🚀Roadmap to Becoming a Data Analyst🚀

Start your journey with these key steps:-

1️⃣ SQL: Master querying and managing data from databases.
2️⃣ Python: Use Python for data manipulation and automation.
3️⃣ Visualization: Present data using Matplotlib/Seaborn.
4️⃣ Excel: Handle data and create quick insights.
5️⃣ Power BI/Tableau: Build interactive dashboards.
6️⃣ Statistics: Understand key concepts for data interpretation.
7️⃣ Data Analytics: Apply everything in real-world projects!

#DataAnalyst

Data Analyst: Analyzes data to provide insights and reports for decision-making.

Data Scientist: Builds models to predict outcomes and uncover deeper insights from data.

Data Engineer: Creates and maintains the systems that store and process data.

Don't make this mistake as a beginner data analyst:

Not learning SQL

There's a reason it's been around for 40+ years.

Get started with:

- SQL basics (syntax + structure)
- Data Manipulation (JOINs, GROUP BY etc)
- Aggregation Functions (SUM, AVG etc)

How to annoy a data analyst in 2024:


☑ Assume the analysis you're asking is "just a quick SQL thing."
☑ Ask to "tweak" a finished dashboard. It's never just a small change.
☑ Question why the numbers in their carefully crafted dashboard don't match your hastily pulled spreadsheet.
☑ Assume all data is clean, structured, and readily available. Spoiler: it's not.
☑ After receiving a detailed, interactive dashboard, ask, "Can I just get this as a printable PDF?" 🤦🏽♂️🤦🏽♂️

Hi guys,

Many people charge too much to teach Excel, Power BI, SQL, Python & Tableau but my mission is to break down barriers. I have shared complete learning series to start your data analytics journey from scratch.

For those of you who are new to this channel, here are some quick links to navigate this channel easily.

Data Analyst Learning Plan 👇
https://t.iss.one/sqlspecialist/752

Python Learning Plan 👇
https://t.iss.one/sqlspecialist/749

Power BI Learning Plan 👇
https://t.iss.one/sqlspecialist/745

SQL Learning Plan 👇
https://t.iss.one/sqlspecialist/738

SQL Learning Series 👇
https://t.iss.one/sqlspecialist/567

Excel Learning Series 👇
https://t.iss.one/sqlspecialist/664

Power BI Learning Series 👇
https://t.iss.one/sqlspecialist/768

Python Learning Series 👇
https://t.iss.one/sqlspecialist/615

Tableau Essential Topics 👇
https://t.iss.one/sqlspecialist/667

Best Data Analytics Resources 👇
https://heylink.me/DataAnalytics

You can find more resources on Medium & Linkedin

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Thanks to all who support our channel and share it with friends & loved ones. You guys are really amazing.

Hope it helps :)

✅𝟓-𝐒𝐭𝐞𝐩 𝐑𝐨𝐚𝐝𝐦𝐚𝐩 𝐭𝐨 𝐒𝐰𝐢𝐭𝐜𝐡 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐅𝐢𝐞𝐥𝐝✅

💁‍♀️𝐁𝐮𝐢𝐥𝐝 𝐊𝐞𝐲 𝐒𝐤𝐢𝐥𝐥𝐬: Focus on core skills—Excel, SQL, Power BI, and Python.

💁‍♀️𝐇𝐚𝐧𝐝𝐬-𝐎𝐧 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Apply your skills to real-world data sets. Projects like sales analysis or customer segmentation show your practical experience. You can find projects on Youtube.

💁‍♀️𝐅𝐢𝐧𝐝 𝐚 𝐌𝐞𝐧𝐭𝐨𝐫: Connect with someone experienced in data analytics for guidance(like me 😅). They can provide valuable insights, feedback, and keep you on track.

💁‍♀️𝐂𝐫𝐞𝐚𝐭𝐞 𝐏𝐨𝐫𝐭𝐟𝐨𝐥𝐢𝐨: Compile your projects in a portfolio or on GitHub. A solid portfolio catches a recruiter’s eye.

💁‍♀️𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞 𝐟𝐨𝐫 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬: Practice SQL queries and Python coding challenges on Hackerrank & LeetCode. Strengthening your problem-solving skills will prepare you for interviews.

Join this SQL WhatsApp Group 👇

https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v

Next time you’re asked for data…

Try to learn the WHY.

What’s the business problem this solves.

Why do they think this data will solve it.

You’ll nearly always be able to help more than they realised.

When I started Data Analysis:

• I didnt understand Star Schema
• I didn’t know PowerBi
• I barely knew Excel
• I didn’t know DAX
• I didn’t know SQL

2 years later:

• I can build Data Models for any business
• I know excel to produce any report
• I can easily data with SQL
• I know PowerBi inside out
• I love DAX

I love data.

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What is CRUD?

CRUD stands for Create, Read, Update, and Delete. It represents the basic operations that can be performed on data in a database.

Examples in SQL:

1. Create:
Adding new records to a table.

    INSERT INTO students (id, name, age)
    VALUES (1, 'John Doe', 20);
    
    

2. Read:
Retrieving data from a table.

    SELECT * FROM students;
    
    

3. Update:

Modifying existing records.

    UPDATE students
    SET age = 21
    WHERE id = 1;
    
    

4. Delete:

Removing records.

DELETE FROM students
WHERE id = 1;

Data analyst starter kit:

- Become an expert at SQL and data wrangling.

- Learn to help others understand data through visualisations.

- Seek to answer specific questions and provide clarity.

- Remember, everything ends up in Excel.

Checklist to become a Data Analyst 👇👇
https://www.linkedin.com/posts/sql-analysts_anyone-with-an-internet-connection-can-learn-activity-7266465625185603584-7FS9

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Important Python Functions

✅ 𝐇𝐨𝐰 𝐭𝐨 𝐁𝐮𝐢𝐥𝐝 𝐚 𝐂𝐚𝐫𝐞𝐞𝐫 𝐚𝐬 𝐚 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐢𝐧 𝟐𝟎𝟐𝟓 🧑‍💻

If you are thinking about becoming a data analyst, 2025 is the perfect year to start. Companies need people who can understand data and turn it into useful insights. Here’s a simple step-by-step guide to help you start your journey.

𝟏. 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐑𝐨𝐥𝐞
A data analyst collects and studies data to help companies make better decisions. They find trends, create reports, and suggest solutions to business problems.

𝟐. 𝐋𝐞𝐚𝐫𝐧 𝐍𝐞𝐜𝐞𝐬𝐬𝐚𝐫𝐲 𝐒𝐤𝐢𝐥𝐥𝐬
𝐄𝐱𝐜𝐞𝐥: Start with PivotTables, VLOOKUP, and creating dashboards.
𝐒𝐐𝐋: Master queries to extract and manipulate data.
𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐓𝐨𝐨𝐥𝐬: Learn Power BI and Tableau to present insights effectively.
𝐏𝐲𝐭𝐡𝐨𝐧: Focus on libraries like Pandas, NumPy, Matplotlib, and Seaborn.
𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬: Basic concepts- mean, median, mode, standard deviation, regression.

𝟑. 𝐖𝐨𝐫𝐤 𝐨𝐧 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬
https://t.iss.one/sqlproject
https://t.iss.one/pythonspecialist

𝟒. 𝐆𝐚𝐢𝐧 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧
Certifications add credibility to your resume. Some popular ones include:
Google Data Analytics Professional Certificate
Microsoft Certified: Data Analyst Associate
Tableau Desktop Specialist Certification

𝟓. 𝐂𝐫𝐞𝐚𝐭𝐞 𝐏𝐨𝐫𝐭𝐟𝐨𝐥𝐢𝐨
𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: Treat your LinkedIn profile as your portfolio. Update it with skills, certifications, and projects.
𝐆𝐢𝐭𝐇𝐮𝐛: Add links to your GitHub repositories with coding projects and Power BI/Tableau dashboards.

𝟔. 𝐆𝐚𝐢𝐧 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 (𝐅𝐨𝐫 𝐅𝐫𝐞𝐬𝐡𝐞𝐫𝐬)
If you're a fresher, here are some ideas to gain experience:
𝐈𝐧𝐭𝐞𝐫𝐧𝐬𝐡𝐢𝐩𝐬: Apply for internships at companies where you can work on real data problems.
𝐅𝐫𝐞𝐞𝐥𝐚𝐧𝐜𝐢𝐧𝐠: Offer data analysis services on platforms like Upwork, Fiverr, or Freelancer.
𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Build your own projects, such as analyzing public datasets (e.g., from Kaggle), and share them on GitHub.
𝐎𝐧𝐥𝐢𝐧𝐞 𝐂𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐢𝐨𝐧𝐬: Participate in data analysis competitions on Kaggle or DrivenData to build your skills and gain recognition.
𝐎𝐩𝐞𝐧-𝐒𝐨𝐮𝐫𝐜𝐞: Contribute to open-source data analysis projects on GitHub.

𝟕. 𝐒𝐭𝐚𝐫𝐭 𝐀𝐩𝐩𝐥𝐲𝐢𝐧𝐠 𝐟𝐨𝐫 𝐉𝐨𝐛𝐬
Tailor your resume and portfolio for each role. Highlight projects and key skills. Consider entry-level roles like:
Junior Data Analyst, Business Analyst, Reporting Analyst
Use platforms like LinkedIn & Naukri to apply for jobs.

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 👍👍