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8 (and forever): keep learning! The learning doesn’t stop when you start applying for jobs or even when you get one! Always be learning something new. Note: even with hard work, this can be a tough, time-intensive process. Hang in there and keep the faith! You got this. Learn Data Analytics

There are a lot of data analyst jobs that don’t use SQL. There are a lot of jobs that don’t use Tableau/Power BI. There are even jobs that don’t use Excel/Google sheets. BUT, the probability you’ll use at least one of those tools in your data analyst job is close to 100%. That’s why I recommend every data analyst learn SQL, Excel, and Tableau or Power BI. That way you’re ready for almost all entry-level data analyst jobs.

7. Start applying to jobs Leverage any connections you have including friends, family, and former colleagues. If you already work for a company that hires data analysts, transitioning to a data role within the company can be the easiest route. Once you have the skills, it’s your job to get the attention of recruiters and hiring managers. Learn Data Analytics

6. Build a great resume Highlight transferrable skills from past experience, add a project section to showcase your analytics skills, and link your portfolio in the header. Alex the Analyst did a great video on this like a year ago including a portfolio template I’ve used. Learn Data Analytics

5. Build a portfolio to showcase your skills There are a ton of options for hosting portfolios including GitHub Pages, Carrd, and Maven Portfolio Showcase. Learn Data Analytics

Data Analyst is NOT a Business Analyst. BOTH Works with DATA,but the way they do it is Vastly Different. Data Analyst Mainly :- ✔️ Work in DATABASE. ✔️ Use SQL. ✔️ Propose actionable INSIGHTS. ✔️ Report to Team Memeber's. Business Analyst Mainly :- ✔️ Work in SPREADSHEET. ✔️ Use EXCEL. ✔️ Make Strategic Recommendations. ✔️ Report to Upper Management. Awareness of the difference's between Data Analyst & Business Analyst.

4. Learn Tableau Software: Tableau Public Learn with Tableau Public website and YouTube channels such as Alex the Analyst, Andy Kriebel, and Tableau Tim Learn Data Analytics

If I need to teach someone data analytics from the basics, here is my strategy: 1. I will first remove the fear of tools from that person 2. i will start with the excel because it looks familiar and easy to use 3. I put more emphasis on projects like at least 5 to 6 with the excel. because in industry you learn by doing things 4. I will release the person from the tutorial hell and move into a more action oriented person 5. Then I move to the sql because every job wants it , even with the ai tools you need strong understanding for it if you are going to use it daily 6. After strong understanding, I will push the person to solve 100 to 150 Sql problems from basic to advance 7. It helps the person to develop the analytical thinking 8. Then I push the person to solve 3 case studies as it helps how we pull the data in the real life 9. Then I move the person to power bi to do again 5 projects by using either sql or excel files 10. Now the fear is removed. 11. Now I push the person to solve unguided challenges and present them by video recording as it increases the problem solving, communication and data story telling skills 12. Further it helps you to clear case study round given by most of the companies 13. Now i help the person how to present them in resume and also how these tools are used in real world. 14. You know the interesting fact, all of above is present free in youtube and I also mentor the people through existing youtube videos. 15. But people stuck in the tutorial hell, loose motivation , stay confused that they are either in the right direction or not. 16. As a personal mentor , I help them to get of the tutorial hell, set them in the right direction and they stay motivated when they start to see the difference before amd after mentorship I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope this helps you 😊

3. Learn SQL Software: MySQL Free courses: Khan Academy, W3Schools, SQL Bolt, SQL Zoo, Luke Barousse and Alex the Analyst on YouTube Telegram channel: SQL for data analysis Practice problems: Hacker Rank, Leet Code, Data Lemur Games: SQL Island, SQL Murder Mystery Learn Data Analytics

2. Learn Microsoft Excel: Software: Microsoft Excel Online (it’s not the best, but it will do.) YouTube & Telegram channels to learn: Teacher’s Tech, Excel for Data Analysis, Kevin Stratvert, Leila Gharani, Alex the Analyst Learn Data Analytics

Full Data Analyst Roadmap (with free resources): 1. Optimize LinkedIn Add a friendly, professional profile photo; create a headline that starts with the job title you want (data analyst, not aspiring); add a banner photo (Canva can help with this); create an about section that goes deeper into your skills and background and why you’re good at what you do (add some personality); Highlight transferrable skills in your work and education history sections, quantify your impact where applicable; add your portfolio and anything else you want to show off to your featured section (once you have a portfolio); Use keywords throughout including tools, years of experience, and job titles (data analyst) Learn Data Analytics

🤯🥳ADVANTAGES OF LANDING INTO DATA ANALYTICS FIELD 1. Diverse Career Opportunities: Data analytics opens doors to various career paths 🌐. 2. High Demand: The field is in high demand, ensuring job stability 📈. 3. Lucrative Salaries: Data analysts often enjoy competitive salaries 💰. 4. Problem Solving: Analyzing data allows you to tackle complex problems with precision 🧩. 5. Industry Versatility: Applicable across industries, from healthcare to finance 🏥💼. 6. Continuous Learning: Constantly evolving field, ensuring ongoing skill development 📚. 7. Informed Decision-Making: Empowers businesses to make data-driven decisions 📊. 8. Global Impact: Contribute to solving real-world challenges on a global scale 🌍. 9. Flexibility: Opportunities for remote work and flexible schedules ⌚. 10. Community Engagement: Connect with a vibrant community of data enthusiasts 👩‍💻👨‍💻.

WebScraping with Gen AI During this session, we'll explore the following topics: 1️⃣ Basics of Web Scraping: Understand the fundamental concepts and techniques of web scraping and its legal and ethical considerations. 2️⃣ Scraping with Gen AI: Discover how Gen AI revolutionizes the web scraping landscape with real-world examples. 3️⃣ Jina Reader API: Get acquainted with the Jina Reader API, a powerful tool for obtaining LLM-friendly input from URLs or web searches. 4️⃣ ScrapeGraphAI: Dive into ScrapeGraphAI, a groundbreaking Python library that combines LLMs and direct graph logic for creating robust scraping pipelines. Event Details: 🗓 Date: 22 June, Saturday ⏰ Time: 11:00 AM IST 🔗 Register now: https://www.buildfastwithai.com/events/web-scraping-with-gen-ai Connect with Founder from IIT Delhi; https://www.linkedin.com/in/satvik-paramkusham/

Why learn Excel? Why learn SQL? Why learn a BI tool? Here's why... Excel → Great foundation for the other data tools → It is assumed you know spreadsheets in most data jobs → Just because you don't need it, other teams in the company might SQL → Chances are data is stored in a warehouse. This is how you get it → This is how you clean, manipulate, and aggregate the data for reporting BI Tool → Format the data so it's easier to read → This is specifically for visualizing data → This is where you can SHOW insights and give direction Learning these gets you from stagnant data to insights that answer business questions BONUS: Don't focus on which tool is best. Pick one you like to work with and focus more on becoming good at what you picked I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope this helps you 😊

Selected Scenario Question: Scenario: You are working as a data analyst for a retail company. The company wants to understand the sales performance across different regions and product categories. You have access to a SQL database that stores order details and a Power BI setup for reporting. Your task is to create a comprehensive report that shows: Total sales by product category. Total sales by region. Total number of orders placed by each customer. Identify the top 5 products contributing to sales in each region. Comprehensive Answer: Step 1: SQL Queries to Retrieve Data Total Sales by Product Category: SELECT ProductCategory, SUM(SalesAmount) AS TotalSales FROM Orders GROUP BY ProductCategory; Total Sales by Region: SELECT Region, SUM(SalesAmount) AS TotalSales FROM Orders GROUP BY Region; Total Number of Orders Placed by Each Customer: SELECT CustomerID, COUNT(*) AS TotalOrders FROM Orders GROUP BY CustomerID; Top 5 Products Contributing to Sales in Each Region: SELECT Region, ProductID, ProductName, SUM(SalesAmount) AS TotalSales FROM Orders GROUP BY Region, ProductID, ProductName ORDER BY Region, TotalSales DESC LIMIT 5; Step 2: Import Data into Power BI Load Data: Open Power BI Desktop. Use the "Get Data" feature to connect to your SQL database. Import the result sets from the SQL queries into Power BI. Create Relationships (if necessary): Ensure that the data tables are properly related. For example, link the Orders table to Customers, Products, and Regions tables if they exist separately. Step 3: Create Visualizations Total Sales by Product Category: Create a bar chart. Drag ProductCategory to the Axis. Drag TotalSales to the Values. Total Sales by Region: Create a pie chart. Drag Region to the Legend. Drag TotalSales to the Values. Total Number of Orders Placed by Each Customer: Create a table. Drag CustomerID to the Rows. Drag TotalOrders to the Values. Top 5 Products Contributing to Sales in Each Region: Create a clustered bar chart. Drag Region to the Axis. Drag ProductName to the Legend. Drag TotalSales to the Values. Apply a Top N filter to show only the top 5 products in each region. Step 4: Optimize Performance Data Model Optimization: Reduce the number of columns and rows by filtering unnecessary data. Use summarized tables to pre-aggregate data. DAX Optimization: Simplify calculations by using measures and avoiding complex DAX queries. Visualization Optimization: Limit the number of visuals on each report page. Avoid using too many slicers or custom visuals that can slow down the performance. Scheduled Refresh: Set up scheduled refreshes to ensure the data is up-to-date without manual intervention. By following these steps, you will create a comprehensive and optimized Power BI report that provides valuable insights into sales performance across different regions and product categories for the retail company. I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope this helps you