Data science, without its complex technologies, can easily be defined as a methodology through which data scientists draw useful insights by manipulating data – which is slightly yet distinctly different from previous data analysis mechanisms. Back in the time, business intelligence, scientific computing, exploratory statistics, etc., were employed separately to perform a singular task. Then data science emerged, thanks to the number of renowned scholars that incessantly pushed the idea forward.
The distinctly different aspect of data science that sets it apart from statistics, business intelligence, and scientific computing is the ambitious objective. Any task that is performed in data science has to have an ambitious objective; that objective is to generate informed conclusions that enhance our decision-making abilities. We make better decisions if we base them on better information. Without data science, our uninformed conclusion and decisions are merely based on certain practices and hunches regardless of the scientific knowledge we may have. (Ley and Bordas, 2018)
Big data has enabled the representation of complex environments, which let the possibility of gathering significant knowledge from data. In general, data science has allowed us many wonders that were either simply impossible in the past or were too complex and hence too costly to practice.
Today, we are going to discuss four different strategies of data science to explore the world by using data.
Data science is a multidisciplinary field; its intricacies work through the methodical balance between scientific methods, different technologies, and algorithms to extract the targeted insight from the various data models. (Fridsma, 2018)
To better understand the mechanisms of data science, you need to consider a few steps that the data science requires to finish any project of the data science process:
Data collection or gathering information from various sources is usually the second step in the data science process. It has two main methods of collecting all the relevant material, such as a passive method and an active method. Data can be gathered by passive or by active methods.
A few common examples of passive data is a web browser, mobile devices, websites, etc.
The common examples of active data collection are the user’s personal information, surveys, feedback, etc.
Probing realities focuses on the latter method because active data represents the reaction to our action. The world that we request the data from responses to our certain action. These responses are then analyzed to gain extremely useful insights, behavioral patterns, or preferential trends, etc., especially when the decisions about the following actions are based on these insights.
The best example of the probing realities strategy is web development A/B testing. A/B testing or variant/split testing requires you to test your two choices to measure which one is more apt to accomplish a predefined goal. Like what is the best design for that page or the background color for this section of the website? The most valuable and finest answers can only be brought forward if you probe the world.
Data science takes the A/B testing to another level and allows us to probe the world for the best possible answers, which in turn leads to the best possible decisions.
Data Science is a multidisciplinary field. It is a mixture of many different tools, methods, algorithms, applications, and machine learning principles with an ambitious objective to discover unseen patterns from the raw data. The pattern discovery technique is used to understand users. It holds significant value in many fields, such as programming advertising or digital marketing. In a digital world, everything around its operations can be categorized as patterns. You can observe or read a pattern either by applying various algorithms or deducing it in a physical form.
In data science, pattern discovery is a process based on recognizing different patterns through machine learning algorithms. If you recall an old heuristic about divide and conquer, recall how it has been in use to solve complex problems. Pattern discovery works along with the same structure; only it is not always that simple to figure out how and when to apply this logic to given problems. (Gullo, 2015)
It is one thing when it’s about datified problems because the process of datification encapsulates ideas, behavioral patterns, thoughts, and preferences into a data form. With the help of technology, datification turns the social actions into computerized information or quantified data, which projects varying trends, patterns, and behaviors to make it useable. Problems that are datified are auto-analyzed that can discover many patterns within datasets and natural clusters. The automatic analysis makes it very easy to find solutions to the given problems.
The most frequently used algorithm for pattern discovery is called clustering. It is a machine learning technique that involves dividing data points into different groups based on the similarity among them. The clustering refers to group segregation comprising of data points of similar nature and assigns them in clusters by using the clustering algorithm.
For example, if you are working for a telecommunication company and you have been given a task to create a network in a certain region by putting up signal towers, the technique you would need to locate those specific tower spots that will ensure optimum signal strength for every user is called clustering.
Pattern discovery has the following features:
When we say data science works towards the ambitious objective of assisting and augmenting our decision-making abilities, we refer to predictive analysis. Decisions, more often than not, are targeted at the future course of action. The best bet to base this decision is accurate predictions, and to predict events, we rely on past experiences and information – data. To put it simply, it’s like analyzing old data to predict a future event. Predictive analytics, especially in data science is dependent upon explanatory data analysis – the what, where, and how of data. It allows reactive as well as active decision-making in response to predicted future events.
In recent years, predictive analytics has been the center of attention. It is largely due to the modern technology it uses for advanced scientific analytics, especially in terms of big data and machine learning. Even in times of statistics and business intelligence, the question of devising robust data models to predict future events remains of paramount importance. The data-driven predictive models can solve long-standing problems in efficient and cost-effective ways. (Radinsky et al., 2013)
The process of predictive analytics uses different techniques of data, statistical algorithms, and machine learning to identify future probability. It comprises using historical data for analytics to make predictions. The old data is used to create mathematical models by using analysis, statistics, and machine learning techniques. This model is meant to catch milestones or important trends in the historical data and develop a relative predictive model. Once we have a predictive model, it is then used on present-day data to enable quantitative prediction of what can/will happen next. The process does not stop at forecasting future events; predictive analytics also suggests a specific course of action to ensure an optimal outcome.
The most common traditional or classical statistical and machine learning predictive analytics methods are as follows:
The data science industry often refers to some of the classical statistical methods as machine learning too, but typically machine learning methods are more advanced and sophisticated.
Remember that, predictive analytics in any environment does not forecast future events with 100% accuracy. There is always a margin for unpredictable events. We are talking about data science here, not magic. Even that can’t promise 100% accurate outcome. The point of predictive analytics is to identify predictable events to gain valuable knowledge.
Have you noticed the recent interest of many multinational companies and governments of different countries in the areas of natural language, computer vision, psychology, neuroscience, etc.? If you have, then chances are you are also familiar with huge investments of a considerable amount of money that has been going into the research of the above-mentioned areas. The constant improvement in the development of deep learning methods to understand natural languages and visual object recognition are some of the finest examples of the kind of research in question. (Cox and Dean, 2014)
So, why do you think is that? Because data science is the future; it is based on enhancing decision making from minor to nuclear testing or robotic space exploration program level.
Understanding people and the world is essential to optimal decisions. Yet it is an objective not quite in the scope of many companies and people, except for the class that makes for the global elite. Scientific understanding of natural language, computer vision, neuroscience, etc., derives the foundation of data science. To be able to know and understand the motivation behind various processes that influence people’s behavior and decisions, this science is necessary for business, health, security, etc., decision making.
Lastly, it is important for people hiring data science professionals, specialized individuals themselves, aspiring data science experts, or even data enthusiasts, to know that creating an effective data strategy is not about something as simple as hiring new data scientists, data analysts, etc., or just making decisions based on data. It is about working to have an ecosystem where developing the right data metrics, and resources are not walking in hell. Creating an effective data strategy is about building and encouraging a culture that questions data, explores it from different angles, and subjects it to different processes before making the ultimate conclusion.
Below are some of the simple alterations that can serve to improve your approach to data.
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