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
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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!
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https://www.linkedin.com/posts/sql-analysts_data-analysis-books-activity-7136045145229041664-uVUm?utm_source=share&utm_medium=member_android

Data Analytics Short Roadmap
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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:
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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 😄❤️

Machine Learning Resources 🤯🤯

📌 A-Z Machine Learning
📌 AI
📌 Python
📌 ChatGPT (Bonus)

https://www.linkedin.com/posts/sql-analysts_ai-machinelearning-python-activity-7139859095532072960-H1RZ?utm_source=share&utm_medium=member_android

33 companies that are CURRENTLY HIRING for 100% REMOTE JOBS
👇👇
https://www.linkedin.com/posts/sql-analysts_jobboard-remotehiring-remoteworking-activity-7141483435960832000-2k4s?utm_source=share&utm_medium=member_android

Like this LinkedIn post and bookmark it for your future reference