AI, Data Science and Analytics have gained a burst of hype over the last decade and for good reasons. However, compared to other IT and academic fields/courses, Data Science is not as concrete and clear-cut. But what was the reason for this sudden outburst of demand for a vague and emerging field?
There are a few highly contributing factors which are involved in making Data Science an extremely enticing and desirable field of action. The upsurge in the demand for data science jobs can be credited to the following factors:
- Increasing Data: As is known, almost every digital action we take generates bits of information which gets recorded as potentially useful data. Every action leaves behind a trail of data which is also sometimes referred to as digital footprints. A larger volume of data helps to track down trends and patterns over a wide range so that smart analysis can be undertaken and informed decisions can be formulated based on best performance trends observed from past data.
- Better Computation: Just a few decades ago, in spite of having a sufficient volume of data, it was almost impossible to implement sophisticated operations on them. This was largely due to primitive computation and technology which could only handle a few units of data at any given time. However, advanced statistical theories were already in place and just lacked the right tools for application. This time period is referred to as the AI winter when in spite of extremely advanced algorithm formulation, implementation was next to impossible. In the last decade or so, once this problem was solved and processors and storage capacities evolved to be in their present state, the theoretical concepts could be applied across several recurring problems. This was when the demand for data specialists like statisticians, data engineers, data scientists and machine learning engineers outgrew the average count.
Who should consider signing up for Data Science / AI / Machine Learning courses?
As was seen above, the demand for data science jobs is in fact, quite overwhelming. The question, however, arises that in spite of an increasing number of data science enthusiasts, why still, is there a largely visible vacancy in this field? The answer is extremely simple, yet harsh.
The available supply of people in this field is extremely under-skilled.
Learning open-source library syntax codes and having an overview of modelling algorithms is simply not enough. Sadly, however, most of the aspiring data scientists of this generation, barely have enough knowledge beyond these basic skills.
It is imperative to understand that Data Science is not simple, contrary to the general belief that media has propagated over the years. Indeed, most people from an engineering or mathematical background can learn and understand coding syntax, modelling concepts and visualization basics with ease, but how many of them can actually modify the already existing foundations to build something better?
Data Science is the science of patterns in data. Therefore, as data patterns and data type changes, which happens very frequently in real-world scenarios, the need arises for new solutions which suites the new input. Good companies and research organizations ask for highly experienced candidates so that they can smartly handle any new challenge, and with data, it is almost always a new challenge. Even the same problems end up getting solved with different techniques when the input data changes.
Therefore, if an individual is looking to opt for data science because of its simplicity or hype, they definitely need to look elsewhere because this field will test his/her extreme limits over time. On the other hand, an individual who is truly interested in the mechanisms of Data Science and really wishes to be invested in the learning and development of improved skills every day must delve into the field right away and gather as much knowledge as possible when the time is good with the help of AI courses, Data Science Courses or Machine Learning Courses.
What are the prerequisites for taking Data Science/ AI/ Analytics/ Machine Learning courses?
If you have persevered and passed the last question graciously, there is also some good news. Data Science has minimum and very simple requirements. It is mostly desired that people who opt for Data Science are from Engineering or Mathematical backgrounds, but it can suit anybody else just fine if there is a willingness to learn and the ability to grasp a wide range of concepts. Here are a few basic prerequisites:
- Basic Coding skills: Even ground-level coding knowledge works in order to implement Data Science/Machine Learning techniques. The two most popular languages in Data Science, Python and R, are extremely simple languages which are easy to learn, implement and work with in general. Both these languages have built-in libraries which have optimized code and can be implemented in just a few lines, speeding things up many-fold.
- Statistical Understanding: Statistical concepts are paramount in data science. They form the foundation of every concept and every algorithm. So, even though an individual never had any relevant education in statistical subjects, it must be noted that she/he must be able to grasp these concepts with ease. Going through a few good courses in statistics can help individuals progress really well in this front. Anything can be explained with simplicity, and if it cannot be, more work has to be done in order to make it simple! Good Analytics and Data Science courses provide you just that, simplicity. You can go through the skills and concepts required to be fluent with statistics here.
Can a person shift career paths to data science?
For those looking to shift career paths, online data science courses, Machine Learning courses and AI courses are a really effective way to do it in case one does not want to invest in a large scale or lose out on years of experience. Good data science courses provide well-guided instructions and simplified concepts so that real-life projects can be solved. If an individual follows the instructions well and implements them in real-life projects in order to achieve satisfying results, he/she can showcase these in their resume during the application of jobs. You can read about the top 5 careers in Data Science in order to be more informed about your next steps during career shiting.
Tip: Use the tips and tricks from the Data Science and Machine Learning courses to solve problems in online hackathons and competitions. Compete for top ranks so that it can be displayed on your CV with a digital proof. Machine Hack (Relatively Easy), Hacker Earth (Medium Level), Kaggle (Advanced) and Analytics Vidya (Relatively Easy).
What is the scope for freshers in Data Science?
If you are a fresher in Data Science, your scope of advancement and learning is immense. Concentrate on learning with the depth-first approach. In other words, take one concept and dive to extreme depths with it so that you can decipher the nuts and bolts of the technique if asked to. This helps to increase one’s ability to tune and tweak the algorithms to the best possible optimizations as per the problem at hand.
As a fresher, staying motivated and discovering your true calling is the key to gaining from these young years. Therefore, experiment with as many problems and techniques as possible in order to understand what really aligns with you and your interests. In case Data Science turns out to be your best possible alignment, in few years’ time, with enough variance and experience by your side, you will be able to work with extremely good projects with large scale firms and research institutes as per your preference. All come with patience, knowledge and hard work.
What is the road map which one needs to follow to leverage any data science course?
Just studying AI, Machine Learning or Data Science courses and memorizing the approaches theoretically will not give good returns. Only take a course you are willing to implement the learnings on a practical front. With every concept you learn, see how and where you can apply it, and then go ahead and do it. This way, whatever was learned, will last much longer in the mind and will also be more effective since you will know the applications and shortcomings at one go. Therefore, follow this approach to make maximum use of any data science/ AI/ ML course:
Learn -> Write -> Code -> Visualize -> Document -> Repeat
If you are unable to do so, there is hardly any benefit of going through an entire course as you will forget it soon by this method.
What are the courses that one can go through to improvise and learn Data Science skills?
Few of the good Data Science / AI / Machine Learning courses which one can follow to learn about data science, machine learning, or Artificial Intelligence are as follows:
Data Science Course: All about data science, analytics and machine learning.
Big Data course: A step further into advanced data analysis and processing. This course deals with Big Data and its operations on Cloud (Amazon Web Services)
Artificial Intelligence course: This is a level higher than the ordinary machine learning. This course delves into deep learning techniques, a branch of AI which deals with biologically similar mechanisms which try to mimic human intelligence. Deep learning is extremely efficient when it comes to data interpretation.
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