This tutorial will familiarize you with data science. We are also discussing the 3 major fields of data science. Beginners should start learning data science from basics tutorials.This tutorial will familiarize you with data science. We are also discussing the 3 major fields of data science. Beginners should start learning data science from basics tutorials.
In this section are going to provide you the basics details of Data Science. Fresher's will be able to understand the basics of Data Science and its 3 major fields. We have large number of tutorials of Data Science and Machine learning on our website. We are also providing technical coding examples and projects for learning Data Science programming. So, let's understand the basics of Data Science.
Data science a multifaceted discipline that helps meaningful utilisation of the potential of insights hidden inside data. Data science is a multidisciplinary field of computing science that through the use of scientific methods, data processing, algorithms and data systems helps extracting valuable data-driven insights from all types of data including both structured and unstructured data types. Data Science field includes knowledge and expertise from various fields of science. Data science is the field of collecting, processing, understand, processing, finding the hidden trends in data and visualizing the findings of data.
There are 5 steps involved in the Data Science projects:
In the following sections we will learn all these steps with many programming exercises. We have programming examples in Python, Spark, TensorFlow and R programming languages. You can go through all these programming exercises to learn it in more detail.
Data Science and Machine learning technologies are continuously evolving field with vast potential in near future. There is great demand of skilled Data Science professionals in the IT Industry and many skilled professionals are earning decent salary in the industry.
Developers with the right sets of skills like data ingestion, data processing, model development, data analytics and data visualizations are getting highly paid jobs in the industry. So, its right time to learn these new skills to be competent in the job industry.
The core of data science comprises of basically data of all types from all sources and pertaining to both structured and unstructured data categories. Bulk of raw data that comes into the large enterprise warehouses can be mined or analysed to derive most useful data-driven insights for relevant and real-life business uses. The ultimate objective is to facilitate multiple ways to utilise this multifaceted data for generating business value.
The primary and most widely adopted utilisation of the Data Science is data-driven decision making by utilising predictive analytics, prescriptive analytics and Machine Learning. Let us now have a brief understanding of these 3 major fields of data science.
Predictive analytics is the particular field of data science that helps predicting any event of the future by analysing the present data streams that relate to those possible events. For instance, for lending bank getting the credited amount in the bank is a matter of grave concern. Predictive analytics by analysing the payment history of the respective customer can precisely predict whether the customer is trustworthy for timely payment or not.
All those machine models that can take decisions and modify the same dynamically on a wide range of parameters, prescriptive analytics is used. This data science field is relatively new and is used mainly for making machines more equipped in respect of faster decision making for the desired actions and outcomes. The autonomous car of Google is a great example of this data analytics tool. The data collected by an autonomous vehicle can be utilised to train the self-driving cars for better and more precise on-road driving decisions.
In all those cases where you don't have enough user data to analyse their preferences and behaviour, you need to observe the user behaviour and collect data from their use of the respective machine. This allows easily detecting hidden patterns in the user behaviour data. Based on the data-driven insights processed by analysing the data gathered and learned by the machine, precise recommendations and interactions for the users can be created. A great example of the utilisation of Machine Learning model is the way intelligent Chatbots are used across mobile and web interfaces.
To conclude, we must point out that data science is not the same discipline as we know by data analysis. Data analysis is a crucial and most commonly referred utilisation of the data science discipline. Data science is actually broad discipline comprising of all data-centric fields focused on utilisation of the data for relevant, real-life and practical uses.
Related Data Science Tutorials: