We are on the brink of a massive technological revolution as we slowly move from the water and steam-powered first industrial revolution to the artificial intelligence-powered fourth industrial revolution. The theories backing data science and machine learning have existed for hundreds of years. There used to be times when proto-computers would take almost forever to compute a billion calculations. No one dared think of artificial intelligence or related technology. All thanks to machine learning and data science, we can now calculate data at a capacity of 5 billion calculations per second.

The growing role of data science (DS) and machine learning (ML) in high-energy physics (HEP) is well established and pertinent given the complex detectors, large data, sets and sophisticated analyses at the heart of HEP research. Moreover, exploiting symmetries inherent in physics data have inspired physics-informed ML as a vibrant sub-field of computer science research. HEP researchers benefit greatly from materials widely available materials for use in education, training and workforce development. They are also contributing to these materials and providing software to DS/ML-related fields. Increasingly, physics departments are offering courses at the intersection of DS, ML and physics, often using curricula developed by HEP researchers and involving open software and data used in HEP. In this white paper, we explore synergies between HEP research and DS/ML education, discuss opportunities and challenges at this intersection, and propose community activities that will be mutually beneficial.

Data science and machine learning deal with complex mathematical concepts and programming tools to build the right kind of algorithms for business decisions. Collaborations and discussions while undertaking and building these projects can be of great help for data scientists and machine learning practitioners. Just like GitHub exists for collaborating on software development in an open-source capacity, a 2019-launched platform named DagsHub is becoming increasingly popular for data scientists and machine learning engineers to come together at a common ground to build their work. "It is like GitHub for data science and machine learning," is how DagsHub describes itself. It is a web platform for data version control and collaboration for data scientists and machine learning engineers and is based on open-source tools, optimised for data science and oriented towards the open-source community.

I had endless curiosity and excitement -- doe-eyed and optimistic. But ringing in the back of my mind was the insecurity that I didn't come from any of the traditional backgrounds, for example, computer science, statistics, or business. Instead, I graduated with a bachelor's in cognitive science. However, as time passed and my experience grew, an idea began to slowly unravel -- perhaps, my background provided a much more solid foundation than I had initially anticipated. "Cognitive Science is an interdisciplinary field of neuroscience, artificial intelligence, computer science, philosophy, psychology, linguistics, and anthropology."

Data science and machine learning can be practiced with varying degrees of efficiency and productivity. Let's imagine somebody is teaching a "Productive Data Science" course or writing a book about it -- using Python as the language framework. What should the typical expectations be from such a course or book? The course/book should be intended for those who wish to leapfrog beyond the standard way of performing data science and machine learning tasks and utilize the full spectrum of the Python data science ecosystem for a much higher level of productivity. Readers should be taught how to look out for inefficiencies and bottlenecks in the standard process and how to think beyond the box.

Data science and machine learning professionals have driven adoption of the Python programming language, but data science and machine learning are still lacking key tools in business and has room to grow before becoming essential for decision-making, according to Anaconda, the maker of a data science distribution of Python. Python could soon be the most popular programming language, battling it out for top spot with JavaScript, Java and C, depending on which language ranking you look at. But while Python adoption is booming, the fields that are driving it -- data science and machine learning -- are still in their infancy. Most respondents (63%) said they used Python frequently or always while 71% of educators said they're teaching machine learning and data science with Python, which has become popular because of its ease of use and easy learning curve. An impressive 88% of students said they were being taught Python in preparation to enter the data science/machine learning field.

If you're learning Data Science and Machine Learning, you definitely need a laptop. This is because you need to write and run your own code to get hands-on experience. When you also consider portability, the laptop is the best option instead of a desktop. A traditional laptop may not be perfect for your data science and machine learning tasks. You need to consider laptop specifications carefully to choose the right laptop.

Data science and machine learning are scientific disciplines that are ruled by programming and mathematics. Nowadays, most corporations globally generate immense amounts of data that can be further analyzed and visualized by experts to understand trends and forecast predictions. For instance, we can only perform accurate data visualization if our data is clear and understandable. However, organizations' data is (frequently) too messy to tinker with -- therefore, finding structures and important patterns in data is a crucial task for data science. Statistics provides the methods and tools to find hidden structures and patterns in data so that specialists can make predictions from them -- making statistics the most fundamental step in the data science and machine learning scope.

Data science and machine learning are scientific disciplines that are ruled by programming and mathematics. Nowadays, most corporations globally generate immense amounts of data that can be further analyzed and visualized by experts to understand trends and forecast predictions. For instance, we can only perform accurate data visualization if our data is clear and understandable. However, organizations' data is (frequently) too messy to tinker with -- therefore, finding structures and important patterns in data is a crucial task for data science. Statistics provides the methods and tools to find hidden structures and patterns in data so that specialists can make predictions from them -- making statistics the most fundamental step in the data science and machine learning scope.

LinkedIn recently published a report naming the fastest growing jobs in the US based on the site's data. The social networking site compared data from 2012 and from 2017 to complete the report. The top two spots were machine learning jobs, which grew by 9.8X in the past five years, and data scientist, which grew 6.5X since 2012. In all the top ten positions, four relate to data science and three out of those four are in the top five spots. So why are data science positions, and specifically machine learning positions, growing so fast?