It is believed that that data is the new oil because it has changed the entire working pattern of industries and organisations. Data plays a key role in decision-making at all levels. Whether it is government organisations or private firms all seem to be building strategies and plans by analysing data. To be sure, this data has been accumulated for many years. Due to this, the field of Data Science has become one of the most sought-after fields for job aspirants. Please find a guide on this with experts’ advice.
Explore different specialisations in Data Science
Experts suggest that rather than opting for general courses, selecting specialised courses in the field is more helpful in getting jobs. As per media reports, companies and organisations mostly look for specialised candidates for specific roles. . So it is better to understand the needs of the market by researching and consulting with experts and exploring the different specialisation courses in the field. Here are some of the specialisation courses in the field of Data Science you can look for.
Machine Learning Engineer: Designing and implementing machine learning algorithms and models.
Skills required: Strong programming skills, expertise in machine learning frameworks and understanding of data structures and algorithms.
Data Engineer: Building and maintaining the infrastructure for data generation, transformation and storage.
Skills required: Proficiency in database management, ETL (Extract, Transform, Load) processes and knowledge of big data technologies.
Data Analyst: Analysing and interpreting complex data sets to provide actionable insights.
Skills required: Statistical analysis, data visualisation, and proficiency in tools like SQL, Excel and data visualisation tools.
Business Intelligence (BI) Analyst: Leveraging data to help businesses make informed decisions.
Skills required: Data visualisation, reporting tools, understanding of business processes and effective communication skills.
Data Scientist: Combining statistical analysis, machine learning and domain knowledge to solve complex problems.
Skills required: Programming, statistical modelling, machine learning and domain expertise.
Big Data Analyst: Dealing with large volumes of data using big data technologies.
Skills required: Hadoop, Spark, NoSQL databases and distributed computing.
Quantitative Analyst: Applying mathematical and statistical techniques to financial and risk management.
Skills required: Strong mathematical and statistical modelling skills, programming and financial domain knowledge.
Geospatial Data Scientist: Analysing and interpreting geospatial data for applications in areas like urban planning, environmental science and logistics.
Skills required: GIS (Geographic Information System), spatial analysis and domain-specific knowledge.
Healthcare Data Scientist: Applying data science techniques to healthcare data for improving patient outcomes, predicting diseases and optimising healthcare processes.
Skills required: Healthcare domain knowledge, data analysis and machine learning.
Social Media Analyst: Analysing social media data to understand trends, user behaviour and sentiment.
Skills required: Text mining, sentiment analysis and proficiency in social media analytics tools.
Short-term and long-term courses
If students plan to make a career in Data Science after grade 12th, then there are a lot of full-time Undergraduate (UG) courses available in the market including at IITs and IIMs. Students can opt for specialised courses after sufficient research and consulting with experts. However, students should choose a college or institute with better placement rates and affordable tuition fees. Aspiring students can visit the official websites of multiple universities and colleges to understand which institution is more suitable for them by considering their financial background and geographical location. To gain information about Data Science colleges in India and Delhi-NCR, students can visit this website collegevidya.com/blog/data-science-colleges-india/.
For students aspiring to enter the field of Data Science post-graduation, various full-time and short-term certificate courses are available. Full-time options include Post Graduation (PG) courses and one-year PG Diploma courses offered by different institutions. Additionally, online certification courses, provided by ed-tech companies and some colleges, come in short-term (15-30 hours) and long-term (two months to one year) formats. Students can explore these opportunities on the official websites of ed-tech companies and colleges. For instance, here is one of the websites students can visit http://www.coursera.org/browse/data-science.
What expert suggests
“Whether you’re starting college or graduating, a basic grasp of programming is crucial. For freshmen, exposure to programming concepts is essential, even if you’re not aiming to be a software developer. Strengthen analytical skills by revisiting +2 level maths topics like linear algebra, probability and calculus. Non-science majors can benefit from exploring how their field intersects with data-driven decisions, addressing ethical concerns in biassed data and understanding economic and regulatory implications. Graduating students should enhance their skills with tools like SQL for data access and visualisation tools such as Tableau or PowerBI. The ability to communicate findings through compelling visual stories is increasingly valuable in a world where AI/ML education is more accessible, offering a competitive edge in your career,” Lalit Sachan, director, Centre for Data Science, AI Research, UPES ON, suggested.