Today’s precise and smart technological developments and solutions available in the market in almost every sector are upgrading rapidly; the data is the heart of these upgradations. Various sensors collect data and transfer it to the system. This data goes through multiple processes such as understanding, analysing, concluding, and extracting meaningful information.
These procedures use a scientific approach used, and thus it is known as ‘Data Science’. It is a trending interdisciplinary field of the 21st century. Various scientific methods, algorithms, and unstructured systems extract insights and knowledge from structured and unstructured data. It is closely related to data mining, big data, and machine learning.
The global data science platform’s market size is rising exponentially due to its applications in various fields. The demands for intelligent systems are increasing in multiples with the adoption of advanced technology. The value of data science’s market size was 3.93 billion USD (United States Dollar) in 2019.
It is estimated to expand at a CAGR (Compound Annual Growth Rate) of 26.9% between 2020 and 2027. Rising investments in data science research, development, and technological advances are causing such rapid market growth.
The data science field is exciting and grabbing the attention of professionals and freshers. IT professionals are leaning towards making a career in the evolving data science domain.
Evolution of Data Science
Data analysis started in the 1960s that has resemblance with data science. The term data science was used first in 1985 in the lecture given at the Chinese Academy of Sciences in Beijing by C. F. Jeff Wu as an alternative word for the statistics. In 1992, three aspects did the successful introduction of a new, interdisciplinary and emerging field of data science:
- Data collection
- Data design
- Data analysis
These theoretical concepts and arguments turned into modern data science in 2001 to expand statistics in technical areas. Though it has been 20 years now, there is no consensus on the definition of data science. It is still a buzzword for many professionals as well as freshers.
Data Science Course Syllabus
In-depth research is improving our understanding and knowledge in Data Science, and thus the study material keeps updating every day for Data Science. There are numerous courses, workshops, training programs, and degrees available for data science held by institutions, universities, and organisations.
With advancements, the data science course syllabus is updated. Some freshers want to start their careers in data science and look for introductory courses that include concepts, hands-on practice, and projects that provide them with the skillset to start working in data science companies.
Most organisations/institutes offer a Data science course syllabus. If we see upGrad’s course syllabus for data science, it includes:
- The concepts of data analysis in excel, Python, and SQL.
- Introductory sessions on Python’s application for Data Science.
- Assignments to strengthen beginners’ ideas. Python is a widely used programming tool for data science and is thus part of all organisations’ data science course syllabus.
- Concepts and hands-on practice on modern technologies such as machine learning, deep learning, natural language processing, computer vision, business intelligence, data analytics, and data engineering.
- Real-time projects for candidates opt to be data scientists, analysts, and developers. These projects help candidates clearly understand technologies and their relevance with data science and finally, how to use them in real-time business development and growth.
upGrad has created one of the most suitable data science course syllabi for professionals. This course is delivered online with learner’s pace and different formats such as certification or Post-Graduate Diploma.
The course contains preparatory sessions covering data analysis and introduction to the programming language used in data science. Various toolkits like Python, MySQL, and Excel are focused on data toolkits that help candidates visualise, programming, and solve assignments given as a part of the data science course.
As machine learning, deep learning, natural language processing, business intelligence, business analysis, and data engineering are fundamental, data science is evolving rapidly. Most of the available data science courses, such as upGrad’s PG Diploma in Data Science course, have dedicated individual topics for these technologies and their use for data science using different tools.
IT (Information Technology) professionals have experience solving various problems logically and developing the best suitable algorithms. To switch their career into data science, they need to upgrade their analytic skills and apply programming language specifically for data science. There are courses developed particularly for professionals looking to upgrade themselves and their abilities to work on data science projects.
Professionals who are willing to work in data science should focus on upskilling their capabilities and knowledge and looking for a suitable course. Their interest lies in the course syllabus rather than other less relevant aspects of the system. Professionals must choose a data science course that concentrates on data science.