Technological advancement and innovation lead us to progress and expansion. Data science is one of the segments in this development field. There is an abundance of data it can be structured or unstructured, to sit back and derive meaning out of that data is an endless and exhaustive process. Data Science is extracting knowledge and insights from that data by using scientific methods, processes, and algorithms. In the field of data science, digital data is only considered as data.
Data Science process
1. Outline the problem
At the beginning before solving any problem, firstly it should be identified and defined what exactly it is. Data questions should be interpreted into something actionable. People having problems will often come up with inconclusive inputs but you need to develop an understanding to turn insufficient inputs into actionable outputs and raise query which is not asked by anyone. Any problem to be solved needs basic understanding and an appropriate way of finding a problem is to ask the right question.
2. Obtain raw data
After defining the problem, the next step is to collect the right data, on top which insights are to be generated. To obtain raw data, the first thing which is taken care of is to analyze which data is required and how to obtain that data, whether it can be obtained by questioning internal databases or by buying external datasets or by scraping the data of the websites.
3. Data Processing
It is the process of making data accessible for analysis by taking off or making alterations to the data that is inappropriate, insufficient, redundant, not pertinent or inappropriately structured. This data is generally not necessary or valuable when it comes to analyzing data as it may obstruct the process or come up with inaccurate results. There are various ways for cleaning data to be conditional on how it is stored along with the answers being sought. Data cleaning is not all about removing information to make space for new data but instead finding a way to augment a data set’s precision without necessarily deleting information.
4. Explore the data
Clean data needs someone who can delve into it. The problem does not arise in testing the ideas, it is approaching the ideas which can be turned into insights. Exploring the data helps in making better analysis.
5. Perform in-depth analysis
In this, the data insights are obtained by applying statistical, mathematical and technical knowledge. There is a probability where there will be a need to develop a predictive model that helps in the differentiating groups with the average customer. It is helpful in scrutinizing the age and social media activity that are important factors in anticipating who will purchase the product. It can be the case that the younger audience is more likely to buy things off the social media, we can obtain a similar conclusion once we analyze the data. Hence, along with the data, our end goal should be clear. If we ask the right questions, we would end up making the most out of the data.
6. Data Visualization
It is the representation of data in a pictorial or graphical form. Using data-viz, decision-makers can view analytics in a better and pleasing way, which can make it easy for them to find new patterns out of the data or understand some difficult concepts.. Making use of interactive visualization assists in taking the concept one step forward, with the use of technology to dissect it into charts and graphs for further description, drilling down or slicing the data, or performing any form of analysis that may be necessary.
Digital data is information that cannot be analyzed easily and interpretation of its meaning is a strenuous task by a particular person, it is dependent on machines to elucidate, process and make changes in it. Data Science’s future is dependent on some factors which are:
1. Making data practicable for data science
Faultily generated data is one of the considerable hindrances in the accomplishment of data science. To speed up the data science projects and bring down the failures, the target should be on enhancing the data quality and hand over data to teams who are associated with the projects and are actionable.
2. Dearth in data science expertise
Data science is one of the demanding fields for new graduates, the requirement surpasses the available supply. The solution proceeds with fasten up hiring along with the search of an alternative means for an expert in areas such as analytics and BI so that the data science process can get faster and can be accessed by everyone. Automation can have an impact on data science over here.
3. Accelerating time to value
Data Science is a monotonous process. It involves creating a “hypothesis” and then putting it to the testing. This reverse and onward process demands numerous experts- data scientists to subject matter experts and data analysts. Any organization whether they are small or big needs to lay hands to speed up the “effort, repeat test” procedures and hasten up the process of data science for greater foreseeing.
4. Enhancing operationalization
Data science extension faces hindrances regarding how difficult it can be to operationalize. There are cases where various models work in the laboratory but they don’t when it comes to the production environment. Constant modification and increase in production data can adversely damage the model over time which were successfully deployed. The “fine-tuning” the ML model to be an operative post-production method is a critical part of this process.
5. An overwhelming amount of data growth
Human beings generate abundant data on a regular basis, but presumably, they don’t have time to think about it. As per a study about the present and future growth of data, 5 billion people interact with data on a day-to-day basis, and this figure will be increased to approximately 6 billion by 2025, showing three-quarters of the world’s population.
Regardless of the area of work, whether it is medicine, media, finance or any other industry, data science can play an integral role in the overall progress. Every area faces the major dilemma of big questions, big data so every field needs data scientists. Being enthusiastic about data science is a perfect opportunity to explore it. Whether you are a big enterprise or a small scale start-up, if you have data and some problems to solve, let data science be your beacon and guide you out of the mess.