DS Question and Answer.pdf
16.7 MB
Data Science Question and Answer!!
Hope this helps !!
Do react for more post like these!! ➡️➡️📖
Hope this helps !!
Do react for more post like these!! ➡️➡️📖
👍6
PySpark_Key_Points_1_Basics_PySpark_Python_API_for_Apache_Spark.pdf
12.7 MB
🐍 PySpark vs Pandas
1. Data Handling
✅Pandas:
Best for small to medium-sized datasets.
Works on a single machine (in-memory processing).
Suitable for datasets that fit in memory.
✅PySpark:
Designed for large-scale data processing.
Can handle big datasets that don’t fit in memory (distributed processing).
Works across multiple machines (clusters).
2. Performance
✅Pandas:
Faster for small datasets (single-machine operations).
May slow down with very large datasets.
✅PySpark:
Faster for large datasets (distributed computing).
Optimized for parallel processing.
3. Ease of Use
✅Pandas:
Simple and easy to use for data manipulation and analysis.
Rich set of functions and operations.
✅PySpark:
More complex and requires setup (cluster, Spark context).
Similar operations to Pandas, but for distributed data.
Hope this helps !!
Do react for more post like these!! ➡️➡️📖
1. Data Handling
✅Pandas:
Best for small to medium-sized datasets.
Works on a single machine (in-memory processing).
Suitable for datasets that fit in memory.
✅PySpark:
Designed for large-scale data processing.
Can handle big datasets that don’t fit in memory (distributed processing).
Works across multiple machines (clusters).
2. Performance
✅Pandas:
Faster for small datasets (single-machine operations).
May slow down with very large datasets.
✅PySpark:
Faster for large datasets (distributed computing).
Optimized for parallel processing.
3. Ease of Use
✅Pandas:
Simple and easy to use for data manipulation and analysis.
Rich set of functions and operations.
✅PySpark:
More complex and requires setup (cluster, Spark context).
Similar operations to Pandas, but for distributed data.
Hope this helps !!
Do react for more post like these!! ➡️➡️📖
👍6
What is Apache Spark and Where to learn them?
Apache Spark is a powerful distributed data processing framework used for big data and machine learning tasks. Here are some excellent resources to learn Apache Spark, catering to various levels of expertise:
1. Follow - Apache Spark Official Documentation
- Great starting point with detailed tutorials and guides.
- Covers installation, core concepts, and APIs for Scala, Python (PySpark), Java, and R.
2. YouTube Tutorials
- Free video tutorials by channels like Simplilearn or Data Engineering Simplified.
3. Coursera and edX Courses
- Coursera: Big Data Analysis with Scala and Spark (offered by École Polytechnique Fédérale de Lausanne).
- edX: Introduction to Big Data with Apache Spark (offered by UC Berkeley).
Apache Spark is a powerful distributed data processing framework used for big data and machine learning tasks. Here are some excellent resources to learn Apache Spark, catering to various levels of expertise:
1. Follow - Apache Spark Official Documentation
- Great starting point with detailed tutorials and guides.
- Covers installation, core concepts, and APIs for Scala, Python (PySpark), Java, and R.
2. YouTube Tutorials
- Free video tutorials by channels like Simplilearn or Data Engineering Simplified.
3. Coursera and edX Courses
- Coursera: Big Data Analysis with Scala and Spark (offered by École Polytechnique Fédérale de Lausanne).
- edX: Introduction to Big Data with Apache Spark (offered by UC Berkeley).
👍1
Capgemini is hiring!
Position: HR Analyst
Qualification: Bachelor’s/ Master’s/ MBA
Salary: 4 - 6 LPA (Expected)
Experience: Freshers/ Experienced
Location: Bangalore; Kolkata, India
📌Apply Now: https://careers.capgemini.com/job/Bangalore-HR-Operational-Excellence-Analyst-A/1134863701/
https://careers.capgemini.com/job/Kolkata-HR-Global-Shared-Services-Analyst-A/1153641201/
Position: HR Analyst
Qualification: Bachelor’s/ Master’s/ MBA
Salary: 4 - 6 LPA (Expected)
Experience: Freshers/ Experienced
Location: Bangalore; Kolkata, India
📌Apply Now: https://careers.capgemini.com/job/Bangalore-HR-Operational-Excellence-Analyst-A/1134863701/
https://careers.capgemini.com/job/Kolkata-HR-Global-Shared-Services-Analyst-A/1153641201/
❤2
Exploratory Data Analysis (EDA) (1).pdf
968.3 KB
✅ Hope this helps !!
Do react for more post like these !! ➡️➡️
Do react for more post like these !! ➡️➡️
❤3👍1
Here are some SQL project ideas tailored for data analysis:
🔟 SQL Project Ideas for Data Analysts
1. Sales Database Analysis: Create a database to track sales transactions. Write SQL queries to analyze sales performance by product, region, and time period.
2. Customer Churn Analysis: Build a database with customer data and track churn rates. Use SQL to identify factors contributing to churn and segment customers.
3. E-commerce Order Tracking: Design a database for an e-commerce platform. Write queries to analyze order trends, average order value, and customer purchase history.
4. Employee Performance Metrics: Create a database for employee records and performance reviews. Analyze employee performance trends and identify high performers using SQL.
5. Inventory Management System: Set up a database to track inventory levels. Write SQL queries to monitor stock levels, identify slow-moving items, and generate restock reports.
6. Healthcare Patient Analysis: Build a database to manage patient records and treatments. Use SQL to analyze treatment outcomes, readmission rates, and patient demographics.
7. Social Media Engagement Analysis: Create a database to track user interactions on a social media platform. Write queries to analyze engagement metrics like likes, shares, and comments.
8. Financial Transaction Analysis: Set up a database for financial transactions. Use SQL to identify spending patterns, categorize expenses, and generate monthly financial reports.
9. Website Traffic Analysis: Build a database to track website visitors. Write queries to analyze traffic sources, user behavior, and page performance.
10. Survey Results Analysis: Create a database to store survey responses. Use SQL to analyze responses, identify trends, and visualize findings based on demographic data.
Here you can find essential SQL Interview Resources👇
https://topmate.io/codingdidi
Like this post if you need more 👍❤️
Hope it helps :)
🔟 SQL Project Ideas for Data Analysts
1. Sales Database Analysis: Create a database to track sales transactions. Write SQL queries to analyze sales performance by product, region, and time period.
2. Customer Churn Analysis: Build a database with customer data and track churn rates. Use SQL to identify factors contributing to churn and segment customers.
3. E-commerce Order Tracking: Design a database for an e-commerce platform. Write queries to analyze order trends, average order value, and customer purchase history.
4. Employee Performance Metrics: Create a database for employee records and performance reviews. Analyze employee performance trends and identify high performers using SQL.
5. Inventory Management System: Set up a database to track inventory levels. Write SQL queries to monitor stock levels, identify slow-moving items, and generate restock reports.
6. Healthcare Patient Analysis: Build a database to manage patient records and treatments. Use SQL to analyze treatment outcomes, readmission rates, and patient demographics.
7. Social Media Engagement Analysis: Create a database to track user interactions on a social media platform. Write queries to analyze engagement metrics like likes, shares, and comments.
8. Financial Transaction Analysis: Set up a database for financial transactions. Use SQL to identify spending patterns, categorize expenses, and generate monthly financial reports.
9. Website Traffic Analysis: Build a database to track website visitors. Write queries to analyze traffic sources, user behavior, and page performance.
10. Survey Results Analysis: Create a database to store survey responses. Use SQL to analyze responses, identify trends, and visualize findings based on demographic data.
Here you can find essential SQL Interview Resources👇
https://topmate.io/codingdidi
Like this post if you need more 👍❤️
Hope it helps :)
topmate.io
Codingdidi
Content Creator
👍4❤2
Leetcode Db question with solution.pdf
349.9 KB
Leetcode Questions and Solution.
Do react with 😍, if you found this helpful.
Do react with 😍, if you found this helpful.
👍6
Data cleaning within Python.pdf
207.4 KB
"📊 Data Cleaning with Python 🐍
Credits to the original author for this amazing resource! 🙌
Sharing it with the community to help you master the essential concepts of data cleaning. 🌟
Let’s learn and grow together! 💡
Do React with 🤩, if you found this helpful.
Credits to the original author for this amazing resource! 🙌
Sharing it with the community to help you master the essential concepts of data cleaning. 🌟
Let’s learn and grow together! 💡
Do React with 🤩, if you found this helpful.
👍1
Learning Python has never been this engaging! 🍔🍟🧋
👉 Learn Python ZERO TO HERO
🔥 7000+ Free Courses | Free Access:
🔗 https://freecoderzone.blogspot.com/2025/01/7000-free-courses.html
📘 Top Python Learning Resources:
1️⃣ Python for Everybody Specialization
🔗 https://www.coursera.org/specializations/python
2️⃣ Crash Course on Python
🔗 https://www.coursera.org/learn/python-crash-course
3️⃣ Get Started with Python
🔗 https://developer.mozilla.org/en-US/docs/Learn/Server-side/Python/Introduction
4️⃣ Python for Data Science, AI & Development
🔗 https://www.edx.org/course/python-for-data-science-ai-development
5️⃣ Google Data Analytics
🔗 https://www.coursera.org/professional-certificates/google-data-analytics
6️⃣ Google Advanced Data Analytics
🔗 https://www.coursera.org/professional-certificates/google-advanced-data-analytics
7️⃣ IBM Data Science Professional Certificate
🔗 https://www.coursera.org/professional-certificates/ibm-data-science
8️⃣ IBM Data Warehouse Engineer Professional Certificate
🔗 https://www.coursera.org/professional-certificates/ibm-data-warehouse-engineer
9️⃣ IBM Cybersecurity Analyst Professional Certificate
🔗 https://www.coursera.org/professional-certificates/ibm-cybersecurity-analyst
🔟 IBM AI Engineering Professional Certificate
🔗 https://www.coursera.org/professional-certificates/ai-engineering
1️⃣1️⃣ IBM DevOps and Software Engineering Professional Certificate
🔗 https://www.coursera.org/professional-certificates/ibm-devops-and-software-engineering
Let’s make Python learning fun and interactive! 🚀
#Python #LearnPython #Programming #CodingSkills #DataScience
👉 Learn Python ZERO TO HERO
🔥 7000+ Free Courses | Free Access:
🔗 https://freecoderzone.blogspot.com/2025/01/7000-free-courses.html
📘 Top Python Learning Resources:
1️⃣ Python for Everybody Specialization
🔗 https://www.coursera.org/specializations/python
2️⃣ Crash Course on Python
🔗 https://www.coursera.org/learn/python-crash-course
3️⃣ Get Started with Python
🔗 https://developer.mozilla.org/en-US/docs/Learn/Server-side/Python/Introduction
4️⃣ Python for Data Science, AI & Development
🔗 https://www.edx.org/course/python-for-data-science-ai-development
5️⃣ Google Data Analytics
🔗 https://www.coursera.org/professional-certificates/google-data-analytics
6️⃣ Google Advanced Data Analytics
🔗 https://www.coursera.org/professional-certificates/google-advanced-data-analytics
7️⃣ IBM Data Science Professional Certificate
🔗 https://www.coursera.org/professional-certificates/ibm-data-science
8️⃣ IBM Data Warehouse Engineer Professional Certificate
🔗 https://www.coursera.org/professional-certificates/ibm-data-warehouse-engineer
9️⃣ IBM Cybersecurity Analyst Professional Certificate
🔗 https://www.coursera.org/professional-certificates/ibm-cybersecurity-analyst
🔟 IBM AI Engineering Professional Certificate
🔗 https://www.coursera.org/professional-certificates/ai-engineering
1️⃣1️⃣ IBM DevOps and Software Engineering Professional Certificate
🔗 https://www.coursera.org/professional-certificates/ibm-devops-and-software-engineering
Let’s make Python learning fun and interactive! 🚀
#Python #LearnPython #Programming #CodingSkills #DataScience
Coursera
Python for Everybody
Offered by University of Michigan. Learn to Program and ... Enroll for free.
👍5❤1
Don't Limit Yourself to Just One Title, "𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭" in Your Job Search!
Don't get caught up in the confines of a single job title! There are countless roles out there that might align perfectly with your skills and interests. Here are a few alternative titles for data analyst roles to broaden your search horizons:
1. QI Analyst
2. Risk Analyst
3. Data Modeler
4. Research Analyst
5. Business Analyst
6. Reporting Analyst
7. Operations Analyst
8. Social Media Analyst
9. Statistical Analyst
10. Statistical Analyst
11. Product Data Analyst
12. Analytics Engineer
13. Supply Chain Analyst
14. Data Mining Engineer
15. Data Science Associate
16. Financial Data Analyst
17. Cybersecurity Analyst
18. Marketing Data Analyst
19. Quantitative Analyst
20. HR Analytics Specialist
21. Decision Support Analyst
22. Machine Learning Analyst
23. Fraud Detection Analyst
24. Healthcare Data Analyst
25. Data Insights Specialist
26. Data Visualization Specialist
27. Customer Insights Analyst
28. Business Intelligence Analyst
29. Predictive Analytics Analyst
Remember, the right opportunity might be hiding behind a different title than you expect. Keep an open mind and explore all avenues in your job search journey!
Also, there might be fewer applicants for these roles as many don't search for titles other than data Analyst or Business Analyst. Maybe you can get more calls or interviews this way.
You don't have to try all the titles, filter out based on your interests and skills!
After all, 𝐉𝐨𝐛 𝐃𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐨𝐧 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐭𝐡𝐞 𝐭𝐢𝐭𝐥𝐞!! 😉
like for more❤️
Don't get caught up in the confines of a single job title! There are countless roles out there that might align perfectly with your skills and interests. Here are a few alternative titles for data analyst roles to broaden your search horizons:
1. QI Analyst
2. Risk Analyst
3. Data Modeler
4. Research Analyst
5. Business Analyst
6. Reporting Analyst
7. Operations Analyst
8. Social Media Analyst
9. Statistical Analyst
10. Statistical Analyst
11. Product Data Analyst
12. Analytics Engineer
13. Supply Chain Analyst
14. Data Mining Engineer
15. Data Science Associate
16. Financial Data Analyst
17. Cybersecurity Analyst
18. Marketing Data Analyst
19. Quantitative Analyst
20. HR Analytics Specialist
21. Decision Support Analyst
22. Machine Learning Analyst
23. Fraud Detection Analyst
24. Healthcare Data Analyst
25. Data Insights Specialist
26. Data Visualization Specialist
27. Customer Insights Analyst
28. Business Intelligence Analyst
29. Predictive Analytics Analyst
Remember, the right opportunity might be hiding behind a different title than you expect. Keep an open mind and explore all avenues in your job search journey!
Also, there might be fewer applicants for these roles as many don't search for titles other than data Analyst or Business Analyst. Maybe you can get more calls or interviews this way.
You don't have to try all the titles, filter out based on your interests and skills!
After all, 𝐉𝐨𝐛 𝐃𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐨𝐧 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐭𝐡𝐞 𝐭𝐢𝐭𝐥𝐞!! 😉
like for more❤️
👍9❤4👏1
Societe Generale is hiring!
Position: Analyst
Qualifications: Bachelor’s/ Master's Degree
Salary: 4 - 7 LPA (Expected)
Experience: Entry Level
Location: Bangalore, India (Hybrid)
📌Apply Now: https://careers.societegenerale.com/en/job-offers/analyst-24000PV0-en?src=JB-14381
Position: Analyst
Qualifications: Bachelor’s/ Master's Degree
Salary: 4 - 7 LPA (Expected)
Experience: Entry Level
Location: Bangalore, India (Hybrid)
📌Apply Now: https://careers.societegenerale.com/en/job-offers/analyst-24000PV0-en?src=JB-14381
👍1
7 Best GitHub Repositories to Break into Data Analytics and Data Science
If you're diving into data science or data analytics, these repositories will give you the edge you need. Check them out:
1️⃣ 100-Days-Of-ML-Code
🔗 https://github.com/Avik-Jain/100-Days-Of-ML-Code
⭐️ Stars: ~42k
2️⃣ awesome-datascience
🔗 https://github.com/academic/awesome-datascience
⭐️ Stars: ~22.7k
3️⃣ Data-Science-For-Beginners
🔗 https://github.com/microsoft/Data-Science-For-Beginners
⭐️ Stars: ~14.5k
4️⃣ data-science-interviews
🔗 https://github.com/alexeygrigorev/data-science-interviews
⭐️ Stars: ~5.8k
5️⃣ Coding and ML System Design
🔗 https://github.com/weeeBox/coding-and-ml-system-design
⭐️ Stars: ~3.5k
6️⃣ Machine Learning Interviews from MAANG
🔗 https://github.com/arunkumarpillai/Machine-Learning-Interviews
⭐️ Stars: ~8.1k
7️⃣ data-science-ipython-notebooks
🔗 https://github.com/donnemartin/data-science-ipython-notebooks
⭐️ Stars: ~27.2k
Explore these amazing resources and take your data science journey to the next level! 🚀
#DataScience #DataAnalytics #GitHub #MachineLearning #CodingSkills
If you're diving into data science or data analytics, these repositories will give you the edge you need. Check them out:
1️⃣ 100-Days-Of-ML-Code
🔗 https://github.com/Avik-Jain/100-Days-Of-ML-Code
⭐️ Stars: ~42k
2️⃣ awesome-datascience
🔗 https://github.com/academic/awesome-datascience
⭐️ Stars: ~22.7k
3️⃣ Data-Science-For-Beginners
🔗 https://github.com/microsoft/Data-Science-For-Beginners
⭐️ Stars: ~14.5k
4️⃣ data-science-interviews
🔗 https://github.com/alexeygrigorev/data-science-interviews
⭐️ Stars: ~5.8k
5️⃣ Coding and ML System Design
🔗 https://github.com/weeeBox/coding-and-ml-system-design
⭐️ Stars: ~3.5k
6️⃣ Machine Learning Interviews from MAANG
🔗 https://github.com/arunkumarpillai/Machine-Learning-Interviews
⭐️ Stars: ~8.1k
7️⃣ data-science-ipython-notebooks
🔗 https://github.com/donnemartin/data-science-ipython-notebooks
⭐️ Stars: ~27.2k
Explore these amazing resources and take your data science journey to the next level! 🚀
#DataScience #DataAnalytics #GitHub #MachineLearning #CodingSkills
GitHub
GitHub - Avik-Jain/100-Days-Of-ML-Code: 100 Days of ML Coding
100 Days of ML Coding. Contribute to Avik-Jain/100-Days-Of-ML-Code development by creating an account on GitHub.
👍3❤2
if you're a data analyst.
you need to clean data as your job
This is how you should learn data cleaning for 2025:
✅Learn how to handle missing values
✅Learn data normalization and standardization
✅Learn to remove duplicates
✅Learn how to handle outliers
✅Learn how to merge and join datasets
✅Learn to identify and correct data inconsistencies
Data cleaning is an essential step to make your analysis meaningful.
you need to clean data as your job
This is how you should learn data cleaning for 2025:
✅Learn how to handle missing values
✅Learn data normalization and standardization
✅Learn to remove duplicates
✅Learn how to handle outliers
✅Learn how to merge and join datasets
✅Learn to identify and correct data inconsistencies
Data cleaning is an essential step to make your analysis meaningful.
❤3👍2
THOMSON REUTERS is Hiring for DATA ENGINEER
Role:- DATA ENGINEER
Qualifications:- GRADUATION
Experience:- Fresher's and Experienced
Mode:- WORK FROM OFFICE
CTC:- 15 LPA
Location:- BANGALORE, KARNATAKA & HYDERABAD, TELANGANA
Apply Now:- https://careers.thomsonreuters.com/us/en/job/THTTRUUSJREQ185931EXTERNALENUS/Data-Engineer
Role:- DATA ENGINEER
Qualifications:- GRADUATION
Experience:- Fresher's and Experienced
Mode:- WORK FROM OFFICE
CTC:- 15 LPA
Location:- BANGALORE, KARNATAKA & HYDERABAD, TELANGANA
Apply Now:- https://careers.thomsonreuters.com/us/en/job/THTTRUUSJREQ185931EXTERNALENUS/Data-Engineer
👍1
1. Handling Missing Values
- Kaggle Tutorial: [Handling Missing Values](https://www.kaggle.com/learn/data-cleaning)
- YouTube Video: ["Dealing with Missing Data in Python"](https://www.youtube.com/watch?v=wvsE8jm1GzE) by Data School
- Blog Post: [Complete Guide to Handling Missing Data in Python](https://towardsdatascience.com/complete-guide-to-handling-missing-data-in-python-95c1221fba0e)
---
2. Data Normalization and Standardization
- Blog Post: [Normalization vs. Standardization Explained](https://machinelearningmastery.com/normalize-standardize-machine-learning-data-python/)
- Interactive Course: [Feature Scaling and Normalization (DataCamp)](https://www.datacamp.com/)
- YouTube Video: ["Feature Scaling in Machine Learning"](https://www.youtube.com/watch?v=UvK0B5JZpM8) by StatQuest
---
3. Removing Duplicates
- Official Pandas Documentation: [Pandas drop_duplicates()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop_duplicates.html)
- Video: ["Removing Duplicates in Python"](https://www.youtube.com/watch?v=QHRrl4Il2Og) by Corey Schafer
- Blog Post: [How to Remove Duplicates Using Python](https://realpython.com/python-data-cleaning-numpy-pandas/)
---
4. Handling Outliers
- Blog Post: [5 Methods to Deal with Outliers in Data](https://towardsdatascience.com/handling-outliers-in-your-data-7cde6b4d76bb)
- Video: ["Identifying and Handling Outliers in Python"](https://www.youtube.com/watch?v=TN-xVNUcDk8) by Krish Naik
- Jupyter Notebook Example: [Outlier Detection with Python](https://github.com/datascience-projects)
---
5. Merging and Joining Datasets
- Pandas Documentation: [Merging, Joining, and Concatenating](https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html)
- Video: ["Pandas Merging and Joining"](https://www.youtube.com/watch?v=g7n1MKo7WgQ) by Corey Schafer
- Interactive Course: [Data Manipulation with Pandas (DataCamp)](https://www.datacamp.com/)
---
6. Identifying and Correcting Data Inconsistencies
- Blog Post: [Python for Data Cleaning](https://towardsdatascience.com/python-for-data-cleaning-a-step-by-step-guide-to-deal-with-data-inconsistencies-c08f06fca8c8)
- Video Tutorial: ["Python for Data Cleaning"](https://www.youtube.com/watch?v=B_L0v1xRb6E)
- Project-Based Learning: [Data Cleaning in Python Mini-Projects](https://github.com/topics/data-cleaning)
---
💡 Pro Tip: Practice real-world data cleaning tasks using open datasets on platforms like:
- [Kaggle Datasets](https://www.kaggle.com/datasets)
- [Data World](https://data.world/)
- [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php)
- Kaggle Tutorial: [Handling Missing Values](https://www.kaggle.com/learn/data-cleaning)
- YouTube Video: ["Dealing with Missing Data in Python"](https://www.youtube.com/watch?v=wvsE8jm1GzE) by Data School
- Blog Post: [Complete Guide to Handling Missing Data in Python](https://towardsdatascience.com/complete-guide-to-handling-missing-data-in-python-95c1221fba0e)
---
2. Data Normalization and Standardization
- Blog Post: [Normalization vs. Standardization Explained](https://machinelearningmastery.com/normalize-standardize-machine-learning-data-python/)
- Interactive Course: [Feature Scaling and Normalization (DataCamp)](https://www.datacamp.com/)
- YouTube Video: ["Feature Scaling in Machine Learning"](https://www.youtube.com/watch?v=UvK0B5JZpM8) by StatQuest
---
3. Removing Duplicates
- Official Pandas Documentation: [Pandas drop_duplicates()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop_duplicates.html)
- Video: ["Removing Duplicates in Python"](https://www.youtube.com/watch?v=QHRrl4Il2Og) by Corey Schafer
- Blog Post: [How to Remove Duplicates Using Python](https://realpython.com/python-data-cleaning-numpy-pandas/)
---
4. Handling Outliers
- Blog Post: [5 Methods to Deal with Outliers in Data](https://towardsdatascience.com/handling-outliers-in-your-data-7cde6b4d76bb)
- Video: ["Identifying and Handling Outliers in Python"](https://www.youtube.com/watch?v=TN-xVNUcDk8) by Krish Naik
- Jupyter Notebook Example: [Outlier Detection with Python](https://github.com/datascience-projects)
---
5. Merging and Joining Datasets
- Pandas Documentation: [Merging, Joining, and Concatenating](https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html)
- Video: ["Pandas Merging and Joining"](https://www.youtube.com/watch?v=g7n1MKo7WgQ) by Corey Schafer
- Interactive Course: [Data Manipulation with Pandas (DataCamp)](https://www.datacamp.com/)
---
6. Identifying and Correcting Data Inconsistencies
- Blog Post: [Python for Data Cleaning](https://towardsdatascience.com/python-for-data-cleaning-a-step-by-step-guide-to-deal-with-data-inconsistencies-c08f06fca8c8)
- Video Tutorial: ["Python for Data Cleaning"](https://www.youtube.com/watch?v=B_L0v1xRb6E)
- Project-Based Learning: [Data Cleaning in Python Mini-Projects](https://github.com/topics/data-cleaning)
---
💡 Pro Tip: Practice real-world data cleaning tasks using open datasets on platforms like:
- [Kaggle Datasets](https://www.kaggle.com/datasets)
- [Data World](https://data.world/)
- [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php)
Kaggle
Learn Data Cleaning Tutorials
Master efficient workflows for cleaning real-world, messy data.
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Position: Business Analyst
Qualification: Bachelor's/ Master's Degree
Salary: 5 - 8 LPA (Expected)
Experience: Freshers/ Experienced
Location: Work From Home (Remote)
📌Apply Now: https://www.revolut.com/careers/position/75445311-c0e0-4b6d-90d4-d4a23226831c/
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Atlassian is hiring!
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Position: Data Engineer, Analytics & Data Science
Qualifications: Bachelor’s/ Master’s Degree
Salary: 15 - 21 LPA (Expected)
Experience: 1 - 2 (Years)
Location: Bengaluru, India
📌Apply Now: https://careers-apac-atlassian.icims.com/jobs/17667/data-engineer/job?iis=LinkedIn&iisn=LinkedIn_Job_Ad&mobile=false&width=1215&height=500&bga=true&needsRedirect=false&jan1offset=330&jun1offset=330
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Working at Atlassian
Atlassians can choose where they work – whether in an office, from home, or a combination of the two. That way, Atlassians have more control over supporting their family, personal goals, and other priorities. We can hire people in any…
Atlassians can choose where they work – whether in an office, from home, or a combination of the two. That way, Atlassians have more control over supporting their family, personal goals, and other priorities. We can hire people in any…
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python cheetsheet.pdf
2.4 MB
*Python*
✅ High-Level & Easy to Learn – Simple syntax, readable code, and beginner-friendly.
✅ Interpreted Language – No need for compilation; executed line by line.
✅ Dynamically Typed – No need to declare variable types; Python determines them at runtime.
✅ Versatile & Multi-Purpose – Used in Web Development, Data Science, AI, ML, Automation, Cybersecurity, and more.
✅ Extensive Libraries & Frameworks – Supports TensorFlow, NumPy, Pandas, Flask, Django, OpenCV, etc.
✅ Object-Oriented & Functional – Supports both OOP and functional programming paradigms.
✅ Cross-Platform – Runs on Windows, macOS, Linux, and even mobile devices.
✅ Large Community & Support – One of the most widely used languages with extensive documentation and forums.
✅ Automation & Scripting – Ideal for task automation, web scraping, and workflow management.
✅ Strong Integration – Works with C, C++, Java, SQL, and various APIs.
✅ High-Level & Easy to Learn – Simple syntax, readable code, and beginner-friendly.
✅ Interpreted Language – No need for compilation; executed line by line.
✅ Dynamically Typed – No need to declare variable types; Python determines them at runtime.
✅ Versatile & Multi-Purpose – Used in Web Development, Data Science, AI, ML, Automation, Cybersecurity, and more.
✅ Extensive Libraries & Frameworks – Supports TensorFlow, NumPy, Pandas, Flask, Django, OpenCV, etc.
✅ Object-Oriented & Functional – Supports both OOP and functional programming paradigms.
✅ Cross-Platform – Runs on Windows, macOS, Linux, and even mobile devices.
✅ Large Community & Support – One of the most widely used languages with extensive documentation and forums.
✅ Automation & Scripting – Ideal for task automation, web scraping, and workflow management.
✅ Strong Integration – Works with C, C++, Java, SQL, and various APIs.
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