 
Web3 is the third generation of the internet based on emerging technologies like blockchains, tokens, DAOs, digital assets, decentralised finance that has the potential to give back control of digital assets back to the users with greater trust and transparency.
Typical web3 applications focus on DAOs, DeFi, Stablecoins, Privacy and digital infrastructure, the creator economy amongst others. The web3 ecosystem represents a promising green space for creators, developers, and various types of tech and non-tech professionals as well. In my talk (video and slides shared above) for Crater's Encrypt 2022 hackathon, I describe how AI can be leveraged to build commercially viable web3 applications for India. I cover a number of relevant AI/ML datasets, models, resources and applications for these domains, recognized by the Ministry of Electronics and Information Technology's National Strategy on Blockchain:
Related Blogs Published by Andela Introduction
Modern tech companies realize that data teams need to consist of professionals with varied expertise, including data analysts, data engineers, data scientists, applied scientists, and machine learning engineers. Data teams work closely with cross-functional stakeholders to build data-driven products that are powered by predictive analytics as well as machine learning. Data-driven organizations rely on robust data infrastructure and ETL processes for downstream machine learning use cases. This recent development is accompanied by the rise of data engineering as a specialized discipline. As more organizations undergo digital and AI transformation journeys, the demand for data engineers has increased concomitantly. Data engineers are required to build the data infrastructure and pipelines and facilitate easy access to processed data for data scientists to build machine learning models. In this article, we’ll dive into the differences between the profiles of a data engineer and a data scientist along several dimensions, including their roles and responsibilities, educational requirements, specializations, and career growth. Roles and responsibilities of data engineers and data scientists Data engineers primarily build the pipeline system for data scientists to consume with models for various use cases. Therefore, data engineers are often hired earlier to build the data platform before onboarding data scientists. In smaller companies and startups, it is not uncommon for data professionals to do both data engineering and data science. As a company grows and scales its data science efforts, specialized data engineering and data science professionals become necessary. Data engineer’s responsibilities
Data scientist’s responsibilities
Every day, data engineers usually write code, build data pipelines, and maintain various pieces of the data infrastructure as well as serve requests for cleaned and processed data from data scientists. Data scientists typically spend most of the day developing and training machine learning models, conducting multiple experiments to optimize the model performance, and meeting cross-functional stakeholders from engineering, product, and business teams to discuss results and develop new use cases. Education differences between data engineers and data scientists Data engineers typically have a bachelor’s degree in computer science or information technology. Their core expertise is focused on software engineering skills such as programming, algorithms, data structures, systems architecture, and building software tools. With the advent of cloud computing as the foundation for any tech organization, data engineers are also expected to be familiar with relevant cloud-based technologies (like AWS, Microsoft Azure, and Google Cloud Platform) focused on data warehousing, data visualization, and data analytics. Similarly, data scientists are also able to leverage cloud-based machine learning services and APIs for common use cases such as recommender systems, computer vision, and NLP, instead of starting from scratch. Certifications provided by these cloud companies are often mandated as compulsory training during the onboarding phase for new data scientist and data engineer candidates. As data engineering is focused on building data systems for data scientists, engineers require a better understanding of statistics or machine learning to help communicate and collaborate with the rest of the data team. Data scientists have a more diverse background with undergraduate-level training in computer science, statistics, mathematics, physics, psychology, and life sciences. Data scientists often have more advanced degrees, such as a master’s degree or a PhD, in any of the above disciplines. Though data scientists traditionally had more advanced degrees, particularly the first wave which emerged a decade ago, it is becoming increasingly common for entry-level data science jobs to not have such requirements. Additionally, data scientists work with multiple stakeholders from engineering, analytics, product, and business teams, and it is helpful for them to know a bit about these areas for a smoother and more efficient collaboration. Building a successful, collaborative data product with diverse cross-functional teams requires efficient communication and storytelling skills from data scientists. Specializations With the rising popularity of data science and data engineering jobs, a number of upskilling platforms, courses, and boot camps now offer specialized, practical, hands-on training. These specializations are industry oriented and often developed by leading tech companies such as Google, Microsoft, AWS, IBM, etc. There are also many certification courses that allow candidates to learn specific data skills and signal their motivation and skill set to prospective employers. The following are a selection of specializations or certifications that a successful data engineer may have:
The following are a selection of specializations or certifications that a successful data scientist may have:
However, prospective data engineers or scientists must carefully consider which course is best suited to them given the constraints of finances, time, and interests. It is not feasible nor necessary to undertake as many courses as possible, and it is more important to focus on the courses that can truly improve your understanding and improve your candidature as a data engineer or a data scientist. Career growth differences between data engineers and data scientists Career growth prospects for both data engineers and data scientists are promising. Data engineers can evolve into related roles such as data architect or solutions architect. They can become leaders who envision and lead teams working on data platforms and also transition into more traditional engineering leadership roles. With a better understanding of core data science skills such as statistics and machine learning, data engineers can also switch to data scientist roles. The demand for data scientists has remained consistently strong for over a decade now. There are numerous entry-level positions at companies of all sizes and business domains. Initially restricted to experts with deep domain expertise and doctoral training, data science has now become more democratic with the development of tools and technologies that simplify and automate the various nuts and bolts of the data science lifecycle. Data scientists can progress further to become recognized domain experts as individual contributors or build data science teams and organizations as data science leaders. With a better grasp of software engineering fundamentals such as data structures, algorithms, and optimized coding, data scientists can also switch laterally to become data engineers or machine learning engineers. Final thoughts With rapid advances in data science and the increasing appreciation for its value in business growth, companies are actively building their data science teams and capabilities. The first step involves building the foundational infrastructure for data, a job that is carried out by data engineers. They take care of building data warehouses and pipelines and provide data that is ready to be consumed by data scientists for building various machine learning models and applications. Related Blogs
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