How do you start AI and ML ?

Where do you go to learn these skills? What courses are the best?

There’s no best answer🥺. Everyone’s path will be different. Some people learn better with books, others learn better through videos.

What’s more important than how you start is why you start.

Start with why.

Why do you want to learn these skills?
Do you want to make money?
Do you want to build things?
Do you want to make a difference?
Again, no right reason. All are valid in their own way.

Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, you’ve got something to turn to. Something to remind you why you started.

Got a why? Good. Time for some hard skills.

I can only recommend what I’ve tried every week new course lauch better than others its difficult to recommend any course

You can completed courses from (in order):

Treehouse / youtube( free) - Introduction to Python

Udacity - Deep Learning & AI Nanodegree

fast.ai - Part 1and Part 2

They’re all world class. I’m a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that.

If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI.

Join for more: https://t.me/machinelearning_deeplearning

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All the best 👍👍

SQL Basics for Data Analysts

SQL (Structured Query Language) is used to retrieve, manipulate, and analyze data stored in databases.

1️⃣ Understanding Databases & Tables

Databases store structured data in tables.

Tables contain rows (records) and columns (fields).

Each column has a specific data type (INTEGER, VARCHAR, DATE, etc.).

2️⃣ Basic SQL Commands

Let's start with some fundamental queries:

🔹 SELECT – Retrieve Data

SELECT * FROM employees; -- Fetch all columns from 'employees' table SELECT name, salary FROM employees; -- Fetch specific columns 

🔹 WHERE – Filter Data

SELECT * FROM employees WHERE department = 'Sales'; -- Filter by department SELECT * FROM employees WHERE salary > 50000; -- Filter by salary 

🔹 ORDER BY – Sort Data

SELECT * FROM employees ORDER BY salary DESC; -- Sort by salary (highest first) SELECT name, hire_date FROM employees ORDER BY hire_date ASC; -- Sort by hire date (oldest first) 

🔹 LIMIT – Restrict Number of Results

SELECT * FROM employees LIMIT 5; -- Fetch only 5 rows SELECT * FROM employees WHERE department = 'HR' LIMIT 10; -- Fetch first 10 HR employees 

🔹 DISTINCT – Remove Duplicates

SELECT DISTINCT department FROM employees; -- Show unique departments 

Mini Task for You: Try to write an SQL query to fetch the top 3 highest-paid employees from an "employees" table.

You can find free SQL Resources here
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#sql

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Amazon Data Analyst Interview Questions for 1-3 years of experience role :-

A. SQL:

1. You have two tables: Employee and Department.
- Employee Table Columns: Employee_id, Employee_Name, Department_id, Salary
- Department Table Columns: Department_id, Department_Name, Location

Write an SQL query to find the name of the employee with the highest salary in each location.

2. You have two tables: Orders and Customers.
- Orders Table Columns: Order_id, Customer_id, Order_Date, Amount
- Customers Table Columns: Customer_id, Customer_Name, Join_Date

Write an SQL query to calculate the total order amount for each customer who joined in the current year. The output should contain Customer_Name and the total amount.

B. Python:

1. Basic oral questions on NumPy (e.g., array creation, slicing, broadcasting) and Matplotlib (e.g., plot types, customization).

2. Basic oral questions on pandas (like: groupby, loc/iloc, merge & join, etc.)

2. Write the code in NumPy and Pandas to replicate the functionality of your answer to the second SQL question.

C. Leadership or Situational Questions:

(Based on the leadership principle of Bias for Action)

- Describe a situation where you had to make a quick decision with limited information. How did you proceed, and what was the outcome?

(Based on the leadership principle of Dive Deep)

- Can you share an example of a project where you had to delve deeply into the data to uncover insights or solve a problem? What steps did you take, and what were the results?

(Based on the leadership principle of Customer Obsession)

- Tell us about a time when you went above and beyond to meet a customer's needs or expectations. How did you identify their requirements, and what actions did you take to deliver exceptional service?

D. Excel:

Questions on advanced functions like VLOOKUP, XLookup, SUMPRODUCT, INDIRECT, TEXT functions, SUMIFS, COUNTIFS, LOOKUPS, INDEX & MATCH, AVERAGEIFS. Plus, some basic questions on pivot tables, conditional formatting, data validation, and charts.

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Most people learn SQL just enough to pull some data. But if you really understand it, you can analyze massive datasets without touching Excel or Python.

Here are 8 game-changing SQL concepts that will make you a data pro:

👇

1. Stop pulling raw data. Start pulling insights.

The biggest mistake? Running a query that gives you everything and then filtering it later.

Good analysts don’t pull raw data. They shape the data before it even reaches them.

2. “SELECT ” is a rookie move.

Pulling all columns is lazy and slow.

A pro only selects what they need.
✔️ Fewer columns = Faster queries
✔️ Less noise = Clearer insights

The more precise your query, the less time you waste cleaning data.

3. GROUP BY is your best friend.

You don’t need 100,000 rows of transactions. What you need is:
✔️ Sales per region
✔️ Average order size per customer
✔️ Number of signups per month

Grouping turns chaotic data into useful summaries.

4. Joins = Connecting the dots.

Your most important data is split across multiple tables.

Want to know how much each customer spent? You need to join:
✔️ Customer info
✔️ Order history
✔️ Payments

Joins = unlocking hidden insights.

5. Window functions will blow your mind.

They let you:
✔️ Rank customers by total purchases
✔️ Calculate rolling averages
✔️ Compare each row to the overall trend

It’s like pivot tables, but way more powerful.

6. CTEs will save you from spaghetti SQL.

Instead of writing a 50-line nested query, break it into steps.

CTEs (Common Table Expressions) make your SQL:
✔️ Easier to read
✔️ Easier to debug
✔️ Reusable

Good SQL is clean SQL.

7. Indexes = Speed.

If your queries take forever, your database is probably doing unnecessary work.

Indexes help databases find data faster.

If you work with large datasets, this is a game changer.

SQL isn’t just about pulling data. It’s about analyzing, transforming, and optimizing it.

Master these 7 concepts, and you’ll never look at SQL the same way again.

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