11 popular Data Science Careers

Data Science Course is a growing field, and it has great career opportunities for individuals who have relevant expertise, knowledge, and qualification. Here is a list of some popular and in-demand careers in the field of data science.

Data Scientist:

Data Scientists are the most highly trained and relevantly qualified experts in the field of data science. Their primary job includes the transformation of large amounts of data into insights. These insights are vital for organisations to take necessary actions. This means that a data scientist’s job is also vital for any business as he aids in the decision-making process. Data Scientists are not only hired in companies and various kinds of industries but also in government agencies. This is the most in-demand career in the field of data science. That means there are more minor challenges in the job hunt process. A proficient data scientist is fluent in the use of programming languages like Python, SQL, and R. The average salary of a data scientist is assumed to be $139840.

Data Analyst:

As the name implies, the job of a data analyst is to analyze and interpret valuable data. Just like a data scientist, a data analyst is also vital for the decision-making process in organisations. A data analyst may help an organisation find potential opportunities for lowering the cost and maximizing the revenues. They understand and interpret complex sets of information through different methods and present them in a more straightforward way to read and induct that information into their essential decisions. They also make strategies to enhance and optimize the quality of statistical results. A data analyst must have a Bachelor’s degree in the relevant field or related fields like big data management and data science. The software with which a data analyst much be proficient includes Python, SQL, Microsoft Excel, and Tableau. The average salary of a data analyst ranges between $100 to $120.

Data Manager:

Data Manager manages the flow of data, processes it, and coordinates people. He must have sufficient information regarding the business aspect of data as they help in the achievement of organisational goals and objectives. A data manager must have keen knowledge about data security, storage and operations, warehousing and business intelligence, modeling and architecture, integration, data governance, and quality. The primary job of a data manager is the management of reference data, document data, content, master data, and metadata. He may deal with the data of the entire organisation, a department, or a specific domain. The job is to make use of data efficiently for the people who need it. Cheap dissertation help UK has Data Managers to maintain the data and to coordinate with people.

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Data Engineer:

Data engineers perform a more technical job as they deal with raw data. They provide the processed data to the data scientists for processing further. This job requires high proficiency in programming languages such as Python, Apache Spark, Hadoop, SQL, NoSQL, Java, C++, and R. A data engineer processes data containing any human, instrument, or machine error, and data that is not adequately validated. This requires high expertise as this job involves specific codes for certain systems. As the data is also not formatted, a data engineer has to do a challenging job. To handle this job efficiently, a data engineer should be not only relevantly qualified but also creative and intelligent enough to unique methods for the processing of raw data. They create data tools and data architecture. The average salary of a data engineer lies between $120 to $140.

Data Architect:

Data architects deal with the data architecture of an enterprise. They not only design but also implement and manage the data architecture of an organisation. It is one of the most critical jobs in data science. A data architect must have a Master’s degree in data science for a handsome job. He must also have sufficient experience after his bachelor’s degree. This will make him more worthy of a competitive job. Industries like finance, education, insurance, and business hire data architects for vital jobs. They also have great opportunities in the technology manufacturing industry and software companies as they deal with vast amounts of data.

Business Analyst:

A business analyst is simply responsible for the examination and analysis of business processes. He provides essential technical information to the business organisation. The most demanding sector for this job is Information Technology. They also work in other sectors as well. They have to opportunities and threats of a business. They must be proficient with the advanced use of spreadsheets and produce detailed documents containing essential business information after identifying problems; they also have to craft solutions for them. They present the data effectively to an organisation dealing in data related to budget forecast, pricing, and planning. To get an excellent job in this field, an individual must be relevantly qualified, having at least a Bachelor’s degree.

Marketing Analyst:

This is a vital job post for any enterprise. Nowadays, when there is more emphasis on marketing, a marketing analyst is one of the most in-demand jobs. They must have strong statistical and software skills. With these skills, their more focus is on the business side of data than data scientists, whose primary focus is data processing. A marketing analyst helps an organisation achieve its goals in an efficient manner. Their job includes:

  • Analyzing data.
  • Creating a marketing plan to provide marketing insights.
  • Offering solutions to enhance the marketing campaign of a product or service.
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They are also responsible for the creation of metrics and strategies for improvement and testing of performance. A marketing analyst must be relevantly qualified and have the necessary skills required for the job.

Clinical Data Manager:

A clinical data manager requires skills and expertise in the fields of Information Technology and health care. A clinical data manager is responsible for the collection and dissemination of every part of clinical data. A clinical data manager plays a crucial role in the decision-making process. He determines the methods and ways for effective data collection. Other parts of the job include technical duties and project management. If you wish to develop a career as a clinical data manager, you must be qualified in the fields of scientific research, healthcare, and technology.

Freelance Data Scientist:

As the trend of freelancing is growing over time, the freelance data scientist is an innovative and popular career choice that has many opportunities for the experts in this field. It does require not only expertise in the field of data science but also adequate practical business knowledge. As a freelance data scientist, a person performs various administrative tasks. As it is a popular career choice, you must be proficient in getting clients. You must have great expertise in this field to prove yourself better than others so that people choose you over others. A good strategy is to put your focus on a specific niche and specialize in it. It will help you get better work opportunities. 

Data Modeller:

A data modeller provides the foundation for the data scientist to work. Data Modellers are responsible for building blueprints for databases of organisations. Data scientists then use these databases as the storage house of essential data. They help a business get helpful information from raw data and use it efficiently in the decision-making process. They incorporate data from various systems and departments and present them in an easily accessible form for the decision-makers. The average salary of a data modeller is $108000.

Machine Learning Engineer:

Machine learning is a growing field nowadays, and machine learning engineers are in great demand. An individual must have data science expertise as well as software engineering expertise to become a machine learning engineer. He is responsible for creating working software. The skills needed to be a machine learning engineer include data evaluation and modeling, statistics and probability, application of machine learning algorithms, and computer science and programming.