Data science has been around for a few decades, but it was only lately that companies realized they need to leverage the approach to use enormous data stacks for decision making. The employment prospects expand as the profession diversifies and rises in prominence. Here are the top five data science careers in terms of roles and responsibilities, skills, certifications, job prospects, and average pay.
Data science has been around since the 1990s, but its importance was acknowledged only when organizations found themselves unable to utilize massive amounts of data for decision-making. Data science has aided organizations in expanding beyond the traditional boundaries of data consolidation. It helps enterprises to have access to more and more information and to perceive new things in a different light.
Are you a certified data scientist with a relevant bachelor’s degree looking to move beyond your current entry-level job? Data science positions can encompass everything from business intelligence and machine learning to data architecture and big data management–all of which make it extremely confusing when charting a career path for yourself. Which position will match your talents and goals? To help you decide, here we compare five top data science careers in terms of job objectives and responsibilities, skills, certifications, job outlook, and average salary.
See More: What Is Data Science? Definition, Lifecycle, and Applications
Top Five Data Science Careers to Consider in 2022
Business intelligence (BI) analyst
This career path is for those possessing both business acumen and consulting skills and an excellent understanding of data. As a BI Analyst, your focus will be on analyzing your organization’s existing data, such as monthly sales, quarterly expenses, or customer churn. You’ll examine the data in terms of your organization’s key performance indicators (KPIs) and business performance and recommend where improvements need to be made. In addition to mining your own company’s data, you will be gathering data from various sources, including your competitors’ and industry data. Among the goals of your analysis will be to find ways your company can improve its market position, profit margins, and the efficiency of its systems, procedures and functions, as well as new ways to better its data collection and analysis methodologies.
- Database design and data architecture
- ETL (extract, transform and load)
- Data mining and analytics
- Data security and privacy,
- Advanced Excel
- SQL, programming languages such as Python, R and SAS
- Advanced Tableau, PowerBI, data interpretation and recommendation skills, Hadoop, cloud computing
- Data storage technology such as Google’s Big Query and Amazon’s Redshift
- Strong leadership, communication, presentation and team building skills
Certification: Certifications available to BI analysts include the Microsoft Certified: Data Analyst Associate and TDWI’s Certified Business Intelligence Professional certification. Certifications in specific computer languages like SAS are also available.
Job outlook: 11% growth rate through 2029 (US Bureau of Labor Statistics (BLS)
Average annual salary: $66,000 – $79,000 (Glassdoor and Payscale)
Individuals pursuing this career path are more interested in using data to help companies make better decisions and improve their business practices than in creating the algorithms used for data discovery and acquisition. As a data analyst, you will use existing tools, systems and data sets to generate actionable insights from your organization’s data. You will identify, extract, and analyze key business performance, risk and compliance data and present your findings to the organization’s decision-makers. You will be called upon to write reports and present your findings. You will need to be able to recognize and understand the trends and insights that can be found in big data sets. Many data analysts move on to become data engineers, data architects, or data scientists after they have acquired over ten years of experience.
- Mathematics, statistics, analysis, data modeling, and predictive modeling
- Hadoop, SQL, advanced Excel, advanced Tableau or PowerBI
- Database management, extracting and analyzing data from diverse sources
- Business acumen and excellent presentation, communication and collaboration skills.
Certification: Online certification courses are available for data analytics, including certifications in business analytics, predictive analytics, and data visualization, such as those provided by 365 Data Science and Analytics Vidhya.
Job outlook: 22% growth rate through 2030 (BLS)
Average annual salary: $57,000 – 68,000 (Glassdoor and PayScale)
This career path is for those more interested in building and optimizing data systems than mining them for actionable insights. Unlike the other data science careers, data engineering focuses on the systems and hardware that facilitate an organization’s data activities rather than data analysis. As a data engineer, you will use your analytical and decision-making skills to develop your organization’s data infrastructure and build data pipelines that ensure the relevant departments and decision-makers can access the data they need. Your focus will be on collecting, managing, analyzing and visualizing large datasets, and ensuring that all big data applications are accessible and working properly. The data engineer career path could also be a stepping stone toward a career in machine learning engineering.
- Data modeling and mining
- Database management, data warehousing, large-scale applications design, statistical modeling and regression analysis.
- operating systems, machine learning including Aforge.NET and Scikit-learn
- Programming languages including Python, R, C/C++, SQL, SAS, SPSS, Java, Perl and Ruby.
- Hadoop-based analytics such as HBase, Hive, Pig and MapReduce, SQL, Cassandra, Tableau, and data visualization,
- Business acumen and strong presentation, collaboration and communication skills
Certification: Online certification courses are available, such as the Certified Data Management Professional (CDMP) certification offered by Data Management Association (DAMA) International, Google’s Certified Professional in data engineering, IBM Certified Engineer in Big Data, the CCP Data Engineer from Cloudera, and the Microsoft Certified Solutions Expert certification in data management and analytics.
Job outlook: Between 22% and 33% through 2030 (BLS)
Average annual salary: $103,000 – $117,000 (Glassdoor and PayScale)
This career path is for analytical and creative individuals whose main interest lies in innovating and designing new solutions for storing and managing complex database systems. As a data architect, you will work with software designers and data engineers to develop databases from the ground up, including design patterns, data modeling, and database integration. You will also be charged with integrating, centralizing, protecting and maintaining all data sources within your company. You’re responsible for how your organization’s data is collected, stored and accessed.
- Applied math and statistics, operating system and application server software
- Data migration
- Database management systems skills including MS SQL server and NoSQL
- Cloud computing
- Programming languages including Python, R, Perl, XML, and Java, SQL, ETL,
- Hadoop-based analytics including MapReduce, Hive, Spark and Pig
- Data mining and data modeling tools such as ERWin, Enterprise Architect, and Visio
- Machine learning and systems development
- Database architecture, data warehousing, data governance, data visualization (Tableau), data backup/archival software, data retention concepts and practices, data flow and integration automation.
- Strong communication and leadership skills
Certification: Certified Data Management Professional (CDMP) from the Institute for Certified Computing Professionals.
Job outlook: 9% through 2031. (BLS)
Average annual salary: $104,000 – $125,000 (Glassdoor and Payscale)
This career path is for those excited about the patterns and trends they can learn from building predictive machine learning models. As a data scientist, you need an analytical mindset and a passion for seeing your work improve business outcomes. Individuals pursuing a data scientist career must be able to take on the roles of mathematician, computer scientist, and business strategist and convey their analyzes to technical and non-technical stakeholders. You will build and deploy predictive models that go beyond discovering what has happened to what will happen using machine learning or deep learning techniques. This role requires you to be an excellent problem-solver and be willing to keep your skills current. Many start their data scientist careers as data architects or data analysts.
- Research design, statistics, predictive analytics, data modeling, data mining, and mathematics
- Programming languages including Python, SAS, R, and SQL
- Hadoop-based analytics such as HBase, Hive, Pig and MapReduce
- Machine learning, Pandas, scikit-learn, Matlab, natural language processing (NLP), AI development frameworks, and deep learning (TensorFlow).
- Database management, data cleaning, database architecture, data visualization
- Business acumen and strong presentation, leadership, collaboration, communication and project management skills.
Certification: Online certification courses are available for data science practitioners, such as those provided by 365 Data Science and Analytics Vidhya.
Job outlook: 27.9% growth rate through 2026 (BLS)
Average annual salary: $100,000-118,000 (Glassdoor and Payscale)
See More: Data Scientist: Job Description, Key Skills, and Salary in 2022
How to decide which career is right for you?
When choosing which career path, you should follow, take into consideration the advice of Yvon Chouinard, billionaire and founder of outdoor apparel brand Patagonia:
“I regard purpose as being at the intersection of what the world needs, what you’re good at, what you’re passionate about, and how you can make money.”
The world needs data scientists and is willing to compensate them well for their skills. Thus, choosing the direction of your career comes down to how your skill set matches that required by your chosen career and, more importantly, how passionate you are about it. You can always add to your skill set, but you can never recover the time spent in a job you are not passionate about.
Which data science career path would you like to pursue? Comment below or let us know on LinkedIn, Twitteror Facebook. We would love to hear from you!
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