Industry revolution 4.0 is evolving around the buzzing technologies like Machine Learning, AI, IOT, Big Data, and Data Science. In any industry, the accuracy of the predictive analysis depends on the previous data available. Let us take an example of the car industry – for any business; it is very common to have an offseason where sales will not meet the production. Car manufacturing companies cannot stop production because of the off-season as they are equipped for a certain volume of products for maximum efficiency of the plant. If the company cannot come up with good sales strategy it would end up in piling up the inventory. Based on the predictive analysis companies can change sales strategy to attract customers by offering best deals.
Indirectly statistical analysis would help the company’s sales and profit. With the availability of a huge amount of data, it is necessary to have some software with can do the predictive analysis in a shorter time and accurately.
What is Data Science? What is machine learning? What is Big Data?
Big Data is a collection of complex and large data which is difficult to process using traditional data management approaches such as RDBMS. Data availability can be in multiple formats like structured, unstructured, machine data, natural language, graphical, audio, video, images and streaming data. It is very difficult for traditional approaches to data analysis for all these aspects. There is a need for software which can organize all these unorganized data and make them ready for analysis to get the accurate prediction.
Data Science is a combined field of many disciples which makes use traditional methods and software technologies from different fields such as computer science, databases, mathematics, statistics, and machine learning. Data Science consists of multiple activities around the variety of data in huge quantities like the collection, preparation, analysis, visualization, management, and preservation of data.
Machine learning is a field of study of algorithms and mathematics models that gives computers the ability to learn and models the trillion of data in different formats.
Machine learning algorithms build a mathematical model of sample data and give the ability to computers to make predictions without being explicitly programmed to perform the task. These systems perform a variety of tasks that involve planning, prediction, recognition, robot control, diagnosis, etc.
Machine learning mainly linked to the data modeling phase of data science.
Data Science Process:
Setting the research goal
Presentation and Automation
Key components of Data Science:
As we discussed till now Data science deals with the huge amount of data which cannot handle it in traditional ways. There is a need for programming languages like Python and R Programming are most popular in the data science community.
Specifically for data science, the availability of third-party packages like matplotlib, scipy, etc makes easier to implement programmed data science. Along with the packages, there is an availability of a large number of DSL(domain specific languages) which are in a human-readable format and easy to code using different IDEs like E-macs, VIM and interactive python environments like IPython and Jupiter have made using python easier than other languages.
R is a programming language is designed and developed by statisticians for statistics in order to do a variety of statistical analysis and graphical presentation of the data.
R programming has extensively and a wide variety of packages for data modeling, data mining and data visualization. It is an open source language and huge active community of programmers and domains experts in statistics are part of the community which always enriching to flourish new libraries for new and accurate statistical methods.
So Big Data, Data Science, Machine learning are interlinked to make predictive analysis efficiently and accurately.
Data Science is a craft of tools that data scientist used to combine the business world and data world for better strategies for improving business growth. Data analysts are producing insights with data, usually done by extracting, cleaning, analyzing, visualizing and presenting data using tools.
Data Scientist should possess are skills in encapsulating programming skills and some statistical readiness, along with visualization techniques with a lot of business senses. So the person should have in-depth knowledge on the business (domain knowledge) for data analysis, once analyzed then it should be depicted in a webpage by incorporating business logic and visualization technique to depict the analysis in graphs. A data scientist is an all-rounder for any business analysis.