Data science is considered a blending of various algorithms, tools, and machine learning in order to achieve the goal of discovering the hidden patterns from such raw data. Data science is something which is an improvised way or the new version that does what statistics do in order to find a more detailed solution to the problem. It is said that it is the future of AI. It has evolved over a time span of 50 years though it is not a new profession.
Let’s talk a bit about the history of data science and how they became popular:
After the emergence of data science, the Operations Research became popular which used maths in order to find optimum solutions, after which there came an evolution of computer era where digital doors were opened. Digital data was brought into account were strings, numbers, and characters came into the picture. This increased data storage, collection, and manipulation ability through computer software and programming. Data analytics and data mining emerged as the statistics became computerized. The data mining as initially developed to extract properties and patterns from digital data sets. As time passed by, real-life problems were also digital through stimulation. Also, OR became optimized until then.
This computer intelligence along with OR gave birth to artificial intelligence (AI) or also known as machine cognition which further gave rise to machine learning which helped programs to learn from such data and further make predictions basis on them.
Once AI and machine learning came into the scene, business intelligence became important as it offered much easier to use dashboards for managers and especially to people with no IT experience. By then, the internet had brought big data which are mostly produced by social media, BI platforms, sensors. Etc. With the launch of big data, an explosion occurred which is why many more new opportunities came up that helped business, science, and government to offer better services and solve their problems. This is where data science has played a very big role as it helped in putting all the pieces of techniques and tools together at the right place and time, which helped people in manipulating data in a much more simpler way.
Now that we know the history of data science and its uses, let us know a few facts about data science:
Data Is Everywhere
According to a survey, the digital universe is mostly made up of approximately 3.2 zettabytes of data which is said to increase to approximately 40 zettabytes in the coming 6 years. These data consist of various sources like videos, pictures, GPS signals, social media posts, etc. Social media websites like Facebook, Google, Youtube, LinkedIn are said to receive massive data every day, also the platform is said to process more than 24 petabytes of data for each and every data. The individuals create 80% of the data in such websites where only 12% is analyzed by the organizations that integrate them into a useful asset for the companies. Thus, data is everywhere and in every aspect be it websites, photographs or online purchases.
Data Usage Has Lead To Job Opportunities
Data Science courses are available in a lot of universities and colleges but people are hesitant to take them due to newly introduced courses, which is very normal for them to react likely. Though, there has been an increase in data science graduate programs offered by the universities due to the increase in demand for such data science specialists. There are a number of various programs from which one can choose from, but the only problem is that while preparing one in such a particular field, they end up teaching different technical skills. In such cases one can always opt for tech data solution as they are one of the best institutes and combats these issues, Also it provides data science training in Mumbai and data science training in Pune.
These courses can turn out to be very helpful in acquiring education in the field of data science which will ensure a job in the future in likewise. Proper experience is also required in this field in order to excel. One can opt for part-time or full-time courses, according to their convenience. These subjects are quite tricky, so it can need some guidance which can be best provided by tech data solutions.
Big Data Means Big Money
The obtaining of masters in such degrees can help in getting a job with huge pay. As there is a shortage of personnel with such a degree, therefore the pay in such a field is huge. The starting package for a data scientist is $1,20,000 to $1,60,000. This is an estimate of pay in the US, though other countries also have a similar pay structure.
95% Of Tasks Doesn’t Require Deep Learning
Mostly all work is technical in such a field, but there might be some work like some real problems which will need much more than that which is, understanding the problem, the decision-makers, the problem domain and also the end-users. There will be a few problems that need not be solved using complicated means. Like some complex situations may be solved by simple methods, though it doesn’t mean that one can neglect them.
Big Data Is Just A Tool
Big data is considered as a collection of tools that helps in the handling of a large volume of data. The best part about them is that they can handle massive data within a short period of time. The scrutinizing the eyes of analysts, analytic problem design and modeling best practices are few things that cannot be replaced using Big data, but this doesn’t mean there is no competition. Soon it will be seen that the small data will be long gone due to the increasing advancements in the IT field. Though tools might come and go machine learning is something that will always exist as it will help in the development of any new tool.
Data Science is a very big concept that has been growing for quite a long time with different data and techniques. Though to get hold of them it is very important to take pieces of training and opt for internships and research projects as the real experience is the one that counts. These facts and history are some of the important aspects of data science which can help one in becoming a better data scientist.