10 DATA SCIENCE SPECIALIZATIONS

INTRODUCTION

In this fast-paced world, it becomes difficult to keep a track of miscellaneous things going on around us. Every day you wake up and are encountered terminologies that didn’t exist before. As we all know, data science is a rapidly growing and massive area of expertise today. Hence, it is absolutely normal that we come across a growing number of specializations within the field. In order to establish a successful career, the primary step should be to understand the various specializations within the field. This will help you in understanding in which area you want to work eventually. 

In today’s article, we are going to describe the various specializations within data science along with their key roles. By the end of it, I am sure you will have a better understanding of what you want to opt for as your career choice.

1. DATA MINING

Data mining also known as knowledge discovery in data (KDD) is the process of finding and extracting anomalies and discovering patterns in large data sets to predict outcomes. In simple terms, the main aim of data mining is to extract information with intelligent methods and transform the information into a comprehensible structure. 

After establishing these patterns, one can use them in machine learning and various other data science avenues. Some key elements of data mining are- pattern tracking, association, classification, regression, and prediction.

KEY ROLES– Cartographer, Data Scientist/Analyst, Statistician, Content Data Expert

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2. DATA VISUALISATION

Data visualization is the domain that deals with the graphic representation of data through visual elements like- charts, graphs, maps, and other visualization tools. It provides a convenient way of understanding trends, outliers, and patterns in data. These visuals help in understanding complex data relationships and data insights in a simpler way. Some common types of data visualization are scattered plots, line graphs, bar graphs, pie charts, choropleth maps, etc.

KEY ROLES– Analytics Manager, Marketing Specialist, Data Specialist, Business Intelligence Analyst

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3. DATA PROCESSING

Data processing is when you collect data and transform data into useful information. It is the change in the information in any manner detectable by an observer. It leads to increased profits, more productivity, and more accurate and reliable results. The 4 stages of data processing include- input, processing, storage, and output. We can also call it manipulation of data by computers inclusive of output formatting or transformation. 

KEY ROLES– Data Processing Managers, Data Scientists, Data Processing Technician

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4. DATA CONSULTANCY

A data consultant is responsible for analyzing and documenting a company’s data management processes. It also involves educating companies or clients about various aspects of data technology. They provide a wide range of methods that optimizes business intelligence by leveraging existing data. The work is spread over an array of fields like computing, businesses, retail, and other industrial areas.

KEY ROLES– Data Consultant, Predictive Analysis Modeler, Data Researcher

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5. MARKET DATA ANALYTICS

Market data analysis is the quantitative and qualitative assessment of a market. It looks into the depths of consumer segments, buying patterns, competition, and the economic environment. These insights can help in making informed decisions and influential marketing strategies. Through it, we can identify the strengths, weaknesses, opportunities, and potential threats of a company. 

KEY ROLES– Product Analyst, Market Research Analyst, Web Analyst

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6. CYBERSECURITY DATA ANALYSIS

This branch combines data science and cybersecurity. A cyber-security data analyst applies machine learning methods to quantify cyber risks. They focus on generating security response alerts and optimizing cybersecurity operations. Cybersecurity uses data science to protect software and devices from cyberattacks. 

It is an extremely important thing as large amounts of data can be difficult to manage without help from data scientists. These experts produce intelligence to improve the security and privacy of data of an organization from external and internal threats. 

KEY ROLES– Cybersecurity Analyst, Data Scientist, Data Analyst

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7. DATA ARCHITECTURE

Data architecture refers to how an organization collects, store, transform, distribute and use data. A data architect builds, optimizes, and maintains database models. It is the founding stone of any data strategy. These days it is important for organizations to have centralized data architecture in accordance with industry standards.

KEY ROLES– Data Specialist, Database Professionals, Database Administrator

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8. DATA ENGINEERING

It is the practice of designing and building data systems for collecting, storing, and analyzing data at scale. Data engineers help an organization in storing, extracting, transforming, loading, aggregating, and validating data. Their primary job is to design, manage and optimize the flow of data with databases throughout the organization.

KEY ROLES– Data Engineer

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9. BUSINESS INTELLIGENCE AND DATA ANALYTICS

Business intelligence is used to enhance business decision-making activities. It describes the strategies, technologies, and tools companies further use to obtain important business information. Data analytics then modifies this raw data into a meaningful format. It is essential for monitoring business trends and detecting significant events and increasing operational efficiency. Data analytics ad data analytics are their subsets that provide data management solutions to understand contemporary data and gain relevant insights. 

KEY ROLES– BI Engineer, BI Analyst, BI Developer

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10. COGNITIVE MACHINE LEARNING

Cognitive machine learning includes programmed self-learning systems that use data mining, pattern recognition, and natural language processing to reflect human actions. Cognitive computing systems work with humans and provide them with advice in making informed decisions. It intends to use the best algorithm and come up with an accurate action/result. 

Through this, the professionals can develop algorithms and AI-based solutions. Nowadays, companies are incorporating more AI-based trained algorithms to gain better predictability. It helps in the reduction of monotonous tasks earlier performed by human labor.

KEY ROLES– ML Engineer, AI Specialist, Researchers

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CONCLUSION

I hope you find this blog informative. This blogpost is written by Riya Pokhriyal. I have completed my graduation in B.A (Hons) Sociology from Hindu College, Delhi University. Currently pursuing B. Ed from GGSIPU. An aspiring teacher with a passion for both teaching and learning and transforming young minds. My interests include-content writing and blogging. Also, an experienced Blogger skilled in SEO, social media outreach, creative writing, and editing. You can connect with me on LinkedIn using this link. For more informative content on Data Science & Jobs for freshers subscribe to our blog. You can read more of our works over here. Thanks for reading & Happy Learning !! 🙂

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