Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI

𝗟𝗲𝗮𝗿𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘😍

Want to master Python and level up your data analytics skills?✨️

These high-quality tutorials to help you go from beginner to pro!✅️

𝐋𝐢𝐧𝐤👇:-

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Useful websites to practice and enhance your Data Analytics skills
👇👇

1. SQL

https://mode.com/sql-tutorial/introduction-to-sql

https://t.me/sqlspecialist/738

2. Python

https://www.learnpython.org/

https://t.me/pythondevelopersindia/873

https://bit.ly/3T7y4ta

https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial

3. R

https://www.datacamp.com/courses/free-introduction-to-r

4. Data Structures

https://leetcode.com/study-plan/data-structure/

https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513

5. Data Visualization

https://www.freecodecamp.org/learn/data-visualization/

https://t.me/Data_Visual/2

https://www.tableau.com/learn/training/20223

https://www.workout-wednesday.com/power-bi-challenges/

6. Excel

https://excel-practice-online.com/

https://www.w3schools.com/EXCEL/index.php

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ENJOY LEARNING 👍👍

𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝘆 𝗶𝘀 𝗢𝗳𝗳𝗲𝗿𝗶𝗻𝗴 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀!😍

🔥 Want to learn from one of the world’s top universities?

Now’s your chance!🔗

𝐋𝐢𝐧𝐤👇:-

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✈️ 𝐑𝐨𝐚𝐝𝐦𝐚𝐩 𝐭𝐨 𝐁𝐞𝐜𝐨𝐦𝐢𝐧𝐠 𝐚 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭

𝟏. 𝐄𝐱𝐜𝐞𝐥: 𝐘𝐨𝐮𝐫 𝐂𝐨𝐫𝐞 𝐓𝐨𝐨𝐥
Master Excel skills for effective data analysis by focusing on:

・Cleaning and organizing data
・Using pivot tables for summaries
・Advanced functions like VLOOKUP, INDEX, and MATCH
・Designing impactful visualizations

𝟐. 𝐁𝐮𝐢𝐥𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧
Statistics are essential for interpreting data. Learn:

・Descriptive statistics (mean, median, mode)
・Probability distributions
・Hypothesis testing and confidence intervals

𝟑. 𝐃𝐨𝐦𝐢𝐧𝐚𝐭𝐞 𝐏𝐲𝐭𝐡𝐨𝐧 𝐨𝐫 𝐑
Choose Python or R to boost your analysis game:

・Clean and structure datasets
・Create visualizations (Matplotlib, Seaborn, or Tidyverse)
・Leverage powerful libraries for in-depth analysis

𝟒. 𝐌𝐚𝐬𝐭𝐞𝐫 𝐒𝐐𝐋
SQL is vital for working with databases. Hone these skills:

・Query writing for data extraction
・Combining data with JOINS
・Using aggregate functions
・Optimizing query performance

𝟓. 𝐄𝐱𝐜𝐞𝐥 𝐚𝐭 𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧
Transform data into stories with tools like Power BI or Tableau:

・Build insightful dashboards
・Create interactive visualizations
・Craft compelling, data-driven narratives

𝟔. 𝐏𝐞𝐫𝐟𝐞𝐜𝐭 𝐃𝐚𝐭𝐚 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠
Data cleaning ensures accurate results. Learn to:

・Handle missing values
・Detect and manage outliers
・Normalize and format data for analysis

𝟕. 𝐆𝐞𝐭 𝐇𝐚𝐧𝐝𝐬-𝐎𝐧 𝐰𝐢𝐭𝐡 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬
Practical experience is key! Work on:

・Market or business data analysis
・Financial or sales dashboards
・Customer segmentation

𝟖. 𝐒𝐡𝐚𝐫𝐩𝐞𝐧 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐒𝐤𝐢𝐥𝐥𝐬
Translate data insights into actionable recommendations:

・Write clear, concise reports
・Present to non-technical audiences
・Deliver impactful, data-backed decisions

𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝗤𝗟 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 & 𝗨𝗻𝗹𝗼𝗰𝗸 𝗛𝗶𝗴𝗵-𝗣𝗮𝘆𝗶𝗻𝗴 𝗢𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝗶𝗲𝘀!😍

Top 3 Free YouTube Playlists to Learn SQL

1)SQL Tutorial Videos
2)SQL Mastery: From Basics to Advanced
3)Learn Complete SQL (Beginner to Advanced)

𝗟𝗶𝗻𝗸 👇:-

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Enroll For FREE & Get Certified🎓

Knowing Excel, SQL, PowerBI, Python is great.

But if you don’t know how to "sell" your analysis there's a high chance you'll fail.

Here's what to do:

- Come up with questions to investigate.
- Create easy-to-understand answers.
- Explain what to do next.

It's that simple.

Learn these to become a

1. Data analyst:

📍Excel
📍SQL
📍Data viz tool (Power BI/Tableau)

2. Data engineer:

📍SQL
📍Python + Spark
📍Cloud platform (AWS/Azure/GCP)

3. Data scientist:

📍SQL
📍Python/R
📍Statistics/machine learning

𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗧𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿😍

1) Introduction to Cyber Security
2) AWS Cloud Masterclass
3)Salesforce Developer Catalyst
4) Python Basics
5) Project Management Basics

𝗟𝗶𝗻𝗸 👇:-

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Enroll For FREE & Get Certified🎓

Data Analytics, Data Science & AI Jobs Are Highly Demanding In 2025😍

Learn These Technologies From Top Industry Data Experts 

Curriculum designed and taught by Alumni from IITs & Leading Tech Companies.

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Guide to Become a Data Analyst!

🔍 Foundation: Build Your Basics
1. Understanding Data Fundamentals: Dive into the basics of data types, structures, and formats.
2. Learn Data Tools: Familiarize yourself with popular tools like Excel, SQL, and Python.
3. Master Data Visualization: Develop skills in creating insightful charts and graphs to communicate findings effectively.
4. Introduction to Statistics: Get comfortable with key statistical concepts like mean, median, and standard deviation.

📈 Intermediate: Deepen Your Skills
5. Advanced Data Manipulation: Level up your data wrangling abilities with techniques like pivot tables and data cleaning.
6. Statistical Analysis: Dive deeper into hypothesis testing, regression analysis, and probability distributions.
7. Machine Learning Basics: Explore the fundamentals of machine learning algorithms and their applications in data analysis.
8. Data Storytelling: Hone your ability to craft compelling narratives from data insights.

📊 Advanced: Specialize and Excel
9. Specialize in a Domain: Choose a niche area such as marketing analytics, financial analysis, or healthcare data.
10. Advanced Machine Learning: Deepen your understanding of complex algorithms like neural networks and ensemble methods.
11. Big Data Technologies: Explore tools and platforms for handling large-scale datasets such as Hadoop and Spark.
12. Ethics and Privacy: Understand the ethical considerations and legal implications of handling sensitive data.

𝟱 𝗠𝘂𝘀𝘁-𝗗𝗼 𝗦𝗤𝗟 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗼 𝗜𝗺𝗽𝗿𝗲𝘀𝘀 𝗥𝗲𝗰𝗿𝘂𝗶𝘁𝗲𝗿𝘀!😍

If you’re aiming for a Data Analyst, Business Analyst, or Data Scientist role, mastering SQL is non-negotiable. 📊

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4aUoeER

Don’t just learn SQL—apply it with real-world projects!✅️

Breaking into Data Analysis can be very confusing in 2024!

Should I learn SQL or NoSQL? Tableau or Power BI? Excel or Google Sheets? Python or R?

Fundamental principles are more important than tools:

Understanding data cleaning and preprocessing is more important than SQL vs NoSQL.

Understanding data visualization concepts is more important than Tableau vs Power BI.

Understanding statistical analysis is more important than Excel vs R.

Understanding programming for data manipulation is more important than Python vs R.

Knowing these will allow you to pick up new emerging tools easily.

Stick to fundamentals first.

𝗙𝗿𝗲𝗲 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗕𝘆 𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀😍

- JP Morgan 
- Accenture
- Walmart
- Tata Group
- Accenture

𝗟𝗶𝗻𝗸 👇:-

https://pdlink.in/3WTGGI8

Enroll For FREE & Get Certified🎓

Starting your journey as a data analyst is an amazing start for your career. As you progress, you might find new areas that pique your interest:

• Data Science: If you enjoy diving deep into statistics, predictive modeling, and machine learning, this could be your next challenge.

• Data Engineering: If building and optimizing data pipelines excites you, this might be the path for you.

• Business Analysis: If you're passionate about translating data into strategic business insights, consider transitioning to a business analyst role.

But remember, even if you stick with data analysis, there's always room for growth, especially with the evolving landscape of AI.

No matter where your path leads, the key is to start now.

This is a very COMMON issue that I observe in the projects of aspiring candidates

They download a DATASET from Kaggle or any other website

Export it to a Data Analysis TOOL

And START the project with data cleaning

After cleaning the data, they PLUG it into a dashboard

In the dashboard, they put EVERY column into the visuals

Also they APPLY the filters of top/bottom 10

Once done, they crack their KNUCKLES

And put this project in a list of SUCCESSFULLY completed projects

Over time, I have REVIEWED so many portfolio projects

And I see this ISSUES almost every time

When I go to their portfolio, for every project there is a DASHBOARD

But WHAT should I do after seeing a dashboard?

What is it trying to SAY?

What should I do after SEEING top or bottom 10 cities, states or products?

Every dashboard lacks CONTEXT

And why NOT?

Because they DON'T even know the business problem or problem statement

So the dashboard you created is of NO use

Your job is not just to create DASHBOARDS

Your job would be to create DASHBOARDS to take out important INSIGHTS

And from those insights, you will build RECOMMENDATIONS

And these recommendations will be given to stakeholders as a SOLUTION to their business problem

If they implemented your IDEAS and the problem gets solved

Now you can say your work is DONE

If you are SHOWING bottom 10 states, then what?

You should write the INSIGHTS too

For example, the sales of North India zone are FALLING

The insights can be used like this

Delhi that used to be in TOP 5 states is now in the BOTTOM 10 states

And this might be the REASON why our North India sales are DROPPING so hard

This is just a RANDOM example showing how your charts become UNDERSTANDABLE

Well, everyone can EXTRACT insights from charts

Even a KID can do this after looking at the tallest and smallest bar

The real task is to give RECOMMENDATIONS to solve the BUSINESS problem

And I have NEVER seen this in anyone's portfolio

If you are doing this, then you are easily STANDING out in the crowd

In my PORTFOLIO, I used to keep business problem, insights, dashboard and recommendations

Even in the bullet point of projects in my resume, I included RECOMMENDATIONS

Now this is what you can call a STRONG portfolio

Because your analysis skills are the SAME as those used in the real life by a Data Analyst

I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://t.me/DataSimplifier

Like if it helps 😄

𝗬𝗼𝘂𝗿 𝗨𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝘁𝗼 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁!😍

Want to break into Data Analytics but don’t know where to start?

Follow this step-by-step roadmap to build real-world skills! ✅

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3CHqZg7

🎯 Start today & build a strong career in Data Analytics! 🚀

Applied to 100+ jobs but still struggling?

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To solve this, Newton School has launched a 2-Month Placement Training & Referral Program for Software Development and Data Science roles.

What you get:
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We select only 10 candidates per month for each domain (Software Development & Data Science).

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Your ultimate guide to data analytics jobs 👇👇
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The Real Truth About Junior Data Analytics Interviews DataAnalytics
(From someone who's interviewed 50+ analysts)

Let me save you hours of interview prep...

SQL Round

WHAT THEY SAY:
"Complex SQL knowledge"

WHAT THEY ACTUALLY TEST:

Can you clean messy data
Do you check for NULL values
How do you handle duplicates
Can you explain your logic
Do you verify results

REAL QUESTIONS:

"Find duplicate transactions"
"Calculate monthly sales"
"Show top customers"
That's it. Really. ⤵️

Excel Interview

WHAT THEY SAY:
"Advanced Excel skills"

WHAT THEY ACTUALLY TEST:

VLOOKUP/XLOOKUP usage
Pivot Table comfort
Basic formulas
Data cleaning approach
Problem-solving process

Business Case

WHAT THEY SAY:
"Data analysis presentation"

WHAT THEY REALLY WANT:

Can you explain simply
Do you ask good questions
Can you structure analysis
Do you focus on impact
Are you confident with data ⤵️

Common Scenarios

The "Messy Data" Test
They give you:

Inconsistent formats
Missing values
Duplicate records

They watch:

How you spot issues
What questions you ask
Your cleaning approach

The "Explain It" Challenge

They ask:
"Walk me through your analysis"

They assess:

Communication clarity
Technical understanding
Business thinking
Confidence level ⤵️

How to Actually Prepare

Practice Basics:

Simple SQL queries
Excel fundamentals
Clear explanation

Business Understanding:

Read company metrics
Understand industry
Know basic KPIs
Prepare good questions

Real Scenarios to Practice:

Monthly sales analysis
Customer segmentation
Product performance
Marketing campaign results

Reality Check:

They care more about:

How you think
How you communicate
How you solve problems

Than:
Perfect technical knowledge
Complex code
Advanced statistics

I have curated best 80+ top-notch Data Analytics Resources
👇👇
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𝗠𝗮𝘀𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗜𝗻 𝟮𝟬𝟮𝟱😍

Master industry-standard tools like Excel, SQL, Tableau, and more.

Gain hands-on experience through real-world projects designed to mimic professional challenges

𝗟𝗶𝗻𝗸👇 :- 

https://pdlink.in/4jxUW2K

All The Best 🎉

Don't Limit Yourself to Just One Title, "𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭" in Your Job Search!


Don't get caught up in the confines of a single job title! There are countless roles out there that might align perfectly with your skills and interests. Here are a few alternative titles for data analyst roles to broaden your search horizons:


1. QI Analyst
2. Risk Analyst
3. Data Modeler
4. Research Analyst
5. Business Analyst
6. Reporting Analyst
7. Operations Analyst
8. Social Media Analyst
9. Statistical Analyst
10. Statistical Analyst
11. Product Data Analyst
12. Analytics Engineer
13. Supply Chain Analyst
14. Data Mining Engineer
15. Data Science Associate
16. Financial Data Analyst
17. Cybersecurity Analyst
18. Marketing Data Analyst
19. Quantitative Analyst
20. HR Analytics Specialist
21. Decision Support Analyst
22. Machine Learning Analyst
23. Fraud Detection Analyst
24. Healthcare Data Analyst
25. Data Insights Specialist
26. Data Visualization Specialist
27. Customer Insights Analyst
28. Business Intelligence Analyst
29. Predictive Analytics Analyst

Remember, the right opportunity might be hiding behind a different title than you expect. Keep an open mind and explore all avenues in your job search journey!

Also, there might be fewer applicants for these roles as many don't search for titles other than data Analyst or Business Analyst. Maybe you can get more calls or interviews this way.

You don't have to try all the titles, filter out based on your interests and skills!

After all, 𝐉𝐨𝐛 𝐃𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐨𝐧 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐭𝐡𝐞 𝐭𝐢𝐭𝐥𝐞!! 😉

I have curated best 80+ top-notch Data Analytics Resources
👇👇
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Hope this helps you 😊

𝗠𝗮𝘀𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 𝘄𝗶𝘁𝗵 𝗧𝗵𝗲𝘀𝗲 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱!😍

If you’re serious about becoming a Data Scientist but don’t know where to start, these YouTube channels will take you from 𝗯𝗲𝗴𝗶𝗻𝗻𝗲𝗿 𝘁𝗼 𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗱—all for FREE!

𝐋𝐢𝐧𝐤👇:-

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Start from scratch, master advanced concepts, and land your dream job in Data Science! 🎯

Data analysis is a gateway to becoming a:

- Data Scientist
- Business Analyst
- Data Engineer
- BI Engineer
- Analytics Engineer

And many other roles.

Learning the skills doesn't close doors, if anything, it opens many more.

𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗥𝗼𝗮𝗱𝗺𝗮𝗽

𝟭. 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀: Master Python, SQL, and R for data manipulation and analysis.

𝟮. 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Use Excel, Pandas, and ETL tools like Alteryx and Talend for data processing.

𝟯. 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Learn Tableau, Power BI, and Matplotlib/Seaborn for creating insightful visualizations.

𝟰. 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗮𝗻𝗱 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝘀: Understand Descriptive and Inferential Statistics, Probability, Regression, and Time Series Analysis.

𝟱. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Get proficient in Supervised and Unsupervised Learning, along with Time Series Forecasting.

𝟲. 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗧𝗼𝗼𝗹𝘀: Utilize Google BigQuery, AWS Redshift, and NoSQL databases like MongoDB for large-scale data management.

𝟳. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴: Implement Data Quality Monitoring (Great Expectations) and Performance Tracking (Prometheus, Grafana).

𝟴. 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗧𝗼𝗼𝗹𝘀: Work with Data Orchestration tools (Airflow, Prefect) and visualization tools like D3.js and Plotly.

𝟵. 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗿: Manage resources using Jupyter Notebooks and Power BI.

𝟭𝟬. 𝗗𝗮𝘁𝗮 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝗘𝘁𝗵𝗶𝗰𝘀: Ensure compliance with GDPR, Data Privacy, and Data Quality standards.

𝟭𝟭. 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴: Leverage AWS, Google Cloud, and Azure for scalable data solutions.

𝟭𝟮. 𝗗𝗮𝘁𝗮 𝗪𝗿𝗮𝗻𝗴𝗹𝗶𝗻𝗴 𝗮𝗻𝗱 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴: Master data cleaning (OpenRefine, Trifacta) and transformation techniques.

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8 must-know Data Analytics Terms.

Creating a one-month data analytics roadmap requires a focused approach to cover essential concepts and skills. Here's a structured plan along with free resources:

🗓️Week 1: Foundation of Data Analytics

◾Day 1-2: Basics of Data Analytics
Resource: Khan Academy's Introduction to Statistics
Focus Areas: Understand descriptive statistics, types of data, and data distributions.

◾Day 3-4: Excel for Data Analysis
Resource: Microsoft Excel tutorials on YouTube or Excel Easy
Focus Areas: Learn essential Excel functions for data manipulation and analysis.

◾Day 5-7: Introduction to Python for Data Analysis
Resource: Codecademy's Python course or Google's Python Class
Focus Areas: Basic Python syntax, data structures, and libraries like NumPy and Pandas.

🗓️Week 2: Intermediate Data Analytics Skills

◾Day 8-10: Data Visualization
Resource: Data Visualization with Matplotlib and Seaborn tutorials
Focus Areas: Creating effective charts and graphs to communicate insights.

◾Day 11-12: Exploratory Data Analysis (EDA)
Resource: Towards Data Science articles on EDA techniques
Focus Areas: Techniques to summarize and explore datasets.

◾Day 13-14: SQL Fundamentals
Resource: Mode Analytics SQL Tutorial or SQLZoo
Focus Areas: Writing SQL queries for data manipulation.

🗓️Week 3: Advanced Techniques and Tools

◾Day 15-17: Machine Learning Basics
Resource: Andrew Ng's Machine Learning course on Coursera
Focus Areas: Understand key ML concepts like supervised learning and evaluation metrics.

◾Day 18-20: Data Cleaning and Preprocessing
Resource: Data Cleaning with Python by Packt
Focus Areas: Techniques to handle missing data, outliers, and normalization.

◾Day 21-22: Introduction to Big Data
Resource: Big Data University's courses on Hadoop and Spark
Focus Areas: Basics of distributed computing and big data technologies.


🗓️Week 4: Projects and Practice

◾Day 23-25: Real-World Data Analytics Projects
Resource: Kaggle datasets and competitions
Focus Areas: Apply learned skills to solve practical problems.

◾Day 26-28: Online Webinars and Community Engagement
Resource: Data Science meetups and webinars (Meetup.com, Eventbrite)
Focus Areas: Networking and learning from industry experts.


◾Day 29-30: Portfolio Building and Review
Activity: Create a GitHub repository showcasing projects and code
Focus Areas: Present projects and skills effectively for job applications.

👉Additional Resources:
Books: "Python for Data Analysis" by Wes McKinney, "Data Science from Scratch" by Joel Grus.
Online Platforms: DataSimplifier, Kaggle, Towards Data Science

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Best Free SQL Courses to Get Started

1) Introduction to Databases and SQL
2) Advanced Database and SQL
3) Learn SQL 
4) SQL Tutorial

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Data Analysis Roadmap!

Don't know where to start your Data Analyst journey? Worry not! Here is a 3 month roadmap that coverts everything a beginner needs, with no prior coding experience!

This roadmap covers:

- Technical Skills: Step-by-step guides for Excel, BI tools (Power BI/Tableau), SQL, Python & Pandas

- Soft Skills: Tips for networking, LinkedIn optimization, and business fundamentals

- Assignments and Projects: Real-world applications each week to build your portfolio

- Interview Prep: Practical resources and mock projects to get you job-ready

If you’re ready to learn with structured weekly goals, free resources, and hands-on assignments, this roadmap is a great place to start!

Here is the list of few projects (found on kaggle). They cover Basics of Python, Advanced Statistics, Supervised Learning (Regression and Classification problems) & Data Science

Please also check the discussions and notebook submissions for different approaches and solution after you tried yourself.

1. Basic python and statistics

Pima Indians :- https://www.kaggle.com/uciml/pima-indians-diabetes-database
Cardio Goodness fit :- https://www.kaggle.com/saurav9786/cardiogoodfitness
Automobile :- https://www.kaggle.com/toramky/automobile-dataset

2. Advanced Statistics

Game of Thrones:-https://www.kaggle.com/mylesoneill/game-of-thrones
World University Ranking:-https://www.kaggle.com/mylesoneill/world-university-rankings
IMDB Movie Dataset:- https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset

3. Supervised Learning

a) Regression Problems

How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview
Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand
Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction
Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data
IMDB Box office Prediction:-https://www.kaggle.com/c/tmdb-box-office-prediction/overview

b) Classification problems

Employee Access challenge :- https://www.kaggle.com/c/amazon-employee-access-challenge/overview
Titanic :- https://www.kaggle.com/c/titanic
San Francisco crime:- https://www.kaggle.com/c/sf-crime
Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction
Trip type classification:- https://www.kaggle.com/c/walmart-recruiting-trip-type-classification
Categorize cusine:- https://www.kaggle.com/c/whats-cooking

4. Some helpful Data science projects for beginners

https://www.kaggle.com/c/house-prices-advanced-regression-techniques

https://www.kaggle.com/c/digit-recognizer

https://www.kaggle.com/c/titanic

5. Intermediate Level Data science Projects

Black Friday Data : https://www.kaggle.com/sdolezel/black-friday

Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones

Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset

Million Song Data : https://www.kaggle.com/c/msdchallenge

Census Income Data : https://www.kaggle.com/c/census-income/data

Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset

Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2

Share with credits: https://t.me/sqlproject

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Creating a one-month data analytics roadmap requires a focused approach to cover essential concepts and skills. Here's a structured plan along with free resources:

🗓️Week 1: Foundation of Data Analytics

◾Day 1-2: Basics of Data Analytics
Resource: Khan Academy's Introduction to Statistics
Focus Areas: Understand descriptive statistics, types of data, and data distributions.

◾Day 3-4: Excel for Data Analysis
Resource: Microsoft Excel tutorials on YouTube or Excel Easy
Focus Areas: Learn essential Excel functions for data manipulation and analysis.

◾Day 5-7: Introduction to Python for Data Analysis
Resource: Codecademy's Python course or Google's Python Class
Focus Areas: Basic Python syntax, data structures, and libraries like NumPy and Pandas.

🗓️Week 2: Intermediate Data Analytics Skills

◾Day 8-10: Data Visualization
Resource: Data Visualization with Matplotlib and Seaborn tutorials
Focus Areas: Creating effective charts and graphs to communicate insights.

◾Day 11-12: Exploratory Data Analysis (EDA)
Resource: Towards Data Science articles on EDA techniques
Focus Areas: Techniques to summarize and explore datasets.

◾Day 13-14: SQL Fundamentals
Resource: Mode Analytics SQL Tutorial or SQLZoo
Focus Areas: Writing SQL queries for data manipulation.

🗓️Week 3: Advanced Techniques and Tools

◾Day 15-17: Machine Learning Basics
Resource: Andrew Ng's Machine Learning course on Coursera
Focus Areas: Understand key ML concepts like supervised learning and evaluation metrics.

◾Day 18-20: Data Cleaning and Preprocessing
Resource: Data Cleaning with Python by Packt
Focus Areas: Techniques to handle missing data, outliers, and normalization.

◾Day 21-22: Introduction to Big Data
Resource: Big Data University's courses on Hadoop and Spark
Focus Areas: Basics of distributed computing and big data technologies.


🗓️Week 4: Projects and Practice

◾Day 23-25: Real-World Data Analytics Projects
Resource: Kaggle datasets and competitions
Focus Areas: Apply learned skills to solve practical problems.

◾Day 26-28: Online Webinars and Community Engagement
Resource: Data Science meetups and webinars (Meetup.com, Eventbrite)
Focus Areas: Networking and learning from industry experts.


◾Day 29-30: Portfolio Building and Review
Activity: Create a GitHub repository showcasing projects and code
Focus Areas: Present projects and skills effectively for job applications.

👉Additional Resources:
Books: "Python for Data Analysis" by Wes McKinney, "Data Science from Scratch" by Joel Grus.
Online Platforms: DataSimplifier, Kaggle, Towards Data Science

Tailor this roadmap to your learning pace and adjust the resources based on your preferences. Consistent practice and hands-on projects are crucial for mastering data analytics within a month. Good luck!

𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

- Artificial Intelligence for Beginners
- Data Science for Beginners
- Machine Learning for Beginners
 
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✈️ 𝐑𝐨𝐚𝐝𝐦𝐚𝐩 𝐭𝐨 𝐁𝐞𝐜𝐨𝐦𝐢𝐧𝐠 𝐚 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭

𝟏. 𝐄𝐱𝐜𝐞𝐥: 𝐘𝐨𝐮𝐫 𝐂𝐨𝐫𝐞 𝐓𝐨𝐨𝐥
Master Excel skills for effective data analysis by focusing on:

・Cleaning and organizing data
・Using pivot tables for summaries
・Advanced functions like VLOOKUP, INDEX, and MATCH
・Designing impactful visualizations

𝟐. 𝐁𝐮𝐢𝐥𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧
Statistics are essential for interpreting data. Learn:

・Descriptive statistics (mean, median, mode)
・Probability distributions
・Hypothesis testing and confidence intervals

𝟑. 𝐃𝐨𝐦𝐢𝐧𝐚𝐭𝐞 𝐏𝐲𝐭𝐡𝐨𝐧 𝐨𝐫 𝐑
Choose Python or R to boost your analysis game:

・Clean and structure datasets
・Create visualizations (Matplotlib, Seaborn, or Tidyverse)
・Leverage powerful libraries for in-depth analysis

𝟒. 𝐌𝐚𝐬𝐭𝐞𝐫 𝐒𝐐𝐋
SQL is vital for working with databases. Hone these skills:

・Query writing for data extraction
・Combining data with JOINS
・Using aggregate functions
・Optimizing query performance

𝟓. 𝐄𝐱𝐜𝐞𝐥 𝐚𝐭 𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧
Transform data into stories with tools like Power BI or Tableau:

・Build insightful dashboards
・Create interactive visualizations
・Craft compelling, data-driven narratives

𝟔. 𝐏𝐞𝐫𝐟𝐞𝐜𝐭 𝐃𝐚𝐭𝐚 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠
Data cleaning ensures accurate results. Learn to:

・Handle missing values
・Detect and manage outliers
・Normalize and format data for analysis

𝟕. 𝐆𝐞𝐭 𝐇𝐚𝐧𝐝𝐬-𝐎𝐧 𝐰𝐢𝐭𝐡 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬
Practical experience is key! Work on:

・Market or business data analysis
・Financial or sales dashboards
・Customer segmentation

𝟖. 𝐒𝐡𝐚𝐫𝐩𝐞𝐧 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐒𝐤𝐢𝐥𝐥𝐬
Translate data insights into actionable recommendations:

・Write clear, concise reports
・Present to non-technical audiences
・Deliver impactful, data-backed decisions

I have curated best 80+ top-notch Data Analytics Resources 👇👇
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Can you use Chat GPT as a data analyst?

The answer to this question is yes, but you need to be cautious about using Chat GPT on the job (and even while learning analytics) for the following reasons.

1. Chat GPT gets things wrong. A lot.

If you use Chat GPT to write code, you better know that coding language extremely well, because you gotta be able to fact check and alter the response you get from Chat GPT.

For this reason, I would recommend staying away from Chat GPT when you’re learning SQL, Python, etc so you thoroughly learn the code without becoming dependent on AI.

2. You absolutely CANNOT paste company data into Chat GPT

As data analysts we work with highly confidential data that we must exercise great caution to protect.

For this reason, no matter how secure Chat GPT says it is, you must never paste company data into the application.

3. Some companies and bosses may not allow the use of Chat GPT

This is a reality in the world of tech and data since the avalanche of AI tools and features over the last couple years.

I’ve heard of some companies that block Chat GPT altogether, and some managers who advise against using it out of fears for security and other reasons.

Given all three of these reasons, feel free to play around with Chat GPT and AI and learn about them, but don’t become overly dependent on these tools.

𝗠𝗮𝘀𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀, 𝗣𝘆𝘁𝗵𝗼𝗻, 𝗔𝗜 & 𝗦𝗤𝗟 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 𝘄𝗶𝘁𝗵 𝗜𝗕𝗠!😍

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1)Data Science Foundations

2)SQL for Data Science

3)Python for Data Science

4)Introduction to Data Science

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It contains comprehensive recorded video lectures on Data Analytics, covering key tools and languages like SQL, Python, Excel, and Power BI along with hands-on projects to ensure you gain practical experience alongside theoretical knowledge.

Please use the above link to avail them!👆

NOTE: -Most data aspirants hoard resources without actually opening them even once! The reason for keeping a small price for these resources is to ensure that you value the content available inside this and encourage you to make the best out of it.

Hope this helps in your data analytics journey... All the best!👍✌️

👉✔️Here are Data Analytics-related questions along with their answers:

1.Question: What is the purpose of exploratory data analysis (EDA)?

Answer: EDA is used to analyze and summarize data sets, often through visual methods, to understand patterns, relationships, and potential outliers.

2. Question: What is the difference between supervised and unsupervised learning?

Answer: Supervised learning involves training a model on a labeled dataset, while unsupervised learning deals with unlabeled data to discover patterns without explicit guidance.

3.Question: Explain the concept of normalization in the context of data preprocessing.

Answer: Normalization scales numeric features to a standard range, preventing certain features from dominating due to their larger scales.

4. Question: What is the purpose of a correlation coefficient in statistics?

Answer: A correlation coefficient measures the strength and direction of a linear relationship between two variables, ranging from -1 to 1.

5. Question: What is the role of a decision tree in machine learning?

Answer: A decision tree is a predictive model that maps features to outcomes by recursively splitting data based on feature conditions.

6. Question: Define precision and recall in the context of classification models.

Answer: Precision is the ratio of correctly predicted positive observations to the total predicted positives, while recall is the ratio of correctly predicted positive observations to all actual positives.

7. Question: What is the purpose of cross-validation in machine learning?

Answer: Cross-validation assesses a model's performance by dividing the dataset into multiple subsets, training the model on some, and testing it on others, helping to evaluate its generalization ability.

8. Question: Explain the concept of a data warehouse.

Answer: A data warehouse is a centralized repository that stores, integrates, and manages large volumes of data from different sources, providing a unified view for analysis and reporting.

9. Question: What is the difference between structured and unstructured data?

Answer: Structured data is organized and easily searchable (e.g., databases), while unstructured data lacks a predefined structure (e.g., text documents, images).

10. Question: What is clustering in machine learning?

Answer: Clustering is a technique that groups similar data points together based on certain features, helping to identify patterns or relationships within the data.

𝗧𝗮𝘁𝗮 𝗚𝗿𝗼𝘂𝗽 𝗙𝗥𝗘𝗘 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀😍

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Here are the SQL interview questions:

Basic SQL Questions

1.⁠ ⁠What is SQL, and what is its purpose?
2.⁠ ⁠Write a SQL query to retrieve all records from a table.
3.⁠ ⁠How do you select specific columns from a table?
4.⁠ ⁠What is the difference between WHERE and HAVING clauses?
5.⁠ ⁠How do you sort data in ascending/descending order?


SQL Query Questions

1.⁠ ⁠Write a SQL query to retrieve the top 10 records from a table based on a specific column.
2.⁠ ⁠How do you join two tables based on a common column?
3.⁠ ⁠Write a SQL query to retrieve data from multiple tables using subqueries.
4.⁠ ⁠How do you use aggregate functions (SUM, AVG, MAX, MIN)?
5.⁠ ⁠Write a SQL query to retrieve data from a table for a specific date range.


SQL Optimization Questions

1.⁠ ⁠How do you optimize SQL query performance?
2.⁠ ⁠What is indexing, and how does it improve query performance?
3.⁠ ⁠How do you avoid full table scans?
4.⁠ ⁠What is query caching, and how does it work?
5.⁠ ⁠How do you optimize SQL queries for large datasets?


SQL Joins and Subqueries

1.⁠ ⁠Explain the difference between INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN.
2.⁠ ⁠Write a SQL query to retrieve data from two tables using a subquery.
3.⁠ ⁠How do you use EXISTS and IN operators in SQL?
4.⁠ ⁠Write a SQL query to retrieve data from multiple tables using a self-join.
5.⁠ ⁠Explain the concept of correlated subqueries.


SQL Data Modeling

1.⁠ ⁠Explain the concept of normalization and denormalization.
2.⁠ ⁠How do you design a database schema for a given application?
3.⁠ ⁠What is data redundancy, and how do you avoid it?
4.⁠ ⁠Explain the concept of primary and foreign keys.
5.⁠ ⁠How do you handle data inconsistencies and anomalies?


SQL Advanced Questions

1.⁠ ⁠Explain the concept of window functions (ROW_NUMBER, RANK, etc.).
2.⁠ ⁠Write a SQL query to retrieve data using Common Table Expressions (CTEs).
3.⁠ ⁠How do you use dynamic SQL?
4.⁠ ⁠Explain the concept of stored procedures and functions.
5.⁠ ⁠Write a SQL query to retrieve data using pivot tables.


SQL Scenario-Based Questions

1.⁠ ⁠You have two tables, Orders and Customers. Write a SQL query to retrieve all orders for customers from a specific region.
2.⁠ ⁠You have a table with duplicate records. Write a SQL query to remove duplicates.
3.⁠ ⁠You have a table with missing values. Write a SQL query to replace missing values with a default value.
4.⁠ ⁠You have a table with data in an incorrect format. Write a SQL query to correct the format.
5.⁠ ⁠You have two tables with different data types for a common column. Write a SQL query to join the tables.


SQL Behavioral Questions

1.⁠ ⁠Can you explain a time when you optimized a slow-running SQL query?
2.⁠ ⁠How do you handle database errors and exceptions?
3.⁠ ⁠Can you describe a complex SQL query you wrote and why?
4.⁠ ⁠How do you stay up-to-date with new SQL features and best practices?
5.⁠ ⁠Can you walk me through your process for troubleshooting SQL issues?

Starting as a data analyst is a great first step in your career. As you grow, you might discover new interests:

• If you love working with statistics and machine learning, you could move into Data Science.

• If you're excited by building data systems and pipelines, Data Engineering might be your next step.

• If you're more interested in understanding the business side, you could become a Business Analyst.

Even if you decide to stay in your data analyst role, there's always something new to learn, especially with advancements in AI.

There are many paths to explore, but what's important is taking that first step.

I have curated best 80+ top-notch Data Analytics Resources 👇👇
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If you can't find a data role, follow this path (that I tried and tested):

📍 1. Get skills (Excel, SQL, Power BI)
📍 2. Build projects
📍 3. Get a semi-data role (any role that only needs basic data skills e.g. Excel)

Heres what you should use your data skills for in this role:

📍 1. Help your team (eg. automate reports, build dashboards)
📍 2. Add this experience to your resume
📍 3. Share this experience online

This allows you to gain real world experience while practicing your skills

Career Path for a Data Analyst

Education: Start by earning a bachelor's degree in fields like math, stats, economics, or computer science.

Skills Growth: Learn programming (Python/R), data tools (SQL/Excel), and visualization. Master data analysis basics.

Entry-Level Role: Begin as a Junior Data Analyst. Learn data cleaning, organization, and basic analysis.

Specialization: Deepen your expertise in a specific industry. Explore advanced analytics and visualization tools.

Advanced Analytics: Move up to Senior Data Analyst. Tackle complex projects and predictive modeling.

Machine Learning: Explore machine learning and data modeling techniques. Familiarize yourself with algorithms, and learn how to implement predictive and classification models.

Domain Expertise: Develop expertise in a particular industry, such as healthcare, finance, e-commerce, etc. This knowledge will enable you to provide more valuable insights from data.

Leadership Roles: As you gain experience, you can move into roles like Data Analytics Manager or Data Science Manager, where you'll oversee teams and projects.

Continuous Learning: Stay updated with the latest tools, techniques, and industry trends. Attend workshops, conferences, and online courses to keep your skills relevant.

Networking: Build a strong professional network within the data analytics community. This can open up opportunities and help you stay informed about industry developments.

Remember, your career path can be personalized based on your interests and strengths. Continuous learning and adaptability are key in the ever-evolving field of data analysis :)

𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 😍

1) Generative AI

2) Big data artificial intelligence

3 ) Microsoft Al for beginners

4) Prompt Engineering for Chat GPT

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Data Science Roadmap

𝗚𝗲𝘁 𝗬𝗼𝘂𝗿 𝗗𝗿𝗲𝗮𝗺 𝗝𝗼𝗯 𝗜𝗻 𝗔𝗺𝗮𝘇𝗼𝗻, 𝗚𝗼𝗼𝗴𝗹𝗲, 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁, 𝗡𝗩𝗜𝗗𝗜𝗔, 𝗮𝗻𝗱 𝗠𝗲𝘁𝗮 (𝗙𝗮𝗰𝗲𝗯𝗼𝗼𝗸) 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲𝘀𝗲 𝗰𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝘃𝗲 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀😍

1️⃣ Amazon Interviewing Guide
2️⃣ Google Interview Tips
3️⃣ Microsoft Hiring Tips
4️⃣ NVIDIA Hiring Process
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7 Baby steps to start with Machine Learning:

1. Start with Python
2. Learn to use Google Colab
3. Take a Pandas tutorial
4. Then a Seaborn tutorial
5. Decision Trees are a good first algorithm
6. Finish Kaggle's "Intro to Machine Learning"
7. Solve the Titanic challenge

𝟱 𝗙𝗥𝗘𝗘 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

Ready to dive into the world of Machine Learning? Here are 5 powerful resources that will guide you every step of the way—from beginner concepts to advanced techniques.

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Data Analyst Roadmap

Like if it helps ❤️

Data Analytics Roadmap 👆

Types Of Databases

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.me/sqlspecialist/752

Python Learning Plan 👇
https://t.me/sqlspecialist/749

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

SQL Learning Plan 👇
https://t.me/sqlspecialist/738

SQL Learning Series 👇
https://t.me/sqlspecialist/567

Excel Learning Series 👇
https://t.me/sqlspecialist/664

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

Python Learning Series 👇
https://t.me/sqlspecialist/615

Tableau Essential Topics 👇
https://t.me/sqlspecialist/667

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

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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 :)

Data Analyst vs Data Engineer vs Data Scientist ✅

Skills required to become a Data Analyst 👇

- Advanced Excel: Proficiency in Excel is crucial for data manipulation, analysis, and creating dashboards.
- SQL/Oracle: SQL is essential for querying databases to extract, manipulate, and analyze data.
- Python/R: Basic scripting knowledge in Python or R for data cleaning, analysis, and simple automations.
- Data Visualization: Tools like Power BI or Tableau for creating interactive reports and dashboards.
- Statistical Analysis: Understanding of basic statistical concepts to analyze data trends and patterns.


Skills required to become a Data Engineer: 👇

- Programming Languages: Strong skills in Python or Java for building data pipelines and processing data.
- SQL and NoSQL: Knowledge of relational databases (SQL) and non-relational databases (NoSQL) like Cassandra or MongoDB.
- Big Data Technologies: Proficiency in Hadoop, Hive, Pig, or Spark for processing and managing large data sets.
- Data Warehousing: Experience with tools like Amazon Redshift, Google BigQuery, or Snowflake for storing and querying large datasets.
- ETL Processes: Expertise in Extract, Transform, Load (ETL) tools and processes for data integration.


Skills required to become a Data Scientist: 👇

- Advanced Tools: Deep knowledge of R, Python, or SAS for statistical analysis and data modeling.
- Machine Learning Algorithms: Understanding and implementation of algorithms using libraries like scikit-learn, TensorFlow, and Keras.
- SQL and NoSQL: Ability to work with both structured and unstructured data using SQL and NoSQL databases.
- Data Wrangling & Preprocessing: Skills in cleaning, transforming, and preparing data for analysis.
- Statistical and Mathematical Modeling: Strong grasp of statistics, probability, and mathematical techniques for building predictive models.
- Cloud Computing: Familiarity with AWS, Azure, or Google Cloud for deploying machine learning models.

Bonus Skills Across All Roles:

- Data Visualization: Mastery in tools like Power BI and Tableau to visualize and communicate insights effectively.
- Advanced Statistics: Strong statistical foundation to interpret and validate data findings.
- Domain Knowledge: Industry-specific knowledge (e.g., finance, healthcare) to apply data insights in context.
- Communication Skills: Ability to explain complex technical concepts to non-technical stakeholders.

I have curated best 80+ top-notch Data Analytics Resources 👇👇
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Share with credits: https://t.me/sqlspecialist

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Quick Recap of Essential SQL Concepts

1️⃣ FROM clause: Specifies the tables from which data will be retrieved.
2️⃣ WHERE clause: Filters rows based on specified conditions.
3️⃣ GROUP BY clause: Groups rows that have the same values into summary rows.
4️⃣ HAVING clause: Filters groups based on specified conditions.
5️⃣ SELECT clause: Specifies the columns to be retrieved.
6️⃣ WINDOW functions: Functions that perform calculations across a set of table rows.
7️⃣ AGGREGATE functions: Functions like COUNT, SUM, AVG that perform calculations on a set of values.
8️⃣ UNION / UNION ALL: Combines the result sets of multiple SELECT statements.
9️⃣ ORDER BY clause: Sorts the result set based on specified columns.
🔟 LIMIT / OFFSET (or FETCH / OFFSET in some databases): Controls the number of rows returned and starting point for retrieval.

Here you can find quick SQL Revision Notes👇
https://topmate.io/analyst/864817

Hope it helps :)

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?" 🤦🏽♂️🤦🏽♂️

Breaking into Data Analysis can be very confusing in 2024!

Should I learn SQL or NoSQL? Tableau or Power BI? Excel or Google Sheets? Python or R?

Fundamental principles are more important than tools:

Understanding data cleaning and preprocessing is more important than SQL vs NoSQL.

Understanding data visualization concepts is more important than Tableau vs Power BI.

Understanding statistical analysis is more important than Excel vs R.

Understanding programming for data manipulation is more important than Python vs R.

Knowing these will allow you to pick up new emerging tools easily.

Stick to fundamentals first.

I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://topmate.io/analyst/861634

Hope this helps you 😊

Data Analyst Skills Required by Employers

Hey guys 👋

I was working on something big from last few days.

Finally, I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://topmate.io/analyst/861634

If you go on purchasing these books, it will cost you more than 15000 but I kept the minimal price for everyone's benefit.

I hope these resources will help you in data analytics journey.

I will add more resources here in the future without any additional cost.

All the best for your career ❤️

Top 5 Tools to master Data Analytics

1. Python:
- Versatile programming language.
- Offers powerful libraries like Pandas, NumPy, and Scikit-learn.
- Used for data manipulation, analysis, and machine learning tasks.

2. R:
- Statistical programming language.
- Provides extensive statistical capabilities.
- Popular for data analysis in academia.
- Offers visualization libraries like ggplot2.

3. SQL (Structured Query Language):
- Essential for working with relational databases.
- Allows querying, manipulation, and management of data.
- Standard language for database management systems.

4. Tableau:
- Data visualization tool.
- Enables creation of interactive dashboards.
- Helps in communicating insights effectively.
- Widely used in business intelligence.

5. Apache Spark:
- Framework for large-scale data processing.
- Offers distributed computing capabilities.
- Libraries like Spark SQL and MLlib for data manipulation and machine learning.
- Ideal for processing big data efficiently.

I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://topmate.io/analyst/861634

Like if it helps :)

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)

Hey guys 👋

Since many of you requested for data analytics recorded video lectures, here you go!
👇👇
https://topmate.io/analyst/1068350

It contains comprehensive recorded video lectures on Data Analytics, covering key tools and languages like SQL, Python, Excel, and Power BI along with hands-on projects to ensure you gain practical experience alongside theoretical knowledge.

Please use the above link to avail them!👆

NOTE: -Most data aspirants hoard resources without actually opening them even once! The reason for keeping a small price for these resources is to ensure that you value the content available inside this and encourage you to make the best out of it.

Hope this helps in your data analytics journey... All the best!👍✌️

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.

🚀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

Hey guys 👋

I was working on something big from last few days.

Finally, I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://topmate.io/analyst/861634

If you go on purchasing these books, it will cost you more than 15000 but I kept the minimal price for everyone's benefit.

I hope these resources will help you in data analytics journey.

I will add more resources here in the future without any additional cost.

All the best for your career ❤️

How Data Analytics Helps to Grow Business to Best

Analytics are the analysis of raw data to draw meaningful insights from it. In other words, applying algorithms, statistical models, or even machine learning on large volumes of data will seek to discover patterns, trends, and correlations. In this way, the bottom line is to support businesses in making much more informed, data-driven decisions.

In simple words, think about running a retail store. You’ve got years of sales data, customer feedback, and inventory reports. However, do you know which are the best-sellers or where you’re losing money? By applying data analytics, you would find out some hidden opportunities, adjust your strategies, and improve your business outcome accordingly.

read more......

7/ Use metaphors or analogies to explain difficult concepts. Don't use professional jargon.

8/ Include both the big picture and the details—it appeals to different stakeholders.

9/ Conclude with a call to action—what should they do next?

4/ Visualise trends over time to tell a story.

5/ Add context to your data—it makes your insights relevant.

6/ Speak the language of your audience—simplify complex terms.

9 secrets about Data Storytelling every analyst should know (number 6 is a must):

1/ Start with the end in mind—what’s the key takeaway?

2/ Don’t just present numbers—explain the 'so what' behind them.

3/ Data should drive decisions—frame your analysis as a solution to a problem.

#DataAnalytics

Must Study: These are the important Questions for Data Analyst ✅



SQL
1. How do you handle NULL values in SQL queries, and why is it important?
2. What is the difference between INNER JOIN and OUTER JOIN, and when would you use each?
3. How do you implement transaction control in SQL Server?

Excel
1. How do you use pivot tables to analyze large datasets in Excel?
2. What are Excel's built-in functions for statistical analysis, and how do you use them?
3. How do you create interactive dashboards in Excel?

Power BI
1. How do you optimize Power BI reports for performance?
2. What is the role of DAX (Data Analysis Expressions) in Power BI, and how do you use it?
3. How do you handle real-time data streaming in Power BI?

Python
1. How do you use Pandas for data manipulation, and what are some advanced features?
2. How do you implement machine learning models in Python, from data preparation to deployment?
3. What are the best practices for handling large datasets in Python?

Data Visualization
1. How do you choose the right visualization technique for different types of data?
2. What is the importance of color theory in data visualization?
3. How do you use tools like Tableau or Power BI for advanced data storytelling?

I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://topmate.io/analyst/861634

Hope this helps you 😊

TOP CONCEPTS FOR INTERVIEW PREPARATION!!

🚀TOP 10 SQL Concepts for Job Interview

1. Aggregate Functions (SUM/AVG)
2. Group By and Order By
3. JOINs (Inner/Left/Right)
4. Union and Union All
5. Date and Time processing
6. String processing
7. Window Functions (Partition by)
8. Subquery
9. View and Index
10. Common Table Expression (CTE)


🚀TOP 10 Statistics Concepts for Job Interview

1. Sampling
2. Experiments (A/B tests)
3. Descriptive Statistics
4. p-value
5. Probability Distributions
6. t-test
7. ANOVA
8. Correlation
9. Linear Regression
10. Logistics Regression


🚀TOP 10 Python Concepts for Job Interview

1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming

Like ❤️ the post if it was helpful to you!!!

Goldman Sachs senior data analyst interview asked questions

SQL

1 find avg of salaries department wise from table
2 Write a SQL query to see employee name and manager name using a self-join on 'employees' table with columns 'emp_id', 'name', and 'manager_id'.
3 newest joinee for every department (solved using lead lag)

POWER BI

1. What does Filter context in DAX mean?
2. Explain how to implement Row-Level Security (RLS) in Power BI.
3. Describe different types of filters in Power BI.
4. Explain the difference between 'ALL' and 'ALLSELECTED' in DAX.
5. How do you calculate the total sales for a specific product using DAX?

PYTHON

1. Create a dictionary, add elements to it, modify an element, and then print the dictionary in alphabetical order of keys.
2. Find unique values in a list of assorted numbers and print the count of how many times each value is repeated.
3. Find and print duplicate values in a list of assorted numbers, along with the number of times each value is repeated.

I have curated best 80+ top-notch Data Analytics Resources 👇👇
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Myntra interview questions for Data Analyst 2024.

1. You have a dataset with missing values. How would you use a combination of Pandas and NumPy to fill missing values based on the mean of the column?
2. How would you create a new column in a Pandas DataFrame by normalizing an existing numeric column using NumPy’s np.min() and np.max()?
3. Explain how to group a Pandas DataFrame by one column and apply a NumPy function, like np.std() (standard deviation), to each group.
4. How can you convert a time-series column in a Pandas DataFrame to NumPy’s datetime format for faster time-based calculations?
5. How would you identify and remove outliers from a Pandas DataFrame using NumPy’s Z-score method (scipy.stats.zscore)?
6. How would you use NumPy’s percentile() function to calculate specific quantiles for a numeric column in a Pandas DataFrame?
7. How would you use NumPy's polyfit() function to perform linear regression on a dataset stored in a Pandas DataFrame?
8. How can you use a combination of Pandas and NumPy to transform categorical data into dummy variables (one-hot encoding)?
9. How would you use both Pandas and NumPy to split a dataset into training and testing sets based on a random seed?
10. How can you apply NumPy's vectorize() function on a Pandas Series for better performance?
11. How would you optimize a Pandas DataFrame containing millions of rows by converting columns to NumPy arrays? Explain the benefits in terms of memory and speed.
12. How can you perform complex mathematical operations, such as matrix multiplication, using NumPy on a subset of a Pandas DataFrame?
13. Explain how you can use np.select() to perform conditional column operations in a Pandas DataFrame.
14. How can you handle time series data in Pandas and use NumPy to perform statistical analysis like rolling variance or covariance?
15. How can you integrate NumPy's random module (np.random) to generate random numbers and add them as a new column in a Pandas DataFrame?
16. Explain how you would use Pandas' applymap() function combined with NumPy’s vectorized operations to transform all elements in a DataFrame.
17. How can you apply mathematical transformations (e.g., square root, logarithm) from NumPy to specific columns in a Pandas DataFrame?
18. How would you efficiently perform element-wise operations between a Pandas DataFrame and a NumPy array of different dimensions?
19. How can you use NumPy functions like np.linalg.inv() or np.linalg.det() for linear algebra operations on numeric columns of a Pandas DataFrame?
20. Explain how you would compute the covariance matrix between multiple numeric columns of a DataFrame using NumPy.
21. What are the key differences between a Pandas DataFrame and a NumPy array? When would you use one over the other?
22. How can you convert a NumPy array into a Pandas DataFrame, and vice versa? Provide an example.

You can find the answers here
👇👇
https://medium.com/@data_analyst/myntra-data-analyst-interview-questions-with-answers-97ed86953204

I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://topmate.io/analyst/861634

Hope this helps you 😊

5️⃣ Python in Excel.

Microsoft is providing you with just what you need to scale beyond Excel limitations.

At first, you use Python in Excel because it's the easiest way to scale and tap into a vast amount of DIY data science goodness.

As 99% of the code you write for Python in Excel translates to any tool, you now have a path to move off of Excel if needed.

For example, Jupyter Notebooks and VS Code.

#dataanalysis

4️⃣ SQL is your friend.

If you're unfamiliar, SQL is the language used to query databases.

After Microsoft Excel, SQL is the world's most commonly used data technology.

SQL is easily integrated into Excel, allowing you to leverage the power of the database server to acquire and wrangle data.

The results of all this goodness then show up in your workbook.

Also, SQL is straightforward for Excel users to learn.

#dataanalysis

3️⃣ Microsoft Excel might be your hammer, but not every problem is a nail.

Please, please, please use Excel where it makes sense!

If you reach a point where Excel doesn't make sense, know that you can quickly move on to technologies that are better suited for your needs....

#dataanalysis

2️⃣ Use Microsoft Excel for as long as possible.

Again, on the surface, strange advice from someone who loves SQL and Python.

When I first started learning data analysis, I ignored Microsoft Excel.

I was a coder, and I looked down on Excel.

I was 100% wrong.

Over the years, Excel has become an exceedingly powerful data analysis tool.

For many professionals, it can be all the analytical tooling they need.

For example, Excel is a wonderful tool for visually analyzing data (e.g., PivotCharts).

You can use Excel to conduct powerful Diagnostic Analytics.

The simple reality is that many professionals will never hit Excel's data limit - especially if they have a decent laptop.

#dataanalysis

Don't waste your lot of time when learning data analysis.

Here's how you may start your Data analysis journey

1️⃣ - Avoid learning a programming language (e.g., SQL, R, or Python) for as long as possible.

This advice might seem strange coming from a former software engineer, so let me explain.

The vast majority of data analyses conducted each day worldwide are performed in the "solo analyst" scenario.

In this scenario, nobody cares about how the analysis was completed.

Only the results matter.

Also, the analysis methods (e.g., code) are rarely shared in this scenario.

Like for next steps

#dataanalysis

You don't need to know everything about every data tool. Focus on what will help land you your job.

For Excel:
- IFS (all variations)
- XLOOKUP
- IMPORTRANGE (in GSheets)
- Pivot Tables
- Dynamic functions like TODAY()

For SQL:
- Sum
- Group By
- Window Functions
- CTEs
- Joins

For Tableau:
- Calculated Columns
- Sets
- Groups
- Formatting

For Power BI:
- Power Query for data transformation
- DAX (Data Analysis Expressions) for creating custom calculations
- Relationships between tables
- Creating interactive and dynamic dashboards
- Utilizing slicers and filters effectively

I have created 100-Day Roadmap & Resources for Data Analyst 👇👇
https://topmate.io/analyst/981703

Hope it helps :)

Data Analyst Interview Questions

Data Analyst Roadmap:

- Tier 1: Excel & SQL
- Tier 2: Data Cleaning & Exploratory Data Analysis (EDA)
- Tier 3: Data Visualization & Business Intelligence (BI) Tools
- Tier 4: Statistical Analysis & Machine Learning Basics

Then build projects that include:

- Data Collection
- Data Cleaning
- Data Analysis
- Data Visualization

And if you want to make your portfolio stand out more:

- Solve real business problems
- Provide clear, impactful insights
- Create a presentation
- Record a video presentation
- Target specific industries
- Reach out to companies

I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://topmate.io/analyst/861634

Hope this helps you 😊