Karl Kjer || Data Science an Overview and Its Importance

Karl Kjer is a famous scientist and Science professor. Here he provides some useful knowledge about Data science.

 

Karl Kjer

Data science is the future of Artificial Intelligence. Thus, it is imperative to understand the value of Data Science and how your business gets benefitted from it. Data Science is a blend of different tools, machine learning principles, and algorithms that aim at discovering the hidden patterns from the raw data. Data Scientist besides doing the exploratory analysis makes use of various advanced machine learning algorithms for identifying any occurrence of a particular event in future. A Data Scientist looks at the data from various angles. Thus, Data Science is mainly used for making predictions and decisions with the use of prescriptive analytics, predictive causal analytics, and machine learning.

Importance of Data Science
 
Traditionally, the data was small in size and structured that could be analysed using the simple BI tools. In the present time, data is semi-structured or unstructured. Here arises the need of having a more advanced as well as complex algorithm and analytical tools for analyzing, processing and drawing something meaningful out of it. But this is not the only reason why Data Science has become immensely popular. Nowadays, it is used in various fields. It is the Data Science that helps to a great extent in decision making. 

All about Data Science Course
 
In the recent years, there has been a great demand among the top notch corporate in hiring the data scientist. If you are keen on bagging a dream job in a reputed company, the data scientist is an ideal option. All you need to do is to enrol in a reputed institute for the data science course. If you are a busy professional, the online class is there to get in-depth knowledge about data science. The course will enable you to get a clear idea about the data scientist toolbox. You will get an overview of the questions, data, tools that the data scientists work with. There are two components of this course: the first part deals with ideas behind turning the data into actionable knowledge and the second part deals with the practical introduction to the used by the data scientist. Thus, enrol for the course and become a proficient professional. 

The Data Science lifecycle is divided into six phases. They are as follows:

• Phase 1 is the discovery phase. Here you need to understand the requirements, specifications, required budget and priorities. In this phase, formulate an initial hypothesis and frame the business issues.
• Phase 2 is for preparing data. Here, you need analytical sandbox where you can perform analytics for the project till completion.
• Phase 3 is the model planning stage. Here, you will determine techniques and methods for drawing the relationships between variables.
• Phase 4 is for model building. It is a phase where you need to develop data sets for testing and training purposes.
• Phase 5 is known as an operational phase. Here, you need to deliver the final reports, code, briefings and technical documents. A pilot project is also implemented in a real-time environment.
• Phase 6 is known as communicating results. It is the final phase where you identify all the key findings, communicate with the stakeholders and determine if the project is a successful one or a complete failure based on the criteria developed in phase 1.

The Bottom Line

A common mistake which is made in Data Science project is jumping into collecting data and analysis without thoroughly understanding the requirements or without even framing the business issues rightly. Thus, it is imperative to follow all the phases through the entire lifecycle of data science for ensuring smooth functioning of the project. 

For more science tips and knowledge contact Karl Kjer and follow his blogs as well.