If you’re interested in pursuing a career in data science, you should assess which professions best match your interests and strengths. Someone with a strong mathematical background, for example, might be best suited to work as a statistician, whereas a business-focused data science specialist would be more suited to work as a business analyst.
We’ll look at the many roles within the greater data science business and provide you with the information you need to chart your course.
Here is top 10 Data Science Roles & What They Mean
1. Data Engineer
Raw data is formatted by data engineers so that it can be studied. They gather data that will be needed later, maintain it, and convert it so that business analysts and others on the team can use it. Data engineers create solutions that make massive amounts of data more accessible to a company.
- Data should be sourced and datasets should be created based on the organization’s objectives.
- Create algorithms that transform unprocessed data into useful information.
- Create the data pipeline architecture for data warehouses and databases.
- Ensure that data governance policies are followed.
A data engineer makes an average annual pay of $112,202.
Data engineers typically hold a bachelor’s degree in mathematics or computing. Programming languages such as Python and Scala, as well as database technologies such as SQL, are required. In this position, Apache Spark and Hadoop are also regularly utilized tools.
2. Data Scientist
To analyze and generate insight from massive datasets, data scientists use statistical and analytical skills. To accomplish this, they frequently use a variety of programming languages. Data scientists’ discoveries aid in the resolution of critical business issues
- Create questions depending on the company’s objectives.
- To find answers to those issues, conduct data investigations, and exploratory analysis.
- Data from a number of sources are combined and processed.
- To lead the data analysis process, choose models and algorithms.
A data scientist’s average annual pay is $74,700.
The majority of data scientists have a bachelor’s degree, usually in computer science, engineering, or a mathematical subject such as statistics. Python and R are two popular programming languages in this discipline. Data scientists are occasionally required to show data, which they do with the help of a data visualization tool like Tableau.
3. Data Analyst
To answer specific business challenges, a data analyst analyses the available data using statistical methodologies. This field’s professionals frequently operate in an interdisciplinary setting, collaborating with both business and data teams. Data analysts differ from data scientists in that data scientists focus on developing tools and frameworks for data collection, whereas data analysts uncover data-based insights.
- Analyze data to find patterns and significance.
- Databases and data warehouses are created and maintained.
- Prepare reports that detail the findings of the data analysis process.
- Identify opportunities to improve data processes with the help of management, engineers, and other team members.
A data analyst’s average annual pay is $62,610.
Data analysts must determine which insights are available from a particular dataset. They create data analysis methods using computer languages like Python and R. Data analysts must also present the findings of their job to various company stakeholders.
4. Data Administrator
Processes are created by data administrators to store, retrieve, and maintain accessible data. They make ensure that data from a certain source is up to date and preserved safely. They also establish policies for database settings.
- The data pipeline of an organization should be monitored and maintained.
- Data that is corrupted or irrelevant should be filtered out.
- Data governance policies should be written and updated regularly.
- Collaborate with a variety of stakeholders to increase the efficiency of data storage and retrieval.
Data administrator jobs pay an average of $50,634 per year.
The data lifecycle of an organization must be understood by data administrators. They make use of database management systems like SQL and Oracle. Hadoop is a popular data management tool among administrators.
5. Data Architect
The databases of an organization are built and maintained by data architects. The design database architectures are based on a company’s needs and developed from start to finish. Data architects keep an eye on their databases and perform system migrations when needed.
- Develop and implement database solutions for a company.
- Investigate database implementation techniques to ensure compliance with both internal and external regulations.
- Prepare database architecture reports for members of the executive team.
- Manage the transition of data from legacy systems to new database technology.
A data architect’s average annual pay is $123,000.
Data architects should be well-versed in database systems and data mining techniques. Data architects are frequently required to have a bachelor’s degree in computer science or engineering by their employers. Good communication skills are also required to keep leadership teams informed about an organization’s growing data storage strategy.
6. Machine Learning Engineer
Artificial intelligence is used by machine learning engineers to automate data analysis operations. Predictive modeling, data mining, and pattern recognition are examples of this. It’s a role that mixes engineering, math, and artificial intelligence approaches.
- To automate data-related tasks, create machine learning systems.
- Optimize machine learning algorithms by analyzing statistical data.
- To make your workflow easier, use machine learning libraries and technologies.
- Determine which datasets will be used to train new machine learning models.
A machine learning engineer’s annual compensation averages $132,900.
Machine learning engineer positions demand a bachelor’s degree in computer science or engineering. Statistics and machine learning algorithms are required skills for professionals in this industry. Database architecture and database systems are also needed skills for machine learning engineers.
7. Machine Learning Scientist
Machine learning scientists are primarily involved in research. They investigate the techniques and models that a business intends to use in its data analysis process. Machine learning scientists evaluate the efficiency, applicability, and security of algorithms, while machine learning engineers apply them.
- Determine which algorithms should be used to tackle various data-related business problems.
- Examine various algorithms and note crucial qualities.
- Algorithms for data analysis should be tested and implemented.
- Present their findings to several different parties.
A machine learning scientist’s average income is $137,053.
PhDs with an emphasis on artificial intelligence and neural networks are common among machine learning scientists. The model machine learning algorithms with tools like OpenCV. The capacity to work on distributed systems and model deployment is required for this position.
8. Business Analyst
Data is used by business analysts to analyze changing business needs and assess how changing processes affect a company. They also work as middlemen between different teams, translating business goals into specific objectives.
- Using data, model business processes and assess the impact of various modifications.
- Changes should be communicated and requirements should be translated for diverse stakeholders.
- Examine data analysis proposals and make suggestions for improvements.
The average compensation for a business analyst is $79,000.
Strong analytical capabilities are required of business analysts. They employ Python and R to execute data wrangling and data manipulation analyses. Business analysts frequently utilize tools like Power BI and Tableau to create reports.
9. Database administrator
A database administrator (DBA) is in charge of a firm’s database, which is critical because the organization needs constant access to accurate data. DBAs make ensuring databases are up to date and set up backup procedures.
- Monitor database systems for any issues with performance or security.
- Establish permissions for various parties and prevent unwanted access.
- Create a database architecture and front-end to make it easier for other team members to access information.
- Ascertain that database functionality complies with the company’s data governance requirements.
A database administrator’s average annual compensation is $73,800.
Database managers must have a thorough understanding of database technologies such as SQL, PostgreSQL, and Oracle. A certification like the Microsoft Certified Database Administrator (MCDBA) can help you advance in your profession. DBAs must keep up with changes in their field and advocate new tools or methods.
Analytical approaches are used by statisticians to interpret numerical data. They assist other data professionals in the development of scale models and algorithms for computations and projections.
- To collect numerical data, communicate with multiple departments.
- Using statistical approaches, do computations and forecasts.
- Management will be informed of their data findings.
- Assist data scientists and other professionals in developing models that can be used to extract insights from numerical data.
A statistician’s average annual pay is $92,270.
A bachelor’s degree in statistics is commonly required of professional statisticians working in data science. Working in this profession necessitates good quantitative and analytical abilities. SPSS is a widely used statistical analysis language. R and Python are also used by statisticians in their work.
While some data science jobs will most certainly be automated in the next ten years, there will still be a demand for people who can comprehend a business need, create a data-oriented solution, and then put that solution into action.
From government security to dating applications, data scientists are needed in almost every business. Millions of businesses and government agencies rely on big data to flourish and better serve their customers. Data science jobs are in high demand, and this trend is unlikely to change in the near future, if at all.
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