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𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗥𝗼𝗮𝗱𝗺𝗮𝗽

𝟭. 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀: Master Python, SQL, and R for data manipulation and analysis.

𝟮. 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Use Excel, Pandas, and ETL tools like Alteryx and Talend for data processing.

𝟯. 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Learn Tableau, Power BI, and Matplotlib/Seaborn for creating insightful visualizations.

𝟰. 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗮𝗻𝗱 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝘀: Understand Descriptive and Inferential Statistics, Probability, Regression, and Time Series Analysis.

𝟱. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Get proficient in Supervised and Unsupervised Learning, along with Time Series Forecasting.

𝟲. 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗧𝗼𝗼𝗹𝘀: Utilize Google BigQuery, AWS Redshift, and NoSQL databases like MongoDB for large-scale data management.

𝟳. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴: Implement Data Quality Monitoring (Great Expectations) and Performance Tracking (Prometheus, Grafana).

𝟴. 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗧𝗼𝗼𝗹𝘀: Work with Data Orchestration tools (Airflow, Prefect) and visualization tools like D3.js and Plotly.

𝟵. 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗿: Manage resources using Jupyter Notebooks and Power BI.

𝟭𝟬. 𝗗𝗮𝘁𝗮 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝗘𝘁𝗵𝗶𝗰𝘀: Ensure compliance with GDPR, Data Privacy, and Data Quality standards.

𝟭𝟭. 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴: Leverage AWS, Google Cloud, and Azure for scalable data solutions.

𝟭𝟮. 𝗗𝗮𝘁𝗮 𝗪𝗿𝗮𝗻𝗴𝗹𝗶𝗻𝗴 𝗮𝗻𝗱 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴: Master data cleaning (OpenRefine, Trifacta) and transformation techniques.

I have curated Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

Hope this helps you 😊

Data analysis is a gateway to becoming a:

- Data Scientist
- Business Analyst
- Data Engineer
- BI Engineer
- Analytics Engineer

And many other roles.

Learning the skills doesn't close doors, if anything, it opens many more.

Don't Limit Yourself to Just One Title, "𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭" in Your Job Search!


Don't get caught up in the confines of a single job title! There are countless roles out there that might align perfectly with your skills and interests. Here are a few alternative titles for data analyst roles to broaden your search horizons:


1. QI Analyst
2. Risk Analyst
3. Data Modeler
4. Research Analyst
5. Business Analyst
6. Reporting Analyst
7. Operations Analyst
8. Social Media Analyst
9. Statistical Analyst
10. Statistical Analyst
11. Product Data Analyst
12. Analytics Engineer
13. Supply Chain Analyst
14. Data Mining Engineer
15. Data Science Associate
16. Financial Data Analyst
17. Cybersecurity Analyst
18. Marketing Data Analyst
19. Quantitative Analyst
20. HR Analytics Specialist
21. Decision Support Analyst
22. Machine Learning Analyst
23. Fraud Detection Analyst
24. Healthcare Data Analyst
25. Data Insights Specialist
26. Data Visualization Specialist
27. Customer Insights Analyst
28. Business Intelligence Analyst
29. Predictive Analytics Analyst

Remember, the right opportunity might be hiding behind a different title than you expect. Keep an open mind and explore all avenues in your job search journey!

Also, there might be fewer applicants for these roles as many don't search for titles other than data Analyst or Business Analyst. Maybe you can get more calls or interviews this way.

You don't have to try all the titles, filter out based on your interests and skills!

After all, 𝐉𝐨𝐛 𝐃𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐨𝐧 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐭𝐡𝐞 𝐭𝐢𝐭𝐥𝐞!! 😉

I have curated best 80+ top-notch Data Analytics Resources
👇👇
https://t.iss.one/DataSimplifier

Hope this helps you 😊

The Real Truth About Junior Data Analytics Interviews DataAnalytics
(From someone who's interviewed 50+ analysts)

Let me save you hours of interview prep...

SQL Round

WHAT THEY SAY:
"Complex SQL knowledge"

WHAT THEY ACTUALLY TEST:

Can you clean messy data
Do you check for NULL values
How do you handle duplicates
Can you explain your logic
Do you verify results

REAL QUESTIONS:

"Find duplicate transactions"
"Calculate monthly sales"
"Show top customers"
That's it. Really. ⤵️

Excel Interview

WHAT THEY SAY:
"Advanced Excel skills"

WHAT THEY ACTUALLY TEST:

VLOOKUP/XLOOKUP usage
Pivot Table comfort
Basic formulas
Data cleaning approach
Problem-solving process

Business Case

WHAT THEY SAY:
"Data analysis presentation"

WHAT THEY REALLY WANT:

Can you explain simply
Do you ask good questions
Can you structure analysis
Do you focus on impact
Are you confident with data ⤵️

Common Scenarios

The "Messy Data" Test
They give you:

Inconsistent formats
Missing values
Duplicate records

They watch:

How you spot issues
What questions you ask
Your cleaning approach

The "Explain It" Challenge

They ask:
"Walk me through your analysis"

They assess:

Communication clarity
Technical understanding
Business thinking
Confidence level ⤵️

How to Actually Prepare

Practice Basics:

Simple SQL queries
Excel fundamentals
Clear explanation

Business Understanding:

Read company metrics
Understand industry
Know basic KPIs
Prepare good questions

Real Scenarios to Practice:

Monthly sales analysis
Customer segmentation
Product performance
Marketing campaign results

Reality Check:

They care more about:

How you think
How you communicate
How you solve problems

Than:
Perfect technical knowledge
Complex code
Advanced statistics

I have curated best 80+ top-notch Data Analytics Resources
👇👇
https://t.iss.one/DataSimplifier

Like this post for more content like this 👍♥️

Your ultimate guide to data analytics jobs 👇👇
https://medium.com/write-a-catalyst/your-ultimate-guide-to-data-analytics-jobs-f7fd3d55844c?sk=3740c46ec74bbc8ef830c01e0df30a17

Like for more ❤️

This is a very COMMON issue that I observe in the projects of aspiring candidates

They download a DATASET from Kaggle or any other website

Export it to a Data Analysis TOOL

And START the project with data cleaning

After cleaning the data, they PLUG it into a dashboard

In the dashboard, they put EVERY column into the visuals

Also they APPLY the filters of top/bottom 10

Once done, they crack their KNUCKLES

And put this project in a list of SUCCESSFULLY completed projects

Over time, I have REVIEWED so many portfolio projects

And I see this ISSUES almost every time

When I go to their portfolio, for every project there is a DASHBOARD

But WHAT should I do after seeing a dashboard?

What is it trying to SAY?

What should I do after SEEING top or bottom 10 cities, states or products?

Every dashboard lacks CONTEXT

And why NOT?

Because they DON'T even know the business problem or problem statement

So the dashboard you created is of NO use

Your job is not just to create DASHBOARDS

Your job would be to create DASHBOARDS to take out important INSIGHTS

And from those insights, you will build RECOMMENDATIONS

And these recommendations will be given to stakeholders as a SOLUTION to their business problem

If they implemented your IDEAS and the problem gets solved

Now you can say your work is DONE

If you are SHOWING bottom 10 states, then what?

You should write the INSIGHTS too

For example, the sales of North India zone are FALLING

The insights can be used like this

Delhi that used to be in TOP 5 states is now in the BOTTOM 10 states

And this might be the REASON why our North India sales are DROPPING so hard

This is just a RANDOM example showing how your charts become UNDERSTANDABLE

Well, everyone can EXTRACT insights from charts

Even a KID can do this after looking at the tallest and smallest bar

The real task is to give RECOMMENDATIONS to solve the BUSINESS problem

And I have NEVER seen this in anyone's portfolio

If you are doing this, then you are easily STANDING out in the crowd

In my PORTFOLIO, I used to keep business problem, insights, dashboard and recommendations

Even in the bullet point of projects in my resume, I included RECOMMENDATIONS

Now this is what you can call a STRONG portfolio

Because your analysis skills are the SAME as those used in the real life by a Data Analyst

I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://t.iss.one/DataSimplifier

Like if it helps 😄

Starting your journey as a data analyst is an amazing start for your career. As you progress, you might find new areas that pique your interest:

• Data Science: If you enjoy diving deep into statistics, predictive modeling, and machine learning, this could be your next challenge.

• Data Engineering: If building and optimizing data pipelines excites you, this might be the path for you.

• Business Analysis: If you're passionate about translating data into strategic business insights, consider transitioning to a business analyst role.

But remember, even if you stick with data analysis, there's always room for growth, especially with the evolving landscape of AI.

No matter where your path leads, the key is to start now.

Breaking into Data Analysis can be very confusing in 2024!

Should I learn SQL or NoSQL? Tableau or Power BI? Excel or Google Sheets? Python or R?

Fundamental principles are more important than tools:

Understanding data cleaning and preprocessing is more important than SQL vs NoSQL.

Understanding data visualization concepts is more important than Tableau vs Power BI.

Understanding statistical analysis is more important than Excel vs R.

Understanding programming for data manipulation is more important than Python vs R.

Knowing these will allow you to pick up new emerging tools easily.

Stick to fundamentals first.

Guide to Become a Data Analyst!

🔍 Foundation: Build Your Basics
1. Understanding Data Fundamentals: Dive into the basics of data types, structures, and formats.
2. Learn Data Tools: Familiarize yourself with popular tools like Excel, SQL, and Python.
3. Master Data Visualization: Develop skills in creating insightful charts and graphs to communicate findings effectively.
4. Introduction to Statistics: Get comfortable with key statistical concepts like mean, median, and standard deviation.

📈 Intermediate: Deepen Your Skills
5. Advanced Data Manipulation: Level up your data wrangling abilities with techniques like pivot tables and data cleaning.
6. Statistical Analysis: Dive deeper into hypothesis testing, regression analysis, and probability distributions.
7. Machine Learning Basics: Explore the fundamentals of machine learning algorithms and their applications in data analysis.
8. Data Storytelling: Hone your ability to craft compelling narratives from data insights.

📊 Advanced: Specialize and Excel
9. Specialize in a Domain: Choose a niche area such as marketing analytics, financial analysis, or healthcare data.
10. Advanced Machine Learning: Deepen your understanding of complex algorithms like neural networks and ensemble methods.
11. Big Data Technologies: Explore tools and platforms for handling large-scale datasets such as Hadoop and Spark.
12. Ethics and Privacy: Understand the ethical considerations and legal implications of handling sensitive data.

Learn these to become a

1. Data analyst:

📍Excel
📍SQL
📍Data viz tool (Power BI/Tableau)

2. Data engineer:

📍SQL
📍Python + Spark
📍Cloud platform (AWS/Azure/GCP)

3. Data scientist:

📍SQL
📍Python/R
📍Statistics/machine learning

Knowing Excel, SQL, PowerBI, Python is great.

But if you don’t know how to "sell" your analysis there's a high chance you'll fail.

Here's what to do:

- Come up with questions to investigate.
- Create easy-to-understand answers.
- Explain what to do next.

It's that simple.

✈️ 𝐑𝐨𝐚𝐝𝐦𝐚𝐩 𝐭𝐨 𝐁𝐞𝐜𝐨𝐦𝐢𝐧𝐠 𝐚 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭

𝟏. 𝐄𝐱𝐜𝐞𝐥: 𝐘𝐨𝐮𝐫 𝐂𝐨𝐫𝐞 𝐓𝐨𝐨𝐥
Master Excel skills for effective data analysis by focusing on:

・Cleaning and organizing data
・Using pivot tables for summaries
・Advanced functions like VLOOKUP, INDEX, and MATCH
・Designing impactful visualizations

𝟐. 𝐁𝐮𝐢𝐥𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧
Statistics are essential for interpreting data. Learn:

・Descriptive statistics (mean, median, mode)
・Probability distributions
・Hypothesis testing and confidence intervals

𝟑. 𝐃𝐨𝐦𝐢𝐧𝐚𝐭𝐞 𝐏𝐲𝐭𝐡𝐨𝐧 𝐨𝐫 𝐑
Choose Python or R to boost your analysis game:

・Clean and structure datasets
・Create visualizations (Matplotlib, Seaborn, or Tidyverse)
・Leverage powerful libraries for in-depth analysis

𝟒. 𝐌𝐚𝐬𝐭𝐞𝐫 𝐒𝐐𝐋
SQL is vital for working with databases. Hone these skills:

・Query writing for data extraction
・Combining data with JOINS
・Using aggregate functions
・Optimizing query performance

𝟓. 𝐄𝐱𝐜𝐞𝐥 𝐚𝐭 𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧
Transform data into stories with tools like Power BI or Tableau:

・Build insightful dashboards
・Create interactive visualizations
・Craft compelling, data-driven narratives

𝟔. 𝐏𝐞𝐫𝐟𝐞𝐜𝐭 𝐃𝐚𝐭𝐚 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠
Data cleaning ensures accurate results. Learn to:

・Handle missing values
・Detect and manage outliers
・Normalize and format data for analysis

𝟕. 𝐆𝐞𝐭 𝐇𝐚𝐧𝐝𝐬-𝐎𝐧 𝐰𝐢𝐭𝐡 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬
Practical experience is key! Work on:

・Market or business data analysis
・Financial or sales dashboards
・Customer segmentation

𝟖. 𝐒𝐡𝐚𝐫𝐩𝐞𝐧 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐒𝐤𝐢𝐥𝐥𝐬
Translate data insights into actionable recommendations:

・Write clear, concise reports
・Present to non-technical audiences
・Deliver impactful, data-backed decisions

Useful websites to practice and enhance your Data Analytics skills
👇👇

1. SQL

https://mode.com/sql-tutorial/introduction-to-sql

https://t.iss.one/sqlspecialist/738

2. Python

https://www.learnpython.org/

https://t.iss.one/pythondevelopersindia/873

https://bit.ly/3T7y4ta

https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial

3. R

https://www.datacamp.com/courses/free-introduction-to-r

4. Data Structures

https://leetcode.com/study-plan/data-structure/

https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513

5. Data Visualization

https://www.freecodecamp.org/learn/data-visualization/

https://t.iss.one/Data_Visual/2

https://www.tableau.com/learn/training/20223

https://www.workout-wednesday.com/power-bi-challenges/

6. Excel

https://excel-practice-online.com/

https://www.w3schools.com/EXCEL/index.php

Join @free4unow_backup for more free courses

ENJOY LEARNING 👍👍

The most imp thing data analysts do is to understand the business requirements.

(1) Gathering Data

This means collecting data from different sources. Many a times this is done in collaboration with data engineers and architects hence usually the data analyst doesn’t have to do a lot in this.

(2) Cleaning Data

Going through the data and trying to understand it, making corrections where needed such as removing outliers or data that should not be included in the analysis. This step can take a lot of time, but understanding the data is crucial before you start to process it.

(3) Processing data

The data processing part of the process is where I use my skills and tools to analyze the work and come up with solutions for the problem at hand.

(4) Creating reports for business leaders

As an analyst, a lot of my time goes into creating and maintaining reports/dashboards for stakeholders and business leaders. This means showing the metrics and KPIs in the best manner possible to help drive business decisions.

The best analysts are those that can use data to tell a story.

(5) Collaborating with people

This one is my favorite! As a data analyst, you work with many people across departments, both senior and junior. You’ll also likely collaborate closely with other people who work in data science like data architects and database developers.

Tools I use: Excel,PowerBI,SQL and Python(sometimes)

Don't stress too much on which tools to learn first.

Pickup 2-3 tools and master them. Skills are transferable.

For eg- If you can create an amazing dashboard in Power BI, you can make similar impressive dashboard in Tableau as well.

If you can run efficient queries in MySQL, it's going to be nearly same in PostgreSQL as well.

If you can manipulate fields in Excel, you can do the same stuff in Google Sheets as well.

Continuity is the key 😄

Never stop Learning ❤️

A - Always check your assumptions
B - Backup your data
C - Check your code

D - Do you know your data?
E - Evaluate your results
F - Find the anomalies

G - Get help when you need it
H - Have a backup plan
I - Investigate your outliers

J - Justify your methods
K - Keep your data clean
L - Let your data tell a story

M - Make your visualizations impactful
N - No one knows everything
O - Outline your analysis

P - Practice good documentation
Q - Quality control is key
R - Review your work

S - Stay organized
T - Test your assumptions
U - Use the right tools

V - Verify your results
W - Write clear and concise reports
X - Xamine for gaps in data

Y - Yield to the evidence
Z - Zero in on your findings

If you can master the ABCs of data analysis, you will be well on your way to being a successful Data Analyst.

Data Analyst Roadmap:

- Tier 1: Excel & SQL
- Tier 2: Data Cleaning & Exploratory Data Analysis (EDA)
- Tier 3: Data Visualization & Business Intelligence (BI) Tools
- Tier 4: Statistical Analysis & Machine Learning Basics

Then build projects that include:

- Data Collection
- Data Cleaning
- Data Analysis
- Data Visualization

And if you want to make your portfolio stand out more:

- Solve real business problems
- Provide clear, impactful insights
- Create a presentation
- Record a video presentation
- Target specific industries
- Reach out to companies

Hope this helps you 😊

The Real Truth About Junior Data Analytics Interviews DataAnalytics
(From someone who's interviewed 50+ analysts)

Let me save you hours of interview prep...

SQL Round

WHAT THEY SAY:
"Complex SQL knowledge"

WHAT THEY ACTUALLY TEST:

Can you clean messy data
Do you check for NULL values
How do you handle duplicates
Can you explain your logic
Do you verify results

REAL QUESTIONS:

"Find duplicate transactions"
"Calculate monthly sales"
"Show top customers"
That's it. Really. ⤵️

Excel Interview

WHAT THEY SAY:
"Advanced Excel skills"

WHAT THEY ACTUALLY TEST:

VLOOKUP/XLOOKUP usage
Pivot Table comfort
Basic formulas
Data cleaning approach
Problem-solving process

Business Case

WHAT THEY SAY:
"Data analysis presentation"

WHAT THEY REALLY WANT:

Can you explain simply
Do you ask good questions
Can you structure analysis
Do you focus on impact
Are you confident with data ⤵️

Common Scenarios

The "Messy Data" Test
They give you:

Inconsistent formats
Missing values
Duplicate records

They watch:

How you spot issues
What questions you ask
Your cleaning approach

The "Explain It" Challenge

They ask:
"Walk me through your analysis"

They assess:

Communication clarity
Technical understanding
Business thinking
Confidence level ⤵️

How to Actually Prepare

Practice Basics:

Simple SQL queries
Excel fundamentals
Clear explanation

Business Understanding:

Read company metrics
Understand industry
Know basic KPIs
Prepare good questions

Real Scenarios to Practice:

Monthly sales analysis
Customer segmentation
Product performance
Marketing campaign results

Reality Check:

They care more about:

How you think
How you communicate
How you solve problems

Than:
Perfect technical knowledge
Complex code
Advanced statistics

SQL Basics for Beginners: Must-Know Concepts

1. What is SQL? 
   SQL (Structured Query Language) is a standard language used to communicate with databases. It allows you to query, update, and manage relational databases by writing simple or complex queries.

2. SQL Syntax 
   SQL is written using statements, which consist of keywords like SELECT, FROM, WHERE, etc., to perform operations on the data.
   - SQL keywords are not case-sensitive, but it's common to write them in uppercase (e.g., SELECT, FROM).

3. SQL Data Types 
   Databases store data in different formats. The most common data types are:
   - INT (Integer): For whole numbers.
   - VARCHAR(n) or TEXT: For storing text data.
   - DATE: For dates.
   - DECIMAL: For precise decimal values, often used in financial calculations.

4. Basic SQL Queries 
   Here are some fundamental SQL operations:

   - SELECT Statement: Used to retrieve data from a database.
   

     SELECT column1, column2 FROM table_name;
     

   - WHERE Clause: Filters data based on conditions.
   

     SELECT * FROM table_name WHERE condition;
     

   - ORDER BY: Sorts data in ascending (ASC) or descending (DESC) order.
   

     SELECT column1, column2 FROM table_name ORDER BY column1 ASC;
     

   - LIMIT: Limits the number of rows returned.
   

     SELECT * FROM table_name LIMIT 5;
     

5. Filtering Data with WHERE Clause 
   The WHERE clause helps you filter data based on a condition:
 

   SELECT * FROM employees WHERE salary > 50000;
   

   You can use comparison operators like:
   - =: Equal to
   - >: Greater than
   - <: Less than
   - LIKE: For pattern matching

6. Aggregating Data 
   SQL provides functions to summarize or aggregate data:
   - COUNT(): Counts the number of rows.
   

     SELECT COUNT(*) FROM table_name;
     

   - SUM(): Adds up values in a column.
   

     SELECT SUM(salary) FROM employees;
     

   - AVG(): Calculates the average value.
   

     SELECT AVG(salary) FROM employees;
     

   - GROUP BY: Groups rows that have the same values into summary rows.
   

     SELECT department, AVG(salary) FROM employees GROUP BY department;
     

7. Joins in SQL 
   Joins combine data from two or more tables:
   - INNER JOIN: Retrieves records with matching values in both tables.
   

     SELECT employees.name, departments.department
     FROM employees
     INNER JOIN departments
     ON employees.department_id = departments.id;
     

   - LEFT JOIN: Retrieves all records from the left table and matched records from the right table.
   

     SELECT employees.name, departments.department
     FROM employees
     LEFT JOIN departments
     ON employees.department_id = departments.id;
     

8. Inserting Data
   To add new data to a table, you use the INSERT INTO statement:
 

   INSERT INTO employees (name, position, salary) VALUES ('John Doe', 'Analyst', 60000);
   

9. Updating Data
   You can update existing data in a table using the UPDATE statement:
 

   UPDATE employees SET salary = 65000 WHERE name = 'John Doe';
   

10. Deleting Data
    To remove data from a table, use the DELETE statement:
  

    DELETE FROM employees WHERE name = 'John Doe';