Sachin Kumar Profile picture
May 15 โ€ข 8 tweets โ€ข 3 min read Twitter logo Read on Twitter
Are you completely new to SQL and do not know where to start?

Here is a simple concept roadmap for learning SQL as a complete beginner:

๐Ÿงต Image
1. ๐‹๐ž๐š๐ซ๐ง ๐ญ๐ก๐ž ๐›๐š๐ฌ๐ข๐œ๐ฌ:

- Primary Key vs Foreign Key
- Data Types
- Database diagrams
- Tables
- Records and Fields
- Naming standards for tables and fields
2. ๐‘๐ž๐ญ๐ซ๐ข๐ž๐ฏ๐ข๐ง๐  ๐ƒ๐š๐ญ๐š ๐ฐ๐ข๐ญ๐ก ๐’๐„๐‹๐„๐‚๐“:

- Learn how to write SELECT ๐˜ค๐˜ฐ๐˜ญ๐˜ถ๐˜ฎ๐˜ฏ(๐˜ด) FROM ๐˜ต๐˜ข๐˜ฃ๐˜ญ๐˜ฆ
- Combine with other keywords: WHERE, ORDER BY & LIMIT
- Learn how to use arithmetic operators in SELECT statement
- Retrieve unique values with DISTINCT keyword
3. ๐‹๐ž๐š๐ซ๐ง ๐ญ๐จ ๐‰๐Ž๐ˆ๐ ๐ญ๐š๐›๐ฅ๐ž๐ฌ

- Learn the four basic JOINS (INNER JOIN, FULL OUTER JOIN, LEFT JOIN, RIGHT JOIN)
- Study the Venn Diagrams behind each type of JOIN
- Understand relationship types (one-to-one, one-to-many, many-to-many)
- JOIN multiple tables
4. ๐‹๐ž๐š๐ซ๐ง ๐ƒ๐š๐ญ๐š ๐€๐ ๐ ๐ซ๐ž๐ ๐š๐ญ๐ข๐จ๐งย 

- GROUP BY statement
- SUM, AVG, COUNT, MAX, MIN, ect....
- Learn the difference between WHERE vs HAVING
5. ๐‹๐ž๐š๐ซ๐ง ๐ฆ๐จ๐ซ๐ž ๐š๐๐ฏ๐š๐ง๐œ๐ž๐ ๐œ๐จ๐ง๐œ๐ž๐ฉ๐ญ๐ฌ

- Learn how to use CASE-WHEN statement
- Common Table Expressions (CTEs)
- Write subqueries in SELECT, WHERE & FROM clauses to retrieve data based on condition or calculation
- Write subqueries in tandem with Aggregate Fn
The concepts in the above roadmap will be enough #SQL knowledge required for most entry-level data analyst reporting positions. More SQL knowledge would be required for an entry-level database developer or data engineer role

Source - Drew Zeimetz

#DataScience #MachineLearning
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More from @Sachintukumar

May 15
Day 62 of #100DayswithMachineLearning

Topic - Ridge Regression in ML ( Part 1 )

๐Ÿงต Image
Ridge Regression (RR) is regularization technique used in statistical modeling & ML to handle the problem of multicollinearity (high correlation) among predictor variables Image
It is an extension of linear regression ( LR) that adds a penalty term to the least squares objective function, resulting in a more stable and robust model.
Read 12 tweets
May 14
Day61 of #100DayswithMachineLearning

Topic - Bias Variance Trade-off in ML

๐Ÿงต Image
๐Ÿ”น If ML model is not accurate. it can make predictions error & these prediction errors are usually known as Bias & Variance

๐Ÿ”น In ML these errors will alway be present as there is always slight difference between model predictions & actual predictions
๐Ÿ”นThe main aim of ML/data science analysts is to reduce these errors in order to get more accurate result

๐Ÿ”นIn ML an error is measure of how accurately an algorithm can make predictions for the previously unknown dataset Image
Read 12 tweets
May 13
Day 60 of #100DayswithMachineLearning

Topic - Polynomial Regression in ML

๐Ÿงต Image
Polynomial regression is type of regression analysis where relationship between independent variable(s) and dependent variable is modeled as an nth-degree polynomial function.

It is an extension of simple linear regression which assumes linear relationship between the variable
In polynomial regression, the polynomial function takes the form:

y = ฮฒโ‚€ + ฮฒโ‚x + ฮฒโ‚‚xยฒ + ... + ฮฒโ‚™xโฟ
Read 12 tweets
May 13
๐Ÿ”ธCONCAT_WS() in SQL { Very Helpful }

A Thread ๐Ÿงต Image
CONCAT_WS() function in SQL is used to concatenate multiple strings into single string with specified separator between each string

"WS" stands for "with separator." This function is commonly used to construct strings contain multiple values such create comma-separated list
The syntax for CONCAT_WS() is as follows:

๐Ÿ”ธCONCAT_WS(separator, string1, string2, ..., stringN)
Read 6 tweets
May 12
Day 59 of #100DayswithMachinelearning

Topic - Mini-Batch Gradient Descent

A Thread ๐Ÿงต Image
Mini-batch gradient descent is a variation of the gradient descent optimization algorithm used in ML & DL

It is designed to address the limitations of two other variants: BGD and SGD Image
In BGD the entire training dataset is used to compute the gradient of the cost function for each iteration.

This approach guarantees convergence to the global minimum but can be computationally expensive, especially for large datasets
Read 12 tweets

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