How do you stand out in your career with data science?
The last decade has seen many new trends emerging in science and their immediate application to everything around us. This has led to IoT’s popularity (Internet of Things), Neural Networks, or Data Science.
When scientific concepts find application, it becomes a topic of interest among every segment associated directly with technology and indirectly.
Scientists, researchers, analysts grab at the opportunity to probe more on the subject. While those associated indirectly, such as business owners, retailers look for ways to incorporate the new technology to increase efficiency.
Data Science is one such concept that has gained widespread popularity owing to its application and has resulted in huge demand in businesses.
This, in turn, has led to the growth of Data science-related jobs. Therefore more and more students, particularly engineering students, have been opting for a Data Science Course or specialization in Data Science.
The Scope of Data Science at BMU
At BMU, it is our deep-rooted value that we build a better future. This is why we attempt to provide our students with the opportunity to study technology’s latest subjects.
Our courses, too, are developed in collaboration with the prestigious Imperial College of London and industry experts.
We do offer a specialization in Data Science and Artificial Intelligence for those in Bachelors in Electronic and Computer Engineering because we believe our students should not be deprived of the chance to make it big in the world of Data Science or miss out on the chance to make the most of the growing demand in that field.
We have advanced labs for students to work on enormous data sets and be ready to work in the real world even before they step out of college.
Data Science – a business perspective
Data Science is the next big thing in technology, after Artificial Intelligence (unless you want to consider Data Science as a part of AI).
Organizations are ready to invest millions in Data Science. Gartner had estimated that, on average, there would be around 3.9 trillion US Dollars spending on Data Science in the year 2020. If this Data does not indicate how massively this field is growing, then nothing will.
To add to that figure, here’s what the co-founder of the world’s largest professional network and career portal, LinkedIn has to say: “Data Scientists are almost all already employed because their demand is high.” Mr. Allen Blue said that there is “enormous growth-15 times, 20 times growth”.
He highlighted that this growth is not happening only in the high-tech sectors or information technology industry. Manufacturing, Education, Marketing, Retail, Hospitality are some of the non-software industries where there is a massive increase in Data Science jobs.
Mr. Blue also suggested a concern which at this time is more muted and subtle. But with the years, the concern could become more serious.
He said that there is an increasing gap between the demand for Data Science professionals in the industries and Data Scientists’ availability in the job market.
Demand in the Data Science field
Here is a look at five aspects to study the growth of Data Science:
a) The biggest challenge organizations face these days is to recruit resources in Data Science. They want a good mix of freshers and those who have been in the industry itself for some time.
But it has only been in the latter half of the past decade that students have started taking up the course as a career choice. So there aren’t too many professionals in the industry who are fit to take up these roles.
This has created a huge vacuum and a great demand for Data Scientists. And the US Bureau of Labour Statistics has stated that this demand will go up by another 28%, at least by 2026.
b) According to Glassdoor, one of the world’s largest job recruiting sites, Data Science job postings were 1700 in 2016. This number grew to 4500 in 2018, and it grew to 6500 in 2020.
This figure is of Glassdoor alone. These statistics provide a view of how much the jobs in this field are growing and what rate.
c) With the growing pandemic, there was a fear about the demand for Data Science dropping in businesses.
With the steep dip in revenues and scattered business solutions, and economies slumping a bit, companies that had initially wanted to invest in Data Science and Artificial Intelligence projects took a back foot.
They went into a survival and sustenance mode by spending only on the bare minimum. However, the situation has changed tremendously in the past few months, and the businesses are back to building efficiency. So Data Science and AI jobs have begun to rise once again.
d) With every passing day, newer tools are emerging to analyze data. Tools such as Fivetran, AutoML are lowering the entry barrier for Data Scientists.
With the considerable amount of time being spent on data collection, cleansing, modeling, interpretation, newer and better tools certainly have a big impact on how the technology is beginning to take shape.
These advanced tools allow more and more data scientists to enter the field and do a lot more work, and thus the technology is slated to flourish.
e) Steeper competitions among businesses and better technologies are causing companies to invest more in data analysis and AI.
While Data Science can study more data available to us, AI can build predictive models, which thereby increases the productivity and efficacy of a business.
Companies want to know what the revenues would look like with incredible accuracy when launching a product A.
They want to know why product B failed in the market. Was it that the timing was incorrect, did the competition plan a better launch, was the pricing a problem? It is Data that helps them make these decisions and analyze the failure. That’s the reason why the demand for Data Science can never go down.
What are companies looking for?
Companies have only one expectation from the Data Scientists – the Data Science professionals will study and analyze available data to throw business insights that will ultimately revolutionize the way they do business.
It will give the business an edge over its competitors and will make its operations efficient and profitable.
For this reason, they are ready to invest in a Data Science group that is internal to their business or pay Data Science Consultants to do this work for them. Either way, it is pushing the need for good Data Scientists.
If we break down this “expectation” into smaller, more actionable goals or objectives, this is what they would look like:
1. Good with numbers: A penchant for numbers is the foremost thing for someone who wants to build a Data Science career.
Numbers are to a Data Scientist what people are to a Human Resource professional. So unless a person has a natural inclination towards playing with numbers, Data Science may not seem to be the right choice for them.
2. Analytical abilities: Computation and analyzing are certain basic aspects of a Data Scientist’s role. As soon as the Data is gathered, the analysis starts.
To a certain level, the analysis starts even before the Data is gathered to decide which data can be helpful and which not. Once Data is collected, the cleansing, structuring, modeling involves analysis.
3. Domain knowledge: Businesses want to hire people who have already worked in the same industry.
For example, a Data Scientist who has worked in BFS (Banking and Financial Services) will have more insights into the industry and its trends than someone who has worked in Data Science in the retail field.
However, this is not applicable for someone who is a fresher and is just joining the industry. But later, when a professional gains maturity in a particular industry, it is extremely valuable.
5. A knack for problem-solving: An authentic ability to look at a problem and discover possible solutions to tackle the problem. To do that, they must first identify the problem and then work out a solution by going into the root cause.
6. Creative with a passion for designing: The professionals are expected to have a panache for designing models. They should be creative and think out of the box to develop newer models, each time with the view to look at it from a fresh perspective.
7. Curious: Essentially, people who are interested in learning more. They don’t hesitate to go beyond their chalked out responsibilities in the job description to explore eagerly.
What Data Science Course teaches you?
1. Programming Skills: R and Python are considered to be the top programming languages for Data Science. A data science course may choose to focus on one over the other. Both have an extensive set of mathematical and statistical analysis support packages to master. Besides, the courses may also teach database access/manipulation query languages like SQL.
2. Probability & Statistics: A good data science course would also give you a strong background in probability & statistics, including topics like probability distributions, statistical tests, maximum likelihood estimators, etc.
Data-driven companies like Netflix, Facebook, Google, and others depend on designing experiments, analyzing and predicting consumer behavior through statistical inference and prediction.
3. Machine Learning: Any data-science course would be incomplete without an introduction to the standard machine learning algorithms. This can mean things like k-nearest neighbors, random forests, ensemble methods, linear regression, PCA, clustering algorithms, and more.
4. Linear Algebra: Linear algebra is taught in a Data Science course along with multivariable calculus.
When Data Scientists work in organizations, they can contribute greatly to predictive performance or optimize algorithms. These are things that mean huge success for the business. These are also questions which a future may ask in an interview.
5. Wrangling with data: A course in Data Science can be a fantastic opportunity to get a hand at working with data. When we talk about Data Science, we mean enormous amounts of data that can be structured or unstructured, clean or messy.
A Data Scientist professional should have been exposed to an environment where they have worked with data sets and dealt with data imperfections. It should not come as a shock. A good course provides the option to work with real data sets.
6. Data Intuition: Knowing how to program a language or build algorithms and expertise in handling data is not enough for a Data Scientist. He or she must have a sense of foresight in what method should be applied to solve a problem.
A person can be a great problem solver, but they may not solve it if they don’t know how to approach a problem.
When recruiters arrive at campus, they are interested in knowing if the student is great at solving problems and if they can make the right choice about the approach and decide what is important and what is not.
It is important for a Data Scientist, and so he can choose the right method without getting overwhelmed. A course in Data Science provides the opportunity to hone this attribute.
A course in Data Science or specialization in Data Science during engineering is not just a decorated certificate.
It makes a student stand out from the others. In the current situation, organizations have not yet made it mandatory that only those with a Data Science course can apply for a Data Science job.
The courses are a recent phenomenon, and only a handful of good colleges offer a valuable course in the subject.
However, a recruiter who is hiring for a Data Science job will be weighing a Data Science student’s profile higher than those without.
Simply because the expectations they have from someone who can perform the job well match those who have completed a course in the subject.
The Data Science course in BMU tries to keep up to the industry expectations by providing the latest computing facilities and high-tech.
We strongly believe that this is the right time for students to make the most of the growing demand, and we are helping in every way possible to lead the way for our students who wish to tread that path.
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