Data science field has been at the top place for the past four years. According to some surveys, the Data science sector is expected to grow at an average rate of 28% per annum over the next five years. It is considered to be an important resource for Data Collection, Formatting, Storage and Management and is a field in which computer professionals with Experience, talent and ability to observe and make data- based decisions. Data science can be defined as asking Interesting, Purposeful questions and finding appropriate answers to those questions through Data. The process is as follows.
1. Ask or raise a question.
2. Collect data that are likely to answer the above question.
3. Refine the collected raw data.
4. Thoroughly Analyse and Visualise the refined data.
5. Evaluate Data and develop a Machine learning model.
6. Summarize and publish the results.
❖ Experts are of the opinion that a good foundation in some subjects is required to form a career in the field of Data science. Statistics, Linear Algebra, Programming, Database Systems, Distributed Computing, Machine Learning, Visualization (Experimental Design), Experimental Design (Manufacturing). They must be proficient in subjects like Deep Learning, Natural Language Processing. However, learning all of these are not necessary at the beginning of a career. Some Entry level understanding of the subject is enough to enter this field. For example, one who want to enter this field does not need full proficiency in Mathematics and other subjects, but they should know the basics. Their performance will be improved with experience.
❖ The truth is that career beginners need a lot of flexibility and guts to experiment with data in different ways in the Programming language. Mathematics lays the Foundation for the development of long models. So, the focus should be on the application of Mathematical methods. Also, good model development depends on thinking ability. In short, Analysis and visualization are important. So, learning Mathematics is very important.
❖ The field of Computer science needs skills in Programming. However, the training offered in colleges in terms of syllabus is at the basic level and the focus is on training the student in a systematic manner. It is only necessary but not perfect. The coding capacity on these fundamental foundations should be enhanced. That means using less resources in less time and finding a less complex answer to a question is the requirement.
❖ Python programming should be well learned, especially for a Data science career. This means that Python requires a comprehensive study of various data retention processes (data types), Data structures, and some important Library programs, especially Libraries related to Data science, such as their topical application. The real- life principle of ‘any building can easily grow if the foundation is solid’ applies here as well. It is very useful for Data science, especially through the practice of pandas in Python, to learn how to make changes (manipulations) and Visualizations to suit the context of data analysis.
The basis for Data science is collecting the available data and forming a new data model through Data analysis. This is like predicting the future. Experts say that is a very artistic part. Using the raw material that people buy in an area as raw data, it is not easy to prepare products in a way that pre- identifies what kind of goods and products people would like to buy beforehand. The man has to spend a lot of time doing this. Also, refining and searching for large amounts of data can be a daunting task. Mistakes can also be made with the impatience that comes with Fatigue. The need for creativity in this work is also minimal. This can only be achieved through a well-trained, 'thinking' computer. With such creativity, the Python Psychic package that comes with Python can effectively conduct template- based research. This package is very rewarding to practice. This package has the potential to accommodate a few thousand models. Can modify and correct various parameters over and over and find the required, useful results in a short time.
While a Machine- learning process can find a compelling answer like Psychic, it is not possible to decide whether it is relevant or not. Analyzing an answer to the current problem, questions such as how standard the application is to the dataset, how it can be applied to the context, and whether it will be possible to generalize this answer to all contexts in the future will not be easy. Deep learning has its role to play on this occasion. However, if you are basically good at Machine learning at the beginning of your career, you can learn Deep learning at the second level.
As Data science is a branch affiliated with the Information Technology field, the course seekers must have basic knowledge regarding subjects like Machine learning, Deep learning, Data Analysis, etc,. relating to it. Many opportunities are available in Computer science and IT fields for those who complete the Data Science course. In addition, there is an opportunity for IT- related professionals to join Data science jobs as Freshers, who must have the required skills mentioned below:
❖ It would be a mistake to think that getting a good percentage in a UG course, Learning some skills, doing two or three projects and getting a job in the field of Data science is enough to win. One has to keep abreast of the ever- changing changes in the field and move forward as a perpetual student, making new improvements to knowledge.
❖ A new field will definitely give an opportunity to new jobs. However, there is a lot of potential for Innovation, as the field has not been fully researched. Therefore, those who take such courses must be regular students. With the motivation to join new branches, one should strive for knowledge development every day keeping in view new innovations, expert opinions and Industry expectations. It did not take long to get a job in the IT sector and lose it. It does not differ in age, position or status. At the student level, participating in competitions run by online media such as GitHub and Coggle, as well as sharing in the work of others, provides golden opportunities. The field is still in its infancy. Everyone here is a student. Everyone can grow with mutual co- operation.
❖ There is no use if you join the course just by understanding that the course is desirable and jobs are easily available, and not doing hard work to improve in this field. We have to understand that companies will not recruit anyone if the skills are not at the right level. They only hire people who live up to their expectations. Companies search for people who can sell their products/ commodities. Those who join a new course should look for new skills that they need and learn a lot and be job- ready.
❖ There are many exclusive Job opportunities available for Data science graduates such as Data Scientist, Data Analyst, Machine learning Engineer, Machine learning Scientist, Application Architect, Enterprise Architect, Data Architect, Infrastructure Architect, Data Engineer, Business Engineer, etc,.
❖ Apart from M.Tech and MS, many Management colleges in our country and Abroad, including IIMs, offer specialized education in MBA. Those who join Research courses get top level jobs.
❖ Businesses must constantly come to market with new ideas to reach the goal. Therefore, those who are in the field, especially those working on a critical level, need to think in a new perspective. It causes some stress. But those who endure it will have a future. There are no negatives to a career if you are constantly learning new things and improving skills.
Posted Date: 04/03/2022