Essential Python Libraries for Data Analytics 😄👇
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1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
5. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
6. PyTorch:
- Deep learning library, particularly popular for neural network research.
7. Django:
- High-level web framework for building robust, scalable web applications.
8. Flask:
- Lightweight web framework for building smaller web applications and APIs.
9. Requests:
- HTTP library for making HTTP requests.
10. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
As a beginner, you can start with Pandas and Numpy libraries for data analysis. If you want to transition from Data Analyst to Data Scientist, then you can start applying ML libraries like Scikit-learn, Tensorflow, Pytorch, etc. in your data projects.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Python Free Resources: https://t.iss.one/pythondevelopersindia
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
5. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
6. PyTorch:
- Deep learning library, particularly popular for neural network research.
7. Django:
- High-level web framework for building robust, scalable web applications.
8. Flask:
- Lightweight web framework for building smaller web applications and APIs.
9. Requests:
- HTTP library for making HTTP requests.
10. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
As a beginner, you can start with Pandas and Numpy libraries for data analysis. If you want to transition from Data Analyst to Data Scientist, then you can start applying ML libraries like Scikit-learn, Tensorflow, Pytorch, etc. in your data projects.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
❤5
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𝗕𝗮𝘀𝗶𝗰 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻
-Brief introduction about yourself.
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𝗧𝗼𝗼𝗹𝘀 𝗣𝗿𝗼𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆
-Discussion about the tools you are proficient in.
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𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗘𝘅𝗽𝗹𝗮𝗻𝗮𝘁𝗶𝗼𝗻
Explain about any Data Analytics Project you did, below are some follow-up questions for sales related data analysis project
Follow-up Question:
Was there any improvement in sales after building the report?
Provide a clear before and after scenario in sales post-report creation.
What areas did you identify where the company was losing sales, and what were your recommendations?
- How do you check the quality of data when it's given to you?
Explain your methods for ensuring data quality.
- How do you handle null values? Describe your approach to managing null values in datasets.
𝗦𝗤𝗟 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀
-Explain the order in which SQL clauses are executed.
-Write a query to find the percentage of the 18-year-old population.
Details: You are given two tables:
Table 1: Contains states and their respective populations.
Table 2: Contains three columns (state, gender, and population of 18-year-olds).
-Explain window functions and how to rank values in SQL.
- Difference between JOIN and UNION.
-How to return unique values in SQL.
𝗕𝗲𝗵𝗮𝘃𝗶𝗼𝗿𝗮𝗹 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀
-Solve a puzzle involving 3 gallons of water in one jar and 2 gallons in another to get exactly 4 gallons.
Step-by-step solution for the water puzzle.
- What skills have you learned on your own? Discuss the skills you self-taught and their impact on your career.
-Describe cases when you showcased team spirit.
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Scenario: Choose any social media app (I choose Discord).
Question: What function/feature would you add to the Discord app, and how would you track its success?
- Rate yourself on Excel, SQL, and Python out of 10.
- What are your strengths in data analytics?
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-Brief introduction about yourself.
-Explanation of how you developed an interest in learning Power BI despite having a chemical background.
𝗧𝗼𝗼𝗹𝘀 𝗣𝗿𝗼𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆
-Discussion about the tools you are proficient in.
-Detailed explanation of a project that demonstrated your proficiency in these tools.
𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗘𝘅𝗽𝗹𝗮𝗻𝗮𝘁𝗶𝗼𝗻
Explain about any Data Analytics Project you did, below are some follow-up questions for sales related data analysis project
Follow-up Question:
Was there any improvement in sales after building the report?
Provide a clear before and after scenario in sales post-report creation.
What areas did you identify where the company was losing sales, and what were your recommendations?
- How do you check the quality of data when it's given to you?
Explain your methods for ensuring data quality.
- How do you handle null values? Describe your approach to managing null values in datasets.
𝗦𝗤𝗟 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀
-Explain the order in which SQL clauses are executed.
-Write a query to find the percentage of the 18-year-old population.
Details: You are given two tables:
Table 1: Contains states and their respective populations.
Table 2: Contains three columns (state, gender, and population of 18-year-olds).
-Explain window functions and how to rank values in SQL.
- Difference between JOIN and UNION.
-How to return unique values in SQL.
𝗕𝗲𝗵𝗮𝘃𝗶𝗼𝗿𝗮𝗹 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀
-Solve a puzzle involving 3 gallons of water in one jar and 2 gallons in another to get exactly 4 gallons.
Step-by-step solution for the water puzzle.
- What skills have you learned on your own? Discuss the skills you self-taught and their impact on your career.
-Describe cases when you showcased team spirit.
-⭐ 𝗦𝗼𝗰𝗶𝗮𝗹 𝗠𝗲𝗱𝗶𝗮 𝗔𝗽𝗽 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻
Scenario: Choose any social media app (I choose Discord).
Question: What function/feature would you add to the Discord app, and how would you track its success?
- Rate yourself on Excel, SQL, and Python out of 10.
- What are your strengths in data analytics?
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