The data scientist is the person who can turn a torrential flood of raw information into something understandable and actionable. The data scientist is supposed to know how to code, do analytics, research, and more. This new breed of technologists must be fluent in many disciplines.
In a blockchain network, each participant has a list of valid transactions (a ledger) grouped into blocks with hashes as links between them. If a block is invalid, its hashes do not match the hashes in the rest of the chain. A blockchain can be private, open to the public, or a permissioned blockchain where you must be invited to join.
But Blockchain is only one of the many technologies that data scientists should look into. The future data scientist will need to master Data Science, Machine Learning, Statistics, Mathematics, and computer science. Here are career options in data science and blockchain technology.
Data Analyst:
A data analyst’s role is to learn the business operations, extract the raw data you need, and prepare it into useful reports. The data analyst also learns how business processes work and how to automate them and make them more efficient.
Data scientists can have data analyst skills but should have good communication skills and understand different business processes.
The data analyst must have the technical skills to code in SQL, Python, R, etc. The data scientist can take on this role by working very closely with the data analyst.
Data Visualization:
As an emerging technology, Blockchain is known among only a few individuals. Data Visualization is getting more and more important as new technologies are introduced. A blockchain network requires nodes (computers) for a distributed ledger to work; it also needs users or businesses for a blockchain to work effectively.
Data Visualization supports data analysts in creating reports about the data collected. You can apply for this job if you know how to present and communicate data effectively by creating informative graphs and charts.
Applications Developer:
A blockchain-enabled application doesn’t have a traditional backend or frontend; rather, it has a distributed backend that is the blockchain network itself. Blockchain developers are responsible for building apps on top of blockchain technology.
For example, Ethereum is a blockchain-based development platform that allows you to create decentralized applications using its Solidity programming language. There are many other blockchain platforms to build on, such as Hyperledger and Multichain.
The blockchain developer should have a strong understanding of at least one of these platforms and enough experience developing apps for distributed systems.
Blockchain Developer:
A blockchain developer is responsible for writing the code for smart contracts, which are the rules governing network transactions. The developer should be able to code in a blockchain platform such as Ethereum or Hyperledger.
Blockchain technology is disruptive, and there will be many opportunities to write code for new and upcoming projects that harness the power of blockchains. The blockchain developer should have the technical capability and best practices to implement secure applications.
Decision Support System:
DSS, also known as operational research, is a decision-making system using quantitative analysis and mathematical modeling to solve business problems. It uses mathematical models for decision-making, problem-solving, and optimization.
A DSS system is used in the financial industry to create predictive models for projecting and measuring risk. The DSS allows you to use statistical analysis and optimization to make better business decisions and obtain insights from data.
A blockchain network can support the development of a DSS because it creates a distributed ledger that contains all transactions. A DSS system can use this data to perform predictive analytics and measure risk.
Data Engineers:
Data Engineers are experts in manipulating, analyzing, and storing data in many databases. This broad term can cover many specific roles, including DevOps, Data Scientist, and Data Analyst. Data engineers are strongly involved in a data pipeline from beginning to end.
Blockchain engineers are responsible for building and maintaining the infrastructure of a blockchain network. A blockchain is only as good as its user base; therefore, it should be built with security as one of your paramount concerns. To build a secure blockchain, the blockchain engineer should have a good knowledge of networking, cryptography, and distributed systems.
The need for data scientists will increase in the next few years as these new technologies are introduced. The best choice is to develop an understanding of each technology that can allow you to build these new applications at scale using different paradigms. The future data scientist must be proficient in many areas, such as machine learning, data mining, statistics, and programming.
Database Administrator:
Many types of databases include MySQL, PostgreSQL, MongoDB, and RethinkDB. The database administrator is responsible for maintaining the database’s integrity and ensuring everything is working correctly. The DBA should be able to install, update and manage different software packages and configure firewalls and operating systems to protect data.
The future data scientist will need to have good communication skills and understand the latest technologies for storing, processing, and accessing data that can provide insight into real-time events.
Data Scientist:
Data science, also known as big data, has taken over the world and is a trending topic to look for in the tech field. Data scientist is a broad term that can be applied in many fields. Many companies are starting to realize the importance of performance analytics and data mining to understand trends.
The future data scientist will need to understand statistics, machine learning, and other emerging technologies in this industry.
Machine Learning Engineer:
Machine learning is the next big thing in tech. It helps you to create adaptive systems and applications that can change their performance depending on the data they are given. Machine learning is used in various fields, such as healthcare, retail, fraud detection, and more.
Data Architect:
The blueprints for data management are created by a data architect responsible for ensuring that the databases can be integrated easily, centralized, and protected using the most effective security measures. In addition, they make certain that the data engineers have access to the most effective tools and infrastructure with which to work.
Conclusion:
The future data scientist will need to have a broad knowledge of the technology and be able to adapt and implement it in different situations. This is not limited to Blockchain but many other technologies in the development stages.
If you read this blog, you should notice that all of these roles require a certain level of skill and understanding to do their job effectively. All of these tech roles require a specific set of skills, and you will need to acquire them to succeed and be successful in the industry.
Most importantly, the future data scientist will need to evaluate the requirements of each project and look into how the right people can be hired for each role. Therefore, it is essential to understand the skills required for each role, how they can contribute to new projects, and how they fit into existing projects.
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