Understanding the Career Opportunities in Data Science
Data Science and Artificial Intelligence (AI) have emerged as popular career choices. 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 independently.
Professionals can create algorithms or use tools to organize and manage this 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.
The field of data science is expanding by leaps and bounds with each passing day. There is a huge pool of recruiters that seek data professionals and compensate with competitive salaries. The market is expanding rapidly and is expected to reach USD 25.94 billion by 2026 (Source).
To clear out the question may get asked, “Data scientist comes under which category?” So Data science broadly comes under the category of Computer Science, and so does Data Scientists. Although there are various domains in data science that are existing in the data science field where in the professionals can pursue data science specialization course in order to carve out a niche for themselves and procure a high salary as it a high paying field. The average salary of a data scientist is 10.5 LPA and the maximum salary is 25.3 LPA (Source) and these figures may go higher guided by more experience, skillset, etc.
Also, the field of data science is not condoned to one possibility rather there are different domains in data science wherein the professionals can pursue any domain guided by their interests. Some of the domains of data science are Data Mining, Cloud and Computing, Data visualization, Data Analytics, etc. A young student in the field of data science could definitely have this question in their mind that “for data science which stream is best?” They can decide the best field based on their interest and spend more time in learning the craft.
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.
Data mining allows the users to find hidden patterns. It is not confined to one industry rather it has applications in almost every such as business, marketing, strategy, baking, etc. On the other hand, statistics eases out working with data from the raw stage to the last. A lot of facts can be strategically inferred from the data that helps in producing better end results.
Data mining has a big scope and its market is growing rapidly. The market is expected to reach $1.31 billion by 2026 (Source). And data mining has many real-life examples such as prediction of loan payment, placing of products in a supermarket, customer retention, attrition, etc.
There is either supervised or unsupervised learning from data and there are six main activities of data mining, such as- classification, estimation, prediction, clustering, description and visualization, etc.
Job roles: Statistician, Data analysts, Business Analyst
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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 the business trends of competitors.
Business Intelligence is a very important specialization in today’s time. The organizations can utilize the business intelligence to have a better insight on how their business is performing or what their competitors are doing. Based on the result the businesses can create a strategy and make informed decisions guided by scientific data.
There are various tools present in the market to aid the professionals in order to carry out their tasks. Some of the tools are Microsoft Power BI, Qlik, Looker, SAP, etc.
Job roles: BI Engineer, Data Strategist, BI Analyst, BI Developer
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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.
Data Engineering serves a really great importance in today’s times. Data Engineers optimize the data. It allows the businesses to optimize the data in order to reach better usability from the data.
There are various advantages of data engineering it helps the organization with, such as agility as the business decision making is faster and more accurate as compared to the earlier times, also it helps in better revenue generation as the organizations can predict the future and make the adjustments accordingly, etc.
Also, there are various tools that data engineers utilize in order to perform their tasks better, such as Amazon Redshift, Big Query, Tableau, Looker, etc.
Job roles: Data Analyst, Database professionals, Data Engineers
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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
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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.
Database Management is a very important domain that allows the organizations to organize the information and data. This minimizes the scope of errors that gets infiltrated throughout the decision making process across the organization.
And the database management system allows the organizations to work with larger volume data as it gets stored in one place. There are various tools that can be used while facilitating the database management such as MySQL, Oracle RDBMS, Salesforce, DevOps, etc.
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.
The Operations-Related Data Analytics allows the businesses to streamline their tasks better and provide better operability over their tasks on a real-time basis.
The businesses are more relied on the data so the Operational Analytics streamlines the data directly from the data warehouse to the relevant tools or CRMs that the team sues such as Salesforce and more.
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.
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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
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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.
The businesses are data driven and have a large volume of data with them. Some of the data is sensitive and cannot be exposed to the larger audience which the organisations need to protect.
The cybersecurity data analysis can very well prevent the threats and guard the data. The analysis can predict the potential threats and provide better measures to protect from the attack.
Job roles: Cybersecurity analyst, Data Scientist, Data analyst
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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.
What are the roles and responsibilities of a cyber security analyst?
Cyber security analysis is a data science specialization that involves the use of data analysis in the field of cyber security. A cyber security analyst works on the data extracted from various security tools. Cybersecurity analysts are responsible for finding out the bugs and vulnerabilities on the company’s software and suggest the solution by designing and implementing optimised algorithms to fill those voids. They are crucial to the company as they are the ones that protect the company from all kinds of cybercrimes.
Which sectors use data science in real life?
Although data science is widely used in almost every field, the prominent ones include healthcare, manufacturing industry, automotive sector, telecommunication, marketing, cyber security, and financial sector. Data science has successfully helped make several wonderful discoveries that may not have been possible by human scanning. These achievements include identifying solar systems similar to ours, making breakthroughs in cancer treatment analysis, predicting natural disasters, reducing criminal cases, discovering new species of plants, and many other great achievements.
How has data science evolved as a field?
Data science has seen significant growth both in terms of solving business needs and career development. Data scientists have become one of the most essential contributors to any field. Instead of learning the generalized skill set, data scientists are focusing on specific skill sets required for specific fields such as cloud, ML, and deep learning. Even experienced professionals are constantly focusing on learning new skills and sharpening their existing skills. By targeting certain specific fields and skills, more jobs in those specific areas are being created.
What is the difference between data science and data analytics?
Data Science is much of a broader term is a macro topic and data analytics is a part of it. Both of these fields require separate skillsets such as data science requires the proefssionals to be having the knowledge of data modelling, predictive analytics programming, etc. Whereas the data analytics requires the knowledge of BI tools,SQL, etc. Data Analytics utilise the existing information to unravel new information or insights.
Are data scientists in demand?
The organisations are becoming data driven and so is the requirement of professionals who know how to manage the data effectively. Yes, data science is in a very huge demand, and it comes under one of the high paying field. The market for data science is growing rapidly and is expecte to reach USD 25.94 billion by 2026.
What industries need data scientists?
The field of data science is applicable in almost every field today. Some of the fields are mentioned below- E-Commerce Bnaking Finance Transport IT Education Marketing Sales
How do you venture into data science?
In order to get into data science the following steps can be taken forward- Invest time into learning programming languages Basic mathematics Machine learning concepts Practice Brush up skills
Can I get a job in data science as a fresher?
Yes definitely! There are various opportunities to get a job as a fresher. The freshers can utilize these jobs in order to brush up their skills, gain new skillset and network.