Different Specializations of Data Science & Which One Should You Choose [2020]

Understanding the Career Opportunities in Data Science

Data Science and Artificial Intelligence (AI) have emerged as a popular career choice. Aspirants find this a booming area, as its implementation spreads in research, engineering, cybersecurity, analytics, and marketing. Professionals from several disciplines are diving deep into the specialized domains of data science to implement various analyses and make the systems learn on their own.

Professionals can create algorithms or use tools to organize and manage these humongous data. This domain has brought new career opportunities. The data scientist’s job role is one of the most prominent of them all. In this article, you will learn about the various data science specializations available to opt for better career growth.

Read: Career in Data Science

Data Science Specialization Areas You can Focus on

AI algorithms can power various systems with advanced analysis, predictions, and recommendations. Data is the enabler that makes these algorithms possible to provide better and better outcomes. So, there are various roles and data science specializations that aspirants can opt for to develop a more specific understanding of that sub-domain.

Companies hire such employees having specialized skills in any of the verticals of data science. Note that you have to be very specific while choosing your specialization (as per your interest or background experience).

1. Data Mining and Statistical Analysis

Both data mining and statistical analysis represent the approach of learning from data. In this specialized role, aspirants learn about discovering and identifying meaningful structures from data and mapping them all together to produce meaningful information.

It allows us to provide exploratory data analysis with predictive models for extracting patterns and trends in data. The professional will then look into the business issues and will interpret it into a data question. The statistical tools and algorithms fed with the enormous amount of data help to create predictive models to answer all the business-related queries.

Job roles: Statistician, Data analysts, Business Analyst

Read: Data Mining Project Ideas for Beginners

2. Business Intelligence & Strategy-Making

This is another booming specialization of data science as the majority of companies prefer to have some BI professionals. Business Intelligence is a domain where the analyst has to transform data into insights that can help propel a business with maximum potential. This specialized domain allows cultivating back-end data sources with high accuracy and simplicity.

It also provides tailored analytics solutions to business-critical problems and can identify as well as predict market competitions. This specialized domain of data science is also responsible for discovering critical areas of revenue loss and recognizing where the company needs to improve. It becomes possible by mining complex datasets using BI software and analyzing business trends of competitors.

Job roles: BI Engineer, Data Strategist, BI Analyst, BI Developer

3. Data Engineering and Data Warehousing

Data engineering is the approach of converting data into a meaningful format so professionals can use those data for intense analysis. Data engineers integrate the data from multiple heterogeneous sources and structure them with ad hoc queries for providing meaningful patterns for decision-making.

This data science specialization involves managing the source, arrangement, tone, storing, and retrieving the data for other data analysts and data science professionals. Read on how to become a data engineer.

Job roles: Data Analyst, Database professionals, Data Engineers

4. Data visualization

The culture of human civilization is mostly visual, which includes everything from art and advertising to entertainment and learning. It becomes very impactful and easy to understand when we visualize anything. Data visualization is the data science specialization domain that is responsible for representing data and information into a graphical representation.

It uses visualization tools like graphs, tables, charts, plots, infographics, and maps. These tools provide a far better way to comprehend trends, competitions, outliers, growth rates, data patterns, and situations with color combinations and graphical designs. Presenting the data in a visually appealing form is a specialized domain of business analysts and data scientists. In data visualization, professionals can easily tweak the data to modify the visuals, which make data visualization an essential field of data analysis. Read on data visuzalition projects.

Job roles: Data Visualization Engineer, Software Developer, Data Scientist

5. Database Management and Data Architecture

In this era of data science and big data – enterprises and organizations need to have centralized data architecture aligned with industry standards. After successfully cleaning or scrubbing, the data is deployed and maintained in databases for complex data transactions supporting applications, services, or groups of services.

Job roles: Database professionals, Database Administrator, Data Specialist

6. Operations-Related Data Analytics

This is another data science specialization that does not require high technical skills. So, if you consider yourself passionate about problem-solving, this might be the right job for you.

Operational-related data analytics directly uses tools and data provided by the other employees and members of the organization. The tools fed with data find possibilities of improvement within the various operations (like logistics, technology, human resources, financials) of the business.

Job roles: Planning Analyst, Decisions Analyst

7. Machine Learning and Cognitive Specialist

Machine learning and cognitive algorithm development are some of the top-rated specializations of data science. Through this, aspirants and professionals can develop algorithms and Artificial Intelligence (AI) based solutions.

We can consider this a larger and more complex domain of data mining and statistical analysis. Here professionals focus on feeding data input through the algorithms to train the model, building pipelines of data, A/B testing, convenient data sources, and benchmarking base-systems. The algorithms fed with datasets help prepare the machines to learn cognitive response and decision-making.

Enterprises and companies are incorporating more and more AI-based trained algorithms for better efficiency and predictability. Companies hire ML professionals to build the actual algorithms and models as well as use industry-standard tools and statistical techniques. A machine learning algorithm can multiply the turnover by smartly reducing the repetitive tasks earlier done by tedious human labor.

Job roles: ML Engineer, Data Scientist, Researchers, AI Specialist

8. Market Data Analytics

This role comes with different verticals of expertise on different levels for a company. Here the data source is external such as sales and marketing data, data from customers and their experiences, track performance in e-commerce and logistics, find new opportunities, and customers.

The marketing data analysts are responsible for measuring, analyzing, and managing marketing performance for effectiveness and optimize return on investment. Understanding the market traits and customer needs will reduce wasted web marketing dollars and highlight insights regarding customer preferences and trends.

Marketing data analytics uses tools and techniques like paid search marketing, marketing software products, search engine optimization, etc. and uses those data to leverage the analysis.

Job roles: Product Analyst, Market Analyst, Web Analyst

9. Cybersecurity Data Analysis

Cybersecurity data analyst is another data science specialization in combination with cybersecurity. Here the data analytics are done in content to cybersecurity. Cyber data analysis is dependent on data received from different security tools.

Companies hire cybersecurity data analysts to design and implement algorithms using large datasets to detect bugs and vulnerabilities, predict future attacks and threats, as well as produce useful intelligence for improving the security and privacy of data within that organization.

These analyses are responsible for protecting the organization’s resources and plans from external as well as internal threats and crimes.

Job roles: Cybersecurity analyst, Data Scientist, Data analyst

Also Read: Data Scientist Salary in India

Conclusion

Data science is a term that can easily escape any specific definition because of its versatile nature and a broad range of implementation areas. There are other interdisciplinary domains like cloud, healthcare, finance, design, etc. where Data Science is used to provide insights for the growth of the organization. 

If you are curious to learn about data science, check out IIIT-B & upGrad’s PG Diploma in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

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