From India Today Education Desk: We are in the middle of Industry 4.0. This is the era of smart factories with sensors, embedded software and robotics that generate real-time data. As a result, the demand for professionals with the right skill competencies in data science is skyrocketing. According to industry estimates, data his scientist job has increased by 14% of his, and by 2026 he is expected to create 11 million jobs, resulting in an average salary of The increase can be astonishing. According to an industry report from a job portal, the average annual base salary for a data scientist in the US is USD 141,804 (Rs 1,1089,966), while a data scientist in India earns an annual salary of Rs 8,24,562.
Specialized courses in data science provide individuals with the opportunity to acquire the skills and knowledge necessary to excel in this field. This article details the opportunities associated with such courses and the skills and knowledge you can expect to acquire.
Rohini R Rao, Associate Professor, Department of Data Science and Computer Applications, Manipal Institute of Technology, shared a list of key data science jobs today and the skills required for these jobs.
1. Data Scientist
Data scientists are professionals who use scientific thinking and sophisticated analytical techniques to support strategic decision-making. They are experts in computer science, statistics, analytical techniques, and artificial intelligence. Mathematicians, physicists, statisticians, and computer programmers are just a few of the traditional technical professions that form the role of data scientist.
For example, a data science team using chatbots to automate customer service needs skills in sales, customer relationship management, and natural language processing, as well as deploying models to production.
To deploy data science solutions, data scientists must be familiar with cloud technologies and ML Ops. They design data flow pipelines using heterogeneous technology stacks. The demand for data scientists in India has reached unprecedented levels and he expects that by 2026 India alone will create around 11 million jobs in the field of data science.
2. Data analyst
Data analysts are responsible for transforming raw data into knowledge and insights that can be applied to business choices. Storytelling involves creativity. Data analysts find stories to explain and visualize data to improve communication with decision makers. It also facilitates the collection, investigation, and transformation of relevant data for further analysis.
Data analysis requires technical skills, machine learning expertise, and knowledge of cloud platforms such as AWZ, Azure, and Google Cloud AI platform. You should have excellent interpersonal and technical skills such as Python or R programming, SQL, data analysis (MS Excel), dashboards (PowerBI or Tableau). According to industry statistics, data analytics as a job has grown sevenfold over the past decade.
3. Business Analyst/Business Intelligence Developer
A business analyst is a data analyst who specializes in business models and focuses on maximizing profits or minimizing costs. Business analysts can identify problems in almost any aspect of a company, including employee development, information technology procedures, and organizational structures. Business analytics are becoming an increasingly important part of doing business as companies strive to improve overall efficiency and reduce costs.
On the other hand, the task of creating, managing, and maintaining business interfaces falls within the purview of business intelligence developers. It includes dashboards, data visualizations, routine and ad-hoc reports, and data query tools to help users get the information they need.
4. Artificial Intelligence Engineer/Machine Learning Engineer
AI engineering builds on the principles of systems engineering, computer science, and human-centered design to create AI models that perform specific tasks. For example, supply chains are using autonomous mobile robots, artificial intelligence, and reinforcement learning to improve delivery schedules and timelines.
This role requires a good knowledge of AI technology, robotics and automation. Machine learning, on the other hand, is the branch of AI that enables machines to learn from data. Most machine learning scientists are in the research department of an organization, designing and developing ML-based or deep learning-based models for deployment in various organizational functions.
5. Enterprise Data Architect
Data architects and stewards provide data management services to enterprises at a strategic level while ensuring data quality, accessibility, and security. An enterprise data architect is someone who creates blueprints for data management, pipelines, and repositories at a strategic level.
Build and maintain your organization’s database by determining performance, database capacity, and technology tier needs. We also work with data engineers and administrators to enable strategic use of data while ensuring performance, privacy, and security. Exposure to diverse data technologies such as SQL, NoSQL databases, big data technologies, and data management is required.
6. Data Engineer
Traditionally, organizations hired database administrators or data warehouse administrators to routinely manage data resources typically stored in relational databases or warehouses. Data engineers are responsible for developing and maintaining scalable data pipelines and APIs to support data repositories, as well as managing dataset infrastructure, building and maintaining hardware and structures.
Aware of the huge demand for skilled data scientists, research institutes and universities offer data science courses to equip students with the necessary data science core, programming skills, and related computational mathematics and statistics. doing.
Because data science courses need to be taught with the right mix of academia and data science practitioners from different disciplines, universities offering data science courses work with technology experts to provide students with hands-on experience. data science problems and the techniques and tools used. at work.
A rapidly changing industry demands graduates with soft skills, learning abilities and adaptability, so students should be exposed to a variety of environments, pedagogies and multiple cultures to become global citizens.
