One of the hottest jobs in data science
I often get asked in interviews, what kind of data scientist are you? Data is a relatively new industry and career path. It is a diverse space with lots of paths, and sometimes it is not clear which path to focus on. So I am writing today about the 10 different types of data scientists.
What are some areas you can focus on?
Data Engineering and Data Warehousing
Data Engineering refers to transforming data into a useful format for analysis. This often involves managing the source, structure, quality, storage, and accessibility of the data to be queried and analyzed by other analysts.
Related jobs: Data Engineer, Database Developer, Data Analyst
2. Data Mining and Statistical Analysis
Data Mining refers to applying statistics in the form of exploratory data analysis and predictive models to reveal patterns and trends in data from existing data sources. This person will be able to look at a business problem and translate it to a data question, create predictive models to answer the question and tell about the findings.
Related jobs: Data Scientist, Business Analyst, Statistician
3. Cloud and Distributed Computing
Cloud and System Architecture refers to designing and implementing enterprise infrastructure and platforms required for cloud and distributed computing. The role also analyzes system requirements and ensures that systems will be securely integrated with current applications and business uses.
Related jobs: Cloud Architect, Cloud Engineer , Platform Engineer
4. Database Management and Architecture
This role is responsible for designing, deploying, and maintaining databases to support high volume, complex data transactions for specific services or groups of services.
related jobs: Database Analyst, Database Administrator, Data Specialist
5. Business Intelligence and Strategy
Some of the key responsibilities in BI include improving back-end data sources for increased accuracy and simplicity, building tailored analytics solutions, managing dashboards, reporting to stakeholders, identifying opportunities and recognizing best practices in reporting and analysis: data integrity, test design, analysis, validation, and documentation.
Related jobs: BI Engineer, BI Developer, BI Analyst, Data Strategist
6. ML / Cognitive Computing Development
This is what most people associate with data science: “making robots”. This is a larger, more complex version of data mining and statistical analysis. These people focus more on getting all the input you need to feed the model; building data pipelines, convenient data sources, A/B testing and benchmarking infrastructure. When/if this is done, you might focus on building the actual algorithms/models, but this part more often than not involves well-known, industry-standard tools and statistical techniques. This focus area has become a buzzword in many organizations, so I encourage looking into sub-fields to identify what you truly like.
Related jobs: ML Engineer, AI Specialist, Cognitive Developer, Researcher
7. Data Visualization and Presentation
Being able to present data visually appealing has become part of almost every business analyst and data scientist role. When this focus area becomes an actual role in a company, its main responsibility includes creating BI solutions for teams and customers based on specific business requirements and use cases. In other instances, it can be more graphic design-oriented.
Related jobs: Data Viz Engineer, Data Viz Developer, Software Developer
8. Operations-Related Data Analytics
If you don’t consider yourself very technical yet have a passion for problem solving and processes, these might be the right path for you. These types of roles focus on leveraging the tools and data provided by the other data science team members to find opportunities for improvement within the operations of the business. These can either be focused on logistics, technology, financials, human resources, etc.
Related jobs: Planning Analyst, Decisions Analyst, Communications Analyst, etc
9. Market-Related Data Analytics
This role has different levels of technical expertise depending on the level of analysis and company. These people tend to focus on more external data related to customers, sales and marketing, yet their purpose is similar to those in operations: track performance and finds opportunities.
Related jobs: Web Analyst, Product Analyst, Market Analyst, Sales Analyst
10. Sector-Specific Data Analytics (Healthcare, Finance, Insurance, etc.)
Lastly, if you studied Healthcare, Finance or something that requires domain-knowledge expertise to analyze, you might opt to look into simple analyst positions within organizations in these industries. Again, the technical expertise of these roles will depend on the expectations of the company hiring and the tools they use.
Related jobs: Data Analyst, Business Analyst, Data Scientist — specialized
In conclusion, data scientists remain the most popular job globally, and you need to be able to market yourself and target the right skillsets for your specialization and focus.
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