The 10 Areas of Expertise in Data Science, and Why You Should Choose One

Understanding Your Career Options

Melody Ucros
Jan 8, 2018 · 5 min read

The best way to make yourself extremely valuable in a team is to understand everything, but being a master of something.

I’ve realized during my Masters that it is impossible to be a domain expert in everything there is to know about working with Data.

The truth is that once you are hired as a data “something”, the company will place you in a team and other people will complement the skills that you have or lack in order to get the job done.

The goal is to become a trusted resource about a certain topic.

The focus area you choose will probably be related to the background, experience or interests that you have in other fields of study.

Keep in mind that a related job position might require you to perform some of the tasks related to a different focus area.


What are some areas you can focus on?

  1. 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 so that it can 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 the application of 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 story 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 in support of 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 bench marking 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. These focus area has become a buzzword in many organizations though, so I encourage looking into sub-fields within it in order to truly identify what you like.

related jobs: ML Engineer, AI Specialist, Cognitive Developer, Researcher

7. Data Visualization and Presentation

Being able to present data in a visually appealing way has become part of almost every business analyst and data scientist role. When these focus area becomes an actual role in a company, their 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 to be very technical yet have a passion for problem solving and processes, these might be the right path for you. These type of roles focus on leveraging the tools and data provided by the other members of the data science team in order to find opportunities of 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

These 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 find 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


If you thought this was useful, please SHARE it with your friends and CLAP. And please COMMENT below if you want to improve or disagree upon a certain focus area description or the job titles related to them.


Reach out to me on Linkedin or email at melodyannucros@gmail.com✉️ .

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Author: Melody Ann Ucros

I’m a Masters in Big Data & Business Analytics Candidate @ IEBusinessSchool, and an Entrepreneurship Evangelist wherever I go. Follow Me

Melody Ucros

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Entrepreneurial Techie who loves helping startups, playing with data, leading projects & exchanging knowledge with impact-makers around the world.