What is Data Science? Exclusively written for Freshers into Learning
What is Data Science ?
In data science, computer science, machine learning, statistics, visualization, and human computer interactions combine to collect, clean, analyse, integrate and visualize with data to create data products. Essentially, data science is a skill of extracting knowledge from data. The process of getting insights from a raw data is data science. In short, What is Data Science mean – it is the methodology used to apply advanced statistical tools to the existing data to generate new insights, thereby converting these insights into new effective business processes. Data science toolkit comprises of many statistical methods, user experience and tools which enable in intelligent decision making. Data science mainly involves organizing, packaging, and delivering the data for providing the apt solution for the organization. It analyses data sets, understands it by performing necessary operations and processes the same for the benefit and use of users. Now let us go in brief on What is Data Science and how it is useful for us.
History of data science
In earlier times, although there was a lot of data created, it was not made use of effectively. There were no predictions or decision making done using the data created. Also, there were lack of scientific insights, for example, using the available forecast data, we were not able to forecast the future predictions with the available reports (data). In addition to this, the data storage used to happen in traditional and unstructured format which was a lot more difficult task to resolve. Unstructured means that the data is not stored in proper format of rows and columns. Also, since the data is created at an exponential rate, storing data with lots of traffic is a complex task, needing lots of expertise in data science field.
Why data science now?
Data is everywhere, and the digital data is growing exponentially, and is expected to reach a big value in the coming years. At this juncture, it’s important for the organizations to analyse the corresponding data to bring in improvements and enhancements in their businesses.
Data science is much more than analysing data. The data scientists solve the problems of different data users, as they have a nose for identifying data problems; hence they require curiosity, creativity and willingness to ask a lot of questions. Data science is one of the most dynamic industries today. Data scientists always thrive to recognise and respect the fact that this field is an ever-morphing one and they should always update themselves to be on par with many other data scientists in the world.
Data science has enabled us to move into a decision-making approach where we can view the current status with reports v/s what we can do better to make the situation profitable and predict the profits of an organization based on the data stored in the past few years. Essentially data science helps us to use the existing reports for future predictions, store the data in a structured manner, and use the predetermined data for future purposes.
Present and future of Data science
With dramatically growing technology, it is essential that we also bring in changes with the tools and methodologies used in business processes to produce excellent results. In the due process, there has been exponential growth in the field of data science also. Where there were traditional methods of data collection in the earlier days, and the data was not used effectively, in these modern days, the data is not only collected, but is organized and processed for business profits.
In today’s world, data science has been divided into logical parts, each part performing its duties duly for bringing in data enhancements for an organization:
Statistical methods – Statistical analysis is the key to many data science tasks involving computation of simple, mean and multiple complex regression analysis. The most widely-used statistical method in data science is machine learning. In machine learning, the data scientists train the computers to learn, develop and grow by themselves without having to intervene or explicit programming. Machine learning is a complex process which is understood only through strong analysis, effective training, and appropriate guidance. The complete training module can be learnt from click4learning, which provides introduction and deep dive into machine learning process. Browse through the available topics and enrol for gaining knowledge in machine learning from Machine Learning Courses.
Tools – Just like strong statistical methods are essential for data science, tools also play an equally important role. Data scientists are inquisitive and must possess knowledge in these tools and be proficient in working knowledge of many statistical programming languages for constructing data processing systems, databases and visualization tools. Many programming language consisting of excellent data libraries and which are easily to use and widely available are part of data science. These tools are utilized to analyse truly massive and unstructured datasets and transfer data to and from neatly categorized rows and columns. Some tools you might consider for effective usage are Python, Big data, Hadoop etc. Browse through the available topics and enrol for gaining knowledge in various data science tools from Click4learning and checkout Python with Data Science details.
UX Research – UX is an exponentially growing field and it has captured the interest of many enthusiasts who want to bring in changes in user experiences. Data science is not far behind, but technically speaking, UX design and data science work independently. But in spite of that, data scientists and UX designers have similar workflows and they share the same roof, as they both collect and analyse data for effective user experience. Various tools used for user experience research in What is Data Science provides an insight and knowledge to UV researches, in combination with the data scientists makes it an important decision making aspect. Browse through the available topics and enroll for gaining knowledge in data science tools from https://click4learning.com/.
Skills required for a data scientist
Still Many of them have a doubts like What is Data Science , Who is a data scientist and what are their roles? This is a point to ponder about when a data scientist is being hired. Technically speaking, a data scientist must be capable of examining raw data and provide insights by applying algorithmic and mechanical processes. A data scientist must be able to derive conclusions from the knowledge they have, and by examining the various data sets and analysing significant correlations between them, which is later used by the organisation for making profitable business relations. A data scientist must possess the following skills in order to be able to analyse the data to the utmost accurateness:
Programming skills and knowledge – a data scientist must have impeccable programming skills and knowledge of where and how to use them so organization can take appropriate decisions.
Data squabbling skills – a data scientist must be able to analyse the data to the fullest possible to provide exceptional suggestions on how to organize and present the data in the most influential manner.
Statistical skills – a data scientist must be exceptionally good in statistical skills and must have excellent academics in mathematics, in order to provide the required numbers meticulously.
Apart from the above skills, it goes without saying that a data scientist must possess extraordinary communication skills and the zeal for learning new technologies and tools. Industry-specific trainings, recommendations, and special certifications can be extremely beneficial for a data scientist to excel in their fields.
For any of your Data Science Online Training needs, reach out to us at email@example.com.
Contact us today to learn about Data Science Online Course.