporting metrics.(varies company to company)
Practice explaining data-driven insights in a business-friendly manner.
Be ready to showcase problem-solving skills with real-world examples.
React with โค๏ธ if you want me to also post sample answer for the above questions
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Be prepared with a mix of technical, analytical, and business-oriented interview questions.
1. Technical Questions (Data Analysis & Reporting)
SQL Questions:
How do you write a query to fetch the top 5 highest revenue-generating customers?
Explain the difference between INNER JOIN, LEFT JOIN, and FULL OUTER JOIN.
How would you optimize a slow-running query?
What are CTEs and when would you use them?
Data Visualization (Power BI / Tableau / Excel)
How would you create a dashboard to track key performance metrics?
Explain the difference between measures and calculated columns in Power BI.
How do you handle missing data in Tableau?
What are DAX functions, and can you give an example?
ETL & Data Processing (Alteryx, Power BI, Excel)
What is ETL, and how does it relate to BI?
Have you used Alteryx for data transformation? Explain a complex workflow you built.
How do you automate reporting using Power Query in Excel?
2. Business and Analytical Questions
How do you define KPIs for a business process?
Give an example of how you used data to drive a business decision.
How would you identify cost-saving opportunities in a reporting process?
Explain a time when your report uncovered a hidden business insight.
3. Scenario-Based & Behavioral Questions
Stakeholder Management:
How do you handle a situation where different business units have conflicting reporting requirements?
How do you explain complex data insights to non-technical stakeholders?
Problem-Solving & Debugging:
What would you do if your report is showing incorrect numbers?
How do you ensure the accuracy of a new KPI you introduced?
Project Management & Process Improvement:
Have you led a project to automate or improve a reporting process?
What steps do you take to ensure the timely delivery of reports?
4. Industry-Specific Questions (Credit Reporting & Financial Services)
What are some key credit risk metrics used in financial services?
How would you analyze trends in customer credit behavior?
How do you ensure compliance and data security in reporting?
5. General HR Questions
Why do you want to work at this company?
Tell me about a challenging project and how you handled it.
What are your strengths and weaknesses?
Where do you see yourself in five years?
How to Prepare?
Brush up on SQL, Power BI, and ETL tools (especially Alteryx).
Learn about key financial and credit reporting metrics.(varies company to company)
Practice explaining data-driven insights in a business-friendly manner.
Be ready to showcase problem-solving skills with real-world examples.
React with โค๏ธ if you want me to also post sample answer for the above questions
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค2
Beyond Data Analytics: Expanding Your Career Horizons
Once you've mastered core and advanced analytics skills, it's time to explore career growth opportunities beyond traditional data analyst roles. Here are some potential paths:
1๏ธโฃ Data Science & AI Specialist ๐ค
Dive deeper into machine learning, deep learning, and AI-powered analytics.
Learn advanced Python libraries like TensorFlow, PyTorch, and Scikit-Learn.
Work on predictive modeling, NLP, and AI automation.
2๏ธโฃ Data Engineering ๐๏ธ
Shift towards building scalable data infrastructure.
Master ETL pipelines, cloud databases (BigQuery, Snowflake, Redshift), and Apache Spark.
Learn Docker, Kubernetes, and Airflow for workflow automation.
3๏ธโฃ Business Intelligence & Data Strategy ๐
Transition into high-level decision-making roles.
Become a BI Consultant or Data Strategist, focusing on storytelling and business impact.
Lead data-driven transformation projects in organizations.
4๏ธโฃ Product Analytics & Growth Strategy ๐
Work closely with product managers to optimize user experience and engagement.
Use A/B testing, cohort analysis, and customer segmentation to drive product decisions.
Learn Mixpanel, Amplitude, and Google Analytics.
5๏ธโฃ Data Governance & Privacy Expert ๐
Specialize in data compliance, security, and ethical AI.
Learn about GDPR, CCPA, and industry regulations.
Work on data quality, lineage, and metadata management.
6๏ธโฃ AI-Powered Automation & No-Code Analytics ๐
Explore AutoML tools, AI-assisted analytics, and no-code platforms like Alteryx and DataRobot.
Automate repetitive tasks and create self-service analytics solutions for businesses.
7๏ธโฃ Freelancing & Consulting ๐ผ
Offer data analytics services as an independent consultant.
Build a personal brand through LinkedIn, Medium, or YouTube.
Monetize your expertise via online courses, coaching, or workshops.
8๏ธโฃ Transitioning to Leadership Roles
Become a Data Science Manager, Head of Analytics, or Chief Data Officer.
Focus on mentoring teams, driving data strategy, and influencing business decisions.
Develop stakeholder management, communication, and leadership skills.
Mastering data analytics opens up multiple career pathwaysโwhether in AI, business strategy, engineering, or leadership. Choose your path, keep learning, and stay ahead of industry trends! ๐
#dataanalytics
Once you've mastered core and advanced analytics skills, it's time to explore career growth opportunities beyond traditional data analyst roles. Here are some potential paths:
1๏ธโฃ Data Science & AI Specialist ๐ค
Dive deeper into machine learning, deep learning, and AI-powered analytics.
Learn advanced Python libraries like TensorFlow, PyTorch, and Scikit-Learn.
Work on predictive modeling, NLP, and AI automation.
2๏ธโฃ Data Engineering ๐๏ธ
Shift towards building scalable data infrastructure.
Master ETL pipelines, cloud databases (BigQuery, Snowflake, Redshift), and Apache Spark.
Learn Docker, Kubernetes, and Airflow for workflow automation.
3๏ธโฃ Business Intelligence & Data Strategy ๐
Transition into high-level decision-making roles.
Become a BI Consultant or Data Strategist, focusing on storytelling and business impact.
Lead data-driven transformation projects in organizations.
4๏ธโฃ Product Analytics & Growth Strategy ๐
Work closely with product managers to optimize user experience and engagement.
Use A/B testing, cohort analysis, and customer segmentation to drive product decisions.
Learn Mixpanel, Amplitude, and Google Analytics.
5๏ธโฃ Data Governance & Privacy Expert ๐
Specialize in data compliance, security, and ethical AI.
Learn about GDPR, CCPA, and industry regulations.
Work on data quality, lineage, and metadata management.
6๏ธโฃ AI-Powered Automation & No-Code Analytics ๐
Explore AutoML tools, AI-assisted analytics, and no-code platforms like Alteryx and DataRobot.
Automate repetitive tasks and create self-service analytics solutions for businesses.
7๏ธโฃ Freelancing & Consulting ๐ผ
Offer data analytics services as an independent consultant.
Build a personal brand through LinkedIn, Medium, or YouTube.
Monetize your expertise via online courses, coaching, or workshops.
8๏ธโฃ Transitioning to Leadership Roles
Become a Data Science Manager, Head of Analytics, or Chief Data Officer.
Focus on mentoring teams, driving data strategy, and influencing business decisions.
Develop stakeholder management, communication, and leadership skills.
Mastering data analytics opens up multiple career pathwaysโwhether in AI, business strategy, engineering, or leadership. Choose your path, keep learning, and stay ahead of industry trends! ๐
#dataanalytics
โค1
Preparing for a machine learning interview as a data analyst is a great step.
Here are some common machine learning interview questions :-
1. Explain the steps involved in a machine learning project lifecycle.
2. What is the difference between supervised and unsupervised learning? Give examples of each.
3. What evaluation metrics would you use to assess the performance of a regression model?
4. What is overfitting and how can you prevent it?
5. Describe the bias-variance tradeoff.
6. What is cross-validation, and why is it important in machine learning?
7. What are some feature selection techniques you are familiar with?
8.What are the assumptions of linear regression?
9. How does regularization help in linear models?
10. Explain the difference between classification and regression.
11. What are some common algorithms used for dimensionality reduction?
12. Describe how a decision tree works.
13. What are ensemble methods, and why are they useful?
14. How do you handle missing or corrupted data in a dataset?
15. What are the different kernels used in Support Vector Machines (SVM)?
These questions cover a range of fundamental concepts and techniques in machine learning that are important for a data scientist role.
Good luck with your interview preparation!
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Like if you need similar content ๐๐
Here are some common machine learning interview questions :-
1. Explain the steps involved in a machine learning project lifecycle.
2. What is the difference between supervised and unsupervised learning? Give examples of each.
3. What evaluation metrics would you use to assess the performance of a regression model?
4. What is overfitting and how can you prevent it?
5. Describe the bias-variance tradeoff.
6. What is cross-validation, and why is it important in machine learning?
7. What are some feature selection techniques you are familiar with?
8.What are the assumptions of linear regression?
9. How does regularization help in linear models?
10. Explain the difference between classification and regression.
11. What are some common algorithms used for dimensionality reduction?
12. Describe how a decision tree works.
13. What are ensemble methods, and why are they useful?
14. How do you handle missing or corrupted data in a dataset?
15. What are the different kernels used in Support Vector Machines (SVM)?
These questions cover a range of fundamental concepts and techniques in machine learning that are important for a data scientist role.
Good luck with your interview preparation!
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Like if you need similar content ๐๐
โค2
Forwarded from Artificial Intelligence
๐๐๐ ๐
๐๐๐ ๐๐๐ซ๐ญ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง ๐๐จ๐ฎ๐ซ๐ฌ๐๐ฌ๐
๐ Dive into the world of Data Analytics with these 6 free courses by IBM!
Gain practical knowledge and stand out in your career with tools designed for real-world applications.
All courses come with expert guidance and are free to access!๐
๐๐ข๐ง๐ค ๐:-
https://bit.ly/4iXOmmb
Enroll For FREE & Get Certified ๐
๐ Dive into the world of Data Analytics with these 6 free courses by IBM!
Gain practical knowledge and stand out in your career with tools designed for real-world applications.
All courses come with expert guidance and are free to access!๐
๐๐ข๐ง๐ค ๐:-
https://bit.ly/4iXOmmb
Enroll For FREE & Get Certified ๐
โค3
Roadmap to Become a Data Analyst:
๐ Learn Excel & Google Sheets (Formulas, Pivot Tables)
โ๐ Master SQL (SELECT, JOINs, CTEs, Window Functions)
โ๐ Learn Data Visualization (Power BI / Tableau)
โ๐ Understand Statistics & Probability
โ๐ Learn Python (Pandas, NumPy, Matplotlib, Seaborn)
โ๐ Work with Real Datasets (Kaggle / Public APIs)
โ๐ Learn Data Cleaning & Preprocessing Techniques
โ๐ Build Case Studies & Projects
โ๐ Create Portfolio & Resume
โโ Apply for Internships / Jobs
React โค๏ธ for More ๐ผ
๐ Learn Excel & Google Sheets (Formulas, Pivot Tables)
โ๐ Master SQL (SELECT, JOINs, CTEs, Window Functions)
โ๐ Learn Data Visualization (Power BI / Tableau)
โ๐ Understand Statistics & Probability
โ๐ Learn Python (Pandas, NumPy, Matplotlib, Seaborn)
โ๐ Work with Real Datasets (Kaggle / Public APIs)
โ๐ Learn Data Cleaning & Preprocessing Techniques
โ๐ Build Case Studies & Projects
โ๐ Create Portfolio & Resume
โโ Apply for Internships / Jobs
React โค๏ธ for More ๐ผ
โค12๐ฅ1
Roadmap to Become a Data Analyst:
๐ Learn Excel & Google Sheets (Formulas, Pivot Tables)
โ๐ Master SQL (SELECT, JOINs, CTEs, Window Functions)
โ๐ Learn Data Visualization (Power BI / Tableau)
โ๐ Understand Statistics & Probability
โ๐ Learn Python (Pandas, NumPy, Matplotlib, Seaborn)
โ๐ Work with Real Datasets (Kaggle / Public APIs)
โ๐ Learn Data Cleaning & Preprocessing Techniques
โ๐ Build Case Studies & Projects
โ๐ Create Portfolio & Resume
โโ Apply for Internships / Jobs
React โค๏ธ for More ๐ผ
๐ Learn Excel & Google Sheets (Formulas, Pivot Tables)
โ๐ Master SQL (SELECT, JOINs, CTEs, Window Functions)
โ๐ Learn Data Visualization (Power BI / Tableau)
โ๐ Understand Statistics & Probability
โ๐ Learn Python (Pandas, NumPy, Matplotlib, Seaborn)
โ๐ Work with Real Datasets (Kaggle / Public APIs)
โ๐ Learn Data Cleaning & Preprocessing Techniques
โ๐ Build Case Studies & Projects
โ๐ Create Portfolio & Resume
โโ Apply for Internships / Jobs
React โค๏ธ for More ๐ผ
โค4
๐ Top 10 Data Analytics Concepts Everyone Should Know ๐
1๏ธโฃ Data Cleaning ๐งน
Removing duplicates, fixing missing or inconsistent data.
๐ Tools: Excel, Python (Pandas), SQL
2๏ธโฃ Descriptive Statistics ๐
Mean, median, mode, standard deviationโbasic measures to summarize data.
๐ Used for understanding data distribution
3๏ธโฃ Data Visualization ๐
Creating charts and dashboards to spot patterns.
๐ Tools: Power BI, Tableau, Matplotlib, Seaborn
4๏ธโฃ Exploratory Data Analysis (EDA) ๐
Identifying trends, outliers, and correlations through deep data exploration.
๐ Step before modeling
5๏ธโฃ SQL for Data Extraction ๐๏ธ
Querying databases to retrieve specific information.
๐ Focus on SELECT, JOIN, GROUP BY, WHERE
6๏ธโฃ Hypothesis Testing โ๏ธ
Making decisions using sample data (A/B testing, p-value, confidence intervals).
๐ Useful in product or marketing experiments
7๏ธโฃ Correlation vs Causation ๐
Just because two things are related doesnโt mean one causes the other!
8๏ธโฃ Data Modeling ๐ง
Creating models to predict or explain outcomes.
๐ Linear regression, decision trees, clustering
9๏ธโฃ KPIs & Metrics ๐ฏ
Understanding business performance indicators like ROI, retention rate, churn.
๐ Storytelling with Data ๐ฃ๏ธ
Translating raw numbers into insights stakeholders can act on.
๐ Use clear visuals, simple language, and real-world impact
โค๏ธ React for more
1๏ธโฃ Data Cleaning ๐งน
Removing duplicates, fixing missing or inconsistent data.
๐ Tools: Excel, Python (Pandas), SQL
2๏ธโฃ Descriptive Statistics ๐
Mean, median, mode, standard deviationโbasic measures to summarize data.
๐ Used for understanding data distribution
3๏ธโฃ Data Visualization ๐
Creating charts and dashboards to spot patterns.
๐ Tools: Power BI, Tableau, Matplotlib, Seaborn
4๏ธโฃ Exploratory Data Analysis (EDA) ๐
Identifying trends, outliers, and correlations through deep data exploration.
๐ Step before modeling
5๏ธโฃ SQL for Data Extraction ๐๏ธ
Querying databases to retrieve specific information.
๐ Focus on SELECT, JOIN, GROUP BY, WHERE
6๏ธโฃ Hypothesis Testing โ๏ธ
Making decisions using sample data (A/B testing, p-value, confidence intervals).
๐ Useful in product or marketing experiments
7๏ธโฃ Correlation vs Causation ๐
Just because two things are related doesnโt mean one causes the other!
8๏ธโฃ Data Modeling ๐ง
Creating models to predict or explain outcomes.
๐ Linear regression, decision trees, clustering
9๏ธโฃ KPIs & Metrics ๐ฏ
Understanding business performance indicators like ROI, retention rate, churn.
๐ Storytelling with Data ๐ฃ๏ธ
Translating raw numbers into insights stakeholders can act on.
๐ Use clear visuals, simple language, and real-world impact
โค๏ธ React for more
โค5
๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ ๐๐ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐๐ ๐๐ ๐๐๐๐ถ๐ป๐ฒ๐๐ ๐๐ป๐ฎ๐น๐๐๐ โ ๐ช๐ต๐ถ๐ฐ๐ต ๐ฃ๐ฎ๐๐ต ๐ถ๐ ๐ฅ๐ถ๐ด๐ต๐ ๐ณ๐ผ๐ฟ ๐ฌ๐ผ๐? ๐ค
In todayโs data-driven world, career clarity can make all the difference. Whether youโre starting out in analytics, pivoting into data science, or aligning business with data as an analyst โ understanding the core responsibilities, skills, and tools of each role is crucial.
๐ Hereโs a quick breakdown from a visual I often refer to when mentoring professionals:
๐น ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐
๓ ฏโข๓ Focus: Analyzing historical data to inform decisions.
๓ ฏโข๓ Skills: SQL, basic stats, data visualization, reporting.
๓ ฏโข๓ Tools: Excel, Tableau, Power BI, SQL.
๐น ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐๐
๓ ฏโข๓ Focus: Predictive modeling, ML, complex data analysis.
๓ ฏโข๓ Skills: Programming, ML, deep learning, stats.
๓ ฏโข๓ Tools: Python, R, TensorFlow, Scikit-Learn, Spark.
๐น ๐๐๐๐ถ๐ป๐ฒ๐๐ ๐๐ป๐ฎ๐น๐๐๐
๓ ฏโข๓ Focus: Bridging business needs with data insights.
๓ ฏโข๓ Skills: Communication, stakeholder management, process modeling.
๓ ฏโข๓ Tools: Microsoft Office, BI tools, business process frameworks.
๐ ๐ ๐ ๐๐ฑ๐๐ถ๐ฐ๐ฒ:
Start with what interests you the most and aligns with your current strengths. Are you business-savvy? Start as a Business Analyst. Love solving puzzles with data?
Explore Data Analyst. Want to build models and uncover deep insights? Head into Data Science.
๐ ๐ง๐ฎ๐ธ๐ฒ ๐๐ถ๐บ๐ฒ ๐๐ผ ๐๐ฒ๐น๐ณ-๐ฎ๐๐๐ฒ๐๐ ๐ฎ๐ป๐ฑ ๐ฐ๐ต๐ผ๐ผ๐๐ฒ ๐ฎ ๐ฝ๐ฎ๐๐ต ๐๐ต๐ฎ๐ ๐ฒ๐ป๐ฒ๐ฟ๐ด๐ถ๐๐ฒ๐ ๐๐ผ๐, not just one thatโs trending.
In todayโs data-driven world, career clarity can make all the difference. Whether youโre starting out in analytics, pivoting into data science, or aligning business with data as an analyst โ understanding the core responsibilities, skills, and tools of each role is crucial.
๐ Hereโs a quick breakdown from a visual I often refer to when mentoring professionals:
๐น ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐
๓ ฏโข๓ Focus: Analyzing historical data to inform decisions.
๓ ฏโข๓ Skills: SQL, basic stats, data visualization, reporting.
๓ ฏโข๓ Tools: Excel, Tableau, Power BI, SQL.
๐น ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐๐
๓ ฏโข๓ Focus: Predictive modeling, ML, complex data analysis.
๓ ฏโข๓ Skills: Programming, ML, deep learning, stats.
๓ ฏโข๓ Tools: Python, R, TensorFlow, Scikit-Learn, Spark.
๐น ๐๐๐๐ถ๐ป๐ฒ๐๐ ๐๐ป๐ฎ๐น๐๐๐
๓ ฏโข๓ Focus: Bridging business needs with data insights.
๓ ฏโข๓ Skills: Communication, stakeholder management, process modeling.
๓ ฏโข๓ Tools: Microsoft Office, BI tools, business process frameworks.
๐ ๐ ๐ ๐๐ฑ๐๐ถ๐ฐ๐ฒ:
Start with what interests you the most and aligns with your current strengths. Are you business-savvy? Start as a Business Analyst. Love solving puzzles with data?
Explore Data Analyst. Want to build models and uncover deep insights? Head into Data Science.
๐ ๐ง๐ฎ๐ธ๐ฒ ๐๐ถ๐บ๐ฒ ๐๐ผ ๐๐ฒ๐น๐ณ-๐ฎ๐๐๐ฒ๐๐ ๐ฎ๐ป๐ฑ ๐ฐ๐ต๐ผ๐ผ๐๐ฒ ๐ฎ ๐ฝ๐ฎ๐๐ต ๐๐ต๐ฎ๐ ๐ฒ๐ป๐ฒ๐ฟ๐ด๐ถ๐๐ฒ๐ ๐๐ผ๐, not just one thatโs trending.
โค2
If you are interested to learn SQL for data analytics purpose and clear the interviews, just cover the following topics
1)Install MYSQL workbench
2) Select
3) From
4) where
5) group by
6) having
7) limit
8) Joins (Left, right , inner, self, cross)
9) Aggregate function ( Sum, Max, Min , Avg)
9) windows function ( row num, rank, dense rank, lead, lag, Sum () over)
10)Case
11) Like
12) Sub queries
13) CTE
14) Replace CTE with temp tables
15) Methods to optimize Sql queries
16) Solve problems and case studies at Ankit Bansal youtube channel
Trick: Just copy each term and paste on youtube and watch any 10 to 15 minute on each topic and practise it while learning , By doing this , you get the basics understanding
17) Now time to go on youtube and search data analysis end to end project using sql
18) Watch them and practise them end to end.
17) learn integration with power bi
In this way , you will not only memorize the concepts but also learn how to implement them in your current working and projects and will be able to defend it in your interviews as well.
Like for more
Here you can find essential SQL Interview Resources๐
https://t.iss.one/DataSimplifier
Hope it helps :)
1)Install MYSQL workbench
2) Select
3) From
4) where
5) group by
6) having
7) limit
8) Joins (Left, right , inner, self, cross)
9) Aggregate function ( Sum, Max, Min , Avg)
9) windows function ( row num, rank, dense rank, lead, lag, Sum () over)
10)Case
11) Like
12) Sub queries
13) CTE
14) Replace CTE with temp tables
15) Methods to optimize Sql queries
16) Solve problems and case studies at Ankit Bansal youtube channel
Trick: Just copy each term and paste on youtube and watch any 10 to 15 minute on each topic and practise it while learning , By doing this , you get the basics understanding
17) Now time to go on youtube and search data analysis end to end project using sql
18) Watch them and practise them end to end.
17) learn integration with power bi
In this way , you will not only memorize the concepts but also learn how to implement them in your current working and projects and will be able to defend it in your interviews as well.
Like for more
Here you can find essential SQL Interview Resources๐
https://t.iss.one/DataSimplifier
Hope it helps :)
โค2
Quick SQL functions cheat sheet for beginners โ
Aggregate Functions
COUNT(*): Counts rows.
SUM(column): Total sum.
AVG(column): Average value.
MAX(column): Maximum value.
MIN(column): Minimum value.
String Functions
CONCAT(a, b, โฆ): Concatenates strings.
SUBSTRING(s, start, length): Extracts part of a string.
UPPER(s) / LOWER(s): Converts string case.
TRIM(s): Removes leading/trailing spaces.
Date & Time Functions
CURRENT_DATE / CURRENT_TIME / CURRENT_TIMESTAMP: Current date/time.
EXTRACT(unit FROM date): Retrieves a date part (e.g., year, month).
DATE_ADD(date, INTERVAL n unit): Adds an interval to a date.
Numeric Functions
ROUND(num, decimals): Rounds to a specified decimal.
CEIL(num) / FLOOR(num): Rounds up/down.
ABS(num): Absolute value.
MOD(a, b): Returns the remainder.
Control Flow Functions
CASE: Conditional logic.
COALESCE(val1, val2, โฆ): Returns the first non-null value.
Like for more free Cheatsheets โค๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
#dataanalytics
Aggregate Functions
COUNT(*): Counts rows.
SUM(column): Total sum.
AVG(column): Average value.
MAX(column): Maximum value.
MIN(column): Minimum value.
String Functions
CONCAT(a, b, โฆ): Concatenates strings.
SUBSTRING(s, start, length): Extracts part of a string.
UPPER(s) / LOWER(s): Converts string case.
TRIM(s): Removes leading/trailing spaces.
Date & Time Functions
CURRENT_DATE / CURRENT_TIME / CURRENT_TIMESTAMP: Current date/time.
EXTRACT(unit FROM date): Retrieves a date part (e.g., year, month).
DATE_ADD(date, INTERVAL n unit): Adds an interval to a date.
Numeric Functions
ROUND(num, decimals): Rounds to a specified decimal.
CEIL(num) / FLOOR(num): Rounds up/down.
ABS(num): Absolute value.
MOD(a, b): Returns the remainder.
Control Flow Functions
CASE: Conditional logic.
COALESCE(val1, val2, โฆ): Returns the first non-null value.
Like for more free Cheatsheets โค๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
#dataanalytics
โค2
Python for Data Analysis: Must-Know Libraries ๐๐
Python is one of the most powerful tools for Data Analysts, and these libraries will supercharge your data analysis workflow by helping you clean, manipulate, and visualize data efficiently.
๐ฅ Essential Python Libraries for Data Analysis:
โ Pandas โ The go-to library for data manipulation. It helps in filtering, grouping, merging datasets, handling missing values, and transforming data into a structured format.
๐ Example: Loading a CSV file and displaying the first 5 rows:
โ NumPy โ Used for handling numerical data and performing complex calculations. It provides support for multi-dimensional arrays and efficient mathematical operations.
๐ Example: Creating an array and performing basic operations:
โ Matplotlib & Seaborn โ These are used for creating visualizations like line graphs, bar charts, and scatter plots to understand trends and patterns in data.
๐ Example: Creating a basic bar chart:
โ Scikit-Learn โ A must-learn library if you want to apply machine learning techniques like regression, classification, and clustering on your dataset.
โ OpenPyXL โ Helps in automating Excel reports using Python by reading, writing, and modifying Excel files.
๐ก Challenge for You!
Try writing a Python script that:
1๏ธโฃ Reads a CSV file
2๏ธโฃ Cleans missing data
3๏ธโฃ Creates a simple visualization
React with โฅ๏ธ if you want me to post the script for above challenge! โฌ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Python is one of the most powerful tools for Data Analysts, and these libraries will supercharge your data analysis workflow by helping you clean, manipulate, and visualize data efficiently.
๐ฅ Essential Python Libraries for Data Analysis:
โ Pandas โ The go-to library for data manipulation. It helps in filtering, grouping, merging datasets, handling missing values, and transforming data into a structured format.
๐ Example: Loading a CSV file and displaying the first 5 rows:
import pandas as pd df = pd.read_csv('data.csv') print(df.head()) โ NumPy โ Used for handling numerical data and performing complex calculations. It provides support for multi-dimensional arrays and efficient mathematical operations.
๐ Example: Creating an array and performing basic operations:
import numpy as np arr = np.array([10, 20, 30]) print(arr.mean()) # Calculates the average
โ Matplotlib & Seaborn โ These are used for creating visualizations like line graphs, bar charts, and scatter plots to understand trends and patterns in data.
๐ Example: Creating a basic bar chart:
import matplotlib.pyplot as plt plt.bar(['A', 'B', 'C'], [5, 7, 3]) plt.show()
โ Scikit-Learn โ A must-learn library if you want to apply machine learning techniques like regression, classification, and clustering on your dataset.
โ OpenPyXL โ Helps in automating Excel reports using Python by reading, writing, and modifying Excel files.
๐ก Challenge for You!
Try writing a Python script that:
1๏ธโฃ Reads a CSV file
2๏ธโฃ Cleans missing data
3๏ธโฃ Creates a simple visualization
React with โฅ๏ธ if you want me to post the script for above challenge! โฌ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค2
If you want to get a job as a machine learning engineer, donโt start by diving into the hottest libraries like PyTorch,TensorFlow, Langchain, etc.
Yes, you might hear a lot about them or some other trending technology of the year...but guess what!
Technologies evolve rapidly, especially in the age of AI, but core concepts are always seen as more valuable than expertise in any particular tool. Stop trying to perform a brain surgery without knowing anything about human anatomy.
Instead, here are basic skills that will get you further than mastering any framework:
๐๐๐ญ๐ก๐๐ฆ๐๐ญ๐ข๐๐ฌ ๐๐ง๐ ๐๐ญ๐๐ญ๐ข๐ฌ๐ญ๐ข๐๐ฌ - My first exposure to probability and statistics was in college, and it felt abstract at the time, but these concepts are the backbone of ML.
You can start here: Khan Academy Statistics and Probability - https://www.khanacademy.org/math/statistics-probability
๐๐ข๐ง๐๐๐ซ ๐๐ฅ๐ ๐๐๐ซ๐ ๐๐ง๐ ๐๐๐ฅ๐๐ฎ๐ฅ๐ฎ๐ฌ - Concepts like matrices, vectors, eigenvalues, and derivatives are fundamental to understanding how ml algorithms work. These are used in everything from simple regression to deep learning.
๐๐ซ๐จ๐ ๐ซ๐๐ฆ๐ฆ๐ข๐ง๐ - Should you learn Python, Rust, R, Julia, JavaScript, etc.? The best advice is to pick the language that is most frequently used for the type of work you want to do. I started with Python due to its simplicity and extensive library support, and it remains my go-to language for machine learning tasks.
You can start here: Automate the Boring Stuff with Python - https://automatetheboringstuff.com/
๐๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ ๐๐ง๐๐๐ซ๐ฌ๐ญ๐๐ง๐๐ข๐ง๐ - Understand the fundamental algorithms before jumping to deep learning. This includes linear regression, decision trees, SVMs, and clustering algorithms.
๐๐๐ฉ๐ฅ๐จ๐ฒ๐ฆ๐๐ง๐ญ ๐๐ง๐ ๐๐ซ๐จ๐๐ฎ๐๐ญ๐ข๐จ๐ง:
Knowing how to take a model from development to production is invaluable. This includes understanding APIs, model optimization, and monitoring. Tools like Docker and Flask are often used in this process.
๐๐ฅ๐จ๐ฎ๐ ๐๐จ๐ฆ๐ฉ๐ฎ๐ญ๐ข๐ง๐ ๐๐ง๐ ๐๐ข๐ ๐๐๐ญ๐:
Familiarity with cloud platforms (AWS, Google Cloud, Azure) and big data tools (Spark) is increasingly important as datasets grow larger. These skills help you manage and process large-scale data efficiently.
You can start here: Google Cloud Machine Learning - https://cloud.google.com/learn/training/machinelearning-ai
I love frameworks and libraries, and they can make anyone's job easier.
But the more solid your foundation, the easier it will be to pick up any new technologies and actually validate whether they solve your problems.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best ๐๐
Yes, you might hear a lot about them or some other trending technology of the year...but guess what!
Technologies evolve rapidly, especially in the age of AI, but core concepts are always seen as more valuable than expertise in any particular tool. Stop trying to perform a brain surgery without knowing anything about human anatomy.
Instead, here are basic skills that will get you further than mastering any framework:
๐๐๐ญ๐ก๐๐ฆ๐๐ญ๐ข๐๐ฌ ๐๐ง๐ ๐๐ญ๐๐ญ๐ข๐ฌ๐ญ๐ข๐๐ฌ - My first exposure to probability and statistics was in college, and it felt abstract at the time, but these concepts are the backbone of ML.
You can start here: Khan Academy Statistics and Probability - https://www.khanacademy.org/math/statistics-probability
๐๐ข๐ง๐๐๐ซ ๐๐ฅ๐ ๐๐๐ซ๐ ๐๐ง๐ ๐๐๐ฅ๐๐ฎ๐ฅ๐ฎ๐ฌ - Concepts like matrices, vectors, eigenvalues, and derivatives are fundamental to understanding how ml algorithms work. These are used in everything from simple regression to deep learning.
๐๐ซ๐จ๐ ๐ซ๐๐ฆ๐ฆ๐ข๐ง๐ - Should you learn Python, Rust, R, Julia, JavaScript, etc.? The best advice is to pick the language that is most frequently used for the type of work you want to do. I started with Python due to its simplicity and extensive library support, and it remains my go-to language for machine learning tasks.
You can start here: Automate the Boring Stuff with Python - https://automatetheboringstuff.com/
๐๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ ๐๐ง๐๐๐ซ๐ฌ๐ญ๐๐ง๐๐ข๐ง๐ - Understand the fundamental algorithms before jumping to deep learning. This includes linear regression, decision trees, SVMs, and clustering algorithms.
๐๐๐ฉ๐ฅ๐จ๐ฒ๐ฆ๐๐ง๐ญ ๐๐ง๐ ๐๐ซ๐จ๐๐ฎ๐๐ญ๐ข๐จ๐ง:
Knowing how to take a model from development to production is invaluable. This includes understanding APIs, model optimization, and monitoring. Tools like Docker and Flask are often used in this process.
๐๐ฅ๐จ๐ฎ๐ ๐๐จ๐ฆ๐ฉ๐ฎ๐ญ๐ข๐ง๐ ๐๐ง๐ ๐๐ข๐ ๐๐๐ญ๐:
Familiarity with cloud platforms (AWS, Google Cloud, Azure) and big data tools (Spark) is increasingly important as datasets grow larger. These skills help you manage and process large-scale data efficiently.
You can start here: Google Cloud Machine Learning - https://cloud.google.com/learn/training/machinelearning-ai
I love frameworks and libraries, and they can make anyone's job easier.
But the more solid your foundation, the easier it will be to pick up any new technologies and actually validate whether they solve your problems.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best ๐๐
โค1
80% of people who start learning data analytics never land a job.
Not because they lack skill
but because they get stuck in "preparation mode."
I was almost one of them.
I spent months:
-Taking courses.
-Watching YouTube tutorials.
-Practicing SQL and Power BI.
But when it came time to publish a project or apply for jobs
I hesitated.
โI need to learn more first.โ
โMy portfolio isnโt ready.โ
โMaybe next month.โ
Sound familiar?
You donโt need more knowledge
you need more execution.
Data analysts who build & share projects are 3X more likely to get hired.
The best analysts arenโt the smartest.
Theyโre the ones who take action.
-They publish dashboards, even if they arenโt perfect.
-They post case studies, even when they feel like imposters.
-They apply for jobs before they "feel ready"
Stop overthinking.
Pick a dataset, build something, and share it today.
One messy project is worth more than 100 courses you never use.
Not because they lack skill
but because they get stuck in "preparation mode."
I was almost one of them.
I spent months:
-Taking courses.
-Watching YouTube tutorials.
-Practicing SQL and Power BI.
But when it came time to publish a project or apply for jobs
I hesitated.
โI need to learn more first.โ
โMy portfolio isnโt ready.โ
โMaybe next month.โ
Sound familiar?
You donโt need more knowledge
you need more execution.
Data analysts who build & share projects are 3X more likely to get hired.
The best analysts arenโt the smartest.
Theyโre the ones who take action.
-They publish dashboards, even if they arenโt perfect.
-They post case studies, even when they feel like imposters.
-They apply for jobs before they "feel ready"
Stop overthinking.
Pick a dataset, build something, and share it today.
One messy project is worth more than 100 courses you never use.
โค5๐1