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✅ Complete Roadmap to Master Data Analytics in 3 Months:
Month 1: Foundations
Week 1: Data basics
- What data analytics is
- Business use cases
- Types of data: structured, semi-structured, unstructured
- Tools overview: Excel, SQL, Power BI or Tableau
Outcome: You know where analytics fits in a company.
Week 2: Excel for analysis
- Data cleaning: remove duplicates, handle blanks
- Core formulas: IF, VLOOKUP, XLOOKUP, COUNTIFS, SUMIFS
- Sorting, filtering, conditional formatting
Outcome: You clean and explore datasets fast.
Week 3: SQL fundamentals
- SELECT, WHERE, ORDER BY, LIMIT
- Aggregations: COUNT, SUM, AVG
- GROUP BY and HAVING
Outcome: You pull exact data you need.
Week 4: SQL joins and practice
- INNER, LEFT, RIGHT joins
- Handling NULLs and duplicates
- Daily query practice
Outcome: You combine tables with confidence.
Month 2: Analysis and Visualization
Week 5: Statistics for analysts
- Mean, median, mode
- Variance, standard deviation
- Correlation with real examples
Outcome: You explain numbers clearly.
Week 6: Power BI or Tableau basics
- Import data from Excel and SQL
- Data model basics: relationships
- Simple charts and tables
Outcome: You build clean visuals.
Week 7: Advanced visuals
- KPIs, filters, slicers
- Bar, line, pie, maps
- Dashboard layout rules
Outcome: Your dashboards tell a story.
Week 8: Business analysis skills
- Asking the right questions
- Metrics: revenue, growth, churn
- Turning insights into actions
Outcome: You think like a business analyst.
Month 3: Real World and Job Prep
Week 9: Python basics for analytics
- Python setup
- Pandas basics: read CSV, filter, group
- Simple analysis noscripts
Outcome: You automate analysis.
Week 10: End to end project
- Choose a dataset: sales or marketing
- Clean data, analyze trends, build a dashboard
Outcome: One solid portfolio project.
Week 11: Interview preparation
- SQL interview questions
- Case studies
- Explain your project clearly
Outcome: You answer with structure.
Week 12: Resume and practice
- Analytics focused resume
- GitHub or portfolio setup
- Daily practice on real questions
Outcome: You are job ready.
Practice platforms: Kaggle datasets, LeetCode SQL, HackerRank
Double Tap ♥️ For Detailed Explanation
Month 1: Foundations
Week 1: Data basics
- What data analytics is
- Business use cases
- Types of data: structured, semi-structured, unstructured
- Tools overview: Excel, SQL, Power BI or Tableau
Outcome: You know where analytics fits in a company.
Week 2: Excel for analysis
- Data cleaning: remove duplicates, handle blanks
- Core formulas: IF, VLOOKUP, XLOOKUP, COUNTIFS, SUMIFS
- Sorting, filtering, conditional formatting
Outcome: You clean and explore datasets fast.
Week 3: SQL fundamentals
- SELECT, WHERE, ORDER BY, LIMIT
- Aggregations: COUNT, SUM, AVG
- GROUP BY and HAVING
Outcome: You pull exact data you need.
Week 4: SQL joins and practice
- INNER, LEFT, RIGHT joins
- Handling NULLs and duplicates
- Daily query practice
Outcome: You combine tables with confidence.
Month 2: Analysis and Visualization
Week 5: Statistics for analysts
- Mean, median, mode
- Variance, standard deviation
- Correlation with real examples
Outcome: You explain numbers clearly.
Week 6: Power BI or Tableau basics
- Import data from Excel and SQL
- Data model basics: relationships
- Simple charts and tables
Outcome: You build clean visuals.
Week 7: Advanced visuals
- KPIs, filters, slicers
- Bar, line, pie, maps
- Dashboard layout rules
Outcome: Your dashboards tell a story.
Week 8: Business analysis skills
- Asking the right questions
- Metrics: revenue, growth, churn
- Turning insights into actions
Outcome: You think like a business analyst.
Month 3: Real World and Job Prep
Week 9: Python basics for analytics
- Python setup
- Pandas basics: read CSV, filter, group
- Simple analysis noscripts
Outcome: You automate analysis.
Week 10: End to end project
- Choose a dataset: sales or marketing
- Clean data, analyze trends, build a dashboard
Outcome: One solid portfolio project.
Week 11: Interview preparation
- SQL interview questions
- Case studies
- Explain your project clearly
Outcome: You answer with structure.
Week 12: Resume and practice
- Analytics focused resume
- GitHub or portfolio setup
- Daily practice on real questions
Outcome: You are job ready.
Practice platforms: Kaggle datasets, LeetCode SQL, HackerRank
Double Tap ♥️ For Detailed Explanation
❤16
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❤1
Excel Basics for Data Analytics
Excel sits at the start of most analysis work.
What you use Excel for
• Cleaning raw data
• Exploring patterns
• Quick summaries for teams
Core concepts you must know
• Data setup
– Freeze header row. View → Freeze Top Row.
– Convert range to table. Ctrl + T.
– Use proper headers. No merged cells. One value per cell.
• Data cleaning
– Remove duplicates. Data → Remove Duplicates.
– Trim extra spaces. =TRIM(A2)
– Convert text to numbers. =VALUE(A2)
– Fix date format. Format Cells → Date.
– Handle blanks. Filter blanks, fill or delete.
– Find and replace. Ctrl + H.
• Essential formulas
– Math and counts
▪ SUM. =SUM(A2:A100)
▪ AVERAGE. =AVERAGE(A2:A100)
▪ MIN. =MIN(A2:A100)
▪ MAX. =MAX(A2:A100)
▪ COUNT. Counts numbers.
▪ COUNTA. Counts non blanks.
▪ COUNTBLANK. Counts blanks.
– Conditional formulas
▪ IF. =IF(A2>5000,"High","Low")
▪ IFS. Multiple conditions.
▪ AND. =AND(A2>5000,B2="West")
▪ OR. =OR(A2>5000,A2<1000)
– Lookup formulas
▪ XLOOKUP. =XLOOKUP(A2,Sheet2!A:A,Sheet2!B:B)
▪ VLOOKUP. Old but common.
▪ INDEX + MATCH. Powerful alternative.
– Text formulas
▪ LEFT. =LEFT(A2,4)
▪ RIGHT. =RIGHT(A2,2)
▪ MID. =MID(A2,2,3)
▪ LEN. =LEN(A2)
▪ CONCAT or TEXTJOIN.
▪ LOWER, UPPER, PROPER.
– Date formulas
▪ TODAY. Current date.
▪ NOW. Date and time.
▪ YEAR, MONTH, DAY.
▪ DATEDIF. Date difference.
▪ EOMONTH. Month end.
• Sorting and filtering
– Sort by multiple columns.
– Filter by value, color, condition.
– Top 10 filter for quick insights.
• Conditional formatting
– Highlight duplicates.
– Color scales for trends.
– Rules for thresholds. Example. Sales > 10000 in green.
• Pivot tables
– Insert → PivotTable.
– Rows. Category or Product.
– Values. Sum, Count, Average.
– Filters. Date, Region.
– Refresh after data update.
• Charts you must know
– Column. Comparison.
– Bar. Ranking.
– Line. Trends over time.
– Pie. Share or percentage.
– Combo. Actual vs target.
• Data validation
– Dropdown list. Data → Data Validation → List.
– Prevent wrong entries.
• Useful shortcuts
– Ctrl + Arrow. Jump data.
– Ctrl + Shift + Arrow. Select range.
– Ctrl + 1. Format cells.
– Ctrl + L. Apply filter.
– Alt + =. Auto sum.
– Ctrl + Z / Y. Undo redo.
• Common analyst mistakes to avoid
– Merged cells.
– Hard coded totals.
– Mixed data types in one column.
– No backup before cleaning.
• Daily practice task
– Download any sales CSV.
– Clean it.
– Build one pivot table.
– Create one chart.
Excel Resources: https://whatsapp.com/channel/0029VaifY548qIzv0u1AHz3i
Data Analytics Roadmap: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02/1354
Double Tap ♥️ For More
Excel sits at the start of most analysis work.
What you use Excel for
• Cleaning raw data
• Exploring patterns
• Quick summaries for teams
Core concepts you must know
• Data setup
– Freeze header row. View → Freeze Top Row.
– Convert range to table. Ctrl + T.
– Use proper headers. No merged cells. One value per cell.
• Data cleaning
– Remove duplicates. Data → Remove Duplicates.
– Trim extra spaces. =TRIM(A2)
– Convert text to numbers. =VALUE(A2)
– Fix date format. Format Cells → Date.
– Handle blanks. Filter blanks, fill or delete.
– Find and replace. Ctrl + H.
• Essential formulas
– Math and counts
▪ SUM. =SUM(A2:A100)
▪ AVERAGE. =AVERAGE(A2:A100)
▪ MIN. =MIN(A2:A100)
▪ MAX. =MAX(A2:A100)
▪ COUNT. Counts numbers.
▪ COUNTA. Counts non blanks.
▪ COUNTBLANK. Counts blanks.
– Conditional formulas
▪ IF. =IF(A2>5000,"High","Low")
▪ IFS. Multiple conditions.
▪ AND. =AND(A2>5000,B2="West")
▪ OR. =OR(A2>5000,A2<1000)
– Lookup formulas
▪ XLOOKUP. =XLOOKUP(A2,Sheet2!A:A,Sheet2!B:B)
▪ VLOOKUP. Old but common.
▪ INDEX + MATCH. Powerful alternative.
– Text formulas
▪ LEFT. =LEFT(A2,4)
▪ RIGHT. =RIGHT(A2,2)
▪ MID. =MID(A2,2,3)
▪ LEN. =LEN(A2)
▪ CONCAT or TEXTJOIN.
▪ LOWER, UPPER, PROPER.
– Date formulas
▪ TODAY. Current date.
▪ NOW. Date and time.
▪ YEAR, MONTH, DAY.
▪ DATEDIF. Date difference.
▪ EOMONTH. Month end.
• Sorting and filtering
– Sort by multiple columns.
– Filter by value, color, condition.
– Top 10 filter for quick insights.
• Conditional formatting
– Highlight duplicates.
– Color scales for trends.
– Rules for thresholds. Example. Sales > 10000 in green.
• Pivot tables
– Insert → PivotTable.
– Rows. Category or Product.
– Values. Sum, Count, Average.
– Filters. Date, Region.
– Refresh after data update.
• Charts you must know
– Column. Comparison.
– Bar. Ranking.
– Line. Trends over time.
– Pie. Share or percentage.
– Combo. Actual vs target.
• Data validation
– Dropdown list. Data → Data Validation → List.
– Prevent wrong entries.
• Useful shortcuts
– Ctrl + Arrow. Jump data.
– Ctrl + Shift + Arrow. Select range.
– Ctrl + 1. Format cells.
– Ctrl + L. Apply filter.
– Alt + =. Auto sum.
– Ctrl + Z / Y. Undo redo.
• Common analyst mistakes to avoid
– Merged cells.
– Hard coded totals.
– Mixed data types in one column.
– No backup before cleaning.
• Daily practice task
– Download any sales CSV.
– Clean it.
– Build one pivot table.
– Create one chart.
Excel Resources: https://whatsapp.com/channel/0029VaifY548qIzv0u1AHz3i
Data Analytics Roadmap: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02/1354
Double Tap ♥️ For More
❤7
𝗔𝗜 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲 🔥
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1. What is the difference between the RANK() and DENSE_RANK() functions?
The RANK() function in the result set defines the rank of each row within your ordered partition. If both rows have the same rank, the next number in the ranking will be the previous rank plus a number of duplicates. If we have three records at rank 4, for example, the next level indicated is 7. The DENSE_RANK() function assigns a distinct rank to each row within a partition based on the provided column value, with no gaps. If we have three records at rank 4, for example, the next level indicated is 5.
2. Explain One-hot encoding and Label Encoding. How do they affect the dimensionality of the given dataset?
One-hot encoding is the representation of categorical variables as binary vectors. Label Encoding is converting labels/words into numeric form. Using one-hot encoding increases the dimensionality of the data set. Label encoding doesn’t affect the dimensionality of the data set. One-hot encoding creates a new variable for each level in the variable whereas, in Label encoding, the levels of a variable get encoded as 1 and 0.
3. What is the shortcut to add a filter to a table in EXCEL?
The filter mechanism is used when you want to display only specific data from the entire dataset. By doing so, there is no change being made to the data. The shortcut to add a filter to a table is Ctrl+Shift+L.
4. What is DAX in Power BI?
DAX stands for Data Analysis Expressions. It's a collection of functions, operators, and constants used in formulas to calculate and return values. In other words, it helps you create new info from data you already have.
5. Define shelves and sets in Tableau?
Shelves: Every worksheet in Tableau will have shelves such as columns, rows, marks, filters, pages, and more. By placing filters on shelves we can build our own visualization structure. We can control the marks by including or excluding data.
Sets: The sets are used to compute a condition on which the dataset will be prepared. Data will be grouped together based on a condition. Fields which is responsible for grouping are known assets. For example – students having grades of more than 70%.
The RANK() function in the result set defines the rank of each row within your ordered partition. If both rows have the same rank, the next number in the ranking will be the previous rank plus a number of duplicates. If we have three records at rank 4, for example, the next level indicated is 7. The DENSE_RANK() function assigns a distinct rank to each row within a partition based on the provided column value, with no gaps. If we have three records at rank 4, for example, the next level indicated is 5.
2. Explain One-hot encoding and Label Encoding. How do they affect the dimensionality of the given dataset?
One-hot encoding is the representation of categorical variables as binary vectors. Label Encoding is converting labels/words into numeric form. Using one-hot encoding increases the dimensionality of the data set. Label encoding doesn’t affect the dimensionality of the data set. One-hot encoding creates a new variable for each level in the variable whereas, in Label encoding, the levels of a variable get encoded as 1 and 0.
3. What is the shortcut to add a filter to a table in EXCEL?
The filter mechanism is used when you want to display only specific data from the entire dataset. By doing so, there is no change being made to the data. The shortcut to add a filter to a table is Ctrl+Shift+L.
4. What is DAX in Power BI?
DAX stands for Data Analysis Expressions. It's a collection of functions, operators, and constants used in formulas to calculate and return values. In other words, it helps you create new info from data you already have.
5. Define shelves and sets in Tableau?
Shelves: Every worksheet in Tableau will have shelves such as columns, rows, marks, filters, pages, and more. By placing filters on shelves we can build our own visualization structure. We can control the marks by including or excluding data.
Sets: The sets are used to compute a condition on which the dataset will be prepared. Data will be grouped together based on a condition. Fields which is responsible for grouping are known assets. For example – students having grades of more than 70%.
❤4
𝗜𝗜𝗧 𝗥𝗼𝗼𝗿𝗸𝗲𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝗔𝗜 😍
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Placement Assistance With 5000+ companies.
✅ Open to everyone
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✅ Taught By IIT Roorkee Professors
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⏳ Deadline: 15th Feb 2026
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗡𝗼𝘄 👇 :-
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❤1
Data Analytics Roadmap
|
|-- Fundamentals
| |-- Mathematics
| | |-- Denoscriptive 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)
| |-- Prenoscriptive 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://news.1rj.ru/str/sqlspecialist/738
2. Python
https://www.learnpython.org/
https://news.1rj.ru/str/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://news.1rj.ru/str/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://news.1rj.ru/str/excel_data
https://www.w3schools.com/EXCEL/index.php
Join @free4unow_backup for more free courses
Like for more ❤️
ENJOY LEARNING 👍👍
|
|-- Fundamentals
| |-- Mathematics
| | |-- Denoscriptive 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)
| |-- Prenoscriptive 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://news.1rj.ru/str/sqlspecialist/738
2. Python
https://www.learnpython.org/
https://news.1rj.ru/str/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://news.1rj.ru/str/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://news.1rj.ru/str/excel_data
https://www.w3schools.com/EXCEL/index.php
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Best Data Analytics Resources 👇
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You can find more resources on Medium & Linkedin
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Thanks to all who support our channel and share it with friends & loved ones. You guys are really amazing.
Hope it helps :)
Many people charge too much to teach Excel, Power BI, SQL, Python & Tableau but my mission is to break down barriers. I have shared complete learning series to start your data analytics journey from scratch.
For those of you who are new to this channel, here are some quick links to navigate this channel easily.
Data Analyst Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/752
Python Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/749
Power BI Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/745
SQL Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/738
SQL Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/567
Excel Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/664
Power BI Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/768
Python Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/615
Tableau Essential Topics 👇
https://news.1rj.ru/str/sqlspecialist/667
Best Data Analytics Resources 👇
https://heylink.me/DataAnalytics
You can find more resources on Medium & Linkedin
Like for more ❤️
Thanks to all who support our channel and share it with friends & loved ones. You guys are really amazing.
Hope it helps :)
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15 Excel Formula Tricks & Shortcuts
1. Insert SUM function → Alt + =
2. Insert IF function quickly → Type =IF( and press Tab
3. Insert VLOOKUP / XLOOKUP → Type function name + Tab
4. Toggle between relative/absolute refs → F4
5. Select entire formula → Ctrl + Shift + U
6. Expand or collapse formula bar → Ctrl + Shift + U
7. Paste formula only → Ctrl + Alt + V, F
8. Paste values only → Ctrl + Alt + V, V
9. Calculate selected cells only → Shift + F9
10. Trace precedents → Ctrl + [
11. Trace dependents → Ctrl + ]
12. Remove arrows → Alt + M, A, A
13. Evaluate formula step-by-step → Alt + M, V
14. Insert array formula (legacy) → Ctrl + Shift + Enter
15. Repeat last formula action → F4
Double tap ♥️ if this helped you
1. Insert SUM function → Alt + =
2. Insert IF function quickly → Type =IF( and press Tab
3. Insert VLOOKUP / XLOOKUP → Type function name + Tab
4. Toggle between relative/absolute refs → F4
5. Select entire formula → Ctrl + Shift + U
6. Expand or collapse formula bar → Ctrl + Shift + U
7. Paste formula only → Ctrl + Alt + V, F
8. Paste values only → Ctrl + Alt + V, V
9. Calculate selected cells only → Shift + F9
10. Trace precedents → Ctrl + [
11. Trace dependents → Ctrl + ]
12. Remove arrows → Alt + M, A, A
13. Evaluate formula step-by-step → Alt + M, V
14. Insert array formula (legacy) → Ctrl + Shift + Enter
15. Repeat last formula action → F4
Double tap ♥️ if this helped you
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📊 Data Analytics: A-Z! 🚀
Data Analytics is the art and science of examining raw data to draw conclusions about that information. It's a powerful field that helps businesses and organizations make informed decisions, improve efficiency, and gain a competitive edge.
Here's a journey through Data Analytics, from the basics to advanced topics:
A - Applications:
• Across industries: Finance, Healthcare, Marketing, Retail, Supply Chain, etc.
• Use cases: Customer segmentation, fraud detection, risk management, predictive maintenance, market research, and more.
B - Business Intelligence (BI):
• Tools and technologies for analyzing business data and presenting it in an easily understandable format (dashboards, reports).
• Examples: Power BI, Tableau, Qlik Sense.
C - Cleaning Data:
• The process of identifying and correcting errors, inconsistencies, and inaccuracies in a dataset.
• Techniques: Handling missing values, removing duplicates, correcting typos, standardizing formats.
D - Data Visualization:
• Graphical representation of data using charts, graphs, maps, and other visual elements.
• Goal: Communicate insights effectively and make data easier to understand.
E - ETL (Extract, Transform, Load):
• The process of extracting data from various sources, transforming it into a consistent format, and loading it into a data warehouse or other storage system.
F - Formulas (Excel):
• Essential for performing calculations and data manipulation in Excel.
• Examples: SUM, AVERAGE, IF, VLOOKUP, COUNTIF.
G - Google Analytics:
• A web analytics service that tracks and reports website traffic.
• Used to analyze user behavior, measure the effectiveness of marketing campaigns, and improve website performance.
H - Hypothesis Testing:
• A statistical method used to determine whether there is enough evidence to support a hypothesis about a population.
• Common tests: T-tests, Chi-square tests, ANOVA.
I - Insights:
• Actionable conclusions and discoveries derived from data analysis.
• Insights should be clear, concise, and relevant to the business context.
J - JOINs (SQL):
• A SQL clause used to combine rows from two or more tables based on a related column.
• Types: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN.
K - Key Performance Indicators (KPIs):
• Measurable values that demonstrate how effectively a company is achieving key business objectives.
• Examples: Revenue growth, customer satisfaction, market share.
L - Libraries (Python):
• Essential Python libraries for data analysis:
• Pandas: Data manipulation and analysis.
• NumPy: Numerical computing.
• Matplotlib & Seaborn: Data visualization.
• Scikit-learn: Machine learning.
M - Machine Learning (ML):
• A type of artificial intelligence that enables computers to learn from data without being explicitly programmed.
• Used for tasks like prediction, classification, and clustering.
N - Normalization:
• A data preprocessing technique used to scale numerical data to a common range, improving the performance of machine learning algorithms.
O - Outliers:
• Data points that are significantly different from other values in a dataset.
• Can be caused by errors, anomalies, or natural variations.
P - Pivot Tables (Excel):
• A powerful tool in Excel for summarizing and analyzing large datasets.
• Allows you to quickly group, filter, and aggregate data.
Q - Queries (SQL):
• Requests for data from a database.
• Used to retrieve, insert, update, and delete data.
R - Regression Analysis:
• A statistical method used to model the relationship between a dependent variable and one or more independent variables.
• Types: Linear regression, logistic regression.
S - SQL (Structured Query Language):
• The standard language for interacting with relational databases.
• Used to retrieve, manipulate, and manage data.
T - Tableau:
• A popular data visualization and business intelligence tool.
• Known for its user-friendly interface and powerful analytical capabilities.
Data Analytics is the art and science of examining raw data to draw conclusions about that information. It's a powerful field that helps businesses and organizations make informed decisions, improve efficiency, and gain a competitive edge.
Here's a journey through Data Analytics, from the basics to advanced topics:
A - Applications:
• Across industries: Finance, Healthcare, Marketing, Retail, Supply Chain, etc.
• Use cases: Customer segmentation, fraud detection, risk management, predictive maintenance, market research, and more.
B - Business Intelligence (BI):
• Tools and technologies for analyzing business data and presenting it in an easily understandable format (dashboards, reports).
• Examples: Power BI, Tableau, Qlik Sense.
C - Cleaning Data:
• The process of identifying and correcting errors, inconsistencies, and inaccuracies in a dataset.
• Techniques: Handling missing values, removing duplicates, correcting typos, standardizing formats.
D - Data Visualization:
• Graphical representation of data using charts, graphs, maps, and other visual elements.
• Goal: Communicate insights effectively and make data easier to understand.
E - ETL (Extract, Transform, Load):
• The process of extracting data from various sources, transforming it into a consistent format, and loading it into a data warehouse or other storage system.
F - Formulas (Excel):
• Essential for performing calculations and data manipulation in Excel.
• Examples: SUM, AVERAGE, IF, VLOOKUP, COUNTIF.
G - Google Analytics:
• A web analytics service that tracks and reports website traffic.
• Used to analyze user behavior, measure the effectiveness of marketing campaigns, and improve website performance.
H - Hypothesis Testing:
• A statistical method used to determine whether there is enough evidence to support a hypothesis about a population.
• Common tests: T-tests, Chi-square tests, ANOVA.
I - Insights:
• Actionable conclusions and discoveries derived from data analysis.
• Insights should be clear, concise, and relevant to the business context.
J - JOINs (SQL):
• A SQL clause used to combine rows from two or more tables based on a related column.
• Types: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN.
K - Key Performance Indicators (KPIs):
• Measurable values that demonstrate how effectively a company is achieving key business objectives.
• Examples: Revenue growth, customer satisfaction, market share.
L - Libraries (Python):
• Essential Python libraries for data analysis:
• Pandas: Data manipulation and analysis.
• NumPy: Numerical computing.
• Matplotlib & Seaborn: Data visualization.
• Scikit-learn: Machine learning.
M - Machine Learning (ML):
• A type of artificial intelligence that enables computers to learn from data without being explicitly programmed.
• Used for tasks like prediction, classification, and clustering.
N - Normalization:
• A data preprocessing technique used to scale numerical data to a common range, improving the performance of machine learning algorithms.
O - Outliers:
• Data points that are significantly different from other values in a dataset.
• Can be caused by errors, anomalies, or natural variations.
P - Pivot Tables (Excel):
• A powerful tool in Excel for summarizing and analyzing large datasets.
• Allows you to quickly group, filter, and aggregate data.
Q - Queries (SQL):
• Requests for data from a database.
• Used to retrieve, insert, update, and delete data.
R - Regression Analysis:
• A statistical method used to model the relationship between a dependent variable and one or more independent variables.
• Types: Linear regression, logistic regression.
S - SQL (Structured Query Language):
• The standard language for interacting with relational databases.
• Used to retrieve, manipulate, and manage data.
T - Tableau:
• A popular data visualization and business intelligence tool.
• Known for its user-friendly interface and powerful analytical capabilities.
❤3
U - Unstructured Data:
• Data that does not have a predefined format (e.g., text documents, images, videos, social media posts).
• Requires specialized tools and techniques for analysis.
V - Visualizations:
• Charts, graphs, maps, and other visual elements used to represent data.
• Choose the right visualization to effectively communicate your insights.
W - WHERE Clause (SQL):
• A SQL clause used to filter rows based on specified conditions.
• Essential for retrieving specific data from a table.
X - Exploratory Data Analysis (EDA):
• An approach to analyzing data to summarize its main characteristics, often with visual methods.
• Goal: To gain a better understanding of the data before performing more formal analysis.
Y - Y-axis (Charts):
• The vertical axis in a chart, typically used to represent the dependent variable or the value being measured.
Z - Zero-Based Thinking:
• An approach to data analysis that encourages you to question existing assumptions and look at the data with fresh eyes.
React ❤️ if you found this helpful!
• Data that does not have a predefined format (e.g., text documents, images, videos, social media posts).
• Requires specialized tools and techniques for analysis.
V - Visualizations:
• Charts, graphs, maps, and other visual elements used to represent data.
• Choose the right visualization to effectively communicate your insights.
W - WHERE Clause (SQL):
• A SQL clause used to filter rows based on specified conditions.
• Essential for retrieving specific data from a table.
X - Exploratory Data Analysis (EDA):
• An approach to analyzing data to summarize its main characteristics, often with visual methods.
• Goal: To gain a better understanding of the data before performing more formal analysis.
Y - Y-axis (Charts):
• The vertical axis in a chart, typically used to represent the dependent variable or the value being measured.
Z - Zero-Based Thinking:
• An approach to data analysis that encourages you to question existing assumptions and look at the data with fresh eyes.
React ❤️ if you found this helpful!
❤6
✅ 15 Excel Function Tips for Smart Work
1. Insert recently used function
→ Alt + M, R
2. Open Name Manager
→ Ctrl + F3
3. Create named range
→ Ctrl + Shift + F3
4. Paste named range
→ F3
5. Insert argument names in formula
→ Ctrl + Shift + A
6. Move to next argument in function
→ Tab
7. Move to previous argument
→ Shift + Tab
8. Select entire column in formula
→ Ctrl + Space
9. Select entire row in formula
→ Shift + Space
10. Switch between worksheets in formula
→ Ctrl + Page Up / Page Down
11. Display formula arguments tooltip
→ Ctrl + Shift + A
12. Convert formula to values
→ Copy → Ctrl + Alt + V → V → Enter
13. Check formula errors
→ Alt + M, K
14. Show calculation options
→ Alt + M, X
15. Enable manual calculation
→ Alt + M, M
Double Tap ♥️ For More
1. Insert recently used function
→ Alt + M, R
2. Open Name Manager
→ Ctrl + F3
3. Create named range
→ Ctrl + Shift + F3
4. Paste named range
→ F3
5. Insert argument names in formula
→ Ctrl + Shift + A
6. Move to next argument in function
→ Tab
7. Move to previous argument
→ Shift + Tab
8. Select entire column in formula
→ Ctrl + Space
9. Select entire row in formula
→ Shift + Space
10. Switch between worksheets in formula
→ Ctrl + Page Up / Page Down
11. Display formula arguments tooltip
→ Ctrl + Shift + A
12. Convert formula to values
→ Copy → Ctrl + Alt + V → V → Enter
13. Check formula errors
→ Alt + M, K
14. Show calculation options
→ Alt + M, X
15. Enable manual calculation
→ Alt + M, M
Double Tap ♥️ For More
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Complete Syllabus for Data Analytics interview:
SQL:
1. Basic
- SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Creating and using simple databases and tables
2. Intermediate
- Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Subqueries and nested queries
- Common Table Expressions (WITH clause)
- CASE statements for conditional logic in queries
3. Advanced
- Advanced JOIN techniques (self-join, non-equi join)
- Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- optimization with indexing
- Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Basic
- Syntax, variables, data types (integers, floats, strings, booleans)
- Control structures (if-else, for and while loops)
- Basic data structures (lists, dictionaries, sets, tuples)
- Functions, lambda functions, error handling (try-except)
- Modules and packages
2. Pandas & Numpy
- Creating and manipulating DataFrames and Series
- Indexing, selecting, and filtering data
- Handling missing data (fillna, dropna)
- Data aggregation with groupby, summarizing data
- Merging, joining, and concatenating datasets
3. Basic Visualization
- Basic plotting with Matplotlib (line plots, bar plots, histograms)
- Visualization with Seaborn (scatter plots, box plots, pair plots)
- Customizing plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Basic
- Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Introduction to charts and basic data visualization
- Data sorting and filtering
- Conditional formatting
2. Intermediate
- Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- PivotTables and PivotCharts for summarizing data
- Data validation tools
- What-if analysis tools (Data Tables, Goal Seek)
3. Advanced
- Array formulas and advanced functions
- Data Model & Power Pivot
- Advanced Filter
- Slicers and Timelines in Pivot Tables
- Dynamic charts and interactive dashboards
Power BI:
1. Data Modeling
- Importing data from various sources
- Creating and managing relationships between different datasets
- Data modeling basics (star schema, snowflake schema)
2. Data Transformation
- Using Power Query for data cleaning and transformation
- Advanced data shaping techniques
- Calculated columns and measures using DAX
3. Data Visualization and Reporting
- Creating interactive reports and dashboards
- Visualizations (bar, line, pie charts, maps)
- Publishing and sharing reports, scheduling data refreshes
Statistics Fundamentals:
Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution.
Hope it helps :)
SQL:
1. Basic
- SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Creating and using simple databases and tables
2. Intermediate
- Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Subqueries and nested queries
- Common Table Expressions (WITH clause)
- CASE statements for conditional logic in queries
3. Advanced
- Advanced JOIN techniques (self-join, non-equi join)
- Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- optimization with indexing
- Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Basic
- Syntax, variables, data types (integers, floats, strings, booleans)
- Control structures (if-else, for and while loops)
- Basic data structures (lists, dictionaries, sets, tuples)
- Functions, lambda functions, error handling (try-except)
- Modules and packages
2. Pandas & Numpy
- Creating and manipulating DataFrames and Series
- Indexing, selecting, and filtering data
- Handling missing data (fillna, dropna)
- Data aggregation with groupby, summarizing data
- Merging, joining, and concatenating datasets
3. Basic Visualization
- Basic plotting with Matplotlib (line plots, bar plots, histograms)
- Visualization with Seaborn (scatter plots, box plots, pair plots)
- Customizing plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Basic
- Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Introduction to charts and basic data visualization
- Data sorting and filtering
- Conditional formatting
2. Intermediate
- Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- PivotTables and PivotCharts for summarizing data
- Data validation tools
- What-if analysis tools (Data Tables, Goal Seek)
3. Advanced
- Array formulas and advanced functions
- Data Model & Power Pivot
- Advanced Filter
- Slicers and Timelines in Pivot Tables
- Dynamic charts and interactive dashboards
Power BI:
1. Data Modeling
- Importing data from various sources
- Creating and managing relationships between different datasets
- Data modeling basics (star schema, snowflake schema)
2. Data Transformation
- Using Power Query for data cleaning and transformation
- Advanced data shaping techniques
- Calculated columns and measures using DAX
3. Data Visualization and Reporting
- Creating interactive reports and dashboards
- Visualizations (bar, line, pie charts, maps)
- Publishing and sharing reports, scheduling data refreshes
Statistics Fundamentals:
Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution.
Hope it helps :)
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