The Complete Collection of Data Science Cheat Sheets – Part 2

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Editor's note: For the full scope of cheat sheets included in this 2 part series, please see The Complete Collection of Data Science Cheat Sheets - Part 1.

Searching for the cheat sheet that works for you can take some time as most of them are not easy to comprehend. The blog contains easy-to-follow and summarized sheet cheats to revise the advanced concepts of data science.

The blog series is divided into two parts that includes easy-to-follow and summarized sheet cheats to revise all of the data science concepts. The two part series is further divided into subcategories SQL, Web Scraping, Statistics, Data Analytics, Business Intelligence, Big Data, Data Structures & Algorithms, Machine Learning, Deep Learning, Natural Language Processing, Data Engineering, Web Frameworks, and All in one VIP cheat sheets.

The second blog consist of seven subcategories:

1. Data Structures & Algorithms
2. Machine Learning
3. Deep Learning
4. Natural Language Processing
5. Data Engineering
6. Web Frameworks
7. VIP Cheat Sheets

Data Structures & Algorithms

 
The most common technical interview questions are about data structures and algorithms. If you are a software engineer or data scientist then you must know common data structure operations, search & sorting algorithms, and data structure types. The list was created to help you understand complex sorting functions and algorithms. 

• Big-O Complexity Chart
• Common Data Structure Operations / Array Sorting Algorithms
• Data Structures
• Princeton: Algorithms and Data Structures
• Essential of Data Structures and Algorithms

Machine Learning

 
This is the most in-demand cheat sheet among the data community. Whenever I have a machine learning or deep learning interview, I spend a couple of  hours revising all of the key concepts of machine learning and model architecture . Sometimes hiring managers won't have the technical knowledge, so they will also use cheat sheets for preparations. The collection consists of machine learning frameworks, algorithms and neural network architectures cheat sheets.

• Supervised learning
• Unsupervised learning
• Scikit-Learn: Python Machine Learning
• Scikit-Learn: Machine Learning Algorithm Selection
• Machine Learning Algorithm
• Time Series with R
• Machine Learning tips and tricks
• Caret: Modeling and machine learning in R
• Machine Learning Modeling with R

Deep Learning

 
Modern machine learning applications run on deep neural networks and every data-related job expects you to have some knowledge about deep learning or Advance AI technologies. The deep learning models are driving modern technologies such as computer vision, automatic speech recognition, natural language processing, medical research, and self-driving cars . The list below contains information about deep learning frameworks (Pytorch/ Keras / Tensorflow), model architectures, graph neural networks, and data processing techniques. 

• Deep Learning
• PyTorch
• Neural Network Architectures
• Neural Network Graphs
• Neural Network Cells
• Neural Network Type with Diagram 
• Keras: Neural Networks in Python
• Deep learning with Keras in R
• TensorFlow

Natural Language Processing

 
Natural Language Processing (NLP) is used for  processing and cleaning text, audio, and image data so we can extract useful information. NLP applications are limitless, as it is used for language translation, transcription, conversation AI, question & answering, generative technology, classification, name entity recognition, and many more. The collection of cheat sheets contains bite-size information about the most famous NLP tools and algorithms. 

• spaCy: Advanced NLP in Python
• String manipulation with stringr
• Regular Expressions with R
• NLP for Beginners 
• Python & nltk 
• Advanced NLP 
• Transformers Documentation 
• NLP Python Introduction
• Gensim

Data Engineering

 
The data engineer's job requirement includes proficiency in SQL, Extract-Transform-Load (ETL) operations, creating & managing databases, automating data pipelines, and processing big data. The data engineer jobs are in demand, and companies want to hire the best engineer for creating and managing fully automated data pipelines. The list below contains cheat sheets on the most popular data engineer tools such as Apache Airflow and Kafka.

• Spark DataFrames in Python
• Data Engineering
• Data Engineering on Microsoft Azure
• Apache Kafka
• dbt(data built tool)
• AWS Redshift
• Apache Airflow
• Docker
• BigQuery

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Web Frameworks

 
Even though this is optional, I have been asked in the past by hiring managers about my experience with end-to-end machine learning applications. They will ask you about Django, Flask, and FastAPI or experience in deploying models to production. It is good practice to learn about web frameworks before a technical interview. The list consists of R-shiny, Plumber, Golem, Streamlit, FastAPI, Flask, and Django web frameworks.

• Interactive web apps with shiny
• Web APIs for R with plumber
• Golem with R
• Streamlit
• FastAPI
• Flask
• Django

VIP Cheat Sheet

 
VIP cheat sheets are a data science goldmine that contains sizable information about data science and its core subjects. The cheat sheets include the basic information about data types, algorithms, NLP, machine learning, data analytics, and data processing. If you are preparing for a general data interview, then I will suggest that you download any VIP cheat sheet and revise all the core topics on data science and machine learning. 

• Stanford: Super VIP Cheat Sheet
• Data Science Cheat Sheet by  Aaron Wang
• Data Science Cheat Sheet by  Maverick Lin
• Machine Learning Bites by  Rishabh Anand
• Machine Learning Interviews

Conclusion

 
If you are preparing for an interview or presentation, use these collections of cheat cheats to revise the core concepts of data science. We have covered Data Structures & Algorithms, Machine learning, Deep Learning, Natural Language Processing, Data Engineering, Web Frameworks. If you want to ace your next interview then bookmark this web page so that you can always come back and prepare for the technical interview.

 
 
Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in Technology Management and a bachelor's degree in Telecommunication Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.