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Skills need for everyday data analysis jobs
Must Study:- These are the important Questions for Data Analyst 3.0
Advanced SQL
1. How do you optimize SQL queries for performance?
2. What are window functions, and how are they used in SQL Server?
3. How do you handle data normalization and denormalization in SQL?
Excel Advanced
1. How do you use Power Query for data transformation in Excel?
2. Explain how to create and use dynamic arrays in Excel.
3. How do you implement advanced conditional formatting rules in Excel?
Power BI
1. How do you create and manage relationships between tables in Power BI?
2. What are the different types of Power BI visuals and when should you use each?
3. How do you implement row-level security in Power BI reports?
Python
1. How do you perform web scraping using Python?
2. What are the key libraries for machine learning in Python, and how are they used?
3. How do you handle missing data and outliers in Python?
Data Visualization
1. How do you design visualizations that cater to different audiences?
2. What are the common pitfalls in data visualization, and how do you avoid them?
3. How do you integrate interactive elements in data visualizations?
Soft Skills
1. How do you prioritize tasks and manage deadlines in a data project?
2. What strategies do you use to build strong relationships with stakeholders?
3. How do you approach problem-solving and decision-making in a data-driven environment?
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://topmate.io/analyst/861634
Hope this helps you 😊
Must Study: These are the important Questions for Data Analyst 2.O
Advanced SQL
1. How do you handle recursive queries in SQL Server?
2. What are the differences between clustered and non-clustered indexes?
3. How do you use JSON functions in SQL Server to parse and query JSON data?
Excel Advanced
1. How do you use Power Pivot for data modeling in Excel?
2. Explain how to use the XLOOKUP function and its advantages over VLOOKUP.
3. How do you create and use custom functions with LAMBDA in Excel?
Power BI
1. How do you use DAX functions for advanced calculations in Power BI?
2. What are the best practices for creating a data model in Power BI?
3. How do you handle performance optimization in Power BI reports?
Python
1. How do you use Pandas for data manipulation and analysis?
2. What is the difference between NumPy and Pandas, and when should you use each?
3. What are the key differences between Python 2 and Python 3, and why should you use Python 3?
Data Visualization
1. How do you use Tableau for interactive data visualizations?
2. What are the principles of effective data visualization design?
3. How do you create dashboards that provide actionable insights?
Soft Skills
1. How do you communicate complex data findings to non-technical stakeholders?
2. What are the key considerations when working on a data project as part of a team?
3. How do you stay updated with the latest trends and technologies in data analysis?
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://topmate.io/analyst/861634
Hope this helps you 😊
Must Study: These are the important Questions for Data Analyst.
SQL Server
1. What is a CTE (Common Table Expression), and how is it used in SQL Server?
2. Explain the concept of window functions and provide examples of their usage.
3. How do you optimize SQL queries for better performance?
Excel
1. How do you use Excel’s Power Query for data extraction and transformation?
2. What are dynamic arrays, and how do you use them in Excel?
3. How do you automate tasks in Excel using VBA (Visual Basic for Applications)?
Power BI
1. How do you implement Row-Level Security (RLS) in Power BI?
2. What are custom visuals in Power BI, and how do you create and use them?
3. How do you integrate Power BI with other tools and platforms (e.g., Azure, SQL Server)?
Python
1. How do you use Python for web scraping, and what are the key libraries involved?
2. What is the purpose of the Scikit-Learn library, and how do you use it for machine learning tasks?
3. How do you perform data cleaning and preprocessing using Python?
Data Visualization
1. What are the latest trends in data visualization, and how do they impact your work?
2. How do you create interactive visualizations using Plotly or Bokeh in Python?
3. What are some advanced techniques for storytelling with data?
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://topmate.io/analyst/861634
Hope this helps you 😊
Since most of you voted for SQL, I created this video which contains essential SQL topics & free resources to practice sql.
👇👇
https://youtu.be/VCZxODefTIs?si=1XB44uv5DIpcJA4K
Please like this video & subscribe my youtube channel so that I can bring more awesome videos. I would really appreciate any feedback in th comments :)
What should be the next topic for YouTube video?
Whould I upload next video on SQL?
Complete Guide to Data Analytics for Beginners 👇👇
https://youtu.be/1-T-VBjLpJo?si=fo_RhbXC46Hg-FVE
Let’s go back to the basics...!
Here’s what you do to become a Data Analyst
- Learn SQL (best skill to have)
- Learn Excel (hidden requirement)
- Learn a BI tool (for nice portfolio projects)
Don’t stop there you still have work to do
- Create a portfolio
- Learn how to create an appealing resume
- Learn how to answer interview questions (STAR method)
After this, my favorite, networking
- Comment on posts
- Start posting yourself
- Reach out to all the recruiters
It can take you anywhere from a couple of months to a year!
It all depends on how much time you can dedicate each day!
But the longer you wait, the longer it will take!
Get after it...!
Perfect Resume Template
Will AI Tools for Data Analysis Replace Data Analysts?
AI and Data Analysis are two closely related scientific areas, that have been developing rapidly for the last several years. As technology continues to evolve, the question arises: Will AI tools for data analysis replace data analysts?
This article aims to describe how AI is related to Data Analysis, what it can do, and will AI tools for data analysis replace data analysts. Starting with the introduction to AI and its fundamental aspects, to how it is going to affect the world in the distant future, the article addresses that and also focuses on how AI is associated with Data analysis.
The moderate generation of AI comprises Machine Learning, Deep Learning, and Generative AI. While generative AI is the capability to produce materials and contents like images, sound, and music, Machine Learning is a specific type of GI that prepares an algorithm to feed information to make a prediction.
🥳🚀When delving into data analytics and initiating your SQL journey, prioritize mastering the fundamental concepts that address the majority of problems before delving into other topics.
👉🏻 Basic Aggregation function:
1️⃣ AVG
2️⃣ COUNT
3️⃣ SUM
4️⃣ MIN
5️⃣ MAX
👉🏻 JOINS
1️⃣ Left
2️⃣ Inner
3️⃣ Self (Important, Practice questions on self join)
👉🏻 Windows Function (Important)
1️⃣ Learn how partitioning works
2️⃣ Learn the different use cases where Ranking/Numbering Functions are used? ( ROW_NUMBER,RANK, DENSE_RANK, NTILE)
3️⃣ Use Cases of LEAD & LAG functions
4️⃣ Use cases of Aggregate window functions
👉🏻 GROUP BY
👉🏻 WHERE vs HAVING
👉🏻 CASE STATEMENT
👉🏻 UNION vs Union ALL
👉🏻 LOGICAL OPERATORS
Other Commonly used functions:
👉🏻 IFNULL
👉🏻 COALESCE
👉🏻 ROUND
👉🏻 Working with Date Functions
1️⃣ EXTRACTING YEAR/MONTH/WEEK/DAY
2️⃣ Calculating date differences
👉🏻CTE
👉🏻Views & Triggers (optional)
Here is an amazing resources to learn & practice SQL: https://t.me/sqlanalyst/195
Hope it helps in your SQL learning 📚
Checklist to become a Data Analyst
Mercedes Interview Questions & Answers.pdf0.51 KB
𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 V/S 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞
𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 (𝐁𝐀):
- Acts as a bridge between the business side and the IT side of an organization.
- Gathers and analyzes business requirements.
- Conducts stakeholder meetings.
𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 (𝐁𝐈):
- Focuses on data analysis, reporting, and data visualization using BI tools.
- Extracts and transforms data from various sources into meaningful insights to support decision-making.
- Builds dashboards and reports.
- Identifies trends and patterns in data.
𝐄𝐱𝐚𝐦𝐩𝐥𝐞:
𝐀𝐦𝐚𝐳𝐨𝐧: A BA might analyze customer feedback to improve delivery processes, while a BI professional could create dashboards to monitor sales trends and warehouse efficiency.
𝐆𝐨𝐨𝐠𝐥𝐞: A BA could work on improving user experience based on app usage data, whereas a BI expert might analyze advertising data to optimize ad campaigns.
Starting exploratory data analysis (EDA) can be tricky. Many of us often feel lost at the beginning. Here's a simple way to get on track: start by creating hypothesis questions and defining KPIs based on your dataset and the field you are working in.
𝐅𝐨𝐥𝐥𝐨𝐰 𝐭𝐡𝐞𝐬𝐞 𝐬𝐭𝐞𝐩𝐬 𝐭𝐨 𝐠𝐮𝐢𝐝𝐞 𝐲𝐨𝐮𝐫 𝐄𝐃𝐀:
1. 𝑼𝒏𝒅𝒆𝒓𝒔𝒕𝒂𝒏𝒅 𝒀𝒐𝒖𝒓 𝑭𝒊𝒆𝒍𝒅: Learn about the industry and the specific problems you're trying to solve. This will help you know what to look for in your data.
2. 𝑰𝒅𝒆𝒏𝒕𝒊𝒇𝒚 𝑲𝒆𝒚 𝑴𝒆𝒕𝒓𝒊𝒄𝒔: Decide on the most important KPIs for your analysis. These should align with your business goals and provide clear insights.
3. 𝑪𝒓𝒆𝒂𝒕𝒆 𝑯𝒚𝒑𝒐𝒕𝒉𝒆𝒔𝒆𝒔: Formulate questions that your EDA will try to answer. This keeps your analysis focused and purposeful.
Using these steps will make your EDA process smoother and ensure your results are valuable and relevant.
If you're thinking about building a data analytics projects, you don't need another book, video, or blog post.
Just start.
You'll learn 10x more by failing big time than by reading someone else's advice 🤷♂️
Shiny tools like Power BI and Tableau can be tempting to jump into right away.
Don't fall into that trap!
Before you dive into data visualization, learn SQL first.
Why?
It's the language of databases and, even if you don't use it in your job, it helps you learn:
- databases
- data modeling
- data storytelling
My recommendation? Learn at least...
- the fundamentals of SQL syntax (SELECT, FROM)
- how to get the data you want (WHERE, HAVING, JOIN)
- how to aggregate data (GROUP BY, COUNT, SUM, AVG, MIN, MAX)
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://topmate.io/analyst/861634
Hope this helps you 😊
I don't have a math or statistics degree.
I taught myself SQL, Python, and data visualization tools through online courses and countless practice hours.
I've worked on dozens of projects and helped make data-driven decisions.
But some days, I still feel like I don't know enough. I look at certain projects and think, "Do I really have enough experience?"
Imposter syndrome doesn't care how long you've been in the field.
Here's what I've learned along the way:
1/ The field is vast: Data analytics is huge. It's okay not to know everything. Nobody does.
2/ Learning never stops: Every project teaches me something new. That's not a weakness; it's the nature of the job.
3/ My perspective matters: My non-traditional background brings unique insights to problem-solving.
4/ Mistakes are normal: I've made errors in my analysis. It happens. It's how we learn and improve.
5/ Celebrate the wins: When a stakeholder uses my insights to make a decision, that's a win. I try to remember these moments.
I still catch myself thinking, "Am I good enough?" when faced with a challenging project.
But then I remind myself of how far I've come.
I've learned to reframe "I don't know this" to "I don't know this yet."
To my fellow data enthusiasts feeling the same way: Your journey is valid. Your skills are valuable. You belong here. 💪
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://topmate.io/analyst/861634
Hope this helps you 😊
