Avoid directly copying YouTube projects onto your resume because if everyone looks the same, recruiters might discard resumes.

Instead, for eg, let's say you are working on a SQL case study, download a dataset from Kaggle (usually a CSV file), set up a Postgre/MySQL database, connect it with the data, and prompt ChatGPT with questions ranging from basic to advanced SQL.

Solve the questions step by step. When using PowerBI, connect to the database and create a compelling dashboard. Don't just upload the dataset; employ DAX queries, statistical functions, and avoid relying solely on drag-and-drop features. Use Formatting section to do creative stuff and add your unique element in the project.

ENJOY LEARNING 👍👍

New Giveaway 500 TB Tutorials + Books + Courses + Trainings + Workshops + Educational Resources
👇👇
https://www.linkedin.com/posts/sql-analysts_python-datascience-aws-activity-7134053938898608128-TCyi?utm_source=share&utm_medium=member_android

Best practices for writing SQL queries:

Join for more: https://t.iss.one/learndataanalysis

1- Write SQL keywords in capital letters.

2- Use table aliases with columns when you are joining multiple tables.

3- Never use select *, always mention list of columns in select clause.

4- Add useful comments wherever you write complex logic. Avoid too many comments.

5- Use joins instead of subqueries when possible for better performance.

6- Create CTEs instead of multiple sub queries , it will make your query easy to read.

7- Join tables using JOIN keywords instead of writing join condition in where clause for better readability.

8- Never use order by in sub queries , It will unnecessary increase runtime.

9- If you know there are no duplicates in 2 tables, use UNION ALL instead of UNION for better performance.

SQL Basics: https://t.iss.one/sqlanalyst/105

Free Data sets for Data Science & Machine Learning Projects
👇👇
https://t.iss.one/DataPortfolio

Advanced data analytics book
👇👇
https://www.linkedin.com/posts/sql-analysts_advances-data-analytics-interview-questions-activity-7135479770078658560-U4ip?utm_source=share&utm_medium=member_android

Repost on linkedin and build your network with right-minded people❤️

Master SQL in just 1 hour
😁👇
https://www.linkedin.com/posts/sql-analysts_sql-cheat-sheet-activity-7135851184589840384-YxrG?utm_source=share&utm_medium=member_android

𝟲𝟬 𝗺𝗶𝗻𝗱-𝗯𝗹𝗼𝘄𝗶𝗻𝗴 𝗔𝗜 𝘁𝗼𝗼𝗹𝘀 𝗯𝗲𝘁𝘁𝗲𝗿 𝘁𝗵𝗮𝗻 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 that no one else will tell you about!
👇👇
https://www.linkedin.com/posts/sql-analysts_data-analysis-books-activity-7136045145229041664-uVUm?utm_source=share&utm_medium=member_android

Data Analytics Short Roadmap
👇👇
https://bit.ly/data-analysis-roadmap

Like for more 😄❤️

Data Analytics Practical Guide
👇👇
https://www.linkedin.com/posts/sql-analysts_data-analytics-practical-guide-activity-7136228678446854145-UJnw

Best Free Courses for Absolute Beginners with Certificate:
👇👇
https://bit.ly/3Gq2far

Like if it really helps you. It takes a lot of efforts in posting content for you guys ❤️😄

Guys, like the linkedin post if its helping you so that linkedin algorithm can send it to more data enthusiasts who can't afford paid courses :)

Self guide to become a data analyst

Join for more: https://t.iss.one/DataAnalystInterview

Python Programming & SQL 7 in 1 Book Free Giveaway
👇👇
https://www.linkedin.com/posts/sql-analysts_python-sql-book-activity-7137666902872473600-NG6Y?utm_source=share&utm_medium=member_android

Hopefully everyone got this free book 😄👍

How to build a data portfolio from scratch
👇👇
https://www.linkedin.com/posts/sql-analysts_how-to-build-a-data-portfolio-from-scratch-activity-7138032456917536768-ak_r?utm_source=share&utm_medium=member_android

Comment your data portfolio link if you have any 😄

Python Pandas Basics to Advanced

https://www.linkedin.com/posts/sql-analysts_python-with-pandas-activity-7138391692432371712-sNOz?utm_source=share&utm_medium=member_android

Like for more 👍❤️

Thanks for the likes and comments on LinkedIn. You guys are the reason because of which I am still sharing free resources❤️

5 Mini Projects for Power BI
👇👇
https://t.iss.one/PowerBI_analyst/52

Steps to become data analyst when you are fresher 👇👇

1 - First try to focus 3 mandatory skills i.e. Sql, Ms excel and python -

- For sql you can refer Ankit Bansal Or Thoufiq Mohammed (techtfq) on @sqlanalyst
- For Ms excel refer Leila Gharani or @excel_analyst
- For python refer freecodecamp from YouTube or @pythonanalyst

2 - After that try to be clear with basic idea of tableau or powerbi. (Not mandatory for every job). You can refer this channel for free resources https://t.iss.one/PowerBI_analyst

3 - Add your college project in your resume, if it's a data science related project it will help a lot. If you don't have project then you can make some dashboarding projects from YouTube in tableau/powerbi.

4 - And start applying for jobs which is having 0-1 yr experience required, you can also apply for 1 yr experience required job in analytics because sometimes they may consider fresher also. You can refer this channel @jobs_sql for job opportunities

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.

Like for more 😄❤️