🔄 Window Functions (ROW_NUMBER, RANK, PARTITION BY)

Window functions perform calculations across rows related to the current row — but unlike GROUP BY, they don’t collapse your data!

They are super useful for running totals, rankings, and finding duplicates.


1. ROW_NUMBER()
Gives a unique number to each row within a partition of a result set.

SELECT name, department, salary,
ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) AS row_num
FROM employees;

Ranks employees by salary within each department.

2. RANK() vs DENSE_RANK()

RANK() leaves gaps after ties.

DENSE_RANK() doesn’t.


SELECT name, salary,
RANK() OVER (ORDER BY salary DESC) AS rank,
DENSE_RANK() OVER (ORDER BY salary DESC) AS dense_rank
FROM employees;


3. PARTITION BY
It’s like a GROUP BY, but for window functions.

SELECT department, name, salary,
AVG(salary) OVER (PARTITION BY department) AS dept_avg_salary
FROM employees;

This shows each employee's salary alongside the average salary of their department — without collapsing the rows.


Other Useful Window Functions:

NTILE(n) – Divides rows into n buckets

LAG() / LEAD() – Look at previous/next row’s value

SUM() / AVG() over a window – for running totals


React with ❤️ if you're pumped for the next one: ⚙️ Data Manipulation (INSERT, UPDATE, DELETE).

Share with credits: https://news.1rj.ru/str/sqlspecialist

Hope it helps :)

Let’s now cover a hands-on and frequently used part of SQL:

⚙️ Data Manipulation (INSERT, UPDATE, DELETE)

Data Manipulation Language (DML) commands are used to add, modify, or remove data from your tables.


1. INSERT – Add new records to a table.

INSERT INTO employees (name, department, salary)
VALUES ('John Doe', 'HR', 60000);

Multiple rows:

INSERT INTO employees (name, department, salary)
VALUES
('Alice', 'IT', 70000),
('Bob', 'Finance', 65000);


2. UPDATE – Modify existing records.

UPDATE employees
SET salary = 75000
WHERE name = 'John Doe';

With multiple fields:

UPDATE employees
SET salary = 80000, department = 'IT'
WHERE id = 101;

Always use WHERE in UPDATE to avoid accidental mass updates.


3. DELETE – Remove records from a table.

DELETE FROM employees
WHERE department = 'Temporary';

Again, make sure to use WHERE — or you’ll delete all rows!


Pro Tips:

- Test your WHERE clause with a SELECT first.

- Use BEGIN TRANSACTION and ROLLBACK if supported — for safety.


React with ❤️ if you're ready to learn how to create and structure your database with: 🧱 Data Definition (CREATE, ALTER, DROP).

Share with credits: https://news.1rj.ru/str/sqlspecialist

Hope it helps :)

9 tips to master Power BI for Data Analysis:

📥 Learn to import data from various sources

🧹 Clean and transform data using Power Query

🧠 Understand relationships between tables using the data model

🧾 Write DAX formulas for calculated columns and measures

📊 Create interactive visuals: bar charts, slicers, maps, etc.

🎯 Use filters, slicers, and drill-through for deeper insights

📈 Build dashboards that tell a clear data story

🔄 Refresh and schedule your reports automatically

📚 Explore Power BI community and documentation for new tricks

Power BI Free Resources: https://news.1rj.ru/str/PowerBI_analyst

Hope it helps :)

#powerbi

Let’s move on to the backbone of any SQL database:

🧱 Data Definition (CREATE, ALTER, DROP)

Data Definition Language (DDL) is used to define and manage database structures like tables, columns, and schemas.


1. CREATE – Used to create new tables, databases, or other objects.

CREATE TABLE employees (
id INT PRIMARY KEY,
name VARCHAR(100),
department VARCHAR(50),
salary DECIMAL(10, 2)
);

You can also create other things like databases, indexes, or views:

CREATE DATABASE company_db;


2. ALTER – Modify an existing table’s structure.

Add a column:

ALTER TABLE employees
ADD date_of_joining DATE;

Modify column data type:

ALTER TABLE employees
ALTER COLUMN salary TYPE FLOAT;

Drop a column:

ALTER TABLE employees
DROP COLUMN date_of_joining;


3. DROP – Permanently delete a table, view, or database.

DROP TABLE employees;

Caution: This deletes everything — structure and data. Use with care!


Bonus: TRUNCATE

TRUNCATE TABLE employees;

Deletes all data from the table but keeps the structure intact. It's faster than DELETE but not recoverable.


React with ❤️ if you're ready for the next one: 🔐 Constraints & Relationships (PK, FK, UNIQUE, CHECK).

Share with credits: https://news.1rj.ru/str/sqlspecialist

Hope it helps :)

🔐 Constraints & Relationships (PK, FK, UNIQUE, CHECK)

Constraints are rules applied to columns to ensure valid and consistent data in your tables.


1. PRIMARY KEY (PK)
Uniquely identifies each row in a table.

Only one per table

Cannot be NULL

Often applied to an id column


CREATE TABLE employees (
id INT PRIMARY KEY,
name VARCHAR(100)
);


2. FOREIGN KEY (FK)
Establishes a relationship between tables.
It links a column to the PRIMARY KEY of another table.

CREATE TABLE departments (
dept_id INT PRIMARY KEY,
dept_name VARCHAR(50)
);

CREATE TABLE employees (
id INT PRIMARY KEY,
name VARCHAR(100),
dept_id INT,
FOREIGN KEY (dept_id) REFERENCES departments(dept_id)
);

Now, employees.dept_id must match a valid departments.dept_id.


3. UNIQUE
Ensures that all values in a column are different (can have one NULL if not restricted).

CREATE TABLE users (
user_id INT PRIMARY KEY,
email VARCHAR(100) UNIQUE
);


4. CHECK
Ensures a condition is true for data being inserted or updated.

CREATE TABLE products (
id INT PRIMARY KEY,
price DECIMAL(10, 2),
CHECK (price > 0)
);


5. NOT NULL
Prevents NULL values in a column.

CREATE TABLE orders (
order_id INT PRIMARY KEY,
product_name VARCHAR(100) NOT NULL
);

Using constraints helps keep your data clean, accurate, and relational.

React with ❤️ if you're ready for the final (and most practical) chapter: 🧪 Real-world SQL Scenarios & Challenges.

Share with credits: https://news.1rj.ru/str/sqlspecialist

Hope it helps :)

Most of you responded with first option PRIMARY KEY

Yes, a PRIMARY KEY does enforce: Uniqueness & NOT NULL


So technically, it's correct — making email a primary key will satisfy the condition that emails must be unique & not null


But... here’s the catch:

In real-world database design, email is rarely used as a primary key, because:

- It can change (users may update emails).

- It’s not an ideal unique identifier like a user_id (which is usually an auto-incrementing integer).

Using email as PK can make foreign key relationships messy and inefficient, so we'll have to explicitly use UNIQUE & NOT NULL constraints

Hope it clear most of the doubts :)

🧪 Real-world SQL Scenarios & Challenges

Let’s dive into the types of real-world problems you’ll encounter as a data analyst, data scientist , data engineer, or developer.


1. Finding Duplicates

SELECT name, COUNT(*)
FROM employees
GROUP BY name
HAVING COUNT(*) > 1;

Perfect for data cleaning and validation tasks.


2. Get the Second Highest Salary

SELECT MAX(salary) AS second_highest
FROM employees
WHERE salary < (
SELECT MAX(salary)
FROM employees
);


3. Running Totals

SELECT name, salary,
SUM(salary) OVER (ORDER BY id) AS running_total
FROM employees;

Essential in dashboards and financial reports.


4. Customers with No Orders

SELECT c.customer_id, c.name
FROM customers c
LEFT JOIN orders o ON c.customer_id = o.customer_id
WHERE o.order_id IS NULL;

Very common in e-commerce or CRM platforms.


5. Monthly Aggregates

SELECT DATE_TRUNC('month', order_date) AS month,
COUNT(*) AS total_orders
FROM orders
GROUP BY month
ORDER BY month;

Great for trends and time-based reporting.


6. Pivot-like Output (Using CASE)

SELECT
department,
COUNT(CASE WHEN gender = 'Male' THEN 1 END) AS male_count,
COUNT(CASE WHEN gender = 'Female' THEN 1 END) AS female_count
FROM employees
GROUP BY department;

Super useful for dashboards and insights.


7. Recursive Queries (Org Hierarchy or Tree)

WITH RECURSIVE employee_tree AS (
SELECT id, name, manager_id
FROM employees
WHERE manager_id IS NULL

UNION ALL

SELECT e.id, e.name, e.manager_id
FROM employees e
INNER JOIN employee_tree et ON e.manager_id = et.id
)
SELECT * FROM employee_tree;

Used in advanced data modeling and tree structures.


You don’t just need to know how SQL works — you need to know when to use it smartly!

React with ❤️ if you’d like me to explain more data analytics topics

Share with credits: https://news.1rj.ru/str/sqlspecialist

SQL Roadmap: https://news.1rj.ru/str/sqlspecialist/1340

Hope it helps :)

🔰 Power BI Roadmap for Beginners

├── 🧠 What is Power BI & Why Use It
├── 🧩 Power BI Components (Desktop, Service, Mobile)
├── 🔌 Connecting to Data Sources (Excel, SQL, Web, etc.)
├── 🧹 Power Query Editor (Data Cleaning & Transformation)
├── 🧱 Data Modeling (Relationships, Star & Snowflake Schema)
├── 📐 DAX Basics (Calculated Columns & Measures)
├── ➗ Important DAX Functions (SUM, CALCULATE, FILTER, etc.)
├── 📊 Creating Visuals (Bar, Pie, Table, Matrix, etc.)
├── 🎨 Visual Customizations (Themes, Tooltips, Conditional Formatting)
├── 📎 Bookmarks & Buttons (Navigation & Interactivity)
├── 📆 Time Intelligence in DAX (YTD, MTD, Previous Month, etc.)
├── 📊 Drill Through, Drill Down & Hierarchies
├── ⏱ Performance Optimization Tips (Model Size, DAX, etc.)
├── 🛡 Row-Level Security (RLS)
├── ☁️ Publishing to Power BI Service
├── 🔄 Scheduled Refresh & Gateways
├── 👥 Sharing & Collaboration (Workspaces, Apps, Access)
├── 🧪 Real-World Projects & Dashboard Challenges

React with ❤️ for the detailed explanation of each topic

Share with credits: https://news.1rj.ru/str/sqlspecialist

SQL Roadmap: https://news.1rj.ru/str/sqlspecialist/1340

Hope it helps :)

🔰 Python Roadmap for Beginners

├── 🐍 Introduction to Python
├── 🧾 Installing Python & Setting Up VS Code / Jupyter
├── ✍️ Python Syntax & Indentation Basics
├── 🔤 Variables, Data Types (int, float, str, bool)
├── ➗ Operators (Arithmetic, Comparison, Logical)
├── 🔁 Conditional Statements (if, elif, else)
├── 🔄 Loops (for, while, break, continue)
├── 🧰 Functions (def, return, args, kwargs)
├── 📦 Built-in Data Structures (List, Tuple, Set, Dictionary)
├── 🧠 List Comprehension & Dictionary Comprehension
├── 📂 File Handling (read, write, with open)
├── 🐞 Error Handling (try, except, finally)
├── 🧱 Modules & Packages (import, pip install)
├── 📊 Working with Libraries (NumPy, Pandas, Matplotlib)
├── 🧹 Data Cleaning with Pandas
├── 🧪 Exploratory Data Analysis (EDA)
├── 🤖 Intro to OOP in Python (Class, Objects, Inheritance)
├── 🧠 Real-World Python Projects & Challenges

SQL Roadmap: https://news.1rj.ru/str/sqlspecialist/1340

Power BI Roadmap: https://news.1rj.ru/str/sqlspecialist/1397

Python Resources: https://news.1rj.ru/str/pythonproz

Hope it helps :)

Let's start with the first Python Concept today

1. Data Types & Data Structures

Before you analyze anything, you need to organize and store your data properly. Python offers four main data structures that every data analyst must master.

Lists ([])
A list is an ordered collection of items that can be changed (mutable).

Example:

scores = [85, 90, 78, 92]
print(scores[0]) # Output: 85

Use lists to store rows of data, filtered results, or time-series points.

Tuples (())
Tuples are like lists but immutable — once created, they can't be modified.

Example :

coords = (12.97, 77.59)

Use them when data should not change, like a fixed location or record.

Dictionaries ({})
Dictionaries store data in key-value pairs. They’re extremely useful when dealing with structured data.

Example:

person = {'name': 'Alice', 'age': 30}
print(person['name']) # Output: Alice

Use dictionaries for JSON data, mapping columns, or creating summary statistics.

Sets (set())
Sets are unordered collections with no duplicate values.

Example:

departments = set(['Sales', 'HR', 'Sales'])
print(departments) # Output: {'Sales', 'HR'}

Use sets when you need to find unique values in a dataset.

Here are some important points to remember:

- Lists help you store sequences like rows or values from a column.

- Dictionaries are great for quick lookups and mappings.

- Sets are useful when working with unique entries, like distinct categories.

- Tuples protect data from accidental modification.


You’ll use these structures every day with pandas. For example, each row in a DataFrame can be treated like a dictionary, and columns often act like lists.

React with ♥️ if you want me to cover next important Python concept Loops & Conditions.

Important Python Concepts: https://news.1rj.ru/str/sqlspecialist/749

Data Analyst Jobs: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J

Hope it helps :)

Now, let’s move to the next important Python concept: Loops & Conditional Statements

Once your data is stored in a list, dictionary, or any structure, you’ll often want to loop through it or perform actions based on specific conditions.

Conditional Statements (if, elif, else)
These allow your program to make decisions based on certain conditions.

*Example* :

age = 25
if age >= 18:
print("Adult")
else:
print("Minor")

This code checks if age is 18 or more. If yes, it prints “Adult”. If not, it prints “Minor”.

Use this when:

- You’re filtering data based on certain conditions

- You want to handle missing values or outliers differently

- You want different results for different input categories


Loops (for, while)
Loops help you automate repetitive tasks.

In data analytics, this is especially useful for cleaning and transforming data.

Example (using for):

scores = [45, 67, 89, 90]
for score in scores:
if score >= 50:
print("Pass")
else:
print("Fail")

It loops through each number in the scores list.

If the score is 50 or more, it prints “Pass”.

Otherwise, it prints “Fail”.

So the output will be:

Fail
Pass
Pass
Pass


Example (using while):

count = 0
while count < 3:
print("Loading...")
count += 1

This starts with count = 0.

It keeps printing “Loading...” until count reaches 3.

After each loop, it adds 1 to count.
So it prints “Loading...” three times.


Real Use-Cases in Data Analytics:

- Looping through rows to clean or validate data

- Using conditions to flag anomalies or classify data

- Automating repetitive logic like formatting strings, checking for nulls, or recalculating columns


*Extras – break and continue:*
These help control your loop.

- break stops the loop

- continue skips the current iteration

Example:
for val in [1, 2, 0, 3]:
if val == 0:
continue
print(10 / val)


When it hits 0, it skips the division (to avoid dividing by zero).

For other numbers, it divides 10 by the value and prints it.


So the output is:

10.0
5.0
3.3333333333333335


While loops and conditionals are essential, you'll use them less directly when working with pandas — but understanding how they work under the hood helps you write better, faster code.

React with ♥️ if you're ready for the next important concept: Functions in Python

Python Data Structures: https://news.1rj.ru/str/sqlspecialist/1400

Python Roadmap: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02/459

Data Analyst Jobs: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J

Hope it helps :)

Now let’s move to the most powerful concepts in Python:

Functions – Write Once, Use Forever

Think of functions as your personal data assistant. They allow you to group a set of instructions under a name — and run that code whenever you need it, with just one line!


You’ll often repeat data cleaning steps (e.g., removing nulls, formatting dates) using Functions.

Functions make your code cleaner, reusable, and scalable.

You can also pass parameters to make functions more flexible.


*Basic Function Structure**

def greet(name):
print(f"Hello, {name}!")

What it does:
This defines a function called greet that takes one input (name) and prints a greeting.

When you call it like this:

greet("Alice")

Output:

Hello, Alice!


Real-Life Example:

Clean Column Names:

def clean_column(col_name):
return col_name.strip().lower().replace(" ", "_")


What it does:

.strip() removes extra spaces

.lower() makes everything lowercase

.replace(" ", "_") replaces spaces with underscores


Use-case :
If your dataset has messy column names like " Total Sales ", this function will turn it into "total_sales".

Function with Multiple Parameters

def calculate_discount(price, discount_percent):
return price - (price * discount_percent / 100)


What it does:
Takes in a price and discount percent, and returns the discounted price.

Example :

final_price = calculate_discount(200, 10)
print(final_price)

Output:

180.0


Reusability in Projects

Let’s say you’re working with sales data and you need to:

- Clean up strings

- Convert dates

- Format currency


You can create a function for each, and call them anytime you need across multiple projects. Saves hours in the long run.

React with ❤️ if you are ready for the next important concept: Lambda Functions in Python

Python Data Structures: https://news.1rj.ru/str/sqlspecialist/1400

Data Analyst Interview: https://whatsapp.com/channel/0029Vazm2S1Ae5VuwOzV1v1h

Data Analyst Jobs: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J

Hope it helps :)

Data Analyst Learning Plan in 2025

|-- Week 1: Introduction to Data Analytics
| |-- What is Data Analytics?
| |-- Roles & Responsibilities of a Data Analyst
| |-- Data Analytics Workflow
| |-- Types of Data (Structured, Unstructured, Semi-structured)
|
|-- Week 2: Excel for Data Analysis
| |-- Excel Basics & Interface
| |-- Data Cleaning & Preparation
| |-- Formulas, Functions, Pivot Tables
| |-- Dashboards & Reporting in Excel
|
|-- Week 3: SQL for Data Analysts
| |-- SQL Basics: SELECT, WHERE, ORDER BY
| |-- Aggregations & GROUP BY
| |-- Joins: INNER, LEFT, RIGHT, FULL
| |-- CTEs, Subqueries & Window Functions
|
|-- Week 4: Python for Data Analysis
| |-- Python Basics (Variables, Data Types, Loops)
| |-- Data Analysis with Pandas
| |-- Data Visualization with Matplotlib & Seaborn
| |-- Exploratory Data Analysis (EDA)
|
|-- Week 5: Statistics & Probability
| |-- Denoscriptive Statistics
| |-- Probability Theory Basics
| |-- Distributions (Normal, Binomial, Poisson)
| |-- Hypothesis Testing & A/B Testing
|
|-- Week 6: Data Cleaning & Transformation
| |-- Handling Missing Values
| |-- Duplicates, Outliers, and Data Formatting
| |-- Data Parsing & Regex
| |-- Data Normalization
|
|-- Week 7: Data Visualization Tools
| |-- Power BI Basics
| |-- Creating Reports and Dashboards
| |-- Data Modeling in Power BI
| |-- Filters, Slicers, DAX Basics
|
|-- Week 8: Advanced Excel & Power BI
| |-- Advanced Charts & Dashboards
| |-- Time Intelligence in Power BI
| |-- Calculated Columns & Measures (DAX)
| |-- Performance Optimization Tips
|
|-- Week 9: Business Acumen & Domain Knowledge
| |-- KPIs & Business Metrics
| |-- Understanding Financial, Marketing, Sales Data
| |-- Creating Insightful Reports
| |-- Storytelling with Data
|
|-- Week 10: Real-World Projects & Portfolio
| |-- End-to-End Project on E-commerce/Sales
| |-- Collecting & Cleaning Data
| |-- Analyzing Trends & Presenting Insights
| |-- Uploading Projects on GitHub
|
|-- Week 11: Tools for Data Analysts
| |-- Jupyter Notebooks
| |-- Google Sheets & Google Data Studio
| |-- Tableau Overview
| |-- APIs & Web Scraping (Intro only)
|
|-- Week 12: Career Preparation
| |-- Resume & LinkedIn for Data Analysts
| |-- Common Interview Questions (SQL, Python, Case Studies)
| |-- Mock Interviews & Peer Reviews

Join our WhatsApp channel: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

Like this post for more content like this 👍♥️

Share with credits: https://news.1rj.ru/str/sqlspecialist

Hope it helps :)

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.

𝗛𝗼𝘄 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝗤𝗟 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 (𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗚𝗲𝘁𝘁𝗶𝗻𝗴 𝗢𝘃𝗲𝗿𝘄𝗵𝗲𝗹𝗺𝗲𝗱!)🧠

Let’s be honest:
SQL seems simple… until JOINs, Subqueries, and Window Functions come crashing in.

But mastering SQL doesn’t have to be hard.

You just need the right roadmap—and that’s exactly what this is.

Here’s a 5-step SQL journey to go from beginner to job-ready analyst👇

🔹 𝗦𝘁𝗲𝗽 𝟭: Nail the Basics (Learn to Think in SQL)

Start with the foundations:

✅ SELECT, WHERE, ORDER BY
✅ DISTINCT, LIMIT, BETWEEN, LIKE
✅ COUNT, SUM, AVG, MIN, MAX

Practice with small tables to build confidence.

Use platforms like:
➡️ W3Schools
➡️ Modesql
➡️ LeetCode (easy problems)

🔹 𝗦𝘁𝗲𝗽 𝟮: Understand GROUP BY and Aggregations (The Analyst’s Superpower)

This is where real-world queries begin. Learn:

✅ GROUP BY + HAVING
✅ Combining GROUP BY with COUNT/AVG
✅ Filtering aggregated data

Example:
"Find top 5 cities with the highest total sales in 2023"
That’s GROUP BY magic.

🔹 𝗦𝘁𝗲𝗽 𝟯: MASTER JOINS (Stop Getting Confused)
JOINS scare a lot of people. But they’re just pattern-matching across tables.

Learn one by one:
✅ INNER JOIN
✅ LEFT JOIN
✅ RIGHT JOIN
✅ FULL OUTER JOIN
✅ SELF JOIN
✅ CROSS JOIN (rare, but good to know)
Visualize them using Venn diagrams or draw sample tables—it helps!

🔹 𝗦𝘁𝗲𝗽 𝟰: Learn Subqueries and CTEs (Write Cleaner, Powerful SQL)

✅ Subqueries: Query inside another query
✅ CTEs (WITH clause): Cleaner and reusable queries
✅ Use them to break down complex problems

CTEs = the secret sauce to writing queries recruiters love.

🔹 𝗦𝘁𝗲𝗽 𝟱: Level Up with Window Functions (Your Entry into Advanced SQL)

If you want to stand out, this is it:

✅ ROW_NUMBER(), RANK(), DENSE_RANK()
✅ LAG(), LEAD(), NTILE()
✅ PARTITION BY and ORDER BY combo

Use these to:
➡️ Find top N per group
➡️ Track user behavior over time
➡️ Do cohort analysis

You don’t need 100 LeetCode problems.

You need 10 real-world queries done deeply.

Keep it simple. Keep it useful.

If you’re just starting out in Data Analytics, it’s super important to build the right habits early.

Here’s a simple plan for beginners to grow both technical and problem-solving skills together:

If You Just Started Learning Data Analytics, Focus on These 5 Baby Steps:

1. Don’t Just Watch Tutorials — Build Small Projects

After learning a new tool (like SQL or Excel), create mini-projects:

- Analyze your expenses

- Explore a free dataset (like Netflix movies, COVID data)


2. Ask Business-Like Questions Early

Whenever you see a dataset, practice asking:

- What problem could this data solve?

- Who would care about this insight?


3. Start a ‘Data Journal’

Every day, note down:

- What you learned

- One business question you could answer with data (Helps you build real-world thinking!)


4. Practice the Basics 100x

Get very comfortable with:

- SELECT, WHERE, GROUP BY (SQL)

- Pivot tables and charts (Excel)

- Basic cleaning (Power Query / Python pandas)


_Mastering basics > learning 50 fancy functions._

5. Learn to Communicate Early

Explain your mini-projects like this:

- What was the business goal?

- What did you find?

- What should someone do based on it?

React with ❤️ if you need a beginner-friendly roadmap to start your data analytics career

Data Analytics Free Resources: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

ENJOY LEARNING 👍👍