Reality check on Data Analytics jobs:
⟶ Most recruiters & employers are open to different backgrounds
⟶ The "essential skills" are usually a mix of hard and soft skills
Desired hard skills:
⟶ Excel - every job needs it
⟶ SQL - data retrieval and manipulation
⟶ Data Visualization - Tableau, Power BI, or Excel (Advanced)
⟶ Python - Basics, Numpy, Pandas, Matplotlib, Seaborn, Scikit-learn, etc
Desired soft skills:
⟶ Communication
⟶ Teamwork & Collaboration
⟶ Problem Solver
⟶ Critical Thinking
If you're lacking in some of the hard skills, start learning them through online courses or engaging in personal projects.
But don't forget to highlight your soft skills in your job application - they're equally important.
In short: Excel + SQL + Data Viz + Python + Communication + Teamwork + Problem Solver + Critical Thinking = Data Analytics
⟶ Most recruiters & employers are open to different backgrounds
⟶ The "essential skills" are usually a mix of hard and soft skills
Desired hard skills:
⟶ Excel - every job needs it
⟶ SQL - data retrieval and manipulation
⟶ Data Visualization - Tableau, Power BI, or Excel (Advanced)
⟶ Python - Basics, Numpy, Pandas, Matplotlib, Seaborn, Scikit-learn, etc
Desired soft skills:
⟶ Communication
⟶ Teamwork & Collaboration
⟶ Problem Solver
⟶ Critical Thinking
If you're lacking in some of the hard skills, start learning them through online courses or engaging in personal projects.
But don't forget to highlight your soft skills in your job application - they're equally important.
In short: Excel + SQL + Data Viz + Python + Communication + Teamwork + Problem Solver + Critical Thinking = Data Analytics
❤3
5 Essential Skills Every Data Analyst Must Master in 2025
Data analytics continues to evolve rapidly, and as a data analyst, it's crucial to stay ahead of the curve. In 2025, the skills that were once optional are now essential to stand out in this competitive field. Here are five must-have skills for every data analyst this year.
1. Data Wrangling & Cleaning:
The ability to clean, organize, and prepare data for analysis is critical. No matter how sophisticated your tools are, they can't work with messy, inconsistent data. Mastering data wrangling—removing duplicates, handling missing values, and standardizing formats—will help you deliver accurate and actionable insights.
Tools to master: Python (Pandas), R, SQL
2. Advanced Excel Skills:
Excel remains one of the most widely used tools in the data analysis world. Beyond the basics, you should master advanced formulas, pivot tables, and Power Query. Excel continues to be indispensable for quick analyses and prototype dashboards.
Key skills to learn: VLOOKUP, INDEX/MATCH, Power Pivot, advanced charting
3. Data Visualization:
The ability to convey your findings through compelling data visuals is what sets top analysts apart. Learn how to use tools like Tableau, Power BI, or even D3.js for web-based visualization. Your visuals should tell a story that’s easy for stakeholders to understand at a glance.
Focus areas: Interactive dashboards, storytelling with data, advanced chart types (heat maps, scatter plots)
4. Statistical Analysis & Hypothesis Testing:
Understanding statistics is fundamental for any data analyst. Master concepts like regression analysis, probability theory, and hypothesis testing. This skill will help you not only describe trends but also make data-driven predictions and assess the significance of your findings.
Skills to focus on: T-tests, ANOVA, correlation, regression models
5. Machine Learning Basics:
While you don’t need to be a data scientist, having a basic understanding of machine learning algorithms is increasingly important. Knowledge of supervised vs unsupervised learning, decision trees, and clustering techniques will allow you to push your analysis to the next level.
Begin with: Linear regression, K-means clustering, decision trees (using Python libraries like Scikit-learn)
In 2025, data analysts must embrace a multi-faceted skill set that combines technical expertise, statistical knowledge, and the ability to communicate findings effectively.
Keep learning and adapting to these emerging trends to ensure you're ready for the challenges of tomorrow.
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more content like this 👍♥️
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Data analytics continues to evolve rapidly, and as a data analyst, it's crucial to stay ahead of the curve. In 2025, the skills that were once optional are now essential to stand out in this competitive field. Here are five must-have skills for every data analyst this year.
1. Data Wrangling & Cleaning:
The ability to clean, organize, and prepare data for analysis is critical. No matter how sophisticated your tools are, they can't work with messy, inconsistent data. Mastering data wrangling—removing duplicates, handling missing values, and standardizing formats—will help you deliver accurate and actionable insights.
Tools to master: Python (Pandas), R, SQL
2. Advanced Excel Skills:
Excel remains one of the most widely used tools in the data analysis world. Beyond the basics, you should master advanced formulas, pivot tables, and Power Query. Excel continues to be indispensable for quick analyses and prototype dashboards.
Key skills to learn: VLOOKUP, INDEX/MATCH, Power Pivot, advanced charting
3. Data Visualization:
The ability to convey your findings through compelling data visuals is what sets top analysts apart. Learn how to use tools like Tableau, Power BI, or even D3.js for web-based visualization. Your visuals should tell a story that’s easy for stakeholders to understand at a glance.
Focus areas: Interactive dashboards, storytelling with data, advanced chart types (heat maps, scatter plots)
4. Statistical Analysis & Hypothesis Testing:
Understanding statistics is fundamental for any data analyst. Master concepts like regression analysis, probability theory, and hypothesis testing. This skill will help you not only describe trends but also make data-driven predictions and assess the significance of your findings.
Skills to focus on: T-tests, ANOVA, correlation, regression models
5. Machine Learning Basics:
While you don’t need to be a data scientist, having a basic understanding of machine learning algorithms is increasingly important. Knowledge of supervised vs unsupervised learning, decision trees, and clustering techniques will allow you to push your analysis to the next level.
Begin with: Linear regression, K-means clustering, decision trees (using Python libraries like Scikit-learn)
In 2025, data analysts must embrace a multi-faceted skill set that combines technical expertise, statistical knowledge, and the ability to communicate findings effectively.
Keep learning and adapting to these emerging trends to ensure you're ready for the challenges of tomorrow.
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more content like this 👍♥️
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
❤6
✅ Basic SQL Commands Cheat Sheet 🗃️
⦁ SELECT — Select data from database
⦁ FROM — Specify table
⦁ WHERE — Filter query by condition
⦁ AS — Rename column or table (alias)
⦁ JOIN — Combine rows from 2+ tables
⦁ AND — Combine conditions (all must match)
⦁ OR — Combine conditions (any can match)
⦁ LIMIT — Limit number of rows returned
⦁ IN — Specify multiple values in WHERE
⦁ CASE — Conditional expressions in queries
⦁ IS NULL — Select rows with NULL values
⦁ LIKE — Search patterns in columns
⦁ COMMIT — Write transaction to DB
⦁ ROLLBACK — Undo transaction block
⦁ ALTER TABLE — Add/remove columns
⦁ UPDATE — Update data in table
⦁ CREATE — Create table, DB, indexes, views
⦁ DELETE — Delete rows from table
⦁ INSERT — Add single row to table
⦁ DROP — Delete table, DB, or index
⦁ GROUP BY — Group data into logical sets
⦁ ORDER BY — Sort result (use DESC for reverse)
⦁ HAVING — Filter groups like WHERE but for grouped data
⦁ COUNT — Count number of rows
⦁ SUM — Sum values in a column
⦁ AVG — Average value in a column
⦁ MIN — Minimum value in column
⦁ MAX — Maximum value in column
💬 Tap ❤️ for more!
⦁ SELECT — Select data from database
⦁ FROM — Specify table
⦁ WHERE — Filter query by condition
⦁ AS — Rename column or table (alias)
⦁ JOIN — Combine rows from 2+ tables
⦁ AND — Combine conditions (all must match)
⦁ OR — Combine conditions (any can match)
⦁ LIMIT — Limit number of rows returned
⦁ IN — Specify multiple values in WHERE
⦁ CASE — Conditional expressions in queries
⦁ IS NULL — Select rows with NULL values
⦁ LIKE — Search patterns in columns
⦁ COMMIT — Write transaction to DB
⦁ ROLLBACK — Undo transaction block
⦁ ALTER TABLE — Add/remove columns
⦁ UPDATE — Update data in table
⦁ CREATE — Create table, DB, indexes, views
⦁ DELETE — Delete rows from table
⦁ INSERT — Add single row to table
⦁ DROP — Delete table, DB, or index
⦁ GROUP BY — Group data into logical sets
⦁ ORDER BY — Sort result (use DESC for reverse)
⦁ HAVING — Filter groups like WHERE but for grouped data
⦁ COUNT — Count number of rows
⦁ SUM — Sum values in a column
⦁ AVG — Average value in a column
⦁ MIN — Minimum value in column
⦁ MAX — Maximum value in column
💬 Tap ❤️ for more!
❤9
Data Analyst Roadmap
Like if it helps ❤️
Like if it helps ❤️
❤3
Interview guide for Data Analyst Role
When interviewing for a Data Analyst role as a fresher, you’ll likely encounter questions that focus on your understanding of data analysis concepts, technical skills, and problem-solving abilities. Here’s a comprehensive list of commonly asked interview questions:
1. General and Behavioral Questions
• Tell me about yourself.
• Why do you want to become a Data Analyst?
• What do you know about our company and why do you want to work here?
• Describe a time when you solved a problem using data.
• How do you prioritize tasks and manage deadlines?
• Tell me about a time when you worked in a team to complete a project.
2. Technical Questions
• What are the different types of joins in SQL? (Expect variations of SQL questions)
• How would you handle missing or inconsistent data?
• What is normalization? Why is it important?
• Explain the difference between primary keys and foreign keys in a database.
• What are the most common data types in SQL?
• How do you perform data cleaning in Excel?
3. Analytical Skills and Problem-Solving
• How would you find outliers in a dataset?
• How would you approach analyzing a dataset with 1 million rows?
• If given two datasets, how would you combine them?
• What steps would you take if your results didn’t match stakeholders’ expectations?
• How would you identify trends or patterns in a dataset?
4. Excel-Related Questions
• What are pivot tables and how do you use them?
• Explain VLOOKUP and HLOOKUP.
• How would you handle large datasets in Excel?
• What is the use of conditional formatting?
• How would you create a dashboard in Excel?
• How can you create a custom formula in Excel?
5. SQL Questions
• Write a SQL query to find the second highest salary in a table.
• What is the difference between WHERE and HAVING clauses?
• How would you optimize a slow-running query?
• What is the difference between UNION and UNION ALL?
• What is a subquery, and when would you use it?
6. Statistics and Data Analysis
• Explain the difference between mean, median, and mode.
• What is standard deviation, and why is it important?
• What is regression analysis? Can you explain linear regression?
• What is correlation, and how is it different from causation?
• What are some key metrics you would track for a marketing campaign?
7. Data Visualization and Tools
• What tools have you used for data visualization?
• Explain a situation where you used charts to tell a story.
• What is your experience with tools like Tableau or Power BI?
• How would you decide which chart type to use for visualizing data?
• Have you ever created a dashboard? If yes, what were the key features?
8. Python/R (If mentioned on your resume)
• What libraries do you use in Python for data analysis?
• How would you import a dataset and perform basic analysis in Python?
• What are some common data manipulation functions in pandas?
• How do you handle missing values in Python?
9. Scenario-Based Questions
• Imagine you are given a dataset of customer purchases; how would you segment the customers?
• You are given sales data for the past five years. What steps would you take to forecast the next year’s sales?
• If you find conflicting data in a report, how would you handle the situation?
• Describe a project where you identified key insights using data.
10. Aptitude or Logical Questions
• Some companies also include questions testing your quantitative aptitude, logical reasoning, and pattern recognition to gauge problem-solving skills.
Tips to Prepare:
1. Strengthen your Basics: Brush up on SQL, Excel, and statistical concepts.
2. Mock Interviews: Practice explaining your thought process for data problems.
3. Projects: Be ready to discuss any projects or internships you’ve done.
4. Stay Current: Read about trends in data analysis and business intelligence.
Hope this helps you 😊
When interviewing for a Data Analyst role as a fresher, you’ll likely encounter questions that focus on your understanding of data analysis concepts, technical skills, and problem-solving abilities. Here’s a comprehensive list of commonly asked interview questions:
1. General and Behavioral Questions
• Tell me about yourself.
• Why do you want to become a Data Analyst?
• What do you know about our company and why do you want to work here?
• Describe a time when you solved a problem using data.
• How do you prioritize tasks and manage deadlines?
• Tell me about a time when you worked in a team to complete a project.
2. Technical Questions
• What are the different types of joins in SQL? (Expect variations of SQL questions)
• How would you handle missing or inconsistent data?
• What is normalization? Why is it important?
• Explain the difference between primary keys and foreign keys in a database.
• What are the most common data types in SQL?
• How do you perform data cleaning in Excel?
3. Analytical Skills and Problem-Solving
• How would you find outliers in a dataset?
• How would you approach analyzing a dataset with 1 million rows?
• If given two datasets, how would you combine them?
• What steps would you take if your results didn’t match stakeholders’ expectations?
• How would you identify trends or patterns in a dataset?
4. Excel-Related Questions
• What are pivot tables and how do you use them?
• Explain VLOOKUP and HLOOKUP.
• How would you handle large datasets in Excel?
• What is the use of conditional formatting?
• How would you create a dashboard in Excel?
• How can you create a custom formula in Excel?
5. SQL Questions
• Write a SQL query to find the second highest salary in a table.
• What is the difference between WHERE and HAVING clauses?
• How would you optimize a slow-running query?
• What is the difference between UNION and UNION ALL?
• What is a subquery, and when would you use it?
6. Statistics and Data Analysis
• Explain the difference between mean, median, and mode.
• What is standard deviation, and why is it important?
• What is regression analysis? Can you explain linear regression?
• What is correlation, and how is it different from causation?
• What are some key metrics you would track for a marketing campaign?
7. Data Visualization and Tools
• What tools have you used for data visualization?
• Explain a situation where you used charts to tell a story.
• What is your experience with tools like Tableau or Power BI?
• How would you decide which chart type to use for visualizing data?
• Have you ever created a dashboard? If yes, what were the key features?
8. Python/R (If mentioned on your resume)
• What libraries do you use in Python for data analysis?
• How would you import a dataset and perform basic analysis in Python?
• What are some common data manipulation functions in pandas?
• How do you handle missing values in Python?
9. Scenario-Based Questions
• Imagine you are given a dataset of customer purchases; how would you segment the customers?
• You are given sales data for the past five years. What steps would you take to forecast the next year’s sales?
• If you find conflicting data in a report, how would you handle the situation?
• Describe a project where you identified key insights using data.
10. Aptitude or Logical Questions
• Some companies also include questions testing your quantitative aptitude, logical reasoning, and pattern recognition to gauge problem-solving skills.
Tips to Prepare:
1. Strengthen your Basics: Brush up on SQL, Excel, and statistical concepts.
2. Mock Interviews: Practice explaining your thought process for data problems.
3. Projects: Be ready to discuss any projects or internships you’ve done.
4. Stay Current: Read about trends in data analysis and business intelligence.
Hope this helps you 😊
❤6
You're STILL a data analyst even if...
- you only use Excel
- you forgot the SQL syntax
- you bombed the big interview
- you don't know how to program
- you did an analysis completely wrong
- you can't remember the right function name
- you have to Google how to do something easy you've done before
You're NOT a data analyst when...
- you give up
SO DON'T GIVE UP! KEEP GOING!
- you only use Excel
- you forgot the SQL syntax
- you bombed the big interview
- you don't know how to program
- you did an analysis completely wrong
- you can't remember the right function name
- you have to Google how to do something easy you've done before
You're NOT a data analyst when...
- you give up
SO DON'T GIVE UP! KEEP GOING!
❤8🔥2
✅ Data Analytics A–Z 📊🚀
🅰️ A – Analytics
Understanding, interpreting, and presenting data-driven insights.
🅱️ B – BI Tools (Power BI, Tableau)
For dashboards and data visualization.
©️ C – Cleaning Data
Remove nulls, duplicates, fix types, handle outliers.
🅳 D – Data Wrangling
Transform raw data into a usable format.
🅴 E – EDA (Exploratory Data Analysis)
Analyze distributions, trends, and patterns.
🅵 F – Feature Engineering
Create new variables from existing data to enhance analysis or modeling.
🅶 G – Graphs & Charts
Visuals like histograms, scatter plots, bar charts to make sense of data.
🅷 H – Hypothesis Testing
A/B testing, t-tests, chi-square for validating assumptions.
🅸 I – Insights
Meaningful takeaways that influence decisions.
🅹 J – Joins
Combine data from multiple tables (SQL/Pandas).
🅺 K – KPIs
Key metrics tracked over time to evaluate success.
🅻 L – Linear Regression
A basic predictive model used frequently in analytics.
🅼 M – Metrics
Quantifiable measures of performance.
🅽 N – Normalization
Scale features for consistency or comparison.
🅾️ O – Outlier Detection
Spot and handle anomalies that can skew results.
🅿️ P – Python
Go-to programming language for data manipulation and analysis.
🆀 Q – Queries (SQL)
Use SQL to retrieve and analyze structured data.
🆁 R – Reports
Present insights via dashboards, PPTs, or tools.
🆂 S – SQL
Fundamental querying language for relational databases.
🆃 T – Tableau
Popular BI tool for data visualization.
🆄 U – Univariate Analysis
Analyzing a single variable's distribution or properties.
🆅 V – Visualization
Transform data into understandable visuals.
🆆 W – Web Scraping
Extract public data from websites using tools like BeautifulSoup.
🆇 X – XGBoost (Advanced)
A powerful algorithm used in machine learning-based analytics.
🆈 Y – Year-over-Year (YoY)
Common time-based metric comparison.
🆉 Z – Zero-based Analysis
Analyzing from a baseline or zero point to measure true change.
💬 Tap ❤️ for more!
🅰️ A – Analytics
Understanding, interpreting, and presenting data-driven insights.
🅱️ B – BI Tools (Power BI, Tableau)
For dashboards and data visualization.
©️ C – Cleaning Data
Remove nulls, duplicates, fix types, handle outliers.
🅳 D – Data Wrangling
Transform raw data into a usable format.
🅴 E – EDA (Exploratory Data Analysis)
Analyze distributions, trends, and patterns.
🅵 F – Feature Engineering
Create new variables from existing data to enhance analysis or modeling.
🅶 G – Graphs & Charts
Visuals like histograms, scatter plots, bar charts to make sense of data.
🅷 H – Hypothesis Testing
A/B testing, t-tests, chi-square for validating assumptions.
🅸 I – Insights
Meaningful takeaways that influence decisions.
🅹 J – Joins
Combine data from multiple tables (SQL/Pandas).
🅺 K – KPIs
Key metrics tracked over time to evaluate success.
🅻 L – Linear Regression
A basic predictive model used frequently in analytics.
🅼 M – Metrics
Quantifiable measures of performance.
🅽 N – Normalization
Scale features for consistency or comparison.
🅾️ O – Outlier Detection
Spot and handle anomalies that can skew results.
🅿️ P – Python
Go-to programming language for data manipulation and analysis.
🆀 Q – Queries (SQL)
Use SQL to retrieve and analyze structured data.
🆁 R – Reports
Present insights via dashboards, PPTs, or tools.
🆂 S – SQL
Fundamental querying language for relational databases.
🆃 T – Tableau
Popular BI tool for data visualization.
🆄 U – Univariate Analysis
Analyzing a single variable's distribution or properties.
🆅 V – Visualization
Transform data into understandable visuals.
🆆 W – Web Scraping
Extract public data from websites using tools like BeautifulSoup.
🆇 X – XGBoost (Advanced)
A powerful algorithm used in machine learning-based analytics.
🆈 Y – Year-over-Year (YoY)
Common time-based metric comparison.
🆉 Z – Zero-based Analysis
Analyzing from a baseline or zero point to measure true change.
💬 Tap ❤️ for more!
❤11
Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources
KPMG Data Analyst Interview Questions 🚀.pdf
🚀 KPMG Data Analyst Interview Questions You MUST Practice! 📊🔥
Prepare smart, not hard – these are the exact questions that give you an edge in cracking Big4 interviews. 💼✨
Prepare smart, not hard – these are the exact questions that give you an edge in cracking Big4 interviews. 💼✨
Data Analytics Roadmap
|
|-- Fundamentals
| |-- Mathematics
| | |-- Descriptive Statistics
| | |-- Inferential Statistics
| | |-- Probability Theory
| |
| |-- Programming
| | |-- Python (Focus on Libraries like Pandas, NumPy)
| | |-- R (For Statistical Analysis)
| | |-- SQL (For Data Extraction)
|
|-- Data Collection and Storage
| |-- Data Sources
| | |-- APIs
| | |-- Web Scraping
| | |-- Databases
| |
| |-- Data Storage
| | |-- Relational Databases (MySQL, PostgreSQL)
| | |-- NoSQL Databases (MongoDB, Cassandra)
| | |-- Data Lakes and Warehousing (Snowflake, Redshift)
|
|-- Data Cleaning and Preparation
| |-- Handling Missing Data
| |-- Data Transformation
| |-- Data Normalization and Standardization
| |-- Outlier Detection
|
|-- Exploratory Data Analysis (EDA)
| |-- Data Visualization Tools
| | |-- Matplotlib
| | |-- Seaborn
| | |-- ggplot2
| |
| |-- Identifying Trends and Patterns
| |-- Correlation Analysis
|
|-- Advanced Analytics
| |-- Predictive Analytics (Regression, Forecasting)
| |-- Prescriptive Analytics (Optimization Models)
| |-- Segmentation (Clustering Techniques)
| |-- Sentiment Analysis (Text Data)
|
|-- Data Visualization and Reporting
| |-- Visualization Tools
| | |-- Power BI
| | |-- Tableau
| | |-- Google Data Studio
| |
| |-- Dashboard Design
| |-- Interactive Visualizations
| |-- Storytelling with Data
|
|-- Business Intelligence (BI)
| |-- KPI Design and Implementation
| |-- Decision-Making Frameworks
| |-- Industry-Specific Use Cases (Finance, Marketing, HR)
|
|-- Big Data Analytics
| |-- Tools and Frameworks
| | |-- Hadoop
| | |-- Apache Spark
| |
| |-- Real-Time Data Processing
| |-- Stream Analytics (Kafka, Flink)
|
|-- Domain Knowledge
| |-- Industry Applications
| | |-- E-commerce
| | |-- Healthcare
| | |-- Supply Chain
|
|-- Ethical Data Usage
| |-- Data Privacy Regulations (GDPR, CCPA)
| |-- Bias Mitigation in Analysis
| |-- Transparency in Reporting
Free Resources to learn Data Analytics skills👇👇
1. SQL
https://mode.com/sql-tutorial/introduction-to-sql
https://t.iss.one/sqlspecialist/738
2. Python
https://www.learnpython.org/
https://t.iss.one/pythondevelopersindia/873
https://bit.ly/3T7y4ta
https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
3. R
https://datacamp.pxf.io/vPyB4L
4. Data Structures
https://leetcode.com/study-plan/data-structure/
https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513
5. Data Visualization
https://www.freecodecamp.org/learn/data-visualization/
https://t.iss.one/Data_Visual/2
https://www.tableau.com/learn/training/20223
https://www.workout-wednesday.com/power-bi-challenges/
6. Excel
https://excel-practice-online.com/
https://t.iss.one/excel_data
https://www.w3schools.com/EXCEL/index.php
Join @free4unow_backup for more free courses
Like for more ❤️
ENJOY LEARNING 👍👍
|
|-- Fundamentals
| |-- Mathematics
| | |-- Descriptive Statistics
| | |-- Inferential Statistics
| | |-- Probability Theory
| |
| |-- Programming
| | |-- Python (Focus on Libraries like Pandas, NumPy)
| | |-- R (For Statistical Analysis)
| | |-- SQL (For Data Extraction)
|
|-- Data Collection and Storage
| |-- Data Sources
| | |-- APIs
| | |-- Web Scraping
| | |-- Databases
| |
| |-- Data Storage
| | |-- Relational Databases (MySQL, PostgreSQL)
| | |-- NoSQL Databases (MongoDB, Cassandra)
| | |-- Data Lakes and Warehousing (Snowflake, Redshift)
|
|-- Data Cleaning and Preparation
| |-- Handling Missing Data
| |-- Data Transformation
| |-- Data Normalization and Standardization
| |-- Outlier Detection
|
|-- Exploratory Data Analysis (EDA)
| |-- Data Visualization Tools
| | |-- Matplotlib
| | |-- Seaborn
| | |-- ggplot2
| |
| |-- Identifying Trends and Patterns
| |-- Correlation Analysis
|
|-- Advanced Analytics
| |-- Predictive Analytics (Regression, Forecasting)
| |-- Prescriptive Analytics (Optimization Models)
| |-- Segmentation (Clustering Techniques)
| |-- Sentiment Analysis (Text Data)
|
|-- Data Visualization and Reporting
| |-- Visualization Tools
| | |-- Power BI
| | |-- Tableau
| | |-- Google Data Studio
| |
| |-- Dashboard Design
| |-- Interactive Visualizations
| |-- Storytelling with Data
|
|-- Business Intelligence (BI)
| |-- KPI Design and Implementation
| |-- Decision-Making Frameworks
| |-- Industry-Specific Use Cases (Finance, Marketing, HR)
|
|-- Big Data Analytics
| |-- Tools and Frameworks
| | |-- Hadoop
| | |-- Apache Spark
| |
| |-- Real-Time Data Processing
| |-- Stream Analytics (Kafka, Flink)
|
|-- Domain Knowledge
| |-- Industry Applications
| | |-- E-commerce
| | |-- Healthcare
| | |-- Supply Chain
|
|-- Ethical Data Usage
| |-- Data Privacy Regulations (GDPR, CCPA)
| |-- Bias Mitigation in Analysis
| |-- Transparency in Reporting
Free Resources to learn Data Analytics skills👇👇
1. SQL
https://mode.com/sql-tutorial/introduction-to-sql
https://t.iss.one/sqlspecialist/738
2. Python
https://www.learnpython.org/
https://t.iss.one/pythondevelopersindia/873
https://bit.ly/3T7y4ta
https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
3. R
https://datacamp.pxf.io/vPyB4L
4. Data Structures
https://leetcode.com/study-plan/data-structure/
https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513
5. Data Visualization
https://www.freecodecamp.org/learn/data-visualization/
https://t.iss.one/Data_Visual/2
https://www.tableau.com/learn/training/20223
https://www.workout-wednesday.com/power-bi-challenges/
6. Excel
https://excel-practice-online.com/
https://t.iss.one/excel_data
https://www.w3schools.com/EXCEL/index.php
Join @free4unow_backup for more free courses
Like for more ❤️
ENJOY LEARNING 👍👍
❤6
Top 8 Excel interview questions data analysts 👇👇
1. Advanced Formulas:
- Can you explain the difference between VLOOKUP and INDEX-MATCH functions? When would you prefer one over the other?
- How would you use the SUMIFS function to analyze data with multiple criteria?
2. Data Cleaning and Manipulation:
- Describe a scenario where you had to clean and transform messy data in Excel. What techniques did you use?
- How do you remove duplicates from a dataset, and what considerations should be taken into account?
3. Pivot Tables:
- Explain the purpose of a pivot table. Provide an example of when you used a pivot table to derive meaningful insights.
- What are slicers in a pivot table, and how can they be beneficial in data analysis?
4. Data Visualization:
- Share your approach to creating effective charts and graphs in Excel to communicate data trends.
- How would you use conditional formatting to highlight key information in a dataset?
5. Statistical Analysis:
- Discuss a situation where you applied statistical analysis in Excel to draw conclusions from a dataset.
- Explain the steps you would take to perform regression analysis in Excel.
6. Macros and Automation:
- Have you ever used Excel macros to automate a repetitive task? If so, provide an example.
- What are the potential risks and benefits of using macros in a data analysis workflow?
7. Data Validation:
- How do you implement data validation in Excel, and why is it important in data analysis?
- Can you give an example of when you used Excel's data validation to improve data accuracy?
8. Data Linking and External Data Sources:
- Describe a situation where you had to link data from multiple Excel workbooks. How did you approach this task?
- How would you import data from an external database into Excel for analysis?
ENJOY LEARNING 👍👍
1. Advanced Formulas:
- Can you explain the difference between VLOOKUP and INDEX-MATCH functions? When would you prefer one over the other?
- How would you use the SUMIFS function to analyze data with multiple criteria?
2. Data Cleaning and Manipulation:
- Describe a scenario where you had to clean and transform messy data in Excel. What techniques did you use?
- How do you remove duplicates from a dataset, and what considerations should be taken into account?
3. Pivot Tables:
- Explain the purpose of a pivot table. Provide an example of when you used a pivot table to derive meaningful insights.
- What are slicers in a pivot table, and how can they be beneficial in data analysis?
4. Data Visualization:
- Share your approach to creating effective charts and graphs in Excel to communicate data trends.
- How would you use conditional formatting to highlight key information in a dataset?
5. Statistical Analysis:
- Discuss a situation where you applied statistical analysis in Excel to draw conclusions from a dataset.
- Explain the steps you would take to perform regression analysis in Excel.
6. Macros and Automation:
- Have you ever used Excel macros to automate a repetitive task? If so, provide an example.
- What are the potential risks and benefits of using macros in a data analysis workflow?
7. Data Validation:
- How do you implement data validation in Excel, and why is it important in data analysis?
- Can you give an example of when you used Excel's data validation to improve data accuracy?
8. Data Linking and External Data Sources:
- Describe a situation where you had to link data from multiple Excel workbooks. How did you approach this task?
- How would you import data from an external database into Excel for analysis?
ENJOY LEARNING 👍👍
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📊Here's a breakdown of SQL interview questions covering various topics:
🔺Basic SQL Concepts:
-Differentiate between SQL and NoSQL databases.
-List common data types in SQL.
🔺Querying:
-Retrieve all records from a table named "Customers."
-Contrast SELECT and SELECT DISTINCT.
-Explain the purpose of the WHERE clause.
🔺Joins:
-Describe types of joins (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN).
-Retrieve data from two tables using INNER JOIN.
🔺Aggregate Functions:
-Define aggregate functions and name a few.
-Calculate average, sum, and count of a column in SQL.
🔺Grouping and Filtering:
-Explain the GROUP BY clause and its use.
-Filter SQL query results using the HAVING clause.
🔺Subqueries:
-Define a subquery and provide an example.
🔺Indexes and Optimization:
-Discuss the importance of indexes in a database.
&Optimize a slow-running SQL query.
🔺Normalization and Data Integrity:
-Define database normalization and its significance.
-Enforce data integrity in a SQL database.
🔺Transactions:
-Define a SQL transaction and its purpose.
-Explain ACID properties in database transactions.
🔺Views and Stored Procedures:
-Define a database view and its use.
-Distinguish a stored procedure from a regular SQL query.
🔺Advanced SQL:
-Write a recursive SQL query and explain its use.
-Explain window functions in SQL.
✅👀These questions offer a comprehensive assessment of SQL knowledge, ranging from basics to advanced concepts.
❤️Like if you'd like answers in the next post! 👍
👉Be the first one to know the latest Job openings 👇
https://t.iss.one/jobs_SQL
🔺Basic SQL Concepts:
-Differentiate between SQL and NoSQL databases.
-List common data types in SQL.
🔺Querying:
-Retrieve all records from a table named "Customers."
-Contrast SELECT and SELECT DISTINCT.
-Explain the purpose of the WHERE clause.
🔺Joins:
-Describe types of joins (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN).
-Retrieve data from two tables using INNER JOIN.
🔺Aggregate Functions:
-Define aggregate functions and name a few.
-Calculate average, sum, and count of a column in SQL.
🔺Grouping and Filtering:
-Explain the GROUP BY clause and its use.
-Filter SQL query results using the HAVING clause.
🔺Subqueries:
-Define a subquery and provide an example.
🔺Indexes and Optimization:
-Discuss the importance of indexes in a database.
&Optimize a slow-running SQL query.
🔺Normalization and Data Integrity:
-Define database normalization and its significance.
-Enforce data integrity in a SQL database.
🔺Transactions:
-Define a SQL transaction and its purpose.
-Explain ACID properties in database transactions.
🔺Views and Stored Procedures:
-Define a database view and its use.
-Distinguish a stored procedure from a regular SQL query.
🔺Advanced SQL:
-Write a recursive SQL query and explain its use.
-Explain window functions in SQL.
✅👀These questions offer a comprehensive assessment of SQL knowledge, ranging from basics to advanced concepts.
❤️Like if you'd like answers in the next post! 👍
👉Be the first one to know the latest Job openings 👇
https://t.iss.one/jobs_SQL
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