data-science

What Is Data Science?

As the world entered the era of big data, the requirement for its storage likewise grew. It was the fundamental test and concern for the enterprise industries until 2010. The primary spotlight was on building a framework and solutions to store data. Presently when Hadoop and other frameworks have effectively tackled the problem of storage, the center has moved to the processing of this data. Data Science is the secret sauce here. All the thoughts which you find in Hollywood science fiction films can really turn into reality by Data Science. Data Science is the future of Artificial Intelligence. Therefore, it is very important to understand what is Data Science and how might it increase the value of your business.

In this blog, I will cover the accompanying themes.

  • What is Data Science in basic words?
  • Why Data Science?
  • Who is a Data Scientist?
  • What does a Data Scientist do?
  • How is it different from Business Intelligence (BI) and Data Science?

The lifecycle of Data Science with the assistance of an utilization case

Before the finish of this blog, you will have the option to understand what is Data Science and its role in extracting significant experiences from the mind boggling and large arrangements of data surrounding us. To get inside and out information on Data Science, you can enroll for live Data Science Certification Training by Edureka with every minute of every day support and lifetime access.

What is Data Science in straightforward words?

Data Science is a mix of various tools, algorithms, and machine learning principles with the objective to discover concealed patterns from the raw data. In any case, how is this different from what analysts have been getting along for years?

The answer lies in the difference among clarifying and predicting.

Data Analyst v/s Data Science - Edureka

As should be obvious from the above picture, a Data Analyst as a rule clarifies what is happening by processing history of the data. Then again, Data Scientist not exclusively does the exploratory investigation to discover experiences from it, yet additionally utilizes various progressed machine learning algorithms to distinguish the occurrence of a particular occasion in the future. A Data Scientist will take a gander at the data from numerous edges, now and then points not known earlier.

Thus, Data Science is primarily used to settle on choices and predictions utilizing predictive causal analytics, prescriptive analytics (predictive in addition to choice science) and machine learning.

Predictive causal analytics – If you need a model that can predict the possibilities of a particular occasion in the future, you have to apply predictive causal analytics. State, on the off chance that you are providing cash on credit, at that point the probability of customers making future credit installments on time involves concern for you. Here, you can manufacture a model that can perform predictive analytics on the installment history of the customer to predict if the future installments will be on schedule or not.

Prescriptive analytics: If you need a model that has the intelligence of taking its own choices and the ability to change it with dynamic parameters, you certainly need prescriptive analytics for it. This relatively new field is tied in with providing guidance. In other terms, it predicts as well as proposes a range of prescribed activities and related results.

The best model for this is Google’s self-driving car which I had talked about earlier as well. The data gathered by vehicles can be utilized to train self-driving cars. You can run algorithms on this data to bring intelligence to it. This will empower your car to take choices like when to turn, which way to take, when to back off or accelerate.

Machine learning for making predictions — If you have transactional data of a fund organization and need to fabricate a model to determine the future trend, at that point machine learning algorithms are the smartest choice. This falls under the paradigm of supervised learning. It is called supervised in light of the fact that you already have the data dependent on which you can train your machines. For instance, a fraud location model can be trained utilizing a historical record of fraudulent purchases.

Machine learning for pattern discovery — If you don’t have the parameters dependent on which you can make predictions, at that point you have to discover the concealed patterns within the dataset to have the option to make significant predictions. This is only the unsupervised model as you don’t have any predefined names for grouping. The most well-known algorithm utilized for pattern discovery is Clustering.

Suppose you are working in a phone organization and you have to build up a network by placing towers in a region. At that point, you can utilize the clustering strategy to discover those tower areas which will ensure that all the users receive ideal sign strength.

We should perceive how the proportion of above-described approaches differ for Data Analysis just as Data Science. As should be obvious in the picture beneath, Data Analysis incorporates descriptive analytics and prediction partially. Then again, Data Science is more about Predictive Causal Analytics and Machine Learning.

Why Data Science?

  • Traditionally, the data that we had was mostly structured and small in size, which could be analyzed by using simple BI tools. Unlike data in the traditional systems which was mostly structured, today most of the data is unstructured or semi-structured. Let’s have a look at the data trends in the image given below which shows that by 2020, more than 80 % of the data will be unstructured.

 

  • This data is generated from different sources like financial logs, text files, multimedia forms, sensors, and instruments. Simple BI tools are not capable of processing this huge volume and variety of data. This is why we need more complex and advanced analytical tools and algorithms for processing, analyzing and drawing meaningful insights out of it.

This is not the only reason why Data Science has become so popular. Let’s dig deeper and see how Data Science is being used in various domains.

  • How about if you could understand the precise requirements of your customers from the existing data like the customer’s past browsing history, purchase history, age and income. No doubt you had all this data earlier too, but now with the vast amount and variety of data, you can train models more effectively and recommend the product to your customers with more precision. Wouldn’t it be amazing as it will bring more business to your organization?
  • Let’s take a different scenario to understand the role of Data Science in decision making. How about if your car had the intelligence to drive you home? The self-driving cars collect live data from sensors, including radars, cameras, and lasers to create a map of its surroundings. Based on this data, it takes decisions like when to speed up, when to speed down, when to overtake, where to take a turn – making use of advanced machine learning algorithms.
  • Let’s see how Data Science can be used in predictive analytics. Let’s take weather forecasting as an example. Data from ships, aircraft, radars, satellites can be collected and analyzed to build models. These models will not only forecast the weather but also help in predicting the occurrence of any natural calamities. It will help you to take appropriate measures beforehand and save many precious lives.

Let’s have a look at the below infographic to see all the domains where Data Science is creating its impression.

Data Science Use Cases - Edureka

Who is a Data Scientist?

There are several definitions accessible on Data Scientists. In straightforward words, a Data Scientist is one who practices the art of Data Science. The term “Data Scientist” has been instituted after considering the way that a Data Scientist draws a great deal of information from the logical fields and applications whether it is measurements or arithmetic.

What does a Data Scientist do?

Data researchers are the individuals who crack complex data problems with their strong expertise in certain logical controls. They work with several components related to arithmetic, measurements, computer science, and so forth (however they may not be an expert in every one of these fields). They make a great deal of utilization of the latest innovations in discovering solutions and reaching resolutions that are crucial for an organization’s growth and development. Data Scientists present the data in a considerably more helpful form as compared to the raw data accessible to them from structured just as unstructured forms.

To find out about a Data Scientist you can refer to this article on Who is a Data Scientist?

Moving further, lets currently talk about BI. I am sure you may have heard of Business Intelligence (BI) as well. Regularly Data Science is mistaken for BI. I will express some succinct and clear contrasts between the two which will help you in improving understanding. How about we see.

Business Intelligence (BI) versus Data Science

BI fundamentally breaks down the previous data to discover knowing the past and understanding to describe business trends. BI empowers you to take data from external and internal sources, prepare it, run queries on it and create dashboards to answer questions like quarterly revenue investigation or business problems. BI can assess the effect of certain occasions in the near future.

Data Science is a more forward-looking approach, an exploratory route with the emphasis on breaking down the past or current data and predicting the future results with the point of settling on informed choices. It answers the open-finished questions concerning “what” and “how” occasions occur.

We should examine some contrasting features.

FeaturesBusiness Intelligence (BI)Data Science
Data Sources Structured
(Usually SQL, often Data Warehouse)
 Both Structured and Unstructured( logs, cloud data, SQL, NoSQL, text)
ApproachStatistics and VisualizationStatistics, Machine Learning, Graph Analysis, Neuro- linguistic Programming (NLP)
FocusPast and PresentPresent and Future
ToolsPentaho, Microsoft BI, QlikView, RRapidMiner, BigML, Weka, R

 

 

A common mistake made in Data Science projects is rushing into data collection and analysis, without understanding the requirements or even framing the business problem properly. Therefore, it is very important for you to follow all the phases throughout the lifecycle of Data Science to ensure the smooth functioning of the project.

Lifecycle of Data Science

Here is a brief overview of the main phases of the Data Science Lifecycle:

Lifecycle of Data Science - Edureka


Discovery of Data Science - EdurekaPhase 1—Discovery: 
Before you begin the project, it is important to understand the various specifications, requirements, priorities and required budget. You must possess the ability to ask the right questions. Here, you assess if you have the required resources present in terms of people, technology, time and data to support the project. In this phase, you also need to frame the business problem and formulate initial hypotheses (IH) to test.

 

Data Science data preparation - Edureka

Phase 2—Data preparation: In this phase, you require analytical sandbox in which you can perform analytics for the entire duration of the project. You need to explore, preprocess and condition data prior to modeling. Further, you will perform ETLT (extract, transform, load and transform) to get data into the sandbox. Let’s have a look at the Statistical Analysis flow below.

Lifecycle of Data Science
You can use R for data cleaning, transformation, and visualization. This will help you to spot the outliers and establish a relationship between the variables. Once you have cleaned and prepared the data, it’s time to do exploratory analytics on it. Let’s see how you can achieve that.

Phase 3—Model planning: Data Science model planning - EdurekaHere, you will determine the methods and techniques to draw the relationships between variables. These relationships will set the base for the algorithms which you will implement in the next phase. You will apply Exploratory Data Analytics (EDA) using various statistical formulas and visualization tools.

 

Let’s have a look at various model planning tools.

Model planning tools in Data Science - Edureka

  1. R has a complete set of modeling capabilities and provides a good environment for building interpretive models.
  2. SQL Analysis services can perform in-database analytics using common data mining functions and basic predictive models.
  3. SAS/ACCESS  can be used to access data from Hadoop and is used for creating repeatable and reusable model flow diagrams.

Although, many tools are present in the market but R is the most commonly used tool.

Data Science skills - Edureka

As you can see in the above image, you need to acquire various hard skills and soft skills. You need to be good at statistics and mathematics to analyze and visualize data. Needless to say, Machine Learning forms the heart of Data Science and requires you to be good at it. Also, you need to have a solid understanding of the domain you are working in to understand the business problems clearly. Your task does not end here. You should be capable of implementing various algorithms which require good coding skills. Finally, once you have made certain key decisions, it is important for you to deliver them to the stakeholders. So, good communication will definitely add brownie points to your skills.

I urge you to see this Data Science video tutorial that explains what is Data Science and all that we have discussed in the blog. Go ahead, enjoy the video and tell me what you think.