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
โค6๐4
Complete 3-months roadmap to learn Artificial Intelligence (AI) ๐๐
### Month 1: Fundamentals of AI and Python
Week 1: Introduction to AI
- Key Concepts: What is AI? Categories (Narrow AI, General AI, Super AI), Applications of AI.
- Reading: Research papers and articles on AI.
- Task: Watch introductory AI videos (e.g., Andrew Ng's "What is AI?" on Coursera).
Week 2: Python for AI
- Skills: Basics of Python programming (variables, loops, conditionals, functions, OOP).
- Resources: Python tutorials (W3Schools, Real Python).
- Task: Write simple Python scripts.
Week 3: Libraries for AI
- Key Libraries: NumPy, Pandas, Matplotlib, Scikit-learn.
- Task: Install libraries and practice data manipulation and visualization.
- Resources: Documentation and tutorials on these libraries.
Week 4: Linear Algebra and Probability
- Key Topics: Matrices, Vectors, Eigenvalues, Probability theory.
- Resources: Khan Academy (Linear Algebra), MIT OCW.
- Task: Solve basic linear algebra problems and write Python functions to implement them.
---
### Month 2: Core AI Techniques & Machine Learning
Week 5: Machine Learning Basics
- Key Concepts: Supervised, Unsupervised learning, Model evaluation metrics.
- Algorithms: Linear Regression, Logistic Regression.
- Task: Build basic models using Scikit-learn.
- Resources: Courseraโs Machine Learning by Andrew Ng, Kaggle datasets.
Week 6: Decision Trees, Random Forests, and KNN
- Key Concepts: Decision Trees, Random Forests, K-Nearest Neighbors (KNN).
- Task: Implement these algorithms and analyze their performance.
- Resources: Hands-on Machine Learning with Scikit-learn.
Week 7: Neural Networks & Deep Learning
- Key Concepts: Artificial Neurons, Forward and Backpropagation, Activation Functions.
- Framework: TensorFlow, Keras.
- Task: Build a simple neural network for a classification problem.
- Resources: Fast.ai, Coursera Deep Learning Specialization by Andrew Ng.
Week 8: Convolutional Neural Networks (CNN)
- Key Concepts: Image classification, Convolution, Pooling.
- Task: Build a CNN using Keras/TensorFlow to classify images (e.g., CIFAR-10 dataset).
- Resources: CS231n Stanford Course, Fast.ai Computer Vision.
---
### Month 3: Advanced AI Techniques & Projects
Week 9: Natural Language Processing (NLP)
- Key Concepts: Tokenization, Embeddings, Sentiment Analysis.
- Task: Implement text classification using NLTK/Spacy or transformers.
- Resources: Hugging Face, Coursera NLP courses.
Week 10: Reinforcement Learning
- Key Concepts: Q-learning, Markov Decision Processes (MDP), Policy Gradients.
- Task: Solve a simple RL problem (e.g., OpenAI Gym).
- Resources: Sutton and Bartoโs book on Reinforcement Learning, OpenAI Gym.
Week 11: AI Model Deployment
- Key Concepts: Model deployment using Flask/Streamlit, Model Serving.
- Task: Deploy a trained model using Flask API or Streamlit.
- Resources: Heroku deployment guides, Streamlit documentation.
Week 12: AI Capstone Project
- Task: Create a full-fledged AI project (e.g., Image recognition app, Sentiment analysis, or Chatbot).
- Presentation: Prepare and document your project.
- Goal: Deploy your AI model and share it on GitHub/Portfolio.
### Tools and Platforms:
- Python IDE: Jupyter, PyCharm, or VSCode.
- Datasets: Kaggle, UCI Machine Learning Repository.
- Version Control: GitHub or GitLab for managing code.
Free Books and Courses to Learn Artificial Intelligence๐๐
Introduction to AI for Business Free Course
Top Platforms for Building Data Science Portfolio
Artificial Intelligence: Foundations of Computational Agents Free Book
Learn Basics about AI Free Udemy Course
Amazing AI Reverse Image Search
By following this roadmap, youโll gain a strong understanding of AI concepts and practical skills in Python, machine learning, and neural networks.
Join @free4unow_backup for more free courses
ENJOY LEARNING ๐๐
### Month 1: Fundamentals of AI and Python
Week 1: Introduction to AI
- Key Concepts: What is AI? Categories (Narrow AI, General AI, Super AI), Applications of AI.
- Reading: Research papers and articles on AI.
- Task: Watch introductory AI videos (e.g., Andrew Ng's "What is AI?" on Coursera).
Week 2: Python for AI
- Skills: Basics of Python programming (variables, loops, conditionals, functions, OOP).
- Resources: Python tutorials (W3Schools, Real Python).
- Task: Write simple Python scripts.
Week 3: Libraries for AI
- Key Libraries: NumPy, Pandas, Matplotlib, Scikit-learn.
- Task: Install libraries and practice data manipulation and visualization.
- Resources: Documentation and tutorials on these libraries.
Week 4: Linear Algebra and Probability
- Key Topics: Matrices, Vectors, Eigenvalues, Probability theory.
- Resources: Khan Academy (Linear Algebra), MIT OCW.
- Task: Solve basic linear algebra problems and write Python functions to implement them.
---
### Month 2: Core AI Techniques & Machine Learning
Week 5: Machine Learning Basics
- Key Concepts: Supervised, Unsupervised learning, Model evaluation metrics.
- Algorithms: Linear Regression, Logistic Regression.
- Task: Build basic models using Scikit-learn.
- Resources: Courseraโs Machine Learning by Andrew Ng, Kaggle datasets.
Week 6: Decision Trees, Random Forests, and KNN
- Key Concepts: Decision Trees, Random Forests, K-Nearest Neighbors (KNN).
- Task: Implement these algorithms and analyze their performance.
- Resources: Hands-on Machine Learning with Scikit-learn.
Week 7: Neural Networks & Deep Learning
- Key Concepts: Artificial Neurons, Forward and Backpropagation, Activation Functions.
- Framework: TensorFlow, Keras.
- Task: Build a simple neural network for a classification problem.
- Resources: Fast.ai, Coursera Deep Learning Specialization by Andrew Ng.
Week 8: Convolutional Neural Networks (CNN)
- Key Concepts: Image classification, Convolution, Pooling.
- Task: Build a CNN using Keras/TensorFlow to classify images (e.g., CIFAR-10 dataset).
- Resources: CS231n Stanford Course, Fast.ai Computer Vision.
---
### Month 3: Advanced AI Techniques & Projects
Week 9: Natural Language Processing (NLP)
- Key Concepts: Tokenization, Embeddings, Sentiment Analysis.
- Task: Implement text classification using NLTK/Spacy or transformers.
- Resources: Hugging Face, Coursera NLP courses.
Week 10: Reinforcement Learning
- Key Concepts: Q-learning, Markov Decision Processes (MDP), Policy Gradients.
- Task: Solve a simple RL problem (e.g., OpenAI Gym).
- Resources: Sutton and Bartoโs book on Reinforcement Learning, OpenAI Gym.
Week 11: AI Model Deployment
- Key Concepts: Model deployment using Flask/Streamlit, Model Serving.
- Task: Deploy a trained model using Flask API or Streamlit.
- Resources: Heroku deployment guides, Streamlit documentation.
Week 12: AI Capstone Project
- Task: Create a full-fledged AI project (e.g., Image recognition app, Sentiment analysis, or Chatbot).
- Presentation: Prepare and document your project.
- Goal: Deploy your AI model and share it on GitHub/Portfolio.
### Tools and Platforms:
- Python IDE: Jupyter, PyCharm, or VSCode.
- Datasets: Kaggle, UCI Machine Learning Repository.
- Version Control: GitHub or GitLab for managing code.
Free Books and Courses to Learn Artificial Intelligence๐๐
Introduction to AI for Business Free Course
Top Platforms for Building Data Science Portfolio
Artificial Intelligence: Foundations of Computational Agents Free Book
Learn Basics about AI Free Udemy Course
Amazing AI Reverse Image Search
By following this roadmap, youโll gain a strong understanding of AI concepts and practical skills in Python, machine learning, and neural networks.
Join @free4unow_backup for more free courses
ENJOY LEARNING ๐๐
โค12
Data Science Interview Questions with Answers ๐
Q1: How would you analyze time series data to forecast production rates for a manufacturing unit?
Ans: I'd use tools like Prophet for time series forecasting. After decomposing the data to identify trends and seasonality, I'd build a model to forecast production rates.
Q2: Describe a situation where you had to design a data warehousing solution for large-scale manufacturing data.
Ans: For a project with multiple manufacturing units, I designed a star schema with a central fact table and surrounding dimension tables to allow for efficient querying.
Q3: How would you use data to identify bottlenecks in a production line?
Ans: I'd analyze production metrics, time logs, and machine efficiency data to identify stages in the production line with delays or reduced output, pinpointing potential bottlenecks.
Q4: How do you ensure data accuracy and consistency in a manufacturing environment with multiple data sources?
Ans: I'd implement data validation checks, use standardized data collection protocols across units, and set up regular data reconciliation processes to ensure accuracy and consistency.
Q1: How would you analyze time series data to forecast production rates for a manufacturing unit?
Ans: I'd use tools like Prophet for time series forecasting. After decomposing the data to identify trends and seasonality, I'd build a model to forecast production rates.
Q2: Describe a situation where you had to design a data warehousing solution for large-scale manufacturing data.
Ans: For a project with multiple manufacturing units, I designed a star schema with a central fact table and surrounding dimension tables to allow for efficient querying.
Q3: How would you use data to identify bottlenecks in a production line?
Ans: I'd analyze production metrics, time logs, and machine efficiency data to identify stages in the production line with delays or reduced output, pinpointing potential bottlenecks.
Q4: How do you ensure data accuracy and consistency in a manufacturing environment with multiple data sources?
Ans: I'd implement data validation checks, use standardized data collection protocols across units, and set up regular data reconciliation processes to ensure accuracy and consistency.
โค5๐1
๐ฆ๐ค๐ ๐๐ผ๐ถ๐ป๐ ๐๐ต๐ฒ๐ฎ๐๐๐ต๐ฒ๐ฒ๐ - ๐๐๐น๐น๐ ๐๐
๐ฝ๐น๐ฎ๐ถ๐ป๐ฒ๐ฑ
๐ช๐ต๐ ๐ท๐ผ๐ถ๐ป๐ ๐บ๐ฎ๐๐๐ฒ๐ฟ?
Joins let you combine data from multiple tables to extract meaningful insights.
Every serious data analyst or backend dev should master these.
Letโs break them down with clarity:
๐๐ก๐ก๐๐ฅ ๐๐ข๐๐ก
โ Returns only the rows with matching keys in both tables
โ Think of it as intersection
๐๐ ๐ฎ๐บ๐ฝ๐น๐ฒ:
Customers who have placed at least one order
SELECT *
FROM Customers
INNER JOIN Orders
ON Customers.ID = Orders.CustomerID;
๐๐๐๐ง ๐๐ข๐๐ก (๐ข๐จ๐ง๐๐ฅ)
โ Returns all rows from the left table + matching rows from the right
โ If no match, right side = NULL
๐๐ ๐ฎ๐บ๐ฝ๐น๐ฒ:
List all customers, even if theyโve never ordered
SELECT *
FROM Customers
LEFT JOIN Orders
ON Customers.ID = Orders.CustomerID;
๐ฅ๐๐๐๐ง ๐๐ข๐๐ก (๐ข๐จ๐ง๐๐ฅ)
โ Returns all rows from the right table + matching rows from the left
โ Rarely used, but similar logic
๐๐ ๐ฎ๐บ๐ฝ๐น๐ฒ:
All orders, even from unknown or deleted customers
SELECT *
FROM Customers
RIGHT JOIN Orders
ON Customers.ID = Orders.CustomerID;
๐๐จ๐๐ ๐ข๐จ๐ง๐๐ฅ ๐๐ข๐๐ก
โ Returns all records when thereโs a match in either table
โ Unmatched rows = NULLs
๐๐ ๐ฎ๐บ๐ฝ๐น๐ฒ:
Show all customers and all orders, whether matched or not
SELECT *
FROM Customers
FULL OUTER JOIN Orders
ON Customers.ID = Orders.CustomerID;
๐๐ฅ๐ข๐ฆ๐ฆ ๐๐ข๐๐ก
โ Returns Cartesian product (all combinations)
โ Use with care. 1,000 x 1,000 rows = 1,000,000 results!
๐๐ ๐ฎ๐บ๐ฝ๐น๐ฒ:
Show all possible product and supplier pairings
SELECT *
FROM Products
CROSS JOIN Suppliers;
๐ฆ๐๐๐ ๐๐ข๐๐ก
โ Join a table to itself
โ Used for hierarchical data like employees & managers
๐๐ ๐ฎ๐บ๐ฝ๐น๐ฒ:
Find each employeeโs manager
SELECT A.Name AS Employee, B.Name AS Manager
FROM Employees A
JOIN Employees B
ON A.ManagerID = B.ID;
๐๐ฒ๐๐ ๐ฃ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฒ๐
โ Always use aliases (A, B) to simplify joins
โ Use JOIN ON instead of WHERE for better clarity
โ Test each join with LIMIT first to avoid surprises
---
๐ช๐ต๐ ๐ท๐ผ๐ถ๐ป๐ ๐บ๐ฎ๐๐๐ฒ๐ฟ?
Joins let you combine data from multiple tables to extract meaningful insights.
Every serious data analyst or backend dev should master these.
Letโs break them down with clarity:
๐๐ก๐ก๐๐ฅ ๐๐ข๐๐ก
โ Returns only the rows with matching keys in both tables
โ Think of it as intersection
๐๐ ๐ฎ๐บ๐ฝ๐น๐ฒ:
Customers who have placed at least one order
SELECT *
FROM Customers
INNER JOIN Orders
ON Customers.ID = Orders.CustomerID;
๐๐๐๐ง ๐๐ข๐๐ก (๐ข๐จ๐ง๐๐ฅ)
โ Returns all rows from the left table + matching rows from the right
โ If no match, right side = NULL
๐๐ ๐ฎ๐บ๐ฝ๐น๐ฒ:
List all customers, even if theyโve never ordered
SELECT *
FROM Customers
LEFT JOIN Orders
ON Customers.ID = Orders.CustomerID;
๐ฅ๐๐๐๐ง ๐๐ข๐๐ก (๐ข๐จ๐ง๐๐ฅ)
โ Returns all rows from the right table + matching rows from the left
โ Rarely used, but similar logic
๐๐ ๐ฎ๐บ๐ฝ๐น๐ฒ:
All orders, even from unknown or deleted customers
SELECT *
FROM Customers
RIGHT JOIN Orders
ON Customers.ID = Orders.CustomerID;
๐๐จ๐๐ ๐ข๐จ๐ง๐๐ฅ ๐๐ข๐๐ก
โ Returns all records when thereโs a match in either table
โ Unmatched rows = NULLs
๐๐ ๐ฎ๐บ๐ฝ๐น๐ฒ:
Show all customers and all orders, whether matched or not
SELECT *
FROM Customers
FULL OUTER JOIN Orders
ON Customers.ID = Orders.CustomerID;
๐๐ฅ๐ข๐ฆ๐ฆ ๐๐ข๐๐ก
โ Returns Cartesian product (all combinations)
โ Use with care. 1,000 x 1,000 rows = 1,000,000 results!
๐๐ ๐ฎ๐บ๐ฝ๐น๐ฒ:
Show all possible product and supplier pairings
SELECT *
FROM Products
CROSS JOIN Suppliers;
๐ฆ๐๐๐ ๐๐ข๐๐ก
โ Join a table to itself
โ Used for hierarchical data like employees & managers
๐๐ ๐ฎ๐บ๐ฝ๐น๐ฒ:
Find each employeeโs manager
SELECT A.Name AS Employee, B.Name AS Manager
FROM Employees A
JOIN Employees B
ON A.ManagerID = B.ID;
๐๐ฒ๐๐ ๐ฃ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฒ๐
โ Always use aliases (A, B) to simplify joins
โ Use JOIN ON instead of WHERE for better clarity
โ Test each join with LIMIT first to avoid surprises
---
โค7
๐๐๐ ๐๐๐ฌ๐ ๐๐ญ๐ฎ๐๐ข๐๐ฌ ๐๐จ๐ซ ๐๐ง๐ญ๐๐ซ๐ฏ๐ข๐๐ฐ:
Join for more: https://t.iss.one/sqlanalyst
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
Join for more: https://t.iss.one/sqlanalyst
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
โค4
Getting a job in 2017:
Apply, get interview, get offer, negotiate salary, start job.
Getting a job in 2025:
Find job you are overqualified for that is underpaying market rates, connect with current employees and ask for a recommendation, bake a cake for the potential team youโll be apart of and hope your efforts are better than other candidates, meet with the third cousin of the hiring manager to see if you are a good fit to maybe start the process of interviewing, take a 3-hour long pass
Apply, get interview, get offer, negotiate salary, start job.
Getting a job in 2025:
Find job you are overqualified for that is underpaying market rates, connect with current employees and ask for a recommendation, bake a cake for the potential team youโll be apart of and hope your efforts are better than other candidates, meet with the third cousin of the hiring manager to see if you are a good fit to maybe start the process of interviewing, take a 3-hour long pass
โค7
Cold email template for Freshers ๐
Dear {NAME},
I hope this email finds you in good health and high spirits. I am writing to express my keen interest in the internship opportunity at the {NAME} and to submit my application for your consideration.
Allow me to introduce myself. My name is Ashok Aggarwal, and I am a statistics major with a specialization in Data Science. I have been following the remarkable work conducted by {NAME} and the valuable contributions it has made to the field of biomedical research and public health. I am truly inspired by the {One USP}
Having reviewed the internship description and requirements, I firmly believe that my academic background and skills make me a strong candidate for this opportunity. I have a solid foundation in statistics and data analysis, along with proficiency in relevant software such as Python, NumPy, Pandas, and visualization tools like Matplotlib. Furthermore, my prior project on {xyz} has reinforced my passion for utilizing data-driven insights to understand {XYZ}
Joining {name} for this internship would provide me with a tremendous platform to contribute my statistical expertise and collaborate with esteemed scientists like yourself. I am eager to work closely with the research team, assist in communications campaigns, engage in community programs, and learn from the collective expertise at {Name}.
I have attached my resume and would be grateful if you could review my application. I am available for an interview at your convenience to further discuss my qualifications and how I can contribute to {NAME} initiatives. I genuinely appreciate your time and consideration.
Thank you for your attention to my application. I look forward to the possibility of joining {NAME} and making a meaningful contribution to the organization's mission. Should you require any further information or documentation, please do not hesitate to contact me.
Wishing you a productive day ahead.
Sincerely,
{Full Name}
Dear {NAME},
I hope this email finds you in good health and high spirits. I am writing to express my keen interest in the internship opportunity at the {NAME} and to submit my application for your consideration.
Allow me to introduce myself. My name is Ashok Aggarwal, and I am a statistics major with a specialization in Data Science. I have been following the remarkable work conducted by {NAME} and the valuable contributions it has made to the field of biomedical research and public health. I am truly inspired by the {One USP}
Having reviewed the internship description and requirements, I firmly believe that my academic background and skills make me a strong candidate for this opportunity. I have a solid foundation in statistics and data analysis, along with proficiency in relevant software such as Python, NumPy, Pandas, and visualization tools like Matplotlib. Furthermore, my prior project on {xyz} has reinforced my passion for utilizing data-driven insights to understand {XYZ}
Joining {name} for this internship would provide me with a tremendous platform to contribute my statistical expertise and collaborate with esteemed scientists like yourself. I am eager to work closely with the research team, assist in communications campaigns, engage in community programs, and learn from the collective expertise at {Name}.
I have attached my resume and would be grateful if you could review my application. I am available for an interview at your convenience to further discuss my qualifications and how I can contribute to {NAME} initiatives. I genuinely appreciate your time and consideration.
Thank you for your attention to my application. I look forward to the possibility of joining {NAME} and making a meaningful contribution to the organization's mission. Should you require any further information or documentation, please do not hesitate to contact me.
Wishing you a productive day ahead.
Sincerely,
{Full Name}
โค5
Handling Datasets of All Types โ Part 1 of 5: Introduction and Basic Concepts โ๏ธ
1. What is a Dataset?
โข A dataset is a structured collection of data, usually organized in rows and columns, used for analysis or training machine learning models.
2. Types of Datasets
โข Structured Data: Tables, spreadsheets with rows and columns (e.g., CSV, Excel).
โข Unstructured Data: Images, text, audio, video.
โข Semi-structured Data: JSON, XML files containing hierarchical data.
3. Common Dataset Formats
โข CSV (Comma-Separated Values)
โข Excel (.xls, .xlsx)
โข JSON (JavaScript Object Notation)
โข XML (eXtensible Markup Language)
โข Images (JPEG, PNG, TIFF)
โข Audio (WAV, MP3)
4. Loading Datasets in Python
โข Use libraries like
โข Use libraries like
5. Basic Dataset Exploration
โข Check shape and size:
โข Preview data:
โข Check for missing values:
6. Summary
โข Understanding dataset types is crucial before processing.
โข Loading and exploring datasets helps identify cleaning and preprocessing needs.
Exercise
โข Load a CSV and JSON dataset in Python, print their shapes, and identify missing values.
Hope this helped youโ๏ธ
1. What is a Dataset?
โข A dataset is a structured collection of data, usually organized in rows and columns, used for analysis or training machine learning models.
2. Types of Datasets
โข Structured Data: Tables, spreadsheets with rows and columns (e.g., CSV, Excel).
โข Unstructured Data: Images, text, audio, video.
โข Semi-structured Data: JSON, XML files containing hierarchical data.
3. Common Dataset Formats
โข CSV (Comma-Separated Values)
โข Excel (.xls, .xlsx)
โข JSON (JavaScript Object Notation)
โข XML (eXtensible Markup Language)
โข Images (JPEG, PNG, TIFF)
โข Audio (WAV, MP3)
4. Loading Datasets in Python
โข Use libraries like
pandas for structured data:import pandas as pd
df = pd.read_csv('data.csv')
โข Use libraries like
json for JSON files:import json
with open('data.json') as f:
data = json.load(f)
5. Basic Dataset Exploration
โข Check shape and size:
print(df.shape)
โข Preview data:
print(df.head())
โข Check for missing values:
print(df.isnull().sum())
6. Summary
โข Understanding dataset types is crucial before processing.
โข Loading and exploring datasets helps identify cleaning and preprocessing needs.
Exercise
โข Load a CSV and JSON dataset in Python, print their shapes, and identify missing values.
Hope this helped you
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Data Science & Machine Learning
What's the correct answer ๐๐
a = "10" โ Variable a is assigned the string "10".
b = a โ Variable b also holds the string "10" (but it's not used afterward).
a = a * 2 โ Since a is a string, multiplying it by an integer results in string repetition.
"10" * 2 results in "1010"
print(a) โ prints the new value of a, which is "1010".โ Correct answer: D. 1010
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