What is Data Science?

What is Data Science?

Data Science is a detailed study of the flow of information from the colossal amounts of data present in an organization’s repository. It involves obtaining meaningful insights from raw and unstructured data which is processed through analytical, programming, and business skills. Let's get started with our 'What is Data Science?' blog.

Importance of Data Science: The Current Scenario

In a world that is increasingly becoming a digital space, organizations deal with zettabytes and yottabytes of structured and unstructured data every day. Evolving technologies have enabled cost savings and smarter storage spaces to store critical data.

Currently, in the industry, there is a huge need for skilled and certified Data Scientists. They are among the highest-paid professionals in the IT industry. According to Forbes, ‘the best job in America is of a Data Scientist with an average annual salary of $110,000’. Only a few people have the capability to process it and derive valuable insights out of it.

Furthermore, looking at the huge and ever-increasing requirements, McKinsey has predicted that there will be a 50 percent gap in the supply of Data Scientists versus its demand in the upcoming years. That’s why in this blog we are talking about ‘What is Data Science?’

Check out this Intellipaat Data Science Full Course video:

In recent years, there is a huge growth in the field of Internet of Things (IoT), due to which 90 percent of the data has been generated in the current world. Every day, 2.5 quintillion bytes of data are generated, and it is more accelerated with the growth of IoT. This data comes from all possible sources such as:

• Sensors used in shopping malls to gather shoppers’ information
• Posts on social media platforms
• Digital pictures and videos captured in our phones
• Purchase transactions made through e-commerce

This data is known as big data.

Companies are flooded with colossal amounts of data. Thus, it is very important to know what to do with this exploding data and how to utilize it.

It is here, the concept of Data Science comes into the picture. Data Science brings together a lot of skills like statistics, mathematics, and business domain knowledge and helps an organization find ways to:

• Reduce costs
• Get into new markets
• Tap on different demographics
• Gauge the effectiveness of a marketing campaign
• Launch a new product or service

Learn Data Science from experts, click here to more in this Data Science Training in New york!

And the list is endless!

Therefore, regardless of the industry vertical, Data Science is likely to play a key role in your organization’s success.

Look at the below infographic, and you will be able to understand how Data Science is creating its impression:

How do top industry players use Data Science?

In this section of the ‘What is Data Science?’ blog, we will look at how top industry players like Google, Amazon, and Visa are using Data Science. IT organizations need to address their complex and expanding data environments in order to identify new value sources, exploit opportunities, and grow or optimize themselves, efficiently. Here, the deciding factor for an organization is ‘what value they extract from their data repository using analytics and how well they present it’. Below, we list some of the biggest and best companies that are hiring Data Scientists at top-notch salaries.

Google is by far the biggest company that is on a hiring spree for trained Data Scientists. Since Google is mostly driven by Data Science, Artificial Intelligence, and Machine Learning these days, it offers one of the best Data Science salaries to its employees.

 

Amazon is a global e-commerce and cloud computing giant that is hiring Data Scientists on a big scale. They need Data Scientists to find out customer mindset and enhance the geographical reach of both e-commerce and cloud domains, among other business-driven goals.

 

An online financial gateway for most companies, Visa does transactions worth hundreds and millions in a single day. Due to this, the need for Data Scientists is huge at Visa to generate more revenue, check fraudulent transactions, and customize products and services as per customer requirements, etc.

Data Science Life Cycle

For a better understanding of ‘What is Data Science?’, let’s explore its life cycle. Suppose, Mr. X is the owner of a retail store and his goal is to improve the sales of his store by identifying the drivers of sales. To accomplish the goal, he needs to answer the following questions:

• Which are the most profitable products in the store?
• How are the in-store promotions working?
• Are the product placements effectively deployed?

His primary aim is to answer these questions which would surely influence the outcome of the project. Hence, he appoints you as a Data Scientist. Let’s solve this problem using the Data Science life cycle.

Data Discovery

The first phase in the Data Science life cycle is data discovery for any Data Science problem. It includes ways to discover data from various sources which could be in an unstructured format like videos or images or in a structured format like in text files, or it could be from relational database systems. Organizations are also peeping into customer social media data, and the like, to understand customer mindset better.

In this stage, as a Data Scientist, our objective would be to boost the sales of Mr. X’s retail store. Here, factors affecting the sales could be:

• Store location
• Staff
• Working hours
• Promotions
• Product placement
• Product pricing
• Competitors’ location and promotions, and so on

Keeping these factors in mind, we would develop clarity on the data and procure this data for our analysis. At the end of this stage, we would collect all data that pertain to the elements listed above.

Data Preparation

Once the data discovery phase is completed, the next stage is data preparation. It includes converting disparate data into a common format in order to work with it seamlessly. This process involves collecting clean data subsets and inserting suitable defaults, and it can also involve more complex methods like identifying missing values by modeling, and so on. Once the data cleaning is done, the next step is to integrate and create a conclusion from the dataset for analysis. This involves the integration of data which includes merging two or more tables of the same objects, but storing different information, or summarizing fields in a table using aggregation. Here, we would also try to explore and understand what patterns and values our datasets have.

Mathematical Models

Do you know, all Data Science projects have certain mathematical models driving them. These models are planned and built by the Data Scientists in order to suit the specific need of the business organization. This might involve various areas of the mathematical domain including statistics, logistic and linear regression, differential and integral calculus, etc. Various tools and apparatus used in this regard could be R statistical computing tools, Python programming language, SAS advanced analytical tools, SQL, and various data visualization tools like Tableau and QlikView.

Also, to generate a satisfactory result, one model might not be enough. We need to use two or more models. In this scenario, a Data scientist will create a group of models. After measuring the models, he/she will revise the parameters and fine-tune them for the next modeling run. This process will continue until the Data Scientist is pretty sure that he/she has found the best model.

Become Master of Data Science by going through this online Data Science course in Toronto.

In this stage, as a Data Scientist, you will build mathematical models based on the business needs of Mr. X, i.e., based on if product A or product B is the most profitable in the store, whether the product placements are effectively working in the store, etc.

Getting Things in Action

Once the data is prepared and the models are built, it is time to get these models working in order to achieve the desired results. There might be various discrepancies and a lot of troubleshooting that might be needed, and thus the model might have to be tweaked. Here, model evaluation explains the performance of the model.

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In this stage, you as a Data Scientist will gather information and derive outcomes based on the business requirements of Mr. X.

Communication

Communicating the findings is the last but not the least step in a Data Science endeavor. In this stage, the Data Scientist needs to be a liaison between various teams and should be able to seamlessly communicate his findings to key stakeholders and decision-makers in the organization so that actions can be taken based on the recommendations of the Data Scientist.

In our example, based on the findings, you will communicate and recommend certain changes in the business strategy so that Mr. X can earn the maximum profit.

Check out this insightful Intellipaat Data Science video:

If you have any doubts or queries related to Data Science, do post on Data Science Community.

Data Science Components

Now, in this ‘What is Data Science?’ blog, we will discuss some of the key components of Data Science, which are:

• Data (and Its Various Types)

The raw dataset is the foundation of Data Science, and it can be of various types like structured data (mostly in a tabular form) and unstructured data (images, videos, emails, PDF files, etc.)

• Programming (Python and R)

Data management and analysis is done by computer programming. In Data Science, two programming languages are most popular: Python and R.

• Statistics and Probability

Data is manipulated to extract information out of it. The mathematical foundation of Data Science is statistics and probability. Without having a clear knowledge of statistics and probability, there is a high possibility of misinterpreting data and reaching at incorrect conclusions. That’s the reason why statistics and probability play a crucial role in Data Science.

• Machine Learning

As a Data Scientist, every day, you will be using Machine Learning algorithms such as regression and classification methods. It is very important for a Data Scientist to know Machine learning as a part of their job so that they can predict valuable insights from available data.

• Big Data

In the current world, raw data is compared with crude oil, and the way we extract refined oil from the crude oil, by applying Data Science, we can extract different kinds of information from raw data. Different tools used by Data Scientists to process big data are Java, Hadoop, R, Pig, Apache Spark, etc.

Grab high-paying analytics jobs with the help of these Top Data Science Interview Questions!

How does Intellipaat help you in making a career in Data Science?

Now, you can answer the question ‘What is Data Science?’ and know that Data Science is not all about money. It also allows you to gain immense knowledge throughout your career. So, it is this heady mix of money and deep domain knowledge that makes Data Science such an enviable career option for budding technology professionals.

Intellipaat provides huge opportunities to the aspirants who are willing to establish themselves as all-rounders in this area. Hence, getting trained in Data Science technologies through courses offered by Intellipaat will be the best career move you will ever make. Intellipaat offers a wide range of courses dedicated to providing you an end-to-end knowledge about the trending and highly in-demand Data Science skills in this domain.

It was not joking when Harvard Business Review reported that Data Science is the hottest job opportunity of the twenty-first century. Today, if any digitally driven organization is starved of data even for a short duration of time, then it loses its competitive edge. Data Scientists help organizations make sense of their customers, markets, and the business as a whole.

If you want to become a Google Data Scientist at the best salary, then you need to be at the top of your game. If you are wondering how to learn Data Science, then Intellipaat is just the right place to start with your incredible Data Science journey.

Check out Intellipaat’s Data Scientist Online Course to get ahead in your career!

Originally published at www.intellipaat.com on August 16, 2019.

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How To Build A Data Science Career In 2021

For this week’s data science career interview, we got in touch with Dr Suman Sanyal, Associate Professor of Computer Science and Engineering at NIIT University. In this interview, Dr Sanyal shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.

With industry-linkage, technology and research-driven seamless education, NIIT University has been recognised for addressing the growing demand for data science experts worldwide with its industry-ready courses. The university has recently introduced B.Tech in Data Science course, which aims to deploy data sets models to solve real-world problems. The programme provides industry-academic synergy for the students to establish careers in data science, artificial intelligence and machine learning.

“Students with skills that are aligned to new-age technology will be of huge value. The industry today wants young, ambitious students who have the know-how on how to get things done,” Sanyal said.

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Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

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'Commoditization Is The Biggest Problem In Data Science Education'

The buzz around data science has sent many youngsters and professionals on an upskill/reskilling spree. Prof. Raghunathan Rengasamy, the acting head of Robert Bosch Centre for Data Science and AI, IIT Madras, believes data science knowledge will soon become a necessity.

IIT Madras has been one of India’s prestigious universities offering numerous courses in data science, machine learning, and artificial intelligence in partnership with many edtech startups. For this week’s data science career interview, Analytics India Magazine spoke to Prof. Rengasamy to understand his views on the data science education market.

With more than 15 years of experience, Prof. Rengasamy is currently heading RBCDSAI-IIT Madras and teaching at the department of chemical engineering. He has co-authored a series of review articles on condition monitoring and fault detection and diagnosis. He has also been the recipient of the Young Engineer Award for the year 2000 by the Indian National Academy of Engineering (INAE) for outstanding engineers under the age of 32.

Of late, Rengaswamy has been working on engineering applications of artificial intelligence and computational microfluidics. His research work has also led to the formation of a startup, SysEng LLC, in the US, funded through an NSF STTR grant.

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What Are The Advantages and Disadvantages of Data Science?

Data Science becomes an important part of today industry. It use for transforming business data into assets that help organizations improve revenue, seize business opportunities, improve customer experience, reduce costs, and more. Data science became the trending course to learn in the industries these days.

Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction. In online Data science course you learn how Data Science deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions.

Advantages of Data Science:- In today’s world, data is being generated at an alarming rate in all time lots of data is generated; from the users of social networking site, or from the calls that one makes, or the data which is being generated from different business. Because of that reason the huge amount of data the value of the field of Data Science has many advantages.

Some Of The Advantages Are Mentioned Below:-

Multiple Job Options :- Because of its high demand it provides large number of career opportunities in its various fields like Data Scientist, Data Analyst, Research Analyst, Business Analyst, Analytics Manager, Big Data Engineer, etc.

Business benefits: - By Data Science Online Course you learn how data science helps organizations knowing how and when their products sell well and that’s why the products are delivered always to the right place and right time. Faster and better decisions are taken by the organization to improve efficiency and earn higher profits.

Highly Paid jobs and career opportunities: - As Data Scientist continues working in that profile and the salaries of different position are grand. According to a Dice Salary Survey, the annual average salary of a Data Scientist $106,000 per year as we consider data.

Hiring Benefits:- If you have skills then don’t worry this comparatively easier to sort data and look for best of candidates for an organization. Big Data and data mining have made processing and selection of CVs, aptitude tests and games easier for the recruitment group.

Also Read: How Data Science Programs Become The Reason Of Your Success

Disadvantages of Data Science: - If there are pros then cons also so here we discuss both pros and cons which make you easy to choose Data Science Course without any doubts. Let’s check some of the disadvantages of Data Science:-

Data Privacy: - As we know Data is used to increase the productivity and the revenue of industry by making game-changing business decisions. But the information or the insights obtained from the data may be misused against any organization.

Cost:- The tools used for data science and analytics can cost tons to a corporation as a number of the tools are complex and need the people to undergo a knowledge Science training to use them. Also, it’s very difficult to pick the right tools consistent with the circumstances because their selection is predicated on the proper knowledge of the tools also as their accuracy in analyzing the info and extracting information.

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50 Data Science Jobs That Opened Just Last Week

Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.

In this article, we list down 50 latest job openings in data science that opened just last week.

(The jobs are sorted according to the years of experience r

1| Data Scientist at IBM

**Location: **Bangalore

Skills Required: Real-time anomaly detection solutions, NLP, text analytics, log analysis, cloud migration, AI planning, etc.

Apply here.

2| Associate Data Scientist at PayPal

**Location: **Chennai

Skills Required: Data mining experience in Python, R, H2O and/or SAS, cross-functional, highly complex data science projects, SQL or SQL-like tools, among others.

Apply here.

3| Data Scientist at Citrix

Location: Bangalore

Skills Required: Data modelling, database architecture, database design, database programming such as SQL, Python, etc., forecasting algorithms, cloud platforms, designing and developing ETL and ELT processes, etc.

Apply here.

4| Data Scientist at PayPal

**Location: **Bangalore

Skills Required: SQL and querying relational databases, statistical programming language (SAS, R, Python), data visualisation tool (Tableau, Qlikview), project management, etc.

Apply here.

5| Data Science at Accenture

**Location: **Bibinagar, Telangana

Skills Required: Data science frameworks Jupyter notebook, AWS Sagemaker, querying databases and using statistical computer languages: R, Python, SLQ, statistical and data mining techniques, distributed data/computing tools such as Map/Reduce, Flume, Drill, Hadoop, Hive, Spark, Gurobi, MySQL, among others.

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