An important collection of the 15 best machine learning cheat sheets.

1- Supervised Learning

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-supervised-learning.pdf

2- Unsupervised Learning

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-unsupervised-learning.pdf

3- Deep Learning

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-deep-learning.pdf

4- Machine Learning Tips and Tricks

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-machine-learning-tips-and-tricks.pdf

5- Probabilities and Statistics

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-probabilities-statistics.pdf

6- Comprehensive Stanford Master Cheat Sheet

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/super-cheatsheet-machine-learning.pdf

7- Linear Algebra and Calculus

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-algebra-calculus.pdf

8- Data Science Cheat Sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf

9- Keras Cheat Sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Keras_Cheat_Sheet_Python.pdf

10- Deep Learning with Keras Cheat Sheet

https://github.com/rstudio/cheatsheets/raw/master/keras.pdf

11- Visual Guide to Neural Network Infrastructures

http://www.asimovinstitute.org/wp-content/uploads/2016/09/neuralnetworks.png

12- Skicit-Learn Python Cheat Sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf

13- Scikit-learn Cheat Sheet: Choosing the Right Estimator

https://scikit-learn.org/stable/tutorial/machine_learning_map/

14- Tensorflow Cheat Sheet

https://github.com/kailashahirwar/cheatsheets-ai/blob/master/PDFs/Tensorflow.pdf

15- Machine Learning Test Cheat Sheet

https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/

ENJOY LEARNING 👍👍

🌮 Data Analyst Vs Data Engineer Vs Data Scientist 🌮


Skills required to become data analyst
👉 Advanced Excel, Oracle/SQL
👉 Python/R

Skills required to become data engineer
👉 Python/ Java.
👉 SQL, NoSQL technologies like Cassandra or MongoDB
👉 Big data technologies like Hadoop, Hive/ Pig/ Spark

Skills required to become data Scientist
👉 In-depth knowledge of tools like R/ Python/ SAS.
👉 Well versed in various machine learning algorithms like scikit-learn, karas and tensorflow
👉 SQL and NoSQL

Bonus skill required: Data Visualization (PowerBI/ Tableau) & Statistics

Frequently asked SQL interview questions for Data Analyst/Data Engineer role-

1 - What is SQL and what are its main features?
2 - Order of writing SQL query?
3- Order of execution of SQL query?
4- What are some of the most common SQL commands?
5- What’s a primary key & foreign key?
6 - All types of joins and questions on their outputs?
7 - Explain all window functions and difference between them?
8 - What is stored procedure?
9 - Difference between stored procedure & Functions in SQL?
10 - What is trigger in SQL?
11 - Difference between where and having?

𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 20 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐒𝐩𝐚𝐫𝐤 𝐬𝐜𝐞𝐧𝐚𝐫𝐢𝐨-𝐛𝐚𝐬𝐞𝐝 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬

1. Data Processing Optimization: How would you optimize a Spark job that processes 1 TB of data daily to reduce execution time and cost?

2. Handling Skewed Data: In a Spark job, one partition is taking significantly longer to process due to skewed data. How would you handle this situation?

3. Streaming Data Pipeline: Describe how you would set up a real-time data pipeline using Spark Structured Streaming to process and analyze clickstream data from a website.

4. Fault Tolerance: How does Spark handle node failures during a job, and what strategies would you use to ensure data processing continues smoothly?

5. Data Join Strategies: You need to join two large datasets in Spark, but you encounter memory issues. What strategies would you employ to handle this?

6. Checkpointing: Explain the role of checkpointing in Spark Streaming and how you would implement it in a real-time application.

7. Stateful Processing: Describe a scenario where you would use stateful processing in Spark Streaming and how you would implement it.

8. Performance Tuning: What are the key parameters you would tune in Spark to improve the performance of a real-time analytics application?

9. Window Operations: How would you use window operations in Spark Streaming to compute rolling averages over a sliding window of events?

10. Handling Late Data: In a Spark Streaming job, how would you handle late-arriving data to ensure accurate results?

11. Integration with Kafka: Describe how you would integrate Spark Streaming with Apache Kafka to process real-time data streams.

12. Backpressure Handling: How does Spark handle backpressure in a streaming application, and what configurations can you use to manage it?

13. Data Deduplication: How would you implement data deduplication in a Spark Streaming job to ensure unique records?

14. Cluster Resource Management: How would you manage cluster resources effectively to run multiple concurrent Spark jobs without contention?

15. Real-Time ETL: Explain how you would design a real-time ETL pipeline using Spark to ingest, transform, and load data into a data warehouse.

16. Handling Large Files: You have a #Spark job that needs to process very large files (e.g., 100 GB). How would you optimize the job to handle such files efficiently?

17. Monitoring and Debugging: What tools and techniques would you use to monitor and debug a Spark job running in production?

18. Delta Lake: How would you use Delta Lake with Spark to manage real-time data lakes and ensure data consistency?

19. Partitioning Strategy: How you would design an effective partitioning strategy for a large dataset.

20. Data Serialization: What serialization formats would you use in Spark for real-time data processing, and why?

Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

All the best 👍👍

FREE RESOURCES TO LEARN DATA ENGINEERING
👇👇

Big Data and Hadoop Essentials free course

https://bit.ly/3rLxbul

Data Engineer: Prepare Financial Data for ML and Backtesting FREE UDEMY COURSE
[4.6 stars out of 5]

https://bit.ly/3fGRjLu

Understanding Data Engineering from Datacamp

https://clnk.in/soLY

Data Engineering Free Books

https://ia600201.us.archive.org/4/items/springer_10.1007-978-1-4419-0176-7/10.1007-978-1-4419-0176-7.pdf

https://www.darwinpricing.com/training/Data_Engineering_Cookbook.pdf

Big Data of Data Engineering Free book

https://databricks.com/wp-content/uploads/2021/10/Big-Book-of-Data-Engineering-Final.pdf

https://aimlcommunity.com/wp-content/uploads/2019/09/Data-Engineering.pdf

The Data Engineer’s Guide to Apache Spark

https://t.me/datasciencefun/783?single

Data Engineering with Python

https://t.me/pythondevelopersindia/343

Data Engineering Projects -

1.End-To-End From Web Scraping to Tableau  https://lnkd.in/ePMw63ge

2. Building Data Model and Writing ETL Job https://lnkd.in/eq-e3_3J

3. Data Modeling and Analysis using Semantic Web Technologies https://lnkd.in/e4A86Ypq

4. ETL Project in Azure Data Factory - https://lnkd.in/eP8huQW3

5. ETL Pipeline on AWS Cloud - https://lnkd.in/ebgNtNRR

6. Covid Data Analysis Project - https://lnkd.in/eWZ3JfKD

7. YouTube Data Analysis 
   (End-To-End Data Engineering Project) - https://lnkd.in/eYJTEKwF

8. Twitter Data Pipeline using Airflow - https://lnkd.in/eNxHHZbY

9. Sentiment analysis Twitter:
    Kafka and Spark Structured Streaming -  https://lnkd.in/esVAaqtU

ENJOY LEARNING 👍👍

Data Analyst vs Data Engineer: Must-Know Differences

Data Analyst:
- Role: Focuses on analyzing, interpreting, and visualizing data to extract insights that inform business decisions.
- Best For: Those who enjoy working directly with data to find patterns, trends, and actionable insights.
- Key Responsibilities:
- Collecting, cleaning, and organizing data.
- Using tools like Excel, Power BI, Tableau, and SQL to analyze data.
- Creating reports and dashboards to communicate insights to stakeholders.
- Collaborating with business teams to provide data-driven recommendations.
- Skills Required:
- Strong analytical skills and proficiency with data visualization tools.
- Expertise in SQL, Excel, and reporting tools.
- Familiarity with statistical analysis and business intelligence.
- Outcome: Data analysts focus on making sense of data to guide decision-making processes in business, marketing, finance, etc.

Data Engineer:
- Role: Focuses on designing, building, and maintaining the infrastructure that allows data to be stored, processed, and analyzed efficiently.
- Best For: Those who enjoy working with the technical aspects of data management and creating the architecture that supports large-scale data analysis.
- Key Responsibilities:
- Building and managing databases, data warehouses, and data pipelines.
- Developing and maintaining ETL (Extract, Transform, Load) processes to move data between systems.
- Ensuring data quality, accessibility, and security.
- Working with big data technologies like Hadoop, Spark, and cloud platforms (AWS, Azure, Google Cloud).
- Skills Required:
- Proficiency in programming languages like Python, Java, or Scala.
- Expertise in database management and big data tools.
- Strong understanding of data architecture and cloud technologies.
- Outcome: Data engineers focus on creating the infrastructure and pipelines that allow data to flow efficiently into systems where it can be analyzed by data analysts or data scientists.

Data analysts work with the data to extract insights and help make data-driven decisions, while data engineers build the systems and infrastructure that allow data to be stored, processed, and analyzed. Data analysts focus more on business outcomes, while data engineers are more involved with the technical foundation that supports data analysis.

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

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Hope it helps :)

Azure Data Engineer interview questions:

Project Questions:

1) Tell me about your project. Explain it end to end.
2) On what measure you have mentioned about the storage cost decreased and cost optimization has been done?

ADF and ADLS

1) There are 10 million CSV files in ADLS Gen2. How will you read them and process using Azure Data Factory? Explain in detail what and all would be used.
2) What is Integration Runtime?
3) What is variable and parameter in ADF?
4) Explain different activities and brief about what each activity does.
5) The pipeline is scheduled and it got failed. I need to automate it by sending the mail when it gets failed. How would you implement it?
6) How do you handle the exceptions in ADF?
7) If the ADF pipeline is running very slow, how would you approach and fix it?
8) What is the difference between Blob storage and ADLS Gen2? Why ADLS Gen2 is required?

Azure Databricks

9) How do you connect ADLS Gen 2 with databricks? In where we mention the role assignments?
10) If you are using Service Principal to connect with ADLS from Azure Databricks explain the steps and how would you code it?
11) Why using service principal? How would you create it?
12) What is Databricks runtime? Why we need it?
13) What are Workflows?
14) Explain about the Medallion Architecture in brief.
15) Explain about Delta file format briefly.
16) Consider you are working in Facebook. User is writing data for each record. Since each record is been written, a new json transaction log will get created for each write. But we can use Datalake only right. Why do we require delta file format? It can decrease the performance every now and then right?
17) Consider a job is running very slow in Azure Databricks. How would you approach the issue and make it faster?
18) What are the optimization techniques you have worked on? Explain them in brief.
19) How would you optimize the job with respect to memory management in azure databricks?

DevOps Tech Stack