By what ways data science is different from data analytics and business intelligence – 2019 edition

1. The definition of data science

First and foremost, data science is mostly considered to be a big discipline comprising of a lot of other disciplines. Data science is related to the big data increase, which comes with so many parental disciplines such as software engineering, business intelligence, computer science and so on.

What is more, data science includes a big number of processes linking to the retrieval, collection, ingestion as well as the transferring of very large amounts of data which we often call big data. Data science is considered to contain the allure of big data, the advantages of unstructured data, the benefits of mathematics and statistics, the development of social media and so on.

2.  About data science: big data, machine learning, data mining as well as data analytics

In general, data science includes shaping the structure for big data as well as exploring the patterns inside it. Moreover, it is also about the recommendation given to decision makers. There are a lot of different things processed and operated with the data science.

In terms of big data, it is a very big amount of unorganized data from numerous sources and this amount of data can not be operated suitably just by making use of conventional apps. And this is the establishment for data science. On the contrary, machine learning includes all the AI strategies utilized in data mining. Python is one of the programming language utilized in developing machine learning. Within machine learning, there is the communication between systems such as production databases, data cleansing and some others. This interaction is aimed to develop the predictive models in machine learning.

Another term is data mining, which contains setting up the models being able to predict the values of different variables through making use of machine learning algorithms to big data. This is the process including gathering data and seeking for patterns within that data. It also includes designing algorithms which are mainly utilized for getting insights from very huge amounts of unstructured data through identifying and applying patterns. Pattern identification and clustering are some of the activities in mining data.

Data science relies much on data mining. Actually, data mining is the first step of data science as it lets data specialists to distinguish the findings and the random noise.

What is more, data analytics take advantage of data mining activities to explore the patterns in the data set. This activity is to predict what event is going to take place in the future. ‘

3. The similarities and differences between data science and business intelligence

A lot of people consider data science an innovative form of business intelligence. Nevertheless, they are two different disciplines and data science can not replace business intelligence and vice versa. Actually, both data scientists and business analysts collaborate together in various roles which are related to each other so that they can make raw data become a more helpful information.

Moreover, both data science and business intelligence provide companies the chance to uncover the useful information in raw data. Currently, there are a lot of companies demanding on both professionals in two aspects in order to make the most of big data.

Regarding to business intelligence, it is a process including the retrospective reports in order to assist entrepreneurs in controlling their business status as well as support for historical business performance. To be more specific, business intelligence puts an emphasis on understanding past data.

Business Intelligence seems to concentrate on reporting and alerts which are the value of visualization. The core value of this technology is what called accessibility. In spite of the fact that companies take advantage of business intelligence to making important decisions, it still has a lot of restrictions. More precisely, tools of business intelligence that work with variables have already existed. As a result, users should get to know what they are demanding on when making use of business intelligence tools.

By contrast, data science is very different from business intelligence as it still takes advantage of past data in order to yield the results about future predictions. It means that data scientists can support organizations to make predictions of the future events.

Whereas business intelligence seems to be structured, data science is moving more towards the unstructured. To be more specific, data science is able to work with unstructured data which is not usable at the time without cleaning and organizing.

Both data science and business intelligence lie on the same spectrum although they are at the opposite ends. Business intelligence concentrates on controlling and reporting current business data so as to manage the concern or interest whereas data science creates the predictive insights as well as new products through making use of improved analytics tools and algorithms.

Data science seems to be more complicated than business intelligence with using tools like Hadoop, SQL or Python and so on.

4. The Evolution of the Quantitative analysts

Quantitative analysts (shortly written as quants) are professionals that are skillful at making analysis and controlling the quantitative data. Although data science was still in the early beginning, quants have already dominated the field. They are able to seek for the proverbial needle then clarifying it and making it become useful for skillful programmers who are capable of turning it into a repeatable algorithm.

Today, quantitative analysts have to deal with a lot of obstacles because the only data available was the data which is known to be helpful. With the aim of testing a theory, quantitative analysts should take advantage of a lot of complicated languages to seek for methods to operate the algorithms so that they can repeat the findings. In this case, there would be a need for support systems such as databases and IT infrastructure.

To conclude, big data is going to dominate the field, especially when it comes to the storage costs and the data processing.