๐๐๐ ๐๐๐ฌ๐ ๐๐ญ๐ฎ๐๐ข๐๐ฌ ๐๐จ๐ซ ๐๐ง๐ญ๐๐ซ๐ฏ๐ข๐๐ฐ:
Join for more: https://t.me/TechAndEvents
1. Dannyโs Diner:
Restaurant analytics to understand the customer orders pattern.
Link: https://8weeksqlchallenge.com/case-study-1/
2. Pizza Runner
Pizza shop analytics to optimize the efficiency of the operation
Link: https://8weeksqlchallenge.com/case-study-2/
3. Foodie Fie
Subscription-based food content platform
Link: https://lnkd.in/gzB39qAT
4. Data Bank: Thatโs money
Analytics based on customer activities with the digital bank
Link: https://lnkd.in/gH8pKPyv
5. Data Mart: Fresh is Best
Analytics on Online supermarket
Link: https://lnkd.in/gC5bkcDf
6. Clique Bait: Attention capturing
Analytics on the seafood industry
Link: https://lnkd.in/ggP4JiYG
7. Balanced Tree: Clothing Company
Analytics on the sales performance of clothing store
Link: https://8weeksqlchallenge.com/case-study-7
8. Fresh segments: Extract maximum value
Analytics on online advertising
Link: https://8weeksqlchallenge.com/case-study-8
Placement Material ๐ฏ๐ฏ: https://topmate.io/sumit_kumar80/1151675
Join for more: https://t.me/TechAndEvents
1. Dannyโs Diner:
Restaurant analytics to understand the customer orders pattern.
Link: https://8weeksqlchallenge.com/case-study-1/
2. Pizza Runner
Pizza shop analytics to optimize the efficiency of the operation
Link: https://8weeksqlchallenge.com/case-study-2/
3. Foodie Fie
Subscription-based food content platform
Link: https://lnkd.in/gzB39qAT
4. Data Bank: Thatโs money
Analytics based on customer activities with the digital bank
Link: https://lnkd.in/gH8pKPyv
5. Data Mart: Fresh is Best
Analytics on Online supermarket
Link: https://lnkd.in/gC5bkcDf
6. Clique Bait: Attention capturing
Analytics on the seafood industry
Link: https://lnkd.in/ggP4JiYG
7. Balanced Tree: Clothing Company
Analytics on the sales performance of clothing store
Link: https://8weeksqlchallenge.com/case-study-7
8. Fresh segments: Extract maximum value
Analytics on online advertising
Link: https://8weeksqlchallenge.com/case-study-8
Placement Material ๐ฏ๐ฏ: https://topmate.io/sumit_kumar80/1151675
Telegram
Tech And Events 2026
Sharing Events In 2026-2027
Technology Updates
World Level Hackathons
Up To Date In Tech Soft Skills For Your Knowledge
Technology Updates
World Level Hackathons
Up To Date In Tech Soft Skills For Your Knowledge
โ
Master Exploratory Data Analysis (EDA) ๐๐ก
1๏ธโฃ Understand Your Dataset
โบ Check shape, column types, missing values
โบ Use: df.info(), df.describe(), df.isnull().sum()
2๏ธโฃ Handle Missing & Duplicate Data
โบ Remove or fill missing values
โบ Use: dropna(), fillna(), drop_duplicates()
3๏ธโฃ Univariate Analysis
โบ Analyze one feature at a time
โบ Tools: histograms, box plots, value_counts()
4๏ธโฃ Bivariate & Multivariate Analysis
โบ Explore relations between features
โบ Tools: scatter plots, heatmaps, pair plots (Seaborn)
5๏ธโฃ Outlier Detection
โบ Use box plots, Z-score, IQR method
โบ Crucial for clean modeling
6๏ธโฃ Correlation Check
โบ Find highly correlated features
โบ Use: df.corr() + Seaborn heatmap
7๏ธโฃ Feature Engineering Ideas
โบ Create or remove features based on insights
๐ Tools: Python (Pandas, Matplotlib, Seaborn)
๐ฏ Mini Project: Try EDA on Titanic or Iris dataset!
Data Scienceเฅค Roadmap:๐ฏ๐ฏ
https://topmate.io/sumit_kumar80/1151675
๐ฌ Double Tap โค๏ธ for more!
1๏ธโฃ Understand Your Dataset
โบ Check shape, column types, missing values
โบ Use: df.info(), df.describe(), df.isnull().sum()
2๏ธโฃ Handle Missing & Duplicate Data
โบ Remove or fill missing values
โบ Use: dropna(), fillna(), drop_duplicates()
3๏ธโฃ Univariate Analysis
โบ Analyze one feature at a time
โบ Tools: histograms, box plots, value_counts()
4๏ธโฃ Bivariate & Multivariate Analysis
โบ Explore relations between features
โบ Tools: scatter plots, heatmaps, pair plots (Seaborn)
5๏ธโฃ Outlier Detection
โบ Use box plots, Z-score, IQR method
โบ Crucial for clean modeling
6๏ธโฃ Correlation Check
โบ Find highly correlated features
โบ Use: df.corr() + Seaborn heatmap
7๏ธโฃ Feature Engineering Ideas
โบ Create or remove features based on insights
๐ Tools: Python (Pandas, Matplotlib, Seaborn)
๐ฏ Mini Project: Try EDA on Titanic or Iris dataset!
Data Scienceเฅค Roadmap:๐ฏ๐ฏ
https://topmate.io/sumit_kumar80/1151675
๐ฌ Double Tap โค๏ธ for more!
topmate.io
Data science Job + Placement with Sumit Kumar
For College and Working Professional
๐ค ChatGPT โ Free
๐ Notion โ Free
๐ Gumroad โ Free
๐ Etsy โ Free
๐ฆ Twitter โ Free
๐บ YouTube โ Free
๐ Wix โ Free
๐จ Canva โ Free
๐ Beehiiv โ Free
๐ฌ Discord โ Free
๐ Google โ Free
๐ Courses โ Free
๐ Buffer โ Free best Best resources: https://topmate.io/sumit_kumar80/1153565
๐ Notion โ Free
๐ Gumroad โ Free
๐ Etsy โ Free
๐ฆ Twitter โ Free
๐บ YouTube โ Free
๐ Wix โ Free
๐จ Canva โ Free
๐ Beehiiv โ Free
๐ฌ Discord โ Free
๐ Google โ Free
๐ Courses โ Free
๐ Buffer โ Free best Best resources: https://topmate.io/sumit_kumar80/1153565
topmate.io
Unlocking the Power of AI with Sumit Kumar
Unlock Your Full Potential as Working Professional/Student
DSA INTERVIEW QUESTIONS AND ANSWERS
1. What is the difference between file structure and storage structure?
The difference lies in the memory area accessed. Storage structure refers to the data structure in the memory of the computer system,
whereas file structure represents the storage structure in the auxiliary memory.
2. Are linked lists considered linear or non-linear Data Structures?
Linked lists are considered both linear and non-linear data structures depending upon the application they are used for. When used for
access strategies, it is considered as a linear data-structure. When used for data storage, it is considered a non-linear data structure.
3. How do you reference all of the elements in a one-dimension array?
All of the elements in a one-dimension array can be referenced using an indexed loop as the array subscript so that the counter runs
from 0 to the array size minus one.
4. What are dynamic Data Structures? Name a few.
They are collections of data in memory that expand and contract to grow or shrink in size as a program runs. This enables the programmer
to control exactly how much memory is to be utilized.Examples are the dynamic array, linked list, stack, queue, and heap.
5. What is a Dequeue?
It is a double-ended queue, or a data structure, where the elements can be inserted or deleted at both ends (FRONT and REAR).
6. What operations can be performed on queues?
enqueue() adds an element to the end of the queue
dequeue() removes an element from the front of the queue
init() is used for initializing the queue
isEmpty tests for whether or not the queue is empty
The front is used to get the value of the first data item but does not remove it
The rear is used to get the last item from a queue.
7. What is the merge sort? How does it work?
Merge sort is a divide-and-conquer algorithm for sorting the data. It works by merging and sorting adjacent data to create bigger sorted
lists, which are then merged recursively to form even bigger sorted lists until you have one single sorted list.
8.How does the Selection sort work?
Selection sort works by repeatedly picking the smallest number in ascending order from the list and placing it at the beginning. This process is repeated moving toward the end of the list or sorted subarray.
Scan all items and find the smallest. Switch over the position as the first item. Repeat the selection sort on the remaining N-1 items. We always iterate forward (i from 0 to N-1) and swap with the smallest element (always i).
Time complexity: best case O(n2); worst O(n2)
Space complexity: worst O(1)
9. What are the applications of graph Data Structure?
Transport grids where stations are represented as vertices and routes as the edges of the graph
Utility graphs of power or water, where vertices are connection points and edge the wires or pipes connecting them
Social network graphs to determine the flow of information and hotspots (edges and vertices)
Neural networks where vertices represent neurons and edge the synapses between them
10. What is an AVL tree?
An AVL (Adelson, Velskii, and Landi) tree is a height balancing binary search tree in which the difference of heights of the left
and right subtrees of any node is less than or equal to one. This controls the height of the binary search tree by not letting
it get skewed. This is used when working with a large data set, with continual pruning through insertion and deletion of data.
11. Differentiate NULL and VOID ?
Null is a value, whereas Void is a data type identifier
Null indicates an empty value for a variable, whereas void indicates pointers that have no initial size
Null means it never existed; Void means it existed but is not in effect
Credits:
Placement Resources:
https://topmate.io/sumit_kumar80/1148833
TCS : https://topmate.io/sumit_kumar80/1151668
All the best ๐๐
1. What is the difference between file structure and storage structure?
The difference lies in the memory area accessed. Storage structure refers to the data structure in the memory of the computer system,
whereas file structure represents the storage structure in the auxiliary memory.
2. Are linked lists considered linear or non-linear Data Structures?
Linked lists are considered both linear and non-linear data structures depending upon the application they are used for. When used for
access strategies, it is considered as a linear data-structure. When used for data storage, it is considered a non-linear data structure.
3. How do you reference all of the elements in a one-dimension array?
All of the elements in a one-dimension array can be referenced using an indexed loop as the array subscript so that the counter runs
from 0 to the array size minus one.
4. What are dynamic Data Structures? Name a few.
They are collections of data in memory that expand and contract to grow or shrink in size as a program runs. This enables the programmer
to control exactly how much memory is to be utilized.Examples are the dynamic array, linked list, stack, queue, and heap.
5. What is a Dequeue?
It is a double-ended queue, or a data structure, where the elements can be inserted or deleted at both ends (FRONT and REAR).
6. What operations can be performed on queues?
enqueue() adds an element to the end of the queue
dequeue() removes an element from the front of the queue
init() is used for initializing the queue
isEmpty tests for whether or not the queue is empty
The front is used to get the value of the first data item but does not remove it
The rear is used to get the last item from a queue.
7. What is the merge sort? How does it work?
Merge sort is a divide-and-conquer algorithm for sorting the data. It works by merging and sorting adjacent data to create bigger sorted
lists, which are then merged recursively to form even bigger sorted lists until you have one single sorted list.
8.How does the Selection sort work?
Selection sort works by repeatedly picking the smallest number in ascending order from the list and placing it at the beginning. This process is repeated moving toward the end of the list or sorted subarray.
Scan all items and find the smallest. Switch over the position as the first item. Repeat the selection sort on the remaining N-1 items. We always iterate forward (i from 0 to N-1) and swap with the smallest element (always i).
Time complexity: best case O(n2); worst O(n2)
Space complexity: worst O(1)
9. What are the applications of graph Data Structure?
Transport grids where stations are represented as vertices and routes as the edges of the graph
Utility graphs of power or water, where vertices are connection points and edge the wires or pipes connecting them
Social network graphs to determine the flow of information and hotspots (edges and vertices)
Neural networks where vertices represent neurons and edge the synapses between them
10. What is an AVL tree?
An AVL (Adelson, Velskii, and Landi) tree is a height balancing binary search tree in which the difference of heights of the left
and right subtrees of any node is less than or equal to one. This controls the height of the binary search tree by not letting
it get skewed. This is used when working with a large data set, with continual pruning through insertion and deletion of data.
11. Differentiate NULL and VOID ?
Null is a value, whereas Void is a data type identifier
Null indicates an empty value for a variable, whereas void indicates pointers that have no initial size
Null means it never existed; Void means it existed but is not in effect
Credits:
Placement Resources:
https://topmate.io/sumit_kumar80/1148833
TCS : https://topmate.io/sumit_kumar80/1151668
All the best ๐๐
topmate.io
Ultimate Placement Coding Resources with Sumit Kumar
Ultimate Placement materials (top 10 companies)
Learning DSA wasnโt just about acing interviews, --- it was about thinking better, building faster, and debugging smarter.
๐ฏ ๐๐ฒ๐ฟ๐ฒ ๐ฎ๐ฟ๐ฒ ๐๐ต๐ฒ ๐ต ๐ฐ๐ผ๐ฟ๐ฒ ๐ฝ๐ฎ๐๐๐ฒ๐ฟ๐ป๐ ๐๐ต๐ฎ๐ ๐๐ฟ๐ฎ๐ป๐๐ณ๐ผ๐ฟ๐บ๐ฒ๐ฑ ๐ต๐ผ๐ ๐ ๐๐ผ๐น๐๐ฒ ๐ฝ๐ฟ๐ผ๐ฏ๐น๐ฒ๐บ๐:
โข Sliding Windows
โข Two Pointers
โข Stack Based Patterns
โข Dynamic Programing
โข BFS/DFS (Trees & Graphs)
โข Merge Intervals
โข Backtracking & Subsets
โข top-k Elements (Heaps)
โข Greedy Techniques
๐ค๏ธ ๐ ๐ ๐ฃ๐ฎ๐๐ต ๐๐ผ ๐ ๐ฎ๐๐๐ฒ๐ฟ๐ถ๐ป๐ด ๐๐ฆ๐:
โข Started with basic problems on arrays & strings
โข Solved 1-2 problems a day, consistently for 3 months
โข Focused more on patterns than individual questions
โข Made my own notes, revisited problems I struggled with
โข Used visual tools to understand recursion & DP
โข Practiced explaining my solutions out loud (like system design reviews)
โข Applied patterns in real-world projects (DevOps automation, log parsing, infra tools)
๐ก ๐๐ผ๐ผ๐ธ๐ถ๐ป๐ด ๐ฏ๐ฎ๐ฐ๐ธ, ๐ผ๐ป๐ฒ ๐๐ต๐ถ๐ป๐ด ๐ถ๐ ๐ฐ๐น๐ฒ๐ฎ๐ฟ:
> It's not how many problems you solve, it's how well you can recognize the pattern hiding in each one.
Join for more:
https://whatsapp.com/channel/0029VaCTjQKInlqIZ1vJ851i
You can find more free resources on my WhatsApp channel:
๐ฏ ๐๐ฒ๐ฟ๐ฒ ๐ฎ๐ฟ๐ฒ ๐๐ต๐ฒ ๐ต ๐ฐ๐ผ๐ฟ๐ฒ ๐ฝ๐ฎ๐๐๐ฒ๐ฟ๐ป๐ ๐๐ต๐ฎ๐ ๐๐ฟ๐ฎ๐ป๐๐ณ๐ผ๐ฟ๐บ๐ฒ๐ฑ ๐ต๐ผ๐ ๐ ๐๐ผ๐น๐๐ฒ ๐ฝ๐ฟ๐ผ๐ฏ๐น๐ฒ๐บ๐:
โข Sliding Windows
โข Two Pointers
โข Stack Based Patterns
โข Dynamic Programing
โข BFS/DFS (Trees & Graphs)
โข Merge Intervals
โข Backtracking & Subsets
โข top-k Elements (Heaps)
โข Greedy Techniques
๐ค๏ธ ๐ ๐ ๐ฃ๐ฎ๐๐ต ๐๐ผ ๐ ๐ฎ๐๐๐ฒ๐ฟ๐ถ๐ป๐ด ๐๐ฆ๐:
โข Started with basic problems on arrays & strings
โข Solved 1-2 problems a day, consistently for 3 months
โข Focused more on patterns than individual questions
โข Made my own notes, revisited problems I struggled with
โข Used visual tools to understand recursion & DP
โข Practiced explaining my solutions out loud (like system design reviews)
โข Applied patterns in real-world projects (DevOps automation, log parsing, infra tools)
๐ก ๐๐ผ๐ผ๐ธ๐ถ๐ป๐ด ๐ฏ๐ฎ๐ฐ๐ธ, ๐ผ๐ป๐ฒ ๐๐ต๐ถ๐ป๐ด ๐ถ๐ ๐ฐ๐น๐ฒ๐ฎ๐ฟ:
> It's not how many problems you solve, it's how well you can recognize the pattern hiding in each one.
Join for more:
https://whatsapp.com/channel/0029VaCTjQKInlqIZ1vJ851i
You can find more free resources on my WhatsApp channel:
*How to forget the past?*
Do you know the two usual ways to remember something?
Firstโtrying to remember it. Put up reminders here and there, and you will probably remember.
Secondโby saying daily: I want to forget it. This is another way to remember something and never forget it.
I want to forget A, and so I remember daily to forget A.
And what am I remembering daily? A.
โโจโโจโ
'Truth Without Apology'
Available on Amazon: https://amzn.in/d/61CYEr4
Do you know the two usual ways to remember something?
Firstโtrying to remember it. Put up reminders here and there, and you will probably remember.
Secondโby saying daily: I want to forget it. This is another way to remember something and never forget it.
I want to forget A, and so I remember daily to forget A.
And what am I remembering daily? A.
โโจโโจโ
'Truth Without Apology'
Available on Amazon: https://amzn.in/d/61CYEr4
โ
Top 10 Data Science Interview Questions (2025) ๐ฅ
1๏ธโฃ What is the difference between supervised and unsupervised learning?
โฆ Supervised: trainings with labeled data (e.g., classification)
โฆ Unsupervised: no labels, finds hidden patterns (e.g., clustering)
2๏ธโฃ How is data science different from data analytics?
โฆ Data science builds models & algorithms; data analytics interprets data patterns for decisions.
3๏ธโฃ Explain the steps to build a decision tree.
โฆ Select best feature (e.g., using entropy/Gini) to split data recursively until stopping criteria.
4๏ธโฃ How do you handle a dataset with >30% missing values?
โฆ Options: drop columns/rows, impute using mean/median/mode or advanced methods.
5๏ธโฃ How do you maintain a deployed machine learning model?
โฆ Monitor performance, retrain with new data, handle data drift & errors.
6๏ธโฃ What is overfitting and how do you prevent it?
โฆ Model fits training data too well, generalizes poorly. Use cross-validation, regularization, pruning.
7๏ธโฃ What is A/B testing and why is it important?
โฆ Controlled experiments to compare two versions for better business decisions.
8๏ธโฃ How often should algorithms/models be updated?
โฆ Depends on data drift, new patterns, or model performance decay.
9๏ธโฃ What techniques do you prefer for text analysis?
โฆ NLP basics: Bag of Words, TF-IDF, and advanced ones like word embeddings (Word2Vec, BERT).
๐ What are common evaluation metrics for classification?
โฆ Accuracy, Precision, Recall, F1-score, AUC-ROC.
๐ฌ Tap โค๏ธ for more
1๏ธโฃ What is the difference between supervised and unsupervised learning?
โฆ Supervised: trainings with labeled data (e.g., classification)
โฆ Unsupervised: no labels, finds hidden patterns (e.g., clustering)
2๏ธโฃ How is data science different from data analytics?
โฆ Data science builds models & algorithms; data analytics interprets data patterns for decisions.
3๏ธโฃ Explain the steps to build a decision tree.
โฆ Select best feature (e.g., using entropy/Gini) to split data recursively until stopping criteria.
4๏ธโฃ How do you handle a dataset with >30% missing values?
โฆ Options: drop columns/rows, impute using mean/median/mode or advanced methods.
5๏ธโฃ How do you maintain a deployed machine learning model?
โฆ Monitor performance, retrain with new data, handle data drift & errors.
6๏ธโฃ What is overfitting and how do you prevent it?
โฆ Model fits training data too well, generalizes poorly. Use cross-validation, regularization, pruning.
7๏ธโฃ What is A/B testing and why is it important?
โฆ Controlled experiments to compare two versions for better business decisions.
8๏ธโฃ How often should algorithms/models be updated?
โฆ Depends on data drift, new patterns, or model performance decay.
9๏ธโฃ What techniques do you prefer for text analysis?
โฆ NLP basics: Bag of Words, TF-IDF, and advanced ones like word embeddings (Word2Vec, BERT).
๐ What are common evaluation metrics for classification?
โฆ Accuracy, Precision, Recall, F1-score, AUC-ROC.
๐ฌ Tap โค๏ธ for more
Forwarded from Acharya Prashant
The Pioneer, 13th Sep'25
Before outer revolutions, we first need an inner one
โโจโโจโ
"The Nepali youthโs courage is unquestionable. The demand now is that their sacrifice should not end in repetition. The real honour to their lives lies not in hashtags or fleeting slogans, but in a revolution that strikes at the root of slavery."
โโจโโจโ
๐ Print :
Available in all 8 English editions of The Pioneer across India
๐ Read online : https://www.dailypioneer.com/2025/columnists/before-outer-revolutions--we-first-need-an-inner-one.html
Before outer revolutions, we first need an inner one
โโจโโจโ
"The Nepali youthโs courage is unquestionable. The demand now is that their sacrifice should not end in repetition. The real honour to their lives lies not in hashtags or fleeting slogans, but in a revolution that strikes at the root of slavery."
โโจโโจโ
๐ Print :
Available in all 8 English editions of The Pioneer across India
๐ Read online : https://www.dailypioneer.com/2025/columnists/before-outer-revolutions--we-first-need-an-inner-one.html
*Boost Life & Work with AI: 20 Must-Visit Sites ๐ฅ๐ฏ*
โ ChatGPT โ AI chatbot chat.openai.com
โ DALLยทE โ Image generation openai.com/dall-e
โ Copy AI โ Copywriting copy.ai
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โ Runway โ Video & image AI runwayml.com
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โ CopySmith โ AI writing copysmith.ai
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โ MastPanel โ Social Services mastpanel.online
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โ Neural love โ Image & video AI neural.love
โ Acw Society โ Free courses anoncyberwarrior.com
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โ Glasp โ Highlight & summarize glasp.co
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๐ก Explore, Create & Grow with AI โ The Future is Yours!
For more posts:
https://t.me/TechAndEvents
> React "โค๏ธ" & Share with your Friends!!
โ ChatGPT โ AI chatbot chat.openai.com
โ DALLยทE โ Image generation openai.com/dall-e
โ Copy AI โ Copywriting copy.ai
โ Jasper AI โ Content creation jasper.ai
โ Runway โ Video & image AI runwayml.com
โ Synthesia โ AI videos synthesia.io
โ Notion AI โ Productivity AI notion.so/product/ai
โ Replit โ AI coding replit.com
โ Perplexity AI โ AI Q&A perplexity.ai
โ Lumen5 โ AI video maker lumen5.com
โ CopySmith โ AI writing copysmith.ai
โ Writesonic โ AI assistant writesonic.com
โ MastPanel โ Social Services mastpanel.online
โ Beautiful AI โ Presentations beautiful.ai
โ Pictory โ Video from text pictory.ai
โ Designs AI โ Creative AI designs.ai
โ Neural love โ Image & video AI neural.love
โ Acw Society โ Free courses anoncyberwarrior.com
โ RunDiffusion โ AI art rundiffusion.com
โ DeepL โ AI translation deepl.com
โ Glasp โ Highlight & summarize glasp.co
โ Mubert โ AI music mubert.com
๐ก Explore, Create & Grow with AI โ The Future is Yours!
For more posts:
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> React "โค๏ธ" & Share with your Friends!!
OpenAI
DALLยทE 3
Forwarded from ๐ป Computer Books Chat ๐ป (Admin)
joan-casteel-oracle-12c-sql-3rd-edition-2015.pdf
18.6 MB
Oracle 12c: SQL
Joan Casteel, 2016
Joan Casteel, 2016
Master Power BI with this Cheat Sheet๐ฅ
If you're preparing for a Power BI interview, this cheat sheet covers the key concepts and DAX commands you'll need. Bookmark it for last-minute revision!
๐ ๐ฃ๐ผ๐๐ฒ๐ฟ ๐๐ ๐๐ฎ๐๐ถ๐ฐ๐:
DAX Functions:
- SUMX: Sum of values based on a condition.
- FILTER: Filter data based on a given condition.
- RELATED: Retrieve a related column from another table.
- CALCULATE: Perform dynamic calculations.
- EARLIER: Access a column from a higher context.
- CROSSJOIN: Create a Cartesian product of two tables.
- UNION: Combine the results from multiple tables.
- RANKX: Rank data within a column.
- DISTINCT: Filter unique rows.
Data Modeling:
- Relationships: Create, manage, and modify relationships.
- Hierarchies: Build time-based hierarchies (e.g., Date, Month, Year).
- Calculated Columns: Create calculated columns to extend data.
- Measures: Write powerful measures to analyze data effectively.
Data Visualization:
- Charts: Bar charts, line charts, pie charts, and more.
- Table & Matrix: Display tabular data and matrix visuals.
- Slicers: Create interactive filters.
- Tooltips: Enhance visual interactivity with tooltips.
- Map: Display geographical data effectively.
โจ ๐๐๐๐ฒ๐ป๐๐ถ๐ฎ๐น ๐ฃ๐ผ๐๐ฒ๐ฟ ๐๐ ๐ง๐ถ๐ฝ๐:
โ Use DAX for efficient data analysis.
โ Optimize data models for performance.
โ Utilize drill-through and drill-down for deeper insights.
โ Leverage bookmarks for enhanced navigation.
โ Annotate your reports with comments for clarity.
Like this post if you need more content like this ๐โค๏ธ
If you're preparing for a Power BI interview, this cheat sheet covers the key concepts and DAX commands you'll need. Bookmark it for last-minute revision!
๐ ๐ฃ๐ผ๐๐ฒ๐ฟ ๐๐ ๐๐ฎ๐๐ถ๐ฐ๐:
DAX Functions:
- SUMX: Sum of values based on a condition.
- FILTER: Filter data based on a given condition.
- RELATED: Retrieve a related column from another table.
- CALCULATE: Perform dynamic calculations.
- EARLIER: Access a column from a higher context.
- CROSSJOIN: Create a Cartesian product of two tables.
- UNION: Combine the results from multiple tables.
- RANKX: Rank data within a column.
- DISTINCT: Filter unique rows.
Data Modeling:
- Relationships: Create, manage, and modify relationships.
- Hierarchies: Build time-based hierarchies (e.g., Date, Month, Year).
- Calculated Columns: Create calculated columns to extend data.
- Measures: Write powerful measures to analyze data effectively.
Data Visualization:
- Charts: Bar charts, line charts, pie charts, and more.
- Table & Matrix: Display tabular data and matrix visuals.
- Slicers: Create interactive filters.
- Tooltips: Enhance visual interactivity with tooltips.
- Map: Display geographical data effectively.
โจ ๐๐๐๐ฒ๐ป๐๐ถ๐ฎ๐น ๐ฃ๐ผ๐๐ฒ๐ฟ ๐๐ ๐ง๐ถ๐ฝ๐:
โ Use DAX for efficient data analysis.
โ Optimize data models for performance.
โ Utilize drill-through and drill-down for deeper insights.
โ Leverage bookmarks for enhanced navigation.
โ Annotate your reports with comments for clarity.
Like this post if you need more content like this ๐โค๏ธ
๐๐๐ ๐๐๐ฌ๐ ๐๐ญ๐ฎ๐๐ข๐๐ฌ ๐๐จ๐ซ ๐๐ง๐ญ๐๐ซ๐ฏ๐ข๐๐ฐ:
Join for more: https://t.me/TechAndEvents
1. Dannyโs Diner:
Restaurant analytics to understand the customer orders pattern.
Link: https://8weeksqlchallenge.com/case-study-1/
2. Pizza Runner
Pizza shop analytics to optimize the efficiency of the operation
Link: https://8weeksqlchallenge.com/case-study-2/
3. Foodie Fie
Subscription-based food content platform
Link: https://lnkd.in/gzB39qAT
4. Data Bank: Thatโs money
Analytics based on customer activities with the digital bank
Link: https://lnkd.in/gH8pKPyv
5. Data Mart: Fresh is Best
Analytics on Online supermarket
Link: https://lnkd.in/gC5bkcDf
6. Clique Bait: Attention capturing
Analytics on the seafood industry
Link: https://lnkd.in/ggP4JiYG
7. Balanced Tree: Clothing Company
Analytics on the sales performance of clothing store
Link: https://8weeksqlchallenge.com/case-study-7
8. Fresh segments: Extract maximum value
Analytics on online advertising
Link: https://8weeksqlchallenge.com/case-study-8
Placement Material ๐ฏ๐ฏ: https://topmate.io/sumit_kumar80/1151675
Join for more: https://t.me/TechAndEvents
1. Dannyโs Diner:
Restaurant analytics to understand the customer orders pattern.
Link: https://8weeksqlchallenge.com/case-study-1/
2. Pizza Runner
Pizza shop analytics to optimize the efficiency of the operation
Link: https://8weeksqlchallenge.com/case-study-2/
3. Foodie Fie
Subscription-based food content platform
Link: https://lnkd.in/gzB39qAT
4. Data Bank: Thatโs money
Analytics based on customer activities with the digital bank
Link: https://lnkd.in/gH8pKPyv
5. Data Mart: Fresh is Best
Analytics on Online supermarket
Link: https://lnkd.in/gC5bkcDf
6. Clique Bait: Attention capturing
Analytics on the seafood industry
Link: https://lnkd.in/ggP4JiYG
7. Balanced Tree: Clothing Company
Analytics on the sales performance of clothing store
Link: https://8weeksqlchallenge.com/case-study-7
8. Fresh segments: Extract maximum value
Analytics on online advertising
Link: https://8weeksqlchallenge.com/case-study-8
Placement Material ๐ฏ๐ฏ: https://topmate.io/sumit_kumar80/1151675
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Data Analyst Interview Questions ๐
1.How to create filters in Power BI?
Filters are an integral part of Power BI reports. They are used to slice and dice the data as per the dimensions we want. Filters are created in a couple of ways.
Using Slicers: A slicer is a visual under Visualization Pane. This can be added to the design view to filter our reports. When a slicer is added to the design view, it requires a field to be added to it. For example- Slicer can be added for Country fields. Then the data can be filtered based on countries.
Using Filter Pane: The Power BI team has added a filter pane to the reports, which is a single space where we can add different fields as filters. And these fields can be added depending on whether you want to filter only one visual(Visual level filter), or all the visuals in the report page(Page level filters), or applicable to all the pages of the report(report level filters)
2.How to sort data in Power BI?
Sorting is available in multiple formats. In the data view, a common sorting option of alphabetical order is there. Apart from that, we have the option of Sort by column, where one can sort a column based on another column. The sorting option is available in visuals as well. Sort by ascending and descending option by the fields and measure present in the visual is also available.
3.How to convert pdf to excel?
Open the PDF document you want to convert in XLSX format in Acrobat DC.
Go to the right pane and click on the โExport PDFโ option.
Choose spreadsheet as the Export format.
Select โMicrosoft Excel Workbook.โ
Now click โExport.โ
Download the converted file or share it.
4. How to enable macros in excel?
Click the file tab and then click โOptions.โ
A dialog box will appear. In the โExcel Optionsโ dialog box, click on the โTrust Centerโ and then โTrust Center Settings.โ
Go to the โMacro Settingsโ and select โenable all macros.โ
Click OK to apply the macro settings.
1.How to create filters in Power BI?
Filters are an integral part of Power BI reports. They are used to slice and dice the data as per the dimensions we want. Filters are created in a couple of ways.
Using Slicers: A slicer is a visual under Visualization Pane. This can be added to the design view to filter our reports. When a slicer is added to the design view, it requires a field to be added to it. For example- Slicer can be added for Country fields. Then the data can be filtered based on countries.
Using Filter Pane: The Power BI team has added a filter pane to the reports, which is a single space where we can add different fields as filters. And these fields can be added depending on whether you want to filter only one visual(Visual level filter), or all the visuals in the report page(Page level filters), or applicable to all the pages of the report(report level filters)
2.How to sort data in Power BI?
Sorting is available in multiple formats. In the data view, a common sorting option of alphabetical order is there. Apart from that, we have the option of Sort by column, where one can sort a column based on another column. The sorting option is available in visuals as well. Sort by ascending and descending option by the fields and measure present in the visual is also available.
3.How to convert pdf to excel?
Open the PDF document you want to convert in XLSX format in Acrobat DC.
Go to the right pane and click on the โExport PDFโ option.
Choose spreadsheet as the Export format.
Select โMicrosoft Excel Workbook.โ
Now click โExport.โ
Download the converted file or share it.
4. How to enable macros in excel?
Click the file tab and then click โOptions.โ
A dialog box will appear. In the โExcel Optionsโ dialog box, click on the โTrust Centerโ and then โTrust Center Settings.โ
Go to the โMacro Settingsโ and select โenable all macros.โ
Click OK to apply the macro settings.