5 misconceptions about data analytics (and what's actually true):
❌ The more sophisticated the tool, the better the analyst
✅ Many analysts do their jobs with "basic" tools like Excel
❌ You're just there to crunch the numbers
✅ You need to be able to tell a story with the data
❌ You need super advanced math skills
✅ Understanding basic math and statistics is a good place to start
❌ Data is always clean and accurate
✅ Data is never clean and 100% accurate (without lots of prep work)
❌ You'll work in isolation and not talk to anyone
✅ Communication with your team and your stakeholders is essential
❌ The more sophisticated the tool, the better the analyst
✅ Many analysts do their jobs with "basic" tools like Excel
❌ You're just there to crunch the numbers
✅ You need to be able to tell a story with the data
❌ You need super advanced math skills
✅ Understanding basic math and statistics is a good place to start
❌ Data is always clean and accurate
✅ Data is never clean and 100% accurate (without lots of prep work)
❌ You'll work in isolation and not talk to anyone
✅ Communication with your team and your stakeholders is essential
Python (Pandas) interview questions for Data analyst role(entry level): ⬇️
1. What is Python Pandas and what is it used for?
2. Different types of Data Structures in Pandas?
3. Significant features of Pandas Library?
4. Time series in Pandas?
5. Reindexing in pandas along with its parameters?
6. Data Frames in Pandas?
7. MultiIndexing in Pandas?
8. Operation on Series in Pandas?
9. Different ways of creating Data Frames in Pandas?
10. Categorical Data in Pandas?
11. How to Read Text Files with Pandas?
12. How are iloc() and loc() different?
13. Difference between join() and merge() in Pandas?
14. How to add a row/column to a Pandas DataFrame?
15.GroupBy function in Pandas?
16.Use of pandas.Dataframe.aggregate() function?
17. Statistical functions in Python Pandas?
#Python
1. What is Python Pandas and what is it used for?
2. Different types of Data Structures in Pandas?
3. Significant features of Pandas Library?
4. Time series in Pandas?
5. Reindexing in pandas along with its parameters?
6. Data Frames in Pandas?
7. MultiIndexing in Pandas?
8. Operation on Series in Pandas?
9. Different ways of creating Data Frames in Pandas?
10. Categorical Data in Pandas?
11. How to Read Text Files with Pandas?
12. How are iloc() and loc() different?
13. Difference between join() and merge() in Pandas?
14. How to add a row/column to a Pandas DataFrame?
15.GroupBy function in Pandas?
16.Use of pandas.Dataframe.aggregate() function?
17. Statistical functions in Python Pandas?
#Python
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Steps to become a data analyst
Learn the Basics of Data Analysis:
Familiarize yourself with foundational concepts in data analysis, statistics, and data visualization. Online courses and textbooks can help.
Free books & other useful data analysis resources - https://t.iss.one/learndataanalysis
Develop Technical Skills:
Gain proficiency in essential tools and technologies such as:
SQL: Learn how to query and manipulate data in relational databases.
Free Resources- @sqlanalyst
Excel: Master data manipulation, basic analysis, and visualization.
Free Resources- @excel_analyst
Data Visualization Tools: Become skilled in tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn.
Free Resources- @PowerBI_analyst
Programming: Learn a programming language like Python or R for data analysis and manipulation.
Free Resources- @pythonanalyst
Statistical Packages: Familiarize yourself with packages like Pandas, NumPy, and SciPy (for Python) or ggplot2 (for R).
Hands-On Practice:
Apply your knowledge to real datasets. You can find publicly available datasets on platforms like Kaggle or create your datasets for analysis.
Build a Portfolio:
Create data analysis projects to showcase your skills. Share them on platforms like GitHub, where potential employers can see your work.
Networking:
Attend data-related meetups, conferences, and online communities. Networking can lead to job opportunities and valuable insights.
Data Analysis Projects:
Work on personal or freelance data analysis projects to gain experience and demonstrate your abilities.
Job Search:
Start applying for entry-level data analyst positions or internships. Look for job listings on company websites, job boards, and LinkedIn.
Jobs & Internship opportunities: @getjobss
Prepare for Interviews:
Practice common data analyst interview questions and be ready to discuss your past projects and experiences.
Continual Learning:
The field of data analysis is constantly evolving. Stay updated with new tools, techniques, and industry trends.
Soft Skills:
Develop soft skills like critical thinking, problem-solving, communication, and attention to detail, as they are crucial for data analysts.
Never ever give up:
The journey to becoming a data analyst can be challenging, with complex concepts and technical skills to learn. There may be moments of frustration and self-doubt, but remember that these are normal parts of the learning process. Keep pushing through setbacks, keep learning, and stay committed to your goal.
ENJOY LEARNING 👍👍
Learn the Basics of Data Analysis:
Familiarize yourself with foundational concepts in data analysis, statistics, and data visualization. Online courses and textbooks can help.
Free books & other useful data analysis resources - https://t.iss.one/learndataanalysis
Develop Technical Skills:
Gain proficiency in essential tools and technologies such as:
SQL: Learn how to query and manipulate data in relational databases.
Free Resources- @sqlanalyst
Excel: Master data manipulation, basic analysis, and visualization.
Free Resources- @excel_analyst
Data Visualization Tools: Become skilled in tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn.
Free Resources- @PowerBI_analyst
Programming: Learn a programming language like Python or R for data analysis and manipulation.
Free Resources- @pythonanalyst
Statistical Packages: Familiarize yourself with packages like Pandas, NumPy, and SciPy (for Python) or ggplot2 (for R).
Hands-On Practice:
Apply your knowledge to real datasets. You can find publicly available datasets on platforms like Kaggle or create your datasets for analysis.
Build a Portfolio:
Create data analysis projects to showcase your skills. Share them on platforms like GitHub, where potential employers can see your work.
Networking:
Attend data-related meetups, conferences, and online communities. Networking can lead to job opportunities and valuable insights.
Data Analysis Projects:
Work on personal or freelance data analysis projects to gain experience and demonstrate your abilities.
Job Search:
Start applying for entry-level data analyst positions or internships. Look for job listings on company websites, job boards, and LinkedIn.
Jobs & Internship opportunities: @getjobss
Prepare for Interviews:
Practice common data analyst interview questions and be ready to discuss your past projects and experiences.
Continual Learning:
The field of data analysis is constantly evolving. Stay updated with new tools, techniques, and industry trends.
Soft Skills:
Develop soft skills like critical thinking, problem-solving, communication, and attention to detail, as they are crucial for data analysts.
Never ever give up:
The journey to becoming a data analyst can be challenging, with complex concepts and technical skills to learn. There may be moments of frustration and self-doubt, but remember that these are normal parts of the learning process. Keep pushing through setbacks, keep learning, and stay committed to your goal.
ENJOY LEARNING 👍👍
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Free Session to learn Data Analytics, Data Science & AI
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Register fast, only for first few users
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https://tracking.acciojob.com/g/PUfdDxgHR
Register fast, only for first few users
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𝟯𝟬 𝗠𝗼𝘀𝘁 𝗖𝗼𝗺𝗺𝗼𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗬𝗼𝘂 𝗠𝘂𝘀𝘁 𝗞𝗻𝗼𝘄!😍
Are you preparing for a Data Analytics interview?🗣
Hiring managers often ask a mix of technical & problem-solving questions to evaluate your skills in SQL, Python, Excel, data visualization, & case studies🎯
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4hbmjxf
Which question do you find the toughest? Drop a comment below!⬇️
Are you preparing for a Data Analytics interview?🗣
Hiring managers often ask a mix of technical & problem-solving questions to evaluate your skills in SQL, Python, Excel, data visualization, & case studies🎯
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4hbmjxf
Which question do you find the toughest? Drop a comment below!⬇️
👍1