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.Â
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. Â
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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.
Top Data Science Skills to Learn
SL. No
Top Data Science Skills to Learn in 2022
1
Data Analysis Programs
Inferential Statistics Programs
2
Hypothesis Testing Programs
Logistic Regression Programs
3
Linear Regression Programs
Linear Algebra for Analysis Programs
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 syllabus for professionals. This course is delivered online with learner’s pace and different formats such as certification or Post-Graduate Diploma.
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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.
If you are curious to learn about data science, check out IIIT-B & upGrad’s Executive PG Program in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.
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.Â
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.
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What are the core subjects in Data Science?
With data becoming an essential necessity, data science is governing most of the fields. This leads to immense responsibilities as a Data Scientist. The following are the core fields and skills that every company seeks in a candidate.
1. Probability & Statistics: Mathematical fundamentals such as statistics, probability, and linear algebra constitute the most important portion of Data Science.
2. Business Intelligence: You will be responsible for decision-making at various labels, which is why you should be well versed with the latest BI tools.
3. Programming Languages: Python and R are considered to be the most effective and powerful languages for Data Science.
4. Machine Learning Algorithms: Regression techniques, Naive Bayes algorithm, and regression trees are some of the major ML algorithms that you need to focus on.
5. Data Manipulation: Data manipulation and data visualization become crucial when it comes to analyzing your data sets.
What is the career path of a data scientist?
Data Science is a field that rewards you almost better than any other field but asks you to follow a certain career path to be a deserving data scientist.
1. Bachelor’s degree
First of all, you have to acquire a bachelor’s degree in Computer Science (CS), Information Technology (IT), or Mathematics.
2. Entry-Level Job
After completing your degree, you should get an entry-level job as a data analyst or a junior data scientist for experience before getting into the big games.
Master’s degree
Data Science is a field that requires at least a master’s degree or a Ph.D. to get bigger opportunities. You can get your master’s parallelly with your entry-level job too.
4. Get a Promotion
Once you are done with your studies, there is no one stopping you from applying for higher opportunities.
How much does a data scientist earn on average?
In India, a data scientist earns around ₹698,412 per annum on average, A newbie or an entry-level data scientist having experience of less than 1 year earns around ₹5,00,000 per annum while a data scientist with at least 4 years of experience earns ₹6,10,811 per annum.
A mid-level data scientist having an experience of 5 to 9 years earns ₹10,04,082 per annum in India. The salary rises dramatically with the rise in your experience as senior-level data scientists around more than ₹17,00,000 a year in India!
