𝟳 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗨𝗽𝗴𝗿𝗮𝗱𝗲 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱 𝗮𝗻𝗱 𝗦𝘁𝗮𝗻𝗱 𝗢𝘂𝘁😍

🚀 Want to Make Your Resume Stand Out in 2025?✨️

If you’re aiming to boost your chances in job interviews or want to upgrade your resume with powerful, in-demand skills — start with these 7 free online courses👨‍💻📌

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Empower yourself and take your career to the next level! ✅

Advanced Skills to Elevate Your Data Analytics Career

1️⃣ SQL Optimization & Performance Tuning

🚀 Learn indexing, query optimization, and execution plans to handle large datasets efficiently.

2️⃣ Machine Learning Basics

🤖 Understand supervised and unsupervised learning, feature engineering, and model evaluation to enhance analytical capabilities.

3️⃣ Big Data Technologies

🏗️ Explore Spark, Hadoop, and cloud platforms like AWS, Azure, or Google Cloud for large-scale data processing.

4️⃣ Data Engineering Skills

⚙️ Learn ETL pipelines, data warehousing, and workflow automation to streamline data processing.

5️⃣ Advanced Python for Analytics

🐍 Master libraries like Scikit-Learn, TensorFlow, and Statsmodels for predictive analytics and automation.

6️⃣ A/B Testing & Experimentation

🎯 Design and analyze controlled experiments to drive data-driven decision-making.

7️⃣ Dashboard Design & UX

🎨 Build interactive dashboards with Power BI, Tableau, or Looker that enhance user experience.

8️⃣ Cloud Data Analytics

☁️ Work with cloud databases like BigQuery, Snowflake, and Redshift for scalable analytics.

9️⃣ Domain Expertise

💼 Gain industry-specific knowledge (e.g., finance, healthcare, e-commerce) to provide more relevant insights.

🔟 Soft Skills & Leadership

💡 Develop stakeholder management, storytelling, and mentorship skills to advance in your career.

Hope it helps :)

#dataanalytics

📊 Data Analyst Roadmap (2025)

Master the Skills That Top Companies Are Hiring For!

📍 1. Learn Excel / Google Sheets
Basic formulas & formatting
VLOOKUP, Pivot Tables, Charts
Data cleaning & conditional formatting

📍 2. Master SQL
SELECT, WHERE, ORDER BY
JOINs (INNER, LEFT, RIGHT)
GROUP BY, HAVING, LIMIT
Subqueries, CTEs, Window Functions

📍 3. Learn Data Visualization Tools
Power BI / Tableau (choose one)
Charts, filters, slicers
Dashboards & storytelling

📍 4. Get Comfortable with Statistics
Mean, Median, Mode, Std Dev
Probability basics
A/B Testing, Hypothesis Testing
Correlation & Regression

📍 5. Learn Python for Data Analysis (Optional but Powerful)
Pandas & NumPy for data handling
Seaborn, Matplotlib for visuals
Jupyter Notebooks for analysis

📍 6. Data Cleaning & Wrangling
Handle missing values
Fix data types, remove duplicates
Text processing & date formatting

📍 7. Understand Business Metrics
KPIs: Revenue, Churn, CAC, LTV
Think like a business analyst
Deliver actionable insights

📍 8. Communication & Storytelling
Present insights with clarity
Simplify complex data
Speak the language of stakeholders

📍 9. Version Control (Git & GitHub)
Track your projects
Build a data portfolio
Collaborate with the community

📍 10. Interview & Resume Preparation
Excel, SQL, case-based questions
Mock interviews + real projects
Resume with measurable achievements

✨ React ❤️ for more

Common Mistakes Data Analysts Must Avoid ⚠️📊

Even experienced analysts can fall into these traps. Avoid these mistakes to ensure accurate, impactful analysis!

1️⃣ Ignoring Data Cleaning 🧹
Messy data leads to misleading insights. Always check for missing values, duplicates, and inconsistencies before analysis.

2️⃣ Relying Only on Averages 📉
Averages hide variability. Always check median, percentiles, and distributions for a complete picture.

3️⃣ Confusing Correlation with Causation 🔗
Just because two things move together doesn’t mean one causes the other. Validate assumptions before making decisions.

4️⃣ Overcomplicating Visualizations 🎨
Too many colors, labels, or complex charts confuse your audience. Keep it simple, clear, and focused on key takeaways.

5️⃣ Not Understanding Business Context 🎯
Data without context is meaningless. Always ask: "What problem are we solving?" before diving into numbers.

6️⃣ Ignoring Outliers Without Investigation 🔍
Outliers can signal errors or valuable insights. Always analyze why they exist before deciding to remove them.

7️⃣ Using Small Sample Sizes ⚠️
Drawing conclusions from too little data leads to unreliable insights. Ensure your sample size is statistically significant.

8️⃣ Failing to Communicate Insights Clearly 🗣️
Great analysis means nothing if stakeholders don’t understand it. Tell a story with data—don’t just dump numbers.

9️⃣ Not Keeping Up with Industry Trends 🚀
Data tools and techniques evolve fast. Keep learning SQL, Python, Power BI, Tableau, and machine learning basics.

Avoid these mistakes, and you’ll stand out as a reliable data analyst!

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Python for Data Analytics - Quick Cheatsheet with Code Example 🚀

1️⃣ Data Manipulation with Pandas

import pandas as pd  
df = pd.read_csv("data.csv")  
df.to_excel("output.xlsx")  
df.head()  
df.info()  
df.describe()  
df[df["sales"] > 1000]  
df[["name", "price"]]  
df.fillna(0, inplace=True)  
df.dropna(inplace=True)  

2️⃣ Numerical Operations with NumPy

import numpy as np  
arr = np.array([1, 2, 3, 4])  
print(arr.shape)  
np.mean(arr)  
np.median(arr)  
np.std(arr)  

3️⃣ Data Visualization with Matplotlib & Seaborn

import matplotlib.pyplot as plt  
plt.plot([1, 2, 3, 4], [10, 20, 30, 40])  
plt.bar(["A", "B", "C"], [5, 15, 25])  
plt.show()  
import seaborn as sns  
sns.heatmap(df.corr(), annot=True)  
sns.boxplot(x="category", y="sales", data=df)  
plt.show()  

4️⃣ Exploratory Data Analysis (EDA)

df.isnull().sum()  
df.corr()  
sns.histplot(df["sales"], bins=30)  
sns.boxplot(y=df["price"])  

5️⃣ Working with Databases (SQL + Python)

import sqlite3  
conn = sqlite3.connect("database.db")  
df = pd.read_sql("SELECT * FROM sales", conn)  
conn.close()  
cursor = conn.cursor()  
cursor.execute("SELECT AVG(price) FROM products")  
result = cursor.fetchone()  
print(result)

React with ❤️ for more

This is how data analytics teams work!

Example:
1) Senior Management at Swiggy/Infosys/HDFC/XYZ company needs data-driven insights to solve a critical business challenge.

So, they onboard a data analytics team to provide support.

2) A team from Analytics Team/Consulting Firm/Internal Data Science Division is onboarded.
The team typically consists of a Lead Analyst/Manager and 2-3 Data Analysts/Junior Analysts.

3) This data analytics team (1 manager + 2-3 analysts) is part of a bigger ecosystem that they can rely upon:
- A Senior Data Scientist/Analytics Lead who has industry knowledge and experience solving similar problems.
- Subject Matter Experts (SMEs) from various domains like AI, Machine Learning, or industry-specific fields (e.g., Marketing, Supply Chain, Finance).
- Business Intelligence (BI) Experts and Data Engineers who ensure that the data is well-structured and easy to interpret.
- External Tools & Platforms (e.g., Power BI, Tableau, Google Analytics) that can be leveraged for advanced analytics.
- Data Experts who specialize in various data sources, research, and methods to get the right information.

4) Every member of this ecosystem collaborates to create value for the client:
- The entire team works toward solving the client’s business problem using data-driven insights.
- The Manager & Analysts may not be industry experts but have access to the right tools and people to bring the expertise required.
- If help is needed from a Data Scientist sitting in New York or a Cloud Engineer in Singapore, it’s available—collaboration is key!

End of the day:
1) Data analytics teams aren’t just about crunching numbers—they’re about solving problems using data-driven insights.
2) EVERYONE in this ecosystem plays a vital role and is rewarded well because the value they create helps the business make informed decisions!
3) You should consider working in this field for a few years, at least. It’ll teach you how to break down complex business problems and solve them with data. And trust me, data-driven decision-making is one of the most powerful skills to have today!

I have curated best 80+ top-notch Data Analytics Resources 👇👇
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𝐀𝐦𝐚𝐳𝐨𝐧 𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 😍

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These courses are perfect to upskill in AI and kickstart your journey in this revolutionary field.

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Advanced Skills to Elevate Your Data Analytics Career

1️⃣ SQL Optimization & Performance Tuning

🚀 Learn indexing, query optimization, and execution plans to handle large datasets efficiently.

2️⃣ Machine Learning Basics

🤖 Understand supervised and unsupervised learning, feature engineering, and model evaluation to enhance analytical capabilities.

3️⃣ Big Data Technologies

🏗️ Explore Spark, Hadoop, and cloud platforms like AWS, Azure, or Google Cloud for large-scale data processing.

4️⃣ Data Engineering Skills

⚙️ Learn ETL pipelines, data warehousing, and workflow automation to streamline data processing.

5️⃣ Advanced Python for Analytics

🐍 Master libraries like Scikit-Learn, TensorFlow, and Statsmodels for predictive analytics and automation.

6️⃣ A/B Testing & Experimentation

🎯 Design and analyze controlled experiments to drive data-driven decision-making.

7️⃣ Dashboard Design & UX

🎨 Build interactive dashboards with Power BI, Tableau, or Looker that enhance user experience.

8️⃣ Cloud Data Analytics

☁️ Work with cloud databases like BigQuery, Snowflake, and Redshift for scalable analytics.

9️⃣ Domain Expertise

💼 Gain industry-specific knowledge (e.g., finance, healthcare, e-commerce) to provide more relevant insights.

🔟 Soft Skills & Leadership

💡 Develop stakeholder management, storytelling, and mentorship skills to advance in your career.

Hope it helps :)

#dataanalytics

5 Essential Skills Every Data Analyst Must Master in 2025

Data analytics continues to evolve rapidly, and as a data analyst, it's crucial to stay ahead of the curve. In 2025, the skills that were once optional are now essential to stand out in this competitive field. Here are five must-have skills for every data analyst this year.

1. Data Wrangling & Cleaning:
The ability to clean, organize, and prepare data for analysis is critical. No matter how sophisticated your tools are, they can't work with messy, inconsistent data. Mastering data wrangling—removing duplicates, handling missing values, and standardizing formats—will help you deliver accurate and actionable insights.

Tools to master: Python (Pandas), R, SQL

2. Advanced Excel Skills:
Excel remains one of the most widely used tools in the data analysis world. Beyond the basics, you should master advanced formulas, pivot tables, and Power Query. Excel continues to be indispensable for quick analyses and prototype dashboards.

Key skills to learn: VLOOKUP, INDEX/MATCH, Power Pivot, advanced charting

3. Data Visualization:
The ability to convey your findings through compelling data visuals is what sets top analysts apart. Learn how to use tools like Tableau, Power BI, or even D3.js for web-based visualization. Your visuals should tell a story that’s easy for stakeholders to understand at a glance.

Focus areas: Interactive dashboards, storytelling with data, advanced chart types (heat maps, scatter plots)

4. Statistical Analysis & Hypothesis Testing:
Understanding statistics is fundamental for any data analyst. Master concepts like regression analysis, probability theory, and hypothesis testing. This skill will help you not only describe trends but also make data-driven predictions and assess the significance of your findings.

Skills to focus on: T-tests, ANOVA, correlation, regression models

5. Machine Learning Basics:
While you don’t need to be a data scientist, having a basic understanding of machine learning algorithms is increasingly important. Knowledge of supervised vs unsupervised learning, decision trees, and clustering techniques will allow you to push your analysis to the next level.

Begin with: Linear regression, K-means clustering, decision trees (using Python libraries like Scikit-learn)

In 2025, data analysts must embrace a multi-faceted skill set that combines technical expertise, statistical knowledge, and the ability to communicate findings effectively.

Keep learning and adapting to these emerging trends to ensure you're ready for the challenges of tomorrow.

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How to master Python from scratch🚀

1. Setup and Basics 🏁
   - Install Python 🖥️: Download Python and set it up.
   - Hello, World! 🌍: Write your first Hello World program.

2. Basic Syntax 📜
   - Variables and Data Types 📊: Learn about strings, integers, floats, and booleans.
   - Control Structures 🔄: Understand if-else statements, for loops, and while loops.
   - Functions 🛠️: Write reusable blocks of code.

3. Data Structures 📂
   - Lists 📋: Manage collections of items.
   - Dictionaries 📖: Store key-value pairs.
   - Tuples 📦: Work with immutable sequences.
   - Sets 🔢: Handle collections of unique items.

4. Modules and Packages 📦
   - Standard Library 📚: Explore built-in modules.
   - Third-Party Packages 🌐: Install and use packages with pip.

5. File Handling 📁
   - Read and Write Files 📝
   - CSV and JSON 📑

6. Object-Oriented Programming 🧩
   - Classes and Objects 🏛️
   - Inheritance and Polymorphism 👨‍👩‍👧

7. Web Development 🌐
   - Flask 🍼: Start with a micro web framework.
   - Django 🦄: Dive into a full-fledged web framework.

8. Data Science and Machine Learning 🧠
   - NumPy 📊: Numerical operations.
   - Pandas 🐼: Data manipulation and analysis.
   - Matplotlib 📈 and Seaborn 📊: Data visualization.
   - Scikit-learn 🤖: Machine learning.

9. Automation and Scripting 🤖
   - Automate Tasks 🛠️: Use Python to automate repetitive tasks.
   - APIs 🌐: Interact with web services.

10. Testing and Debugging 🐞
    - Unit Testing 🧪: Write tests for your code.
    - Debugging 🔍: Learn to debug efficiently.

11. Advanced Topics 🚀
    - Concurrency and Parallelism 🕒
    - Decorators 🌀 and Generators ⚙️
    - Web Scraping 🕸️: Extract data from websites using BeautifulSoup and Scrapy.

12. Practice Projects 💡
    - Calculator 🧮
    - To-Do List App 📋
    - Weather App ☀️
    - Personal Blog 📝

13. Community and Collaboration 🤝
    - Contribute to Open Source 🌍
    - Join Coding Communities 💬
    - Participate in Hackathons 🏆

14. Keep Learning and Improving 📈
    - Read Books 📖: Like "Automate the Boring Stuff with Python".
    - Watch Tutorials 🎥: Follow video courses and tutorials.
    - Solve Challenges 🧩: On platforms like LeetCode, HackerRank, and CodeWars.

15. Teach and Share Knowledge 📢
    - Write Blogs ✍️
    - Create Video Tutorials 📹
    - Mentor Others 👨‍🏫

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Python Methods

Python for everything 👆

What seperates a good 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 from a great one?

The journey to becoming an exceptional data analyst requires mastering a blend of technical and soft skills.

☑ Technical skills:
- Querying Data with SQL
- Data Visualization (Tableau/PowerBI)
- Data Storytelling and Reporting
- Data Exploration and Analytics
- Data Modeling

☑ Soft Skills:
- Problem Solving
- Communication
- Business Acumen
- Curiosity
- Critical Thinking
- Learning Mindset

But how do you develop these soft skills?

◆ Tackle real-world data projects or case studies. The more complex, the better.

◆ Practice explaining your analysis to non-technical audiences. If they understand, you’ve nailed it!

◆ Learn how industries use data for decision-making. Align your analysis with business outcomes.

◆ Stay curious, ask 'why,' and dig deeper into your data. Don’t settle for surface-level insights.

◆ Keep evolving. Attend webinars, read books, or engage with industry experts regularly.

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📌 MACHINE LEARNING INTERVIEW QUESTIONS

SQL INTERVIEW Questions

Explain the concept of window functions in SQL. Provide examples to illustrate their usage.

Answer:

Window Functions:
Window functions perform calculations across a set of table rows related to the current row. Unlike aggregate functions, window functions do not group rows into a single output row; instead, they return a value for each row in the query result.

Types of Window Functions:
1. Aggregate Window Functions: Compute aggregate values like SUM, AVG, COUNT, etc.
2. Ranking Window Functions: Assign a rank to each row, such as RANK(), DENSE_RANK(), and ROW_NUMBER().
3. Analytic Window Functions: Perform calculations like LEAD(), LAG(), FIRST_VALUE(), and LAST_VALUE().

Syntax:

SELECT column_name, 
       window_function() OVER (PARTITION BY column_name ORDER BY column_name)
FROM table_name;

Examples:

1. Using ROW_NUMBER():
Assign a unique number to each row within a partition of the result set.

   SELECT employee_name, department_id, salary,
          ROW_NUMBER() OVER (PARTITION BY department_id ORDER BY salary DESC) AS rank
   FROM employees;
   

This query ranks employees within each department based on their salary in descending order.

2. Using AVG() with OVER():
Calculate the average salary within each department without collapsing the result set.

   SELECT employee_name, department_id, salary,
          AVG(salary) OVER (PARTITION BY department_id) AS avg_salary
   FROM employees;
   

This query returns the average salary for each department along with each employee's salary.

3. Using LEAD():
Access the value of a subsequent row in the result set.

   SELECT employee_name, department_id, salary,
          LEAD(salary, 1) OVER (PARTITION BY department_id ORDER BY salary) AS next_salary
   FROM employees;
   

This query retrieves the salary of the next employee within the same department based on the current sorting order.

4. Using RANK():
Assign a rank to each row within the partition, with gaps in the ranking values if there are ties.

   SELECT employee_name, department_id, salary,
          RANK() OVER (PARTITION BY department_id ORDER BY salary DESC) AS rank
   FROM employees;
   

This query ranks employees within each department by their salary in descending order, leaving gaps for ties.

Tip: Window functions are powerful for performing calculations across a set of rows while retaining the individual rows. They are useful for running totals, moving averages, ranking, and accessing data from other rows within the same result set.

Go though SQL Learning Series to refresh your basics

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Top 10 concepts for Data Analyst interviews 👇👇

1. Data Cleaning: Techniques to handle missing, duplicate, and inconsistent data.


2. SQL: Strong knowledge of Joins, Group By, Window Functions, and Subqueries.


3. Excel: Proficiency in Pivot Tables, VLOOKUP, Conditional Formatting, and advanced formulas.


4. Visualization Tools: Expertise in Tableau, Power BI, or similar tools for dashboards and insights.


5. Data Wrangling: Extracting, transforming, and loading (ETL) data from various sources.


6. Statistics: Basic understanding of mean, median, standard deviation, correlation, and hypothesis testing.


7. Python/R: Ability to use libraries like Pandas, NumPy, and Matplotlib for analysis.


8. Business Acumen: Translate data insights into actionable recommendations for stakeholders.


9. Data Modeling: Create relationships between datasets and understand star/snowflake schema.


10. A/B Testing: Design and interpret experiments to compare group performance.

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Most popular Python libraries for data visualization:

Matplotlib – The most fundamental library for static charts. Best for basic visualizations like line, bar, and scatter plots. Highly customizable but requires more coding.

Seaborn – Built on Matplotlib, it simplifies statistical data visualization with beautiful defaults. Ideal for correlation heatmaps, categorical plots, and distribution analysis.

Plotly – Best for interactive visualizations with zooming, hovering, and real-time updates. Great for dashboards, web applications, and 3D plotting.

Bokeh – Designed for interactive and web-based visualizations. Excellent for handling large datasets, streaming data, and integrating with Flask/Django.

Altair – A declarative library that makes complex statistical plots easy with minimal code. Best for quick and clean data exploration.

For static charts, start with Matplotlib or Seaborn. If you need interactivity, use Plotly or Bokeh. For quick EDA, Altair is a great choice.

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Building Your Personal Brand as a Data Analyst 🚀

A strong personal brand can help you land better job opportunities, attract freelance clients, and position you as a thought leader in data analytics.

Here’s how to build and grow your brand effectively:

1️⃣ Optimize Your LinkedIn Profile 🔍

Use a clear, professional profile picture and a compelling headline (e.g., Data Analyst | SQL | Power BI | Python Enthusiast).

Write an engaging "About" section showcasing your skills, experience, and passion for data analytics.

Share projects, case studies, and insights to demonstrate expertise.

Engage with industry leaders, recruiters, and fellow analysts.


2️⃣ Share Valuable Content Consistently ✍️

Post insightful LinkedIn posts, Medium articles, or Twitter threads on SQL, Power BI, Python, and industry trends.

Write about real-world case studies, common mistakes, and career advice.

Share data visualization tips, SQL tricks, or step-by-step tutorials.


3️⃣ Contribute to Open-Source & GitHub 💻

Publish SQL queries, Python noscripts, Jupyter notebooks, and dashboards.

Share projects with real datasets to showcase your hands-on skills.

Collaborate on open-source data analytics projects to gain exposure.


4️⃣ Engage in Online Data Analytics Communities 🌍

Join and contribute to Reddit (r/dataanalysis, r/SQL), Stack Overflow, and Data Science Discord groups.

Participate in Kaggle competitions to gain practical experience.

Answer questions on Quora, LinkedIn, or Twitter to establish credibility.


5️⃣ Speak at Webinars & Meetups 🎤

Host or participate in webinars on LinkedIn, YouTube, or data conferences.

Join local meetups or online communities like DataCamp and Tableau User Groups.

Share insights on career growth, best practices, and analytics trends.


6️⃣ Create a Portfolio Website 🌐

Build a personal website showcasing your projects, resume, and blog.

Include interactive dashboards, case studies, and problem-solving examples.

Use Wix, WordPress, or GitHub Pages to get started.


7️⃣ Network & Collaborate 🤝

Connect with hiring managers, recruiters, and senior analysts.

Collaborate on guest blog posts, podcasts, or YouTube interviews.

Attend data science and analytics conferences to expand your reach.


8️⃣ Start a YouTube Channel or Podcast 🎥

Share short tutorials on SQL, Power BI, Python, and Excel.

Interview industry experts and discuss data analytics career paths.

Offer career guidance, resume tips, and interview prep content.


9️⃣ Offer Free Value Before Monetizing 💡

Give away free e-books, templates, or mini-courses to attract an audience.

Provide LinkedIn Live Q&A sessions, career guidance, or free tutorials.

Once you build trust, you can monetize through consulting, courses, and coaching.


🔟 Stay Consistent & Keep Learning

Building a brand takes time—stay consistent with content creation and engagement.

Keep learning new skills and sharing your journey to stay relevant.

Follow industry leaders, subscribe to analytics blogs, and attend workshops.

A strong personal brand in data analytics can open unlimited opportunities—from job offers to freelance gigs and consulting projects.

Start small, be consistent, and showcase your expertise! 🔥

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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 Denoscriptive 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.

Data Analytics Resources
👇👇
https://news.1rj.ru/str/sqlspecialist

Hope this helps you 😊