Exceptional Resources for Data Science Interview Preparation. Part 1: Live Coding
In this article, we will understand what a live coding interview is and how to prepare for it.
This blog-post will primarily be useful to Data Scientists and ML engineers, while some sections, for example, Algorithms and Data Structures, will be suitable for all IT specialists who will have to go through the live coding section.
Table of contents
- Preparing for an Algorithmic Interview
- Resources
- Algorithms and Data Structures
- Programming in Python
- Solving a Practical Data Science Problem
- Hybrid
- Learning How to Learn
- Let’s sum it up
- What’s next?
NB:
I'm the author of the article.
It was initially published in Russian (on habr.com), then I added additional resources in English to make up for deleting resources in Russian language and published it on medium.com.
So, for Russian speakers I recommend to read Russian version, for English speakers I recommend to read English version and both will benefit from starring the repository, which will be maintained and updated when new resources become available.
Links:
- Medium (eng)
- Habr (rus)
Navigational hashtags: #armknowledgesharing #armarticles
General hashtags: #interview #interviewpreparation #livecoding #leetcode #algorithms #algorithmsdatastructures #datastructures #python #sql #kaggle
@data_science_weekly
In this article, we will understand what a live coding interview is and how to prepare for it.
This blog-post will primarily be useful to Data Scientists and ML engineers, while some sections, for example, Algorithms and Data Structures, will be suitable for all IT specialists who will have to go through the live coding section.
Table of contents
- Preparing for an Algorithmic Interview
- Resources
- Algorithms and Data Structures
- Programming in Python
- Solving a Practical Data Science Problem
- Hybrid
- Learning How to Learn
- Let’s sum it up
- What’s next?
NB:
I'm the author of the article.
It was initially published in Russian (on habr.com), then I added additional resources in English to make up for deleting resources in Russian language and published it on medium.com.
So, for Russian speakers I recommend to read Russian version, for English speakers I recommend to read English version and both will benefit from starring the repository, which will be maintained and updated when new resources become available.
Links:
- Medium (eng)
- Habr (rus)
Navigational hashtags: #armknowledgesharing #armarticles
General hashtags: #interview #interviewpreparation #livecoding #leetcode #algorithms #algorithmsdatastructures #datastructures #python #sql #kaggle
@data_science_weekly
👍8
System Design
Learn how to design systems at scale and prepare for system design interviews
What is system design?
System design is the process of defining the architecture, interfaces, and data for a system that satisfies specific requirements. System design meets the needs of your business or organization through coherent and efficient systems. It requires a systematic approach to building and engineering systems. A good system design requires us to think about everything, from infrastructure all the way down to the data and how it's stored.
Table of contents
- Getting Started
What is system design?
- Chapter I
IP, OSI Model, TCP and UDP, Domain Name System (DNS), Load Balancing, Clustering, Caching, Content Delivery Network (CDN), Proxy, Availability, Scalability, Storage
- Chapter II
Databases and DBMS, SQL databases, NoSQL databases, SQL vs NoSQL databases, Database Replication, Indexes, Normalization and Denormalization, ACID and BASE consistency models, CAP theorem, PACELC Theorem, Transactions, Distributed Transactions, Sharding, Consistent Hashing, Database Federation
- Chapter III
N-tier architecture, Message Brokers, Message Queues, Publish-Subscribe, Enterprise Service Bus (ESB), Monoliths and Microservices, Event-Driven Architecture (EDA), Event Sourcing, Command and Query Responsibility Segregation (CQRS), API Gateway, REST, GraphQL, gRPC, Long polling, WebSockets, Server-Sent Events (SSE)
- Chapter IV
Geohashing and Quadtrees, Circuit breaker, Rate Limiting, Service Discovery, SLA, SLO, SLI, Disaster recovery, Virtual Machines (VMs) and Containers, OAuth 2.0 and OpenID Connect (OIDC), Single Sign-On (SSO), SSL, TLS, mTLS
- Chapter V
System Design Interviews, URL Shortener, WhatsApp, Twitter, Netflix, Uber
- Appendix
Next Steps, References
Links:
- Direct link to the site with the course
- Direct link to the repository for the course
- Content Guide link
- Topic Guide link
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #systemdesign
@data_science_weekly
Learn how to design systems at scale and prepare for system design interviews
What is system design?
System design is the process of defining the architecture, interfaces, and data for a system that satisfies specific requirements. System design meets the needs of your business or organization through coherent and efficient systems. It requires a systematic approach to building and engineering systems. A good system design requires us to think about everything, from infrastructure all the way down to the data and how it's stored.
Table of contents
- Getting Started
What is system design?
- Chapter I
IP, OSI Model, TCP and UDP, Domain Name System (DNS), Load Balancing, Clustering, Caching, Content Delivery Network (CDN), Proxy, Availability, Scalability, Storage
- Chapter II
Databases and DBMS, SQL databases, NoSQL databases, SQL vs NoSQL databases, Database Replication, Indexes, Normalization and Denormalization, ACID and BASE consistency models, CAP theorem, PACELC Theorem, Transactions, Distributed Transactions, Sharding, Consistent Hashing, Database Federation
- Chapter III
N-tier architecture, Message Brokers, Message Queues, Publish-Subscribe, Enterprise Service Bus (ESB), Monoliths and Microservices, Event-Driven Architecture (EDA), Event Sourcing, Command and Query Responsibility Segregation (CQRS), API Gateway, REST, GraphQL, gRPC, Long polling, WebSockets, Server-Sent Events (SSE)
- Chapter IV
Geohashing and Quadtrees, Circuit breaker, Rate Limiting, Service Discovery, SLA, SLO, SLI, Disaster recovery, Virtual Machines (VMs) and Containers, OAuth 2.0 and OpenID Connect (OIDC), Single Sign-On (SSO), SSL, TLS, mTLS
- Chapter V
System Design Interviews, URL Shortener, WhatsApp, Twitter, Netflix, Uber
- Appendix
Next Steps, References
Links:
- Direct link to the site with the course
- Direct link to the repository for the course
- Content Guide link
- Topic Guide link
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #systemdesign
@data_science_weekly
👍5
Prompt Engineering Guide
Generative AI is the world's hottest buzzword, and they have created the most comprehensive (and free) guide on how to use it. This course is tailored to non-technical readers, who may not have even heard of AI, making it the perfect starting point if you are new to Generative AI and Prompt Engineering. Technical readers will find valuable insights within their later modules.
Generative AI refers to tools that can be used to create new content such as articles or images, just like humans can. It is expected to significantly change the way we work (read: your job may be affected). With so much buzz floating around about Generative AI (Gen AI) and Prompt Engineering (PE), it is hard to know what to believe.
They have scoured the internet to find the best techniques and tools for their 1.3 Million readers from companies like OpenAI, Brex, and Deloitte. They are constantly refining their guide, to ensure that they provide you with the latest information.
Link:
- Direct link to the site with the guide
- Content Guide link
- Topic Guide link
Navigational hashtags: #armknowledgesharing #armtutorial
General hashtags: #promptengineering #prompt #prompting #genai #generativeai
@data_science_weekly
Generative AI is the world's hottest buzzword, and they have created the most comprehensive (and free) guide on how to use it. This course is tailored to non-technical readers, who may not have even heard of AI, making it the perfect starting point if you are new to Generative AI and Prompt Engineering. Technical readers will find valuable insights within their later modules.
Generative AI refers to tools that can be used to create new content such as articles or images, just like humans can. It is expected to significantly change the way we work (read: your job may be affected). With so much buzz floating around about Generative AI (Gen AI) and Prompt Engineering (PE), it is hard to know what to believe.
They have scoured the internet to find the best techniques and tools for their 1.3 Million readers from companies like OpenAI, Brex, and Deloitte. They are constantly refining their guide, to ensure that they provide you with the latest information.
Link:
- Direct link to the site with the guide
- Content Guide link
- Topic Guide link
Navigational hashtags: #armknowledgesharing #armtutorial
General hashtags: #promptengineering #prompt #prompting #genai #generativeai
@data_science_weekly
👍4
Designing Machine Learning Systems by Chip Huyen
Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements.
Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references.
This book will help you tackle scenarios such as:
- Engineering data and choosing the right metrics to solve a business problem
- Automating the process for continually developing, evaluating, deploying, and updating models
- Developing a monitoring system to quickly detect and address issues your models might encounter in production
- Architecting an ML platform that serves across use cases
- Developing responsible ML systems
Link: https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #machinelearningsystemdesign #systemdesign #machinelearning #ml #designingmachinelearningsystems
@data_science_weekly
Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements.
Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references.
This book will help you tackle scenarios such as:
- Engineering data and choosing the right metrics to solve a business problem
- Automating the process for continually developing, evaluating, deploying, and updating models
- Developing a monitoring system to quickly detect and address issues your models might encounter in production
- Architecting an ML platform that serves across use cases
- Developing responsible ML systems
Link: https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #machinelearningsystemdesign #systemdesign #machinelearning #ml #designingmachinelearningsystems
@data_science_weekly
👍8
MLU-EXPLAIN
Visual explanations of core machine learning concepts
Machine Learning University (MLU) is an education initiative from Amazon designed to teach machine learning theory and practical application.
As part of that goal, MLU-Explain exists to teach important machine learning concepts through visual essays in a fun, informative, and accessible manner.
Available articles:
- Neural Networks
- Equality of Dots
- Logistic Regression
- Linear Regression
- Reinforcement Learning
- ROC & AUC
- Cross-validation
- Train, Test, and Validation Sets
- Precision & Recall
- Random Forest
- Decision Trees
- The Bias Variance Tradeoff
- Double Descent
Link:
- Direct Link
Navigational hashtags: #armknowledgesharing #armtutorials
General hashtags: #machinelearning #ml #visualisation
@data_science_weekly
Visual explanations of core machine learning concepts
Machine Learning University (MLU) is an education initiative from Amazon designed to teach machine learning theory and practical application.
As part of that goal, MLU-Explain exists to teach important machine learning concepts through visual essays in a fun, informative, and accessible manner.
Available articles:
- Neural Networks
- Equality of Dots
- Logistic Regression
- Linear Regression
- Reinforcement Learning
- ROC & AUC
- Cross-validation
- Train, Test, and Validation Sets
- Precision & Recall
- Random Forest
- Decision Trees
- The Bias Variance Tradeoff
- Double Descent
Link:
- Direct Link
Navigational hashtags: #armknowledgesharing #armtutorials
General hashtags: #machinelearning #ml #visualisation
@data_science_weekly
👍6
Exceptional Resources for Data Science Interview Preparation. Part 2: Classic Machine Learning
In the previous article, I shared materials for preparing for one of the most daunting (for many) stages — Live Coding.
In this article, we will look at materials that can be used to prepare for the section on classic machine learning.
Table of contents
- Classic Machine Learning
- Resources
- Books
- Courses
- Sites
- Cheatsheets
- Other
- Let’s sum it up
- What’s next?
NB:
I'm the author of the article.
It was initially published in Russian (on habr.com), then I published it on medium.com. So, for Russian speakers I recommend to read Russian version, for English speakers I recommend to read English version and both will benefit from starring the repository, which will be maintained and updated when new resources become available.
Links:
- Medium (eng)
- Habr (rus)
Navigational hashtags: #armknowledgesharing #armarticles
General hashtags: #interview #interviewpreparation #machinelearning #ml
@data_science_weekly
In the previous article, I shared materials for preparing for one of the most daunting (for many) stages — Live Coding.
In this article, we will look at materials that can be used to prepare for the section on classic machine learning.
Table of contents
- Classic Machine Learning
- Resources
- Books
- Courses
- Sites
- Cheatsheets
- Other
- Let’s sum it up
- What’s next?
NB:
I'm the author of the article.
It was initially published in Russian (on habr.com), then I published it on medium.com. So, for Russian speakers I recommend to read Russian version, for English speakers I recommend to read English version and both will benefit from starring the repository, which will be maintained and updated when new resources become available.
Links:
- Medium (eng)
- Habr (rus)
Navigational hashtags: #armknowledgesharing #armarticles
General hashtags: #interview #interviewpreparation #machinelearning #ml
@data_science_weekly
👍5
Interpretable Machine Learning. A Guide for Making Black Box Models Explainable by Christoph Molnar
Machine learning has great potential for improving products, processes and research. But computers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book is about making machine learning models and their decisions interpretable.
After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. The focus of the book is on model-agnostic methods for interpreting black box models such as feature importance and accumulated local effects, and explaining individual predictions with Shapley values and LIME. In addition, the book presents methods specific to deep neural networks.
All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project. Reading the book is recommended for machine learning practitioners, data scientists, statisticians, and anyone else interested in making machine learning models interpretable.
Link:
- Direct Link
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #machinelearning #ml #interpretation #explanation #interpretability #blackbox
@data_science_weekly
Machine learning has great potential for improving products, processes and research. But computers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book is about making machine learning models and their decisions interpretable.
After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. The focus of the book is on model-agnostic methods for interpreting black box models such as feature importance and accumulated local effects, and explaining individual predictions with Shapley values and LIME. In addition, the book presents methods specific to deep neural networks.
All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project. Reading the book is recommended for machine learning practitioners, data scientists, statisticians, and anyone else interested in making machine learning models interpretable.
Link:
- Direct Link
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #machinelearning #ml #interpretation #explanation #interpretability #blackbox
@data_science_weekly
👍4
Mathematics for Machine Learning by Marc Peter Deisenroth and A. Aldo Faisal
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines.
For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts.
Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Table of Contents
Part I: Mathematical Foundations
1. Introduction and Motivation
2. Linear Algebra
3. Analytic Geometry
4. Matrix Decompositions
5. Vector Calculus
6. Probability and Distribution
7. Continuous Optimization
Part II: Central Machine Learning Problems
8. When Models Meet Data
9. Linear Regression
10. Dimensionality Reduction with Principal Component Analysis
11. Density Estimation with Gaussian Mixture Models
12. Classification with Support Vector Machines
Link: Direct Link
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #math #mathematics #maths #calculus #algebra #probability #geometry #optimization #machinelearning #ml
@data_science_weekly
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines.
For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts.
Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Table of Contents
Part I: Mathematical Foundations
1. Introduction and Motivation
2. Linear Algebra
3. Analytic Geometry
4. Matrix Decompositions
5. Vector Calculus
6. Probability and Distribution
7. Continuous Optimization
Part II: Central Machine Learning Problems
8. When Models Meet Data
9. Linear Regression
10. Dimensionality Reduction with Principal Component Analysis
11. Density Estimation with Gaussian Mixture Models
12. Classification with Support Vector Machines
Link: Direct Link
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #math #mathematics #maths #calculus #algebra #probability #geometry #optimization #machinelearning #ml
@data_science_weekly
👍10
The Pragmatic Engineer
The #1 technology newsletter on Substack. Highly relevant for software engineers and engineering managers, useful for those working in tech. Written by engineering manager and software engineer Gergely Orosz who was previously at Uber, Skype/Microsoft, and at startups.
What to expect:
- Big Tech and startups, from the inside. Tech is accelerating rapidly: but some fast-moving companies are ahead of the rest of the pack. What are they doing differently and why? He talks with people working at these companies to get insights and details.
- Actionable advice for engineering managers, software engineers and tech workers. Topics covered are relevant to those working at tech companies. Get tools and insights to become a more efficient engineering leader. If you use just one approach to make your project, team, or company more efficient, the weekly newsletter already pays for itself.
- A pulse on the tech market and trends worth knowing about. What is happening in tech, and why? How is the market changing? What does this mean for hiring managers and for those navigating their careers? He covers patterns and trends heard within Big Tech and high-growth startups in the series The Pulse.
Link: Direct Link
Navigational hashtags: #armknowledgesharing #armnewsletters
General hashtags: #technology #engineering #efficiency
@data_science_weekly
The #1 technology newsletter on Substack. Highly relevant for software engineers and engineering managers, useful for those working in tech. Written by engineering manager and software engineer Gergely Orosz who was previously at Uber, Skype/Microsoft, and at startups.
What to expect:
- Big Tech and startups, from the inside. Tech is accelerating rapidly: but some fast-moving companies are ahead of the rest of the pack. What are they doing differently and why? He talks with people working at these companies to get insights and details.
- Actionable advice for engineering managers, software engineers and tech workers. Topics covered are relevant to those working at tech companies. Get tools and insights to become a more efficient engineering leader. If you use just one approach to make your project, team, or company more efficient, the weekly newsletter already pays for itself.
- A pulse on the tech market and trends worth knowing about. What is happening in tech, and why? How is the market changing? What does this mean for hiring managers and for those navigating their careers? He covers patterns and trends heard within Big Tech and high-growth startups in the series The Pulse.
Link: Direct Link
Navigational hashtags: #armknowledgesharing #armnewsletters
General hashtags: #technology #engineering #efficiency
@data_science_weekly
👍3
MLOps Guide by Arthur Olga, Gabriel Monteiro, Guilherme Leite and Vinicius Lima
This site is intended to be a MLOps Guide to help projects and companies to build more reliable MLOps environment. This guide should contemplate the theory behind MLOps and an implementation that should fit for most use cases.
What is MLOps?
MLOps is a methodology of operation that aims to facilitate the process of bringing an experimental Machine Learning model into production and maintaining it efficiently. MLOps focus on bringing the methodology of DevOps used in the software industry to the Machine Learning model lifecycle.
In that way we can define some of the main features of a MLOPs project:
- Data and Model Versioning
- Feature Management and Storing
- Automation of Pipelines and Processes
- CI/CD for Machine Learning
- Continuous Monitoring of Models
What does this guide cover?
- Introduction to MLOps Concepts
- Tutorial for Building a MLOps Environment
Link: Direct
Navigational hashtags: #armknowledgesharing #armguides
General hashtags: #mlops #ml #operations
@data_science_weekly
This site is intended to be a MLOps Guide to help projects and companies to build more reliable MLOps environment. This guide should contemplate the theory behind MLOps and an implementation that should fit for most use cases.
What is MLOps?
MLOps is a methodology of operation that aims to facilitate the process of bringing an experimental Machine Learning model into production and maintaining it efficiently. MLOps focus on bringing the methodology of DevOps used in the software industry to the Machine Learning model lifecycle.
In that way we can define some of the main features of a MLOPs project:
- Data and Model Versioning
- Feature Management and Storing
- Automation of Pipelines and Processes
- CI/CD for Machine Learning
- Continuous Monitoring of Models
What does this guide cover?
- Introduction to MLOps Concepts
- Tutorial for Building a MLOps Environment
Link: Direct
Navigational hashtags: #armknowledgesharing #armguides
General hashtags: #mlops #ml #operations
@data_science_weekly
👍4
Lessons in Statistical Thinking by Daniel Kaplan
One of the oft-stated goals of education is the development of “critical thinking” skills. Although it is rare to see a careful definition of critical thinking, widely accepted elements include framing and recognizing coherent arguments, the application of logic patterns such as deduction, the skeptical evaluation of evidence, consideration of alternative explanations, and a disinclination to accept unsubstantiated claims.
“Statistical thinking” is a variety of critical thinking involving data and inductive reasoning directed to draw reasonable and useful conclusions that can guide decision-making and action.
Surprisingly, many university statistics courses are not primarily about statistical reasoning. They do cover some technical methods used in statistical reasoning, but they have replaced notions of “useful,” “decision-making,” and “action” with doctrines such as “null hypothesis significance testing” and “correlation is not causation.” For example, a core method for drawing responsible conclusions about causal relationships by adjusting for “covariates” is hardly ever even mentioned in conventional statistics courses.
These Lessons in Statistical Thinking present the statistical ideas and methods behind decision-making to guide action.
Link: Direct Link
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #stats #statistics #math #maths
@data_science_weekly
One of the oft-stated goals of education is the development of “critical thinking” skills. Although it is rare to see a careful definition of critical thinking, widely accepted elements include framing and recognizing coherent arguments, the application of logic patterns such as deduction, the skeptical evaluation of evidence, consideration of alternative explanations, and a disinclination to accept unsubstantiated claims.
“Statistical thinking” is a variety of critical thinking involving data and inductive reasoning directed to draw reasonable and useful conclusions that can guide decision-making and action.
Surprisingly, many university statistics courses are not primarily about statistical reasoning. They do cover some technical methods used in statistical reasoning, but they have replaced notions of “useful,” “decision-making,” and “action” with doctrines such as “null hypothesis significance testing” and “correlation is not causation.” For example, a core method for drawing responsible conclusions about causal relationships by adjusting for “covariates” is hardly ever even mentioned in conventional statistics courses.
These Lessons in Statistical Thinking present the statistical ideas and methods behind decision-making to guide action.
Link: Direct Link
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #stats #statistics #math #maths
@data_science_weekly
👍3
Introduction To Algorithms by MIT
This is an introductory course covering elementary data structures (dynamic arrays, heaps, balanced binary search trees, hash tables) and algorithmic approaches to solve classical problems (sorting, graph searching, dynamic programming). Introduction to mathematical modeling of computational problems, as well as common algorithms, algorithmic paradigms, and data structures used to solve these problems. Emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems.
Link: Direct Link
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #algorithms #datastructures #mit
@data_science_weekly
This is an introductory course covering elementary data structures (dynamic arrays, heaps, balanced binary search trees, hash tables) and algorithmic approaches to solve classical problems (sorting, graph searching, dynamic programming). Introduction to mathematical modeling of computational problems, as well as common algorithms, algorithmic paradigms, and data structures used to solve these problems. Emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems.
Link: Direct Link
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #algorithms #datastructures #mit
@data_science_weekly
👍3
What the f*ck Python! 😱
Python, being a beautifully designed high-level and interpreter-based programming language, provides us with many features for the programmer's comfort. But sometimes, the outcomes of a Python snippet may not seem obvious at first sight.
Here's a fun project attempting to explain what exactly is happening under the hood for some counter-intuitive snippets and lesser-known features in Python.
While some of the examples you see below may not be WTFs in the truest sense, but they'll reveal some of the interesting parts of Python that you might be unaware of. I find it a nice way to learn the internals of a programming language, and I believe that you'll find it interesting too!
If you're an experienced Python programmer, you can take it as a challenge to get most of them right in the first attempt. You may have already experienced some of them before, and I might be able to revive sweet old memories of yours! 😅
Links:
- Interactive Website
- Interactive Notebook
- GitHub Version:
- ENG
- RUS
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #python #programming #coding
@data_science_weekly
Python, being a beautifully designed high-level and interpreter-based programming language, provides us with many features for the programmer's comfort. But sometimes, the outcomes of a Python snippet may not seem obvious at first sight.
Here's a fun project attempting to explain what exactly is happening under the hood for some counter-intuitive snippets and lesser-known features in Python.
While some of the examples you see below may not be WTFs in the truest sense, but they'll reveal some of the interesting parts of Python that you might be unaware of. I find it a nice way to learn the internals of a programming language, and I believe that you'll find it interesting too!
If you're an experienced Python programmer, you can take it as a challenge to get most of them right in the first attempt. You may have already experienced some of them before, and I might be able to revive sweet old memories of yours! 😅
Links:
- Interactive Website
- Interactive Notebook
- GitHub Version:
- ENG
- RUS
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #python #programming #coding
@data_science_weekly
👍4
Data Analysis with Python and PySpark by Jonathan Rioux
In Data Analysis with Python and PySpark you will learn how to:
- Manage your data as it scales across multiple machines
- Scale up your data programs with full confidence
- Read and write data to and from a variety of sources and formats
- Deal with messy data with PySpark’s data manipulation functionality
- Discover new data sets and perform exploratory data analysis
- Build automated data pipelines that transform, summarize, and get insights from data
- Troubleshoot common PySpark errors
- Creating reliable long-running jobs
Data Analysis with Python and PySpark is your guide to delivering successful Python-driven data projects. Packed with relevant examples and essential techniques, this practical book teaches you to build pipelines for reporting, machine learning, and other data-centric tasks. Quick exercises in every chapter help you practice what you’ve learned, and rapidly start implementing PySpark into your data systems. No previous knowledge of Spark is required.
Link: Direct
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #spark #pyspark #bigdata
@data_science_weekly
In Data Analysis with Python and PySpark you will learn how to:
- Manage your data as it scales across multiple machines
- Scale up your data programs with full confidence
- Read and write data to and from a variety of sources and formats
- Deal with messy data with PySpark’s data manipulation functionality
- Discover new data sets and perform exploratory data analysis
- Build automated data pipelines that transform, summarize, and get insights from data
- Troubleshoot common PySpark errors
- Creating reliable long-running jobs
Data Analysis with Python and PySpark is your guide to delivering successful Python-driven data projects. Packed with relevant examples and essential techniques, this practical book teaches you to build pipelines for reporting, machine learning, and other data-centric tasks. Quick exercises in every chapter help you practice what you’ve learned, and rapidly start implementing PySpark into your data systems. No previous knowledge of Spark is required.
Link: Direct
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #spark #pyspark #bigdata
@data_science_weekly
👍3
Practical Recommender Systems by Kim Falk
Practical Recommender Systems explains how recommender systems work and shows how to create and apply them for your site. After covering the basics, you’ll see how to collect user data and produce personalized recommendations. You’ll learn how to use the most popular recommendation algorithms and see examples of them in action on sites like Amazon and Netflix. Finally, the book covers scaling problems and other issues you’ll encounter as your site grows.
Link: Direct
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #recsys #recommendersystems
@data_science_weekly
Practical Recommender Systems explains how recommender systems work and shows how to create and apply them for your site. After covering the basics, you’ll see how to collect user data and produce personalized recommendations. You’ll learn how to use the most popular recommendation algorithms and see examples of them in action on sites like Amazon and Netflix. Finally, the book covers scaling problems and other issues you’ll encounter as your site grows.
Link: Direct
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #recsys #recommendersystems
@data_science_weekly
👍3
CS224W: Machine Learning with Graphs
Complex data can be represented as a graph of relationships between objects. Such networks are a fundamental tool for modeling social, technological, and biological systems. This course focuses on the computational, algorithmic, and modeling challenges specific to the analysis of massive graphs. By means of studying the underlying graph structure and its features, students are introduced to machine learning techniques and data mining tools apt to reveal insights on a variety of networks.
Topics include: representation learning and Graph Neural Networks; algorithms for the World Wide Web; reasoning over Knowledge Graphs; influence maximization; disease outbreak detection, social network analysis.
Links:
- Direct
- Videos
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #graphs #graph #gnn #knowledgegraphs #socialnetworks
@data_science_weekly
Complex data can be represented as a graph of relationships between objects. Such networks are a fundamental tool for modeling social, technological, and biological systems. This course focuses on the computational, algorithmic, and modeling challenges specific to the analysis of massive graphs. By means of studying the underlying graph structure and its features, students are introduced to machine learning techniques and data mining tools apt to reveal insights on a variety of networks.
Topics include: representation learning and Graph Neural Networks; algorithms for the World Wide Web; reasoning over Knowledge Graphs; influence maximization; disease outbreak detection, social network analysis.
Links:
- Direct
- Videos
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #graphs #graph #gnn #knowledgegraphs #socialnetworks
@data_science_weekly
👍2
🧠 Awesome ChatGPT Prompts
Welcome to the "Awesome ChatGPT Prompts" repository! This is a collection of prompt examples to be used with the ChatGPT model.
The ChatGPT model is a large language model trained by OpenAI that is capable of generating human-like text. By providing it with a prompt, it can generate responses that continue the conversation or expand on the given prompt.
In this repository, you will find a variety of prompts that can be used with ChatGPT.
To get started, simply clone this repository and use the prompts in the README.md file as input for ChatGPT. You can also use the prompts in this file as inspiration for creating your own.
Link: Direct
Navigational hashtags: #armknowledgesharing #armrepo
General hashtags: #prompts #prompt #promptengineering #chatgpt #gpt
@data_science_weekly
Welcome to the "Awesome ChatGPT Prompts" repository! This is a collection of prompt examples to be used with the ChatGPT model.
The ChatGPT model is a large language model trained by OpenAI that is capable of generating human-like text. By providing it with a prompt, it can generate responses that continue the conversation or expand on the given prompt.
In this repository, you will find a variety of prompts that can be used with ChatGPT.
To get started, simply clone this repository and use the prompts in the README.md file as input for ChatGPT. You can also use the prompts in this file as inspiration for creating your own.
Link: Direct
Navigational hashtags: #armknowledgesharing #armrepo
General hashtags: #prompts #prompt #promptengineering #chatgpt #gpt
@data_science_weekly
👍2
Mathematics Of Machine Learning by MIT
Broadly speaking, Machine Learning refers to the automated identification of patterns in data. As such it has been a fertile ground for new statistical and algorithmic developments. The purpose of this course is to provide a mathematically rigorous introduction to these developments with emphasis on methods and their analysis.
Link: Direct
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #math #maths #mathematics #ml
@data_science_weekly
Broadly speaking, Machine Learning refers to the automated identification of patterns in data. As such it has been a fertile ground for new statistical and algorithmic developments. The purpose of this course is to provide a mathematically rigorous introduction to these developments with emphasis on methods and their analysis.
Link: Direct
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #math #maths #mathematics #ml
@data_science_weekly
👍3
Exceptional Resources for Data Science Interview Preparation. Part 3: Specialized Machine Learning
In the previous article, I shared materials for preparing for the stage on Classical Machine Learning.
In this article, we will look at materials that can be used to prepare for the section on specialized machine learning.
Table of contents
- Resources
- Deep Learning
- Natural Language Processing
- Computer Vision
- Graph Neural Networks
- Reinforcement Learning
- Recommender Systems
- Time Series
- Big Data
- Let’s sum it up
- What’s next?
NB:
I'm the author of the article.
It was initially published in Russian (on habr.com), then I published it on medium.com. So, for Russian speakers I recommend to read Russian version, for English speakers I recommend to read English version and both will benefit from starring the repository, which will be maintained and updated when new resources become available.
Links:
- Medium (eng)
- Habr (rus)
Navigational hashtags: #armknowledgesharing #armarticles
General hashtags: #interview #interviewpreparation #machinelearning #ml #deeplearning #dl #nlp #cv #rl #gnn #recsys
@data_science_weekly
In the previous article, I shared materials for preparing for the stage on Classical Machine Learning.
In this article, we will look at materials that can be used to prepare for the section on specialized machine learning.
Table of contents
- Resources
- Deep Learning
- Natural Language Processing
- Computer Vision
- Graph Neural Networks
- Reinforcement Learning
- Recommender Systems
- Time Series
- Big Data
- Let’s sum it up
- What’s next?
NB:
I'm the author of the article.
It was initially published in Russian (on habr.com), then I published it on medium.com. So, for Russian speakers I recommend to read Russian version, for English speakers I recommend to read English version and both will benefit from starring the repository, which will be maintained and updated when new resources become available.
Links:
- Medium (eng)
- Habr (rus)
Navigational hashtags: #armknowledgesharing #armarticles
General hashtags: #interview #interviewpreparation #machinelearning #ml #deeplearning #dl #nlp #cv #rl #gnn #recsys
@data_science_weekly
👍3
DevOps for Data Science by Alex K Gold
In this book, you’ll learn about DevOps conventions, tools, and practices that can be useful to you as a data scientist. You’ll also learn how to work better with the IT/Admin team at your organization, and even how to do a little server administration of your own if you’re pressed into service.
Link: Direct
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #devops #mlops #datascience
@data_science_weekly
In this book, you’ll learn about DevOps conventions, tools, and practices that can be useful to you as a data scientist. You’ll also learn how to work better with the IT/Admin team at your organization, and even how to do a little server administration of your own if you’re pressed into service.
Link: Direct
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #devops #mlops #datascience
@data_science_weekly
👍5