Data Engineers
8.95K subscribers
354 photos
74 files
340 links
Free Data Engineering Ebooks & Courses
Download Telegram
Essential Data Science Concepts Everyone Should Know:

1. Data Types and Structures:

• Categorical: Nominal (unordered, e.g., colors) and Ordinal (ordered, e.g., education levels)

• Numerical: Discrete (countable, e.g., number of children) and Continuous (measurable, e.g., height)

• Data Structures: Arrays, Lists, Dictionaries, DataFrames (for organizing and manipulating data)

2. Descriptive Statistics:

• Measures of Central Tendency: Mean, Median, Mode (describing the typical value)

• Measures of Dispersion: Variance, Standard Deviation, Range (describing the spread of data)

• Visualizations: Histograms, Boxplots, Scatterplots (for understanding data distribution)

3. Probability and Statistics:

• Probability Distributions: Normal, Binomial, Poisson (modeling data patterns)

• Hypothesis Testing: Formulating and testing claims about data (e.g., A/B testing)

• Confidence Intervals: Estimating the range of plausible values for a population parameter

4. Machine Learning:

• Supervised Learning: Regression (predicting continuous values) and Classification (predicting categories)

• Unsupervised Learning: Clustering (grouping similar data points) and Dimensionality Reduction (simplifying data)

• Model Evaluation: Accuracy, Precision, Recall, F1-score (assessing model performance)

5. Data Cleaning and Preprocessing:

• Missing Value Handling: Imputation, Deletion (dealing with incomplete data)

• Outlier Detection and Removal: Identifying and addressing extreme values

• Feature Engineering: Creating new features from existing ones (e.g., combining variables)

6. Data Visualization:

• Types of Charts: Bar charts, Line charts, Pie charts, Heatmaps (for communicating insights visually)

• Principles of Effective Visualization: Clarity, Accuracy, Aesthetics (for conveying information effectively)

7. Ethical Considerations in Data Science:

• Data Privacy and Security: Protecting sensitive information

• Bias and Fairness: Ensuring algorithms are unbiased and fair

8. Programming Languages and Tools:

• Python: Popular for data science with libraries like NumPy, Pandas, Scikit-learn

• R: Statistical programming language with strong visualization capabilities

• SQL: For querying and manipulating data in databases

9. Big Data and Cloud Computing:

• Hadoop and Spark: Frameworks for processing massive datasets

• Cloud Platforms: AWS, Azure, Google Cloud (for storing and analyzing data)

10. Domain Expertise:

• Understanding the Data: Knowing the context and meaning of data is crucial for effective analysis

• Problem Framing: Defining the right questions and objectives for data-driven decision making

Bonus:

• Data Storytelling: Communicating insights and findings in a clear and engaging manner

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

ENJOY LEARNING 👍👍
1
𝗛𝗶𝗱𝗱𝗲𝗻 𝗚𝗲𝗺 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗿𝗼𝗺 𝗠𝗜𝗧, 𝗛𝗮𝗿𝘃𝗮𝗿𝗱 & 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱!😍

Still searching for quality learning resources?📚

What if I told you there’s a platform offering free full-length courses from top universities like MIT, Stanford, and Harvard — and most people have never even heard of it? 🤯

𝗟𝗶𝗻𝗸𝘀:-👇

https://pdlink.in/4lN7aF1

Don’t skip this chance✅️
Data Engineering Roadmap
4
𝟯 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗦𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱!😍

Want to break into Data Analytics but don’t know where to start? 🤔

These 3 beginner-friendly and 100% FREE courses will help you build real skills — no degree required!👨‍🎓

𝗟𝗶𝗻𝗸:-👇

https://pdlink.in/3IohnJO

No confusion, no fluff — just pure value✅️
Most Asked SQL Interview Questions at MAANG Companies🔥🔥

Preparing for an SQL Interview at MAANG Companies? Here are some crucial SQL Questions you should be ready to tackle:

1. How do you retrieve all columns from a table?

SELECT * FROM table_name;

2. What SQL statement is used to filter records?

SELECT * FROM table_name
WHERE condition;

The WHERE clause is used to filter records based on a specified condition.

3. How can you join multiple tables? Describe different types of JOINs.

SELECT columns
FROM table1
JOIN table2 ON table1.column = table2.column
JOIN table3 ON table2.column = table3.column;

Types of JOINs:

1. INNER JOIN: Returns records with matching values in both tables

SELECT * FROM table1
INNER JOIN table2 ON table1.column = table2.column;

2. LEFT JOIN: Returns all records from the left table & matched records from the right table. Unmatched records will have NULL values.

SELECT * FROM table1
LEFT JOIN table2 ON table1.column = table2.column;

3. RIGHT JOIN: Returns all records from the right table & matched records from the left table. Unmatched records will have NULL values.

SELECT * FROM table1
RIGHT JOIN table2 ON table1.column = table2.column;

4. FULL JOIN: Returns records when there is a match in either left or right table. Unmatched records will have NULL values.

SELECT * FROM table1
FULL JOIN table2 ON table1.column = table2.column;

4. What is the difference between WHERE & HAVING clauses?

WHERE: Filters records before any groupings are made.

SELECT * FROM table_name
WHERE condition;

HAVING: Filters records after groupings are made.

SELECT column, COUNT(*)
FROM table_name
GROUP BY column
HAVING COUNT(*) > value;

5. How do you calculate average, sum, minimum & maximum values in a column?

Average: SELECT AVG(column_name) FROM table_name;

Sum: SELECT SUM(column_name) FROM table_name;

Minimum: SELECT MIN(column_name) FROM table_name;

Maximum: SELECT MAX(column_name) FROM table_name;

Here you can find essential SQL Interview Resources👇
https://t.iss.one/mysqldata

Like this post if you need more 👍❤️

Hope it helps :)
2
Forwarded from Artificial Intelligence
𝟲 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗦𝗤𝗟 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 (𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮𝘀𝗲𝘁𝘀!)😍

🎯 Want to level up your SQL skills with real business scenarios?📚

These 6 hands-on SQL projects will help you go beyond basic SELECT queries and practice what hiring managers actually care about👨‍💻📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/40kF1x0

Save this post — even completing 1 project can power up your SQL profile!✅️
1
What is the difference between data scientist, data engineer, data analyst and business intelligence?

🧑🔬 Data Scientist
Focus: Using data to build models, make predictions, and solve complex problems.
Cleans and analyzes data
Builds machine learning models
Answers “Why is this happening?” and “What will happen next?”
Works with statistics, algorithms, and coding (Python, R)
Example: Predict which customers are likely to cancel next month

🛠️ Data Engineer
Focus: Building and maintaining the systems that move and store data.
Designs and builds data pipelines (ETL/ELT)
Manages databases, data lakes, and warehouses
Ensures data is clean, reliable, and ready for others to use
Uses tools like SQL, Airflow, Spark, and cloud platforms (AWS, Azure, GCP)
Example: Create a system that collects app data every hour and stores it in a warehouse

📊 Data Analyst
Focus: Exploring data and finding insights to answer business questions.
Pulls and visualizes data (dashboards, reports)
Answers “What happened?” or “What’s going on right now?”
Works with SQL, Excel, and tools like Tableau or Power BI
Less coding and modeling than a data scientist
Example: Analyze monthly sales and show trends by region

📈 Business Intelligence (BI) Professional
Focus: Helping teams and leadership understand data through reports and dashboards.
Designs dashboards and KPIs (key performance indicators)
Translates data into stories for non-technical users
Often overlaps with data analyst role but more focused on reporting
Tools: Power BI, Looker, Tableau, Qlik
Example: Build a dashboard showing company performance by department

🧩 Summary Table
Data Scientist - What will happen? Tools: Python, R, ML tools, predictions & models
Data Engineer - How does the data move and get stored? Tools: SQL, Spark, cloud tools, infrastructure & pipelines
Data Analyst - What happened? Tools: SQL, Excel, BI tools, reports & exploration
BI Professional - How can we see business performance clearly? Tools: Power BI, Tableau, dashboards & insights for decision-makers

🎯 In short:
Data Engineers build the roads.
Data Scientists drive smart cars to predict traffic.
Data Analysts look at traffic data to see patterns.
BI Professionals show everyone the traffic report on a screen.
2
📊 Data Science Summarized: The Core Pillars of Success! 🚀

1️⃣ Statistics:
The backbone of data analysis and decision-making.
Used for hypothesis testing, distributions, and drawing actionable insights.

2️⃣ Mathematics:
Critical for building models and understanding algorithms.
Focus on:
Linear Algebra
Calculus
Probability & Statistics

3️⃣ Python:
The most widely used language in data science.
Essential libraries include:
Pandas
NumPy
Scikit-Learn
TensorFlow

4️⃣ Machine Learning:
Use algorithms to uncover patterns and make predictions.
Key types:
Regression
Classification
Clustering

5️⃣ Domain Knowledge:
Context matters.
Understand your industry to build relevant, useful, and accurate models.
1
Greetings from PVR Cloud Tech!! 🌈

We will be starting Full Stack Data Engineering on 19th July 2025, from 10:00 AM to 12:00 PM IST (Saturday).

These sessions are exclusively designed for beginners entering the software industry and individuals transitioning from non-IT to IT backgrounds. Data engineers are the backbone of modern businesses.

Course Content :

https://drive.google.com/file/d/1yejI95UAC5DdD2X83Qiu14pnfpUVX6_l/view?usp=sharing

🔥 Interested candidates, please fill out the form below and join the WhatsApp Group.

https://forms.gle/B2JD2ZUvpwfUtPZN6

https://chat.whatsapp.com/Cdr0oDSoaGZIyoIAkmlOAa

https://www.whatsapp.com/channel/0029Vb60rGU8V0thkpbFFW2n

Please share these details with your friends as these sessions may help them transform their careers, and you will be a part of it by providing information.

Thanks,
Team,PVR Cloud Tech
+91-9346060794
1
𝟲 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗦𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 & 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗝𝗼𝘂𝗿𝗻𝗲𝘆😍

Want to break into Data Science & Analytics but don’t want to spend on expensive courses?👨‍💻

Start here — with 100% FREE courses from Cisco, IBM, Google & LinkedIn, all with certificates you can showcase on LinkedIn or your resume!📚📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3Ix2oxd

This list will set you up with real-world, job-ready skills✅️
1
𝗖𝗿𝗮𝗰𝗸 𝗙𝗔𝗔𝗡𝗚 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱 — 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘!😍

If you’re serious about cracking top tech interviews — from FAANG to startups — this is the roadmap you can’t afford to miss🎊

Thousands have used it to land roles at Google, Amazon, Microsoft, and more — completely free🤩📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3TJlpyW

Your dream job might just start here.✅️
1
Here’s a detailed breakdown of critical roles and their associated responsibilities:


🔘 Data Engineer: Tailored for Data Enthusiasts

1. Data Ingestion: Acquire proficiency in data handling techniques.
2. Data Validation: Master the art of data quality assurance.
3. Data Cleansing: Learn advanced data cleaning methodologies.
4. Data Standardisation: Grasp the principles of data formatting.
5. Data Curation: Efficiently organise and manage datasets.

🔘 Data Scientist: Suited for Analytical Minds

6. Feature Extraction: Hone your skills in identifying data patterns.
7. Feature Selection: Master techniques for efficient feature selection.
8. Model Exploration: Dive into the realm of model selection methodologies.

🔘 Data Scientist & ML Engineer: Designed for Coding Enthusiasts

9. Coding Proficiency: Develop robust programming skills.
10. Model Training: Understand the intricacies of model training.
11. Model Validation: Explore various model validation techniques.
12. Model Evaluation: Master the art of evaluating model performance.
13. Model Refinement: Refine and improve candidate models.
14. Model Selection: Learn to choose the most suitable model for a given task.

🔘 ML Engineer: Tailored for Deployment Enthusiasts

15. Model Packaging: Acquire knowledge of essential packaging techniques.
16. Model Registration: Master the process of model tracking and registration.
17. Model Containerisation: Understand the principles of containerisation.
18. Model Deployment: Explore strategies for effective model deployment.

These roles encompass diverse facets of Data and ML, catering to various interests and skill sets. Delve into these domains, identify your passions, and customise your learning journey accordingly.
3
𝟰 𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱😍

Want to break into data science in 2025—without spending a single rupee?💰👨‍💻

You’re in luck! Microsoft is offering powerful, beginner-friendly resources that teach you everything from Python fundamentals to AI and data analytics—for free🤩✔️

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/42vCIrb

Level up your career in the booming field of data✅️
1
ETL vs REVERSE ETL vs ELT
2
Forwarded from Artificial Intelligence
𝟰 𝗠𝘂𝘀𝘁-𝗪𝗮𝘁𝗰𝗵 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗼𝗿 𝗘𝘃𝗲𝗿𝘆 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗦𝘁𝘂𝗱𝗲𝗻𝘁 𝗶𝗻 𝟮𝟬𝟮𝟱😍

If you’re starting your data analytics journey, these 4 YouTube courses are pure gold — and the best part? 💻🤩

They’re completely free💥💯

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/44DvNP1

Each course can help you build the right foundation for a successful tech career✅️
1
𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐓𝐞𝐜𝐡 𝐒𝐭𝐚𝐜𝐤

What it is: A powerful open-source platform designed to automate deploying, scaling, and operating application containers.

𝐂𝐥𝐮𝐬𝐭𝐞𝐫 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭:
- Organizes containers into groups for easier management.
- Automates tasks like scaling and load balancing.

𝐂𝐨𝐧𝐭𝐚𝐢𝐧𝐞𝐫 𝐑𝐮𝐧𝐭𝐢𝐦𝐞:
- Software responsible for launching and managing containers.
- Ensures containers run efficiently and securely.

𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲:
- Implements measures to protect against unauthorized access and malicious activities.
- Includes features like role-based access control and encryption.

𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 & 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲:
- Tools to monitor system health, performance, and resource usage.
- Helps identify and troubleshoot issues quickly.

𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐢𝐧𝐠:
- Manages network communication between containers and external systems.
- Ensures connectivity and security between different parts of the system.

𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬:
- Handles tasks related to the underlying infrastructure, such as provisioning and scaling.
- Automates repetitive tasks to streamline operations and improve efficiency.

- 𝐊𝐞𝐲 𝐜𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬:
- Cluster Management: Handles grouping and managing multiple containers.
- Container Runtime: Software that runs containers and manages their lifecycle.
- Security: Implements measures to protect containers and the overall system.
- Monitoring & Observability: Tools to track and understand system behavior and performance.
- Networking: Manages communication between containers and external networks.
- Infrastructure Operations: Handles tasks like provisioning, scaling, and maintaining the underlying infrastructure.
2
𝟲 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗙𝗿𝗼𝗺 𝗧𝗼𝗽 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 😍

A power-packed selection of 100% free, certified courses from top institutions:

- Data Analytics – Cisco
- Digital Marketing – Google
- Python for AI – IBM/edX
- SQL & Databases – Stanford
- Generative AI – Google Cloud
- Machine Learning – Harvard

𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:- 
 
https://pdlink.in/3FcwrZK
 
Master in‑demand tech skills with these 6 certified, top-tier free courses
1