Almost everyone knows that these are the tools a Data Analyst works with:
➡️ SQL
➡️ Excel
➡️ Power BI/Tableau
➡️ Python
But people getting started with analytics are confused about the preferences of picking these tools.
There are various kinds of data analytics roles available in the market :
➡️ BI + SQL: Will primarily be involved in BI development.
➡️ SQL + Excel: Will primarily work on Excel reporting.
➡️ SQL + Python: Will primarily do data analysis using python.
Now, If you are getting started with learning analytics, choose any one role that interests you the most and focus on completing the primary tools that the role requires. Learn them VERY WELL.
Learn any of the above combinations that interests you first and then start looking out for opportunities which ask for these primary tools and simultaneously start learning the basics of the 3rd tool.
You don't have to focus on being good with each and every tool but being good with any of the above combinations always works.
Join this channel to learn everything about Data Analytics 👇
https://t.iss.one/sqlspecialist
Hope this helps you 😊
➡️ SQL
➡️ Excel
➡️ Power BI/Tableau
➡️ Python
But people getting started with analytics are confused about the preferences of picking these tools.
There are various kinds of data analytics roles available in the market :
➡️ BI + SQL: Will primarily be involved in BI development.
➡️ SQL + Excel: Will primarily work on Excel reporting.
➡️ SQL + Python: Will primarily do data analysis using python.
Now, If you are getting started with learning analytics, choose any one role that interests you the most and focus on completing the primary tools that the role requires. Learn them VERY WELL.
Learn any of the above combinations that interests you first and then start looking out for opportunities which ask for these primary tools and simultaneously start learning the basics of the 3rd tool.
You don't have to focus on being good with each and every tool but being good with any of the above combinations always works.
Join this channel to learn everything about Data Analytics 👇
https://t.iss.one/sqlspecialist
Hope this helps you 😊
❤4
10 SQL Concepts Every Data Analyst Should Master 👇
✅ SELECT, WHERE, ORDER BY – Core of querying your data
✅ JOINs (INNER, LEFT, RIGHT, FULL) – Combine data from multiple tables
✅ GROUP BY & HAVING – Aggregate and filter grouped data
✅ Subqueries – Nest queries inside queries for complex logic
✅ CTEs (Common Table Expressions) – Write cleaner, reusable SQL logic
✅ Window Functions – Perform advanced analytics like rankings & running totals
✅ Indexes – Boost your query performance
✅ Normalization – Structure your database efficiently
✅ UNION vs UNION ALL – Combine result sets with or without duplicates
✅ Stored Procedures & Functions – Reusable logic inside your DB
React with ❤️ if you want me to cover each topic in detail
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
✅ SELECT, WHERE, ORDER BY – Core of querying your data
✅ JOINs (INNER, LEFT, RIGHT, FULL) – Combine data from multiple tables
✅ GROUP BY & HAVING – Aggregate and filter grouped data
✅ Subqueries – Nest queries inside queries for complex logic
✅ CTEs (Common Table Expressions) – Write cleaner, reusable SQL logic
✅ Window Functions – Perform advanced analytics like rankings & running totals
✅ Indexes – Boost your query performance
✅ Normalization – Structure your database efficiently
✅ UNION vs UNION ALL – Combine result sets with or without duplicates
✅ Stored Procedures & Functions – Reusable logic inside your DB
React with ❤️ if you want me to cover each topic in detail
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
❤4
𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱!😍
Want to communicate with AI like a pro? 🤖
Whether you’re a data analyst, AI developer, content creator, or student, this is the must-have skill of 2025✨️
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/456lMuf
Save this now & unlock your AI potential!⚡
Want to communicate with AI like a pro? 🤖
Whether you’re a data analyst, AI developer, content creator, or student, this is the must-have skill of 2025✨️
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/456lMuf
Save this now & unlock your AI potential!⚡
Data Analyst Interview Questions
[Python, SQL, PowerBI]
1. Is indentation required in python?
Ans: Indentation is necessary for Python. It specifies a block of code. All code within loops, classes, functions, etc is specified within an indented block. It is usually done using four space characters. If your code is not indented necessarily, it will not execute accurately and will throw errors as well.
2. What are Entities and Relationships?
Ans:
Entity: An entity can be a real-world object that can be easily identifiable. For example, in a college database, students, professors, workers, departments, and projects can be referred to as entities.
Relationships: Relations or links between entities that have something to do with each other. For example – The employee’s table in a company’s database can be associated with the salary table in the same database.
3. What are Aggregate and Scalar functions?
Ans: An aggregate function performs operations on a collection of values to return a single scalar value. Aggregate functions are often used with the GROUP BY and HAVING clauses of the SELECT statement. A scalar function returns a single value based on the input value.
4. What are Custom Visuals in Power BI?
Ans: Custom Visuals are like any other visualizations, generated using Power BI. The only difference is that it develops the custom visuals using a custom SDK. The languages like JQuery and JavaScript are used to create custom visuals in Power BI
ENJOY LEARNING 👍👍
[Python, SQL, PowerBI]
1. Is indentation required in python?
Ans: Indentation is necessary for Python. It specifies a block of code. All code within loops, classes, functions, etc is specified within an indented block. It is usually done using four space characters. If your code is not indented necessarily, it will not execute accurately and will throw errors as well.
2. What are Entities and Relationships?
Ans:
Entity: An entity can be a real-world object that can be easily identifiable. For example, in a college database, students, professors, workers, departments, and projects can be referred to as entities.
Relationships: Relations or links between entities that have something to do with each other. For example – The employee’s table in a company’s database can be associated with the salary table in the same database.
3. What are Aggregate and Scalar functions?
Ans: An aggregate function performs operations on a collection of values to return a single scalar value. Aggregate functions are often used with the GROUP BY and HAVING clauses of the SELECT statement. A scalar function returns a single value based on the input value.
4. What are Custom Visuals in Power BI?
Ans: Custom Visuals are like any other visualizations, generated using Power BI. The only difference is that it develops the custom visuals using a custom SDK. The languages like JQuery and JavaScript are used to create custom visuals in Power BI
ENJOY LEARNING 👍👍
❤6
Forwarded from Data Analytics
𝟱 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗚𝗶𝘁𝗛𝘂𝗯 𝗥𝗲𝗽𝗼𝘀𝗶𝘁𝗼𝗿𝗶𝗲𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲😍
Looking to Master Python for Free?✨️
These 5 GitHub repositories are all you need to level up — from beginner to advanced! 💻
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3FG7DcW
📌 Save this post & share it with a Python learner!
Looking to Master Python for Free?✨️
These 5 GitHub repositories are all you need to level up — from beginner to advanced! 💻
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3FG7DcW
📌 Save this post & share it with a Python learner!
Essential Pandas Functions for Data Analysis
Data Loading:
pd.read_csv() - Load data from a CSV file.
pd.read_excel() - Load data from an Excel file.
Data Inspection:
df.head(n) - View the first n rows.
df.info() - Get a summary of the dataset.
df.describe() - Generate summary statistics.
Data Manipulation:
df.drop(columns=['col1', 'col2']) - Remove specific columns.
df.rename(columns={'old_name': 'new_name'}) - Rename columns.
df['col'] = df['col'].apply(func) - Apply a function to a column.
Filtering and Sorting:
df[df['col'] > value] - Filter rows based on a condition.
df.sort_values(by='col', ascending=True) - Sort rows by a column.
Aggregation:
df.groupby('col').sum() - Group data and compute the sum.
df['col'].value_counts() - Count unique values in a column.
Merging and Joining:
pd.merge(df1, df2, on='key') - Merge two DataFrames.
pd.concat([df1, df2]) - Concatenate
Here you can find essential Python Interview Resources👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more resources like this 👍♥️
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Data Loading:
pd.read_csv() - Load data from a CSV file.
pd.read_excel() - Load data from an Excel file.
Data Inspection:
df.head(n) - View the first n rows.
df.info() - Get a summary of the dataset.
df.describe() - Generate summary statistics.
Data Manipulation:
df.drop(columns=['col1', 'col2']) - Remove specific columns.
df.rename(columns={'old_name': 'new_name'}) - Rename columns.
df['col'] = df['col'].apply(func) - Apply a function to a column.
Filtering and Sorting:
df[df['col'] > value] - Filter rows based on a condition.
df.sort_values(by='col', ascending=True) - Sort rows by a column.
Aggregation:
df.groupby('col').sum() - Group data and compute the sum.
df['col'].value_counts() - Count unique values in a column.
Merging and Joining:
pd.merge(df1, df2, on='key') - Merge two DataFrames.
pd.concat([df1, df2]) - Concatenate
Here you can find essential Python Interview Resources👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more resources like this 👍♥️
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
❤3👍1
Preparing for a SQL interview?
Focus on mastering these essential topics:
1. Joins: Get comfortable with inner, left, right, and outer joins.
Knowing when to use what kind of join is important!
2. Window Functions: Understand when to use
ROW_NUMBER, RANK(), DENSE_RANK(), LAG, and LEAD for complex analytical queries.
3. Query Execution Order: Know the sequence from FROM to
ORDER BY. This is crucial for writing efficient, error-free queries.
4. Common Table Expressions (CTEs): Use CTEs to simplify and structure complex queries for better readability.
5. Aggregations & Window Functions: Combine aggregate functions with window functions for in-depth data analysis.
6. Subqueries: Learn how to use subqueries effectively within main SQL statements for complex data manipulations.
7. Handling NULLs: Be adept at managing NULL values to ensure accurate data processing and avoid potential pitfalls.
8. Indexing: Understand how proper indexing can significantly boost query performance.
9. GROUP BY & HAVING: Master grouping data and filtering groups with HAVING to refine your query results.
10. String Manipulation Functions: Get familiar with string functions like CONCAT, SUBSTRING, and REPLACE to handle text data efficiently.
11. Set Operations: Know how to use UNION, INTERSECT, and EXCEPT to combine or compare result sets.
12. Optimizing Queries: Learn techniques to optimize your queries for performance, especially with large datasets.
If we master/ Practice in these topics we can track any SQL interviews..
Like this post if you need more 👍❤️
Hope it helps :)
Focus on mastering these essential topics:
1. Joins: Get comfortable with inner, left, right, and outer joins.
Knowing when to use what kind of join is important!
2. Window Functions: Understand when to use
ROW_NUMBER, RANK(), DENSE_RANK(), LAG, and LEAD for complex analytical queries.
3. Query Execution Order: Know the sequence from FROM to
ORDER BY. This is crucial for writing efficient, error-free queries.
4. Common Table Expressions (CTEs): Use CTEs to simplify and structure complex queries for better readability.
5. Aggregations & Window Functions: Combine aggregate functions with window functions for in-depth data analysis.
6. Subqueries: Learn how to use subqueries effectively within main SQL statements for complex data manipulations.
7. Handling NULLs: Be adept at managing NULL values to ensure accurate data processing and avoid potential pitfalls.
8. Indexing: Understand how proper indexing can significantly boost query performance.
9. GROUP BY & HAVING: Master grouping data and filtering groups with HAVING to refine your query results.
10. String Manipulation Functions: Get familiar with string functions like CONCAT, SUBSTRING, and REPLACE to handle text data efficiently.
11. Set Operations: Know how to use UNION, INTERSECT, and EXCEPT to combine or compare result sets.
12. Optimizing Queries: Learn techniques to optimize your queries for performance, especially with large datasets.
If we master/ Practice in these topics we can track any SQL interviews..
Like this post if you need more 👍❤️
Hope it helps :)
👍4❤2
𝟳 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗨𝗽𝗴𝗿𝗮𝗱𝗲 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱 𝗮𝗻𝗱 𝗦𝘁𝗮𝗻𝗱 𝗢𝘂𝘁😍
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𝐋𝐢𝐧𝐤👇:-
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Empower yourself and take your career to the next level! ✅
🚀 Want to Make Your Resume Stand Out in 2025?✨️
If you’re aiming to boost your chances in job interviews or want to upgrade your resume with powerful, in-demand skills — start with these 7 free online courses👨💻📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3SJ91OV
Empower yourself and take your career to the next level! ✅
Advanced Skills to Elevate Your Data Analytics Career
1️⃣ SQL Optimization & Performance Tuning
🚀 Learn indexing, query optimization, and execution plans to handle large datasets efficiently.
2️⃣ Machine Learning Basics
🤖 Understand supervised and unsupervised learning, feature engineering, and model evaluation to enhance analytical capabilities.
3️⃣ Big Data Technologies
🏗️ Explore Spark, Hadoop, and cloud platforms like AWS, Azure, or Google Cloud for large-scale data processing.
4️⃣ Data Engineering Skills
⚙️ Learn ETL pipelines, data warehousing, and workflow automation to streamline data processing.
5️⃣ Advanced Python for Analytics
🐍 Master libraries like Scikit-Learn, TensorFlow, and Statsmodels for predictive analytics and automation.
6️⃣ A/B Testing & Experimentation
🎯 Design and analyze controlled experiments to drive data-driven decision-making.
7️⃣ Dashboard Design & UX
🎨 Build interactive dashboards with Power BI, Tableau, or Looker that enhance user experience.
8️⃣ Cloud Data Analytics
☁️ Work with cloud databases like BigQuery, Snowflake, and Redshift for scalable analytics.
9️⃣ Domain Expertise
💼 Gain industry-specific knowledge (e.g., finance, healthcare, e-commerce) to provide more relevant insights.
🔟 Soft Skills & Leadership
💡 Develop stakeholder management, storytelling, and mentorship skills to advance in your career.
Hope it helps :)
#dataanalytics
1️⃣ SQL Optimization & Performance Tuning
🚀 Learn indexing, query optimization, and execution plans to handle large datasets efficiently.
2️⃣ Machine Learning Basics
🤖 Understand supervised and unsupervised learning, feature engineering, and model evaluation to enhance analytical capabilities.
3️⃣ Big Data Technologies
🏗️ Explore Spark, Hadoop, and cloud platforms like AWS, Azure, or Google Cloud for large-scale data processing.
4️⃣ Data Engineering Skills
⚙️ Learn ETL pipelines, data warehousing, and workflow automation to streamline data processing.
5️⃣ Advanced Python for Analytics
🐍 Master libraries like Scikit-Learn, TensorFlow, and Statsmodels for predictive analytics and automation.
6️⃣ A/B Testing & Experimentation
🎯 Design and analyze controlled experiments to drive data-driven decision-making.
7️⃣ Dashboard Design & UX
🎨 Build interactive dashboards with Power BI, Tableau, or Looker that enhance user experience.
8️⃣ Cloud Data Analytics
☁️ Work with cloud databases like BigQuery, Snowflake, and Redshift for scalable analytics.
9️⃣ Domain Expertise
💼 Gain industry-specific knowledge (e.g., finance, healthcare, e-commerce) to provide more relevant insights.
🔟 Soft Skills & Leadership
💡 Develop stakeholder management, storytelling, and mentorship skills to advance in your career.
Hope it helps :)
#dataanalytics
❤3
𝟰 𝗛𝗶𝗴𝗵-𝗜𝗺𝗽𝗮𝗰𝘁 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗟𝗮𝘂𝗻𝗰𝗵 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱😍
These globally recognized certifications from platforms like Google, IBM, Microsoft, and DataCamp are beginner-friendly, industry-aligned, and designed to make you job-ready in just a few weeks
𝐋𝐢𝐧𝐤👇:-
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These courses help you gain hands-on experience — exactly what top MNCs look for!✅️
These globally recognized certifications from platforms like Google, IBM, Microsoft, and DataCamp are beginner-friendly, industry-aligned, and designed to make you job-ready in just a few weeks
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4kC18XE
These courses help you gain hands-on experience — exactly what top MNCs look for!✅️
❤2
📊 Data Analyst Roadmap (2025)
Master the Skills That Top Companies Are Hiring For!
📍 1. Learn Excel / Google Sheets
Basic formulas & formatting
VLOOKUP, Pivot Tables, Charts
Data cleaning & conditional formatting
📍 2. Master SQL
SELECT, WHERE, ORDER BY
JOINs (INNER, LEFT, RIGHT)
GROUP BY, HAVING, LIMIT
Subqueries, CTEs, Window Functions
📍 3. Learn Data Visualization Tools
Power BI / Tableau (choose one)
Charts, filters, slicers
Dashboards & storytelling
📍 4. Get Comfortable with Statistics
Mean, Median, Mode, Std Dev
Probability basics
A/B Testing, Hypothesis Testing
Correlation & Regression
📍 5. Learn Python for Data Analysis (Optional but Powerful)
Pandas & NumPy for data handling
Seaborn, Matplotlib for visuals
Jupyter Notebooks for analysis
📍 6. Data Cleaning & Wrangling
Handle missing values
Fix data types, remove duplicates
Text processing & date formatting
📍 7. Understand Business Metrics
KPIs: Revenue, Churn, CAC, LTV
Think like a business analyst
Deliver actionable insights
📍 8. Communication & Storytelling
Present insights with clarity
Simplify complex data
Speak the language of stakeholders
📍 9. Version Control (Git & GitHub)
Track your projects
Build a data portfolio
Collaborate with the community
📍 10. Interview & Resume Preparation
Excel, SQL, case-based questions
Mock interviews + real projects
Resume with measurable achievements
✨ React ❤️ for more
Master the Skills That Top Companies Are Hiring For!
📍 1. Learn Excel / Google Sheets
Basic formulas & formatting
VLOOKUP, Pivot Tables, Charts
Data cleaning & conditional formatting
📍 2. Master SQL
SELECT, WHERE, ORDER BY
JOINs (INNER, LEFT, RIGHT)
GROUP BY, HAVING, LIMIT
Subqueries, CTEs, Window Functions
📍 3. Learn Data Visualization Tools
Power BI / Tableau (choose one)
Charts, filters, slicers
Dashboards & storytelling
📍 4. Get Comfortable with Statistics
Mean, Median, Mode, Std Dev
Probability basics
A/B Testing, Hypothesis Testing
Correlation & Regression
📍 5. Learn Python for Data Analysis (Optional but Powerful)
Pandas & NumPy for data handling
Seaborn, Matplotlib for visuals
Jupyter Notebooks for analysis
📍 6. Data Cleaning & Wrangling
Handle missing values
Fix data types, remove duplicates
Text processing & date formatting
📍 7. Understand Business Metrics
KPIs: Revenue, Churn, CAC, LTV
Think like a business analyst
Deliver actionable insights
📍 8. Communication & Storytelling
Present insights with clarity
Simplify complex data
Speak the language of stakeholders
📍 9. Version Control (Git & GitHub)
Track your projects
Build a data portfolio
Collaborate with the community
📍 10. Interview & Resume Preparation
Excel, SQL, case-based questions
Mock interviews + real projects
Resume with measurable achievements
✨ React ❤️ for more
❤10
𝟭𝟬𝟬𝟬+ 𝗙𝗿𝗲𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗯𝘆 𝗜𝗻𝗳𝗼𝘀𝘆𝘀 – 𝗟𝗲𝗮𝗿𝗻, 𝗚𝗿𝗼𝘄, 𝗦𝘂𝗰𝗰𝗲𝗲𝗱!😍
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𝐋𝐢𝐧𝐤👇:-
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Save this blog, sign up, and start your upskilling journey today!✅️
🚀 Looking to upgrade your skills without spending a rupee?💰
Here’s your golden opportunity to unlock 1,000+ certified online courses across technology, business, communication, leadership, soft skills, and much more — all absolutely FREE on Infosys Springboard!🔥
𝐋𝐢𝐧𝐤👇:-
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Save this blog, sign up, and start your upskilling journey today!✅️
Common Mistakes Data Analysts Must Avoid ⚠️📊
Even experienced analysts can fall into these traps. Avoid these mistakes to ensure accurate, impactful analysis!
1️⃣ Ignoring Data Cleaning 🧹
Messy data leads to misleading insights. Always check for missing values, duplicates, and inconsistencies before analysis.
2️⃣ Relying Only on Averages 📉
Averages hide variability. Always check median, percentiles, and distributions for a complete picture.
3️⃣ Confusing Correlation with Causation 🔗
Just because two things move together doesn’t mean one causes the other. Validate assumptions before making decisions.
4️⃣ Overcomplicating Visualizations 🎨
Too many colors, labels, or complex charts confuse your audience. Keep it simple, clear, and focused on key takeaways.
5️⃣ Not Understanding Business Context 🎯
Data without context is meaningless. Always ask: "What problem are we solving?" before diving into numbers.
6️⃣ Ignoring Outliers Without Investigation 🔍
Outliers can signal errors or valuable insights. Always analyze why they exist before deciding to remove them.
7️⃣ Using Small Sample Sizes ⚠️
Drawing conclusions from too little data leads to unreliable insights. Ensure your sample size is statistically significant.
8️⃣ Failing to Communicate Insights Clearly 🗣️
Great analysis means nothing if stakeholders don’t understand it. Tell a story with data—don’t just dump numbers.
9️⃣ Not Keeping Up with Industry Trends 🚀
Data tools and techniques evolve fast. Keep learning SQL, Python, Power BI, Tableau, and machine learning basics.
Avoid these mistakes, and you’ll stand out as a reliable data analyst!
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Even experienced analysts can fall into these traps. Avoid these mistakes to ensure accurate, impactful analysis!
1️⃣ Ignoring Data Cleaning 🧹
Messy data leads to misleading insights. Always check for missing values, duplicates, and inconsistencies before analysis.
2️⃣ Relying Only on Averages 📉
Averages hide variability. Always check median, percentiles, and distributions for a complete picture.
3️⃣ Confusing Correlation with Causation 🔗
Just because two things move together doesn’t mean one causes the other. Validate assumptions before making decisions.
4️⃣ Overcomplicating Visualizations 🎨
Too many colors, labels, or complex charts confuse your audience. Keep it simple, clear, and focused on key takeaways.
5️⃣ Not Understanding Business Context 🎯
Data without context is meaningless. Always ask: "What problem are we solving?" before diving into numbers.
6️⃣ Ignoring Outliers Without Investigation 🔍
Outliers can signal errors or valuable insights. Always analyze why they exist before deciding to remove them.
7️⃣ Using Small Sample Sizes ⚠️
Drawing conclusions from too little data leads to unreliable insights. Ensure your sample size is statistically significant.
8️⃣ Failing to Communicate Insights Clearly 🗣️
Great analysis means nothing if stakeholders don’t understand it. Tell a story with data—don’t just dump numbers.
9️⃣ Not Keeping Up with Industry Trends 🚀
Data tools and techniques evolve fast. Keep learning SQL, Python, Power BI, Tableau, and machine learning basics.
Avoid these mistakes, and you’ll stand out as a reliable data analyst!
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
❤6
𝗙𝗿𝗲𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲: 𝗧𝗵𝗲 𝗕𝗲𝘀𝘁 𝗦𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝗣𝗼𝗶𝗻𝘁 𝗳𝗼𝗿 𝗧𝗲𝗰𝗵 & 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀😍
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Python is the #1 most in-demand programming language, and Scaler’s free Python for Beginners course is a game-changer for absolute beginners📊✔️
𝐋𝐢𝐧𝐤👇:-
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No coding background needed!✅️
🚀 Want to break into tech or data analytics but don’t know how to start?📌✨️
Python is the #1 most in-demand programming language, and Scaler’s free Python for Beginners course is a game-changer for absolute beginners📊✔️
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/45TroYX
No coding background needed!✅️