𝟲 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗦𝗤𝗟 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 (𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮𝘀𝗲𝘁𝘀!)😍

🎯 Want to level up your SQL skills with real business scenarios?📚

These 6 hands-on SQL projects will help you go beyond basic SELECT queries and practice what hiring managers actually care about👨‍💻📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/40kF1x0

Save this post — even completing 1 project can power up your SQL profile!✅️

Data Analytics Roadmap

1. Fundamentals of Statistics and Mathematics
  - Understand descriptive statistics: mean, median, mode, variance, standard deviation.
  - Basics of probability theory.
  - Hypothesis testing and statistical inference.
  - Some linear algebra and calculus basics (optional depending on needs).

2. Learn Excel and Google Sheets
  - Master spreadsheet basics: formulas, functions, pivot tables.
  - Data visualization with charts and graphs.
  - Basic automation with macros and advanced formulas.

3. Programming for Data Analytics
  - Choose Python or R as your main analytical programming language.
  - Python libraries: pandas (data manipulation), numpy (numerical operations), matplotlib and seaborn (visualization).
  - For R: dplyr, ggplot2.
  - Use Jupyter Notebook (Python) or RStudio for coding environment.

4. Databases and SQL
  - Understand relational databases and how data is stored.
  - Learn SQL queries: SELECT, JOIN, GROUP BY, aggregation functions.
  - Practice querying real databases.

5. Data Visualization Tools
  - Learn tools like Tableau, Power BI, or Looker.
  - Build interactive dashboards and reports.
  - Understand best practices for effective visualization (color, simplicity, clarity).

6. Business Analytics Fundamentals
  - Understand business processes and workflows.
  - Define Key Performance Indicators (KPIs).
  - Translate business questions into analytical problems.

7. Data Cleaning and Preprocessing
  - Handle missing, inconsistent, and outlier data.
  - Data transformation and normalization techniques.
  - Use Python (pandas) or other tools to clean data effectively.

8. Basics of Machine Learning (Optional for Advanced Skills)
  - Understand simple models: linear regression, classification.
  - Use scikit-learn library in Python.
  - Apply models for forecasting and clustering.

9. Hands-on Practice and Projects
  - Work on real datasets from Kaggle or other platforms.
  - Build a portfolio showcasing your data analysis projects.
  - Participate in data competitions and hackathons.

10. Communication and Reporting
  - Develop skills in presenting data insights clearly.
  - Create compelling reports and presentations.
  - Learn to work with stakeholders to tailor insights.

Share with credits: https://t.iss.one/sqlspecialist

React ♥️ for more

Key data science programming languages and tools

Data Science Roadmap

𝟲 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗦𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 & 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗝𝗼𝘂𝗿𝗻𝗲𝘆😍

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𝐋𝐢𝐧𝐤👇:-

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This list will set you up with real-world, job-ready skills✅️

There are several AI tools and libraries available to assist with coding in Python. Here are some of the most popular ones:

1. GitHub Copilot: An AI-powered code completion tool developed by GitHub and OpenAI. It can suggest entire lines or blocks of code based on the context of what you're writing.

2. Tabnine: An AI code completion tool that supports various IDEs and code editors. It uses deep learning models to predict and suggest code completions.

3. Kite: An AI-powered code completion and documentation tool that integrates with many popular IDEs. It offers in-line code completions and documentation for Python.

4. PyCharm's Code Completion: JetBrains' PyCharm IDE comes with advanced code completion features, which are enhanced by AI to provide context-aware suggestions.

5. Jupyter Notebooks with AI Integration: Jupyter notebooks can integrate with various AI tools and libraries for code completion and suggestions, like JupyterLab Code Formatter or extensions that integrate with AI models.

6. DeepCode: An AI-based code review tool that helps identify and fix bugs, security vulnerabilities, and code quality issues.

7. IntelliCode: An extension for Visual Studio Code that uses AI to provide code suggestions and improve productivity.

8. Codota: An AI-powered code suggestion tool that integrates with many IDEs and provides context-aware code completions.

9. Repl.it Ghostwriter: An AI-powered code completion tool available in the Repl.it online coding environment.

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𝗖𝗿𝗮𝗰𝗸 𝗙𝗔𝗔𝗡𝗚 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱 — 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘!😍

If you’re serious about cracking top tech interviews — from FAANG to startups — this is the roadmap you can’t afford to miss🎊

Thousands have used it to land roles at Google, Amazon, Microsoft, and more — completely free🤩📌

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Your dream job might just start here.✅️

𝟰 𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱😍

Want to break into data science in 2025—without spending a single rupee?💰👨‍💻

You’re in luck! Microsoft is offering powerful, beginner-friendly resources that teach you everything from Python fundamentals to AI and data analytics—for free🤩✔️

𝐋𝐢𝐧𝐤👇:-

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Level up your career in the booming field of data✅️

𝟰 𝗠𝘂𝘀𝘁-𝗪𝗮𝘁𝗰𝗵 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗼𝗿 𝗘𝘃𝗲𝗿𝘆 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗦𝘁𝘂𝗱𝗲𝗻𝘁 𝗶𝗻 𝟮𝟬𝟮𝟱😍

If you’re starting your data analytics journey, these 4 YouTube courses are pure gold — and the best part? 💻🤩

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𝐋𝐢𝐧𝐤👇:-

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Each course can help you build the right foundation for a successful tech career✅️

Machine Learning Roadmap

𝟲 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗙𝗿𝗼𝗺 𝗧𝗼𝗽 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 😍

A power-packed selection of 100% free, certified courses from top institutions:

- Data Analytics – Cisco
- Digital Marketing – Google
- Python for AI – IBM/edX
- SQL & Databases – Stanford
- Generative AI – Google Cloud
- Machine Learning – Harvard

𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:- 
 
https://pdlink.in/3FcwrZK
 
Master in‑demand tech skills with these 6 certified, top-tier free courses

As a data analyst, your focus isn't on creating dashboards, writing SQL queries, doing pivot tables, generating reports, or cleaning data.

Your focus should be solving business problems using these skills

- Don’t just write SQL—ask why you're querying that data and what decision it will influence.

- Don’t just build a dashboard—ask who will use it and how it will help them take action.

- Don’t just clean data—know what insight lies beneath the mess.

- Don’t just report metrics—ask what story they’re telling and what recommendation can follow.

🚀 𝟳 𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 + 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱 😍

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𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:- 
 
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💼 Perfect for students, freshers & working professionals

𝗧𝗶𝗿𝗲𝗱 𝗼𝗳 𝘀𝘁𝗿𝘂𝗴𝗴𝗹𝗶𝗻𝗴 𝘁𝗼 𝗳𝗶𝗻𝗱 𝗴𝗼𝗼𝗱 𝗔𝗜/𝗠𝗟 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗼 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲?😍

Stop wasting hours searching — here’s a GOLDMINE 💎

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𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/45gTMU8

✨Save this. Share this. Start building.✅️

Use Chat GPT to prepare for your next Interview

This could be the most helpful thing for people aspiring for new jobs.

A few prompts that can help you here are:

💡Prompt 1: Here is a Job description of a job I am looking to apply for. Can you tell me what skills and questions should I prepare for? {Paste JD}

💡Prompt 2: Here is my resume. Can you tell me what optimization I can do to make it more likely to get selected for this interview? {Paste Resume in text}

💡Prompt 3: Act as an Interviewer for the role of a {product manager} at {Company}. Ask me 5 questions one by one, wait for my response, and then tell me how I did. You should give feedback in the following format: What was good, where are the gaps, and how to address the gaps?

💡Prompt 4: I am interviewing for this job given in the JD. Can you help me understand the company, its role, its products, main competitors, and challenges for the company?

💡Prompt 5: What are the few questions I should ask at the end of the interview which can help me learn about the culture of the company?

Free book to master ChatGPT: https://t.iss.one/InterviewBooks/166

ENJOY LEARNING 👍👍

𝟱 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗧𝗲𝗰𝗵 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲 – 𝗪𝗶𝘁𝗵 𝗙𝘂𝗹𝗹 𝗧𝘂𝘁𝗼𝗿𝗶𝗮𝗹𝘀!😍

Are you ready to build real-world tech projects that don’t just look good on your resume, but actually teach you practical, job-ready skills?🧑‍💻📌

Here’s a curated list of 5 high-value development tutorials — covering everything from full-stack development and real-time chat apps to AI form builders and reinforcement learning✨️💻

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3UtCSLO

They’re real, portfolio-worthy projects you can start today✅️

Complete Syllabus for Data Analytics interview:

SQL:
1. Basic
  - SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
  - Basic JOINS (INNER, LEFT, RIGHT, FULL)
  - Creating and using simple databases and tables

2. Intermediate
  - Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
  - Subqueries and nested queries
  - Common Table Expressions (WITH clause)
  - CASE statements for conditional logic in queries

3. Advanced
  - Advanced JOIN techniques (self-join, non-equi join)
  - Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
  - optimization with indexing
  - Data manipulation (INSERT, UPDATE, DELETE)

Python:
1. Basic
  - Syntax, variables, data types (integers, floats, strings, booleans)
  - Control structures (if-else, for and while loops)
  - Basic data structures (lists, dictionaries, sets, tuples)
  - Functions, lambda functions, error handling (try-except)
  - Modules and packages

2. Pandas & Numpy
  - Creating and manipulating DataFrames and Series
  - Indexing, selecting, and filtering data
  - Handling missing data (fillna, dropna)
  - Data aggregation with groupby, summarizing data
  - Merging, joining, and concatenating datasets

3. Basic Visualization
  - Basic plotting with Matplotlib (line plots, bar plots, histograms)
  - Visualization with Seaborn (scatter plots, box plots, pair plots)
  - Customizing plots (sizes, labels, legends, color palettes)
  - Introduction to interactive visualizations (e.g., Plotly)

Excel:
1. Basic
  - Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
  - Introduction to charts and basic data visualization
  - Data sorting and filtering
  - Conditional formatting

2. Intermediate
  - Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
  - PivotTables and PivotCharts for summarizing data
  - Data validation tools
  - What-if analysis tools (Data Tables, Goal Seek)

3. Advanced
  - Array formulas and advanced functions
  - Data Model & Power Pivot
- Advanced Filter
- Slicers and Timelines in Pivot Tables
  - Dynamic charts and interactive dashboards

Power BI:
1. Data Modeling
  - Importing data from various sources
  - Creating and managing relationships between different datasets
  - Data modeling basics (star schema, snowflake schema)

2. Data Transformation
  - Using Power Query for data cleaning and transformation
  - Advanced data shaping techniques
  - Calculated columns and measures using DAX

3. Data Visualization and Reporting
  - Creating interactive reports and dashboards
  - Visualizations (bar, line, pie charts, maps)
  - Publishing and sharing reports, scheduling data refreshes

Statistics Fundamentals:
Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution.