Data Science

As the world enters the age of modern and massive knowledge, then it also became necessary to store the data, so it was a challenge for enterprises till 2010 producing such frameworks that would help to save and store data, in this regards Hadoop and other frameworks became successful to solve the problem. Now the focus is on the processing of data and data science is the subject behind all about the processing of huge data. The study of data science is very important for those; add some value to their businesses. In this article, we will answer some questions to understand what data science is.

Importance of Data Science:

Before 2000, mostly the data was structured and small in size, so the analysis of data was simple by using BI tools. Today the volume of data is so large and mostly it is unstructured or semi-structured. According to some observations, by 2020 there will be 80% unstructured data.

Data is generated from different sources like text files, financial logs, sensors, multimedia forms, and instruments. This huge amount of data cannot be analyzed by using simple BI tools, so here we need advanced technologies and algorithms for processing of data, to give us a meaningful insight into data.

There are some other reasons as well that why data science has become so important. We will point out the importance of data science in various domains.

•    Data science plays a very important role in sell and marketing of a product. You can understand the precise requirements by analyzing the past data of your customers. Today the data is available in large amount and it trains the models more precisely, result in a precise recommendation of products for customers.

•    Data science becomes so effective in the decision-making process such as we take the example of the self-driving car. The car collect data from the sensor, radars, camera, and lasers to control itself, it takes the decision of speed, turns, overtakes according to the data it collects from the given sources.

•    Data science is used in predictive analytics like we take the example of weather forecasting, here data from satellites, radars, ships, aircraft are gathered and then create models and these not only help in predicting the weather but also make aware of the occurrence of natural calamities.

 What is Data Science?

Nowadays the term data science is becoming so popular, we will discuss here in detail what it means, what the skill you require to become a data scientist and will differentiate between data science and BI. Data science is a combination of various tools, algorithms and machine learning principles that are used to find some hidden patterns from raw data.

There are statisticians or data analysts who do the same job for many years but how we will differentiate it from a data scientist. Data analyst tell you what is happening now by processing history of the data and on other hand data scientist tell you the present and also predict the future of an event.

Data science is mainly used for decision making and prediction by using tools like predictive causal analytics, prescriptive analytics, and machine learning.

Predictive Causal Analytics:

This model is used to predict the occurrence of a particular event in the future by analyzing the past data of the event. If we take the example of a bank, they provide loans to people; here this model is applied to predict the probability of the person making future credit payments on time.

Prescriptive Analytics:

Here we talk about a model that has the intelligence of taking its own decisions and also the ability to changes his decisions according to its dynamics surrounding. This model provides you multiple suggestions with its possible outcomes. The self-driving car is an example of prescriptive analytics, car collects data from its different sources such as radar, camera, sensors, and this data is used to trains self-driving car and intelligence is added to it through algorithms so that it becomes able to make decisions like when speed up or down, when to turn.

Machine learning for making Predictions:

If you have data of financial transactions of any company and want to make a model that predicts the future trends then machine learning algorithms is the best option. This model comes under the paradigm of supervised learning. In this model you have data and you train your machine based on this data.

Machine learning for pattern discovery:

In this model when you have no parameters on which you make predictions, so here you have to find the hidden pattern inside data which helps you to reach meaningful predictions. The algorithms used to find a hidden pattern is called clustering.

Lifecycle of Data Science:

Here we will discuss the main lifecycle of data science:

Phase 1 (Discovery): 

In the beginning when you choose a project then first understand and consider various specifications, requirements, and budget required for completion of the project. The most import aspect of this phase to ask the right question very related to the project and frame the problem then formulate initial hypnosis for testing.

Phase 2 (Data Preparation):

In this phase, you need an analytical sandbox during which you will be able to perform analytics for the complete period of the project and you have to preprocess and apply some condition before placing in the model. Before passing the data into the sandbox, you have to perform ETLT on the data. When the data is transformed and cleans and exploratory analytics is done to it.

Phase 3 (Model Planning):

In this phase, your search for a method or techniques that build relationship between variables and a relationship provide a base for generating algorithms that are useful in the next phase.

Model planning tools are given below:

R:

It is a complete set which has the capabilities of modeling with an easy and good environment to build interpretive models.

SQL Analysis Service:

Data analytics is performed in the database by using functions like data mining, predictive models.

SAS/ACCESS:

This tool is used to access data from Hadoop and mainly used for reusable and repeatable flow diagrams.

R is most commonly used in the market as compared to other models present in the market. When you understand the insight of your data then you produce an algorithm for the data, now the next stage is to apply the algorithms to build a model.

Phase 4 (model building):

You design dataset in this phase for training purposes and consider the tools whether it is so powerful to run the model and need some improvement in the existing tools. Analyzing of various techniques are also considered including classification, association, and clustering to build a model.

Phase 5 (Operationalize):

Here you deliver the final product which includes final reports, codes and technical documents and some time for experience a pilot project is also launched in a real-time production environment to see a picture of performance and some other constraints before launching on a full scale.

Phase 6(Communicate result):

When you achieve your goal then go to evaluate the result and whatever you planned in the first phase, so here highlight the key findings and discuss it with the stakeholders if the outcome you get, is a failure or success based on the principle you developed in the first phase.