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What is Data Science?

Data Science is perfect amalgamation of programming, analytical, and business skills that allows extracting meaningful insights from raw and unstructured data.

What is Data Science?
June 11, 2018      10378 Views     4 Comments

Data Science is a study which deals with identification, representation and extraction of meaningful information from data sources to be used for business purposes.

With enormous amount of facts generating each minute, the requirement to extract the useful insights is a must for the businesses to stand out from the crowd. Data engineers setup the database and data storage in order to facilitatethe process of data mining, data munging and other processes. Every other organization is running behind profits, but the companies that formulate efficient strategies based on fresh and useful insights always win the game in the long-run.

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Data Science Definition

This is an introduction to Data Science. Since Data Science is a wide-ranging field there isn’t a single way to define the role of a data scientist or the domain of data science. You might be wondering what does a data scientist do? So it is tough to get a simple data science meaning or to pinpoint who is a data scientist. The data scientist skill set includes in equal measure the statistics, analytical, programming skills and business acumen. Most of the data scientists have a strong background in mathematics or other domains of science and having a PhD is also a distinct possibility. Without the role of a Data Scientist, the value of big data cannot be harnessed. So in today’s data-driven world data scientists are in huge demand to convert data into valuable business insights. Having knowledge of Data Science basics is quite useful in today’s data-driven world.

Comparing Data Science with Data Analysis:

The Data Scientist and Data Analyst are different in the sense that Data Scientist starts by asking the right questions, Data Analyst starts by mining the data. The Data Scientist needs substantive expertise and non-technical skills whereas a Data Analyst does not need these skills.

CriteriaData ScientistData Analyst
Fundamental goalAsking the right business questions & finding solutionsAnalyzing and mining business data
Various tasksData cleansing, preparation, analysis to gain insightsData querying, aggregation to find patterns
Substantive expertiseNeededNot necessary
Non-technical skillsNeededNot needed

Data Science is a multidisciplinary science and having a Data Science career means you need to master a lot of domains like data inference, working with algorithms, deploying statistics, deductive reasoning, computer programming, substantive expertise among other skills. Data Science applications can straddle across multiple industry domains.

The job of a Data Scientist is to dig into the granular level in order to understand complex behaviors, trends, inferences, analytical creativity, time series analysis, segmentation analysis, inferential model, quantitative reasoning, and more.

“Data Scientist is better at statistics than any software engineer and better at software engineering than any statistician.” ― Josh Wills, Director of Data Engineering at Slack

There is no clear-cut definition of what exactly comprises a Data Scientist’s roles and responsibilities. It can involve anything from optimizing the sales funnel to getting the right strategy for a company to enter the next lucrative international market. So it is a bit tricky trying to define the work of a Data Scientist in a simplistic manner. There can be a lot of ambiguity regarding whether something falls under the purview of a Data Scientist.

Find out what a Data Scientist does in this insightful Forrester video:

Understanding Data Science

Regardless of the problem statement the Data Scientist goes through a set course of action most of the time in order to get a better solution to the issue.

Understanding Data Science

  • Understand the Problem

Learn about the issue at ground, ask the right questions which is at the center of what a Data Scientist does and forms the foundation for the later stages of the Data Scientist’s role. Define the problem and convert it into a concrete framework which can then be worked upon.

  • Collect Enough Data

As the name implies the Data Scientist has to collect enough data in order to make sense of the problem at hand and get a better grip of the issue with respect to the time, money and resources needed to make the process successful.

  • Process the Raw Data

Data can rarely be used in its original form. It needs to be processed and various methods exist to convert it into a usable format. This is an essential part of every Data Scientist’s job routine and this consumes a major chunk of his time and resources.

  • Explore the Data

After the data has been processed and converted into a form that can then be used for the later stages, you need to explore it further so as to get the characteristics of the data and find out more about the obvious trends, correlation and the not so obvious hidden relationships and more.

  • Analyze the Data

This is where the magic happens. The data scientist deploys the various arsenals in his repository like machine learning, statistics and probability, linear and logistic regression, time-series analysis and more in order to make sense of the data. At the end of this step the Data Scientist would be able to gain valuable business insights like predictions, business process optimization, finding new ways of doing the same old things among other things.

  • Communicate the Results

At the end of the entire process there is a need to communicate the findings to the right stake-holders in order to get the groundwork done for the action to be taken and deployment of the decisions that are taken.

John Foreman

“Data scientists are kind of like the new Renaissance folks, because data science is inherently multidisciplinary.”John Foreman, Vice President of Product Management at MailChimp

How does Data Science make working so easy?

Since Data Science is not a single domain, it includes a variety of tools and techniques in order to get the right data, make sense of it and convert it into business insights. It is the combination of man and machine in equal measure. For some tasks humans are good but for some other tasks machines are good at it. So it is about doing what one does best. When it comes to research, intellectual curiosity and such other skills, humans are very good at it. When it comes to automation of repeated tasks, machines are unbeatable.

The role of a data scientist is all about playing with big numbers. It is about making sense of the numbers and hence mathematics especially statistics plays a vital part in the day to day routine of a data scientist. Some of the aspects that are important when it comes to the domain of statistics include: Calculus, Probability, Linear Algebra among other skills.

Peter Skomoroch

“Data scientists are able to think of ways to use data to solve problems that otherwise would have been unsolved, or solved using only intuition.”Peter Skomoroch, Former Principal Data Scientist at LinkedIn.

The data scientist also needs to have a good grounding in some of the most important programming languages like Java, Python, Scala, Structured Query Language, R statistical computing among other languages.

Having an analytical bent of mind is crucial to the working of a Data Scientist on a daily basis. The Data Scientist needs to do a lot of analytical work in order to process huge volumes of data and derive valuable business insights out of it.

Most of the Data Scientists have a common educational background be it engineering degree or even PhDs. But that does not mean that if you have any other degree you cannot pursue a Data Science course and become a Data Scientist.

Since the Data Scientist will be working extensively with Big Data it is important to have a firm knowledge of Hadoop tools and technologies. These set of tools include the Hadoop Distributed File System, MapReduce for processing big data along with the various tools and technologies like HBase, Hive, Pig, Sqoop, among others.

The Data Scientist needs to have a set of skills that include Data Mining which is associated with the technique of identifying patterns and establishing relationships in order to analyze large volumes of data and deploy predictive analysis and come up with inferences for the future. In data mining the association rules are deployed for coming up with if/then patterns, finding out how frequently an item appears in a database and so on. The data scientist is also supposed to know a lot about regression both linear and logistic, clustering, classification, sequencing of data and so on.

Top Data Science Companies

Today the data scientist requirement is across the board cutting across industry verticals. Here we list some of the biggest and best companies that are hiring data scientists at top-notch salaries.

Google : Google is by far the biggest company that is on a hiring spree for top-notch data scientists. Since today most of Google is driven by data scientists, artificial intelligence and machine learning, Google offers some of the best data science salaries.

Amazon : Amazon is another global ecommerce and cloud computing giant that is hiring data scientists on a big scale. They need data scientists to find out about the customer mindset, enhance the geographical reach of both the ecommerce domain and cloud domain among other business-driven goals.

Visa : Visa is an online financial gateway for most of the companies and Visa does transactions in the range of hundreds of millions over the course of a regular day. Due to this the requirement for data scientists is huge at Visa to generate more revenue, check fraudulent transactions, customize the products and services as per the customer requirements among other things.

The various subsets of Data Science

The various subsets of Data Science

Data Analyst :

As the name implies this role involves doing analysis on the data using various tools and techniques. This could be using the various programming languages like R, Python, SQL and so on.

Data Engineer :

The role of a Data Engineer includes working with huge amounts of data by accessing it through large databases, deploy large amount of processing on the data and coming up with inferences and results. The Data Engineer will be well-versed in the domain of statistics and programming languages as well. He would normally have a strong background in software engineering.

Data Architect :

This person takes on a very high-level role in the organization when it comes to working with data and deriving insights from it. The person creates the blueprint for integrating, streamlining, centralizing and protecting the data that others can work with. The Data Architect needs to have a mastery in the various tools like Hive, Pig, Spark and more of such tools in order to work with different types of data.

What can you do with Data Science?

Some of the tasks you can do with Data Science include:

  • Come up with conclusive research and open-ended questions
  • Extract large volumes of data from external and internal sources
  • Deploy statistical, machine learning and analytical methods
  • Clean, prune and get data ready for processing and analysis
  • Look at data from various angles to determine hidden patterns, relations and trends
  • Use a mix of algorithmic and automation tools
  • Redesign processes, systems using a data-driven approach

Working with Data Science

If you want to work in Data Science then there are certain qualities and skills that you need to possess. These are mathematical, statistical and analytical skills. You should be well-versed in the various programming languages like R, Python, SQL, Spark among others. Coming up with new algorithms in order to solve new problems is a very important skillset. Having knowledge of the various databases, combining data from various sources, processing it using the various tools and so on are some important skills. Data munging, data cleansing, data transformation are essential part of the skillset of any Data Scientist. Finally the data has to be converted into visual insights. This is where the various business reporting, charting, mapping and business intelligence dashboard skills come into the picture. All this means you need to have the right data science education.

Every industry is different and the role of a Data Scientist varies from industry to industry but the approach of Data Science is the same in order to get meaningful answers to most pressing business questions.

Check what are the top skills needed for a Data Scientist in this insightful video :

The Data Science Tools

The Data Science Tools

R Programming

The R programming is a statistical programming language that is equipped with a wide range of features, functionalities. It has been the most promising language when it came to data analytics and machine learning.

SQL

SQL refers to the structured programming that is used to work with relational database management systems. This SQL is useful for data follows a certain format like the row and column standard type that is used to depict a huge amount of data even in today’s world of unstructured data. SQL is extensively used by database administrators and developers alike.

Python

Python is a high-level, powerful, object-oriented programming language that is highly versatile. It is used for a variety of applications but none more important than in the data science domain and machine learning applications. It has a huge set of libraries that is one of the distinct features of Python programming language.

Hadoop

This is a tool used for big data applications and it is the most powerful as well as an open source solution. It has a huge ecosystem that comprises of some of the best tools for working with big data. You store, compute, deploy real-time analytics among things on big data through the Hadoop and its ecosystem of tools.

SAS

SAS is a powerful business intelligence and analytical tool. It is a software suite for extracting, analyzing and reporting on a wide range of data and derive valuable business insights from it. It includes a whole set of tools for working across the various steps of converting data into business insights.

Tableau

This is the most powerful data visualization, analysis and reporting tool. The best of Tableau is that you don’t need any technical knowledge or programming skills in order to derive valuable insights from Tableau.

Advantages of Data Science

  • Data scientist helps the management to come up with better and faster decisions
  • It empowers the decision-makers with solid data and outlines a path to achieve business goals
  • You can anticipate new challenges and opportunities through the power of data
  • Spotting trends and capitalizing on it before the competition
  • Setting the guidelines for best practices and tried and tested methodologies
  • Rigorously testing the decisions until it achieves perfection

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How Data Science is different from Big Data?

Though data science almost sounds similar to the concept of Big Data, but actually there is a huge difference between these two terms:

Big DataData Science
Consists of voluminous amounts of unstructured, semi-structured or structured dataCombines statistical, mathematical, programming and problem-solving techniques.
Used to extract meaningful insights from large data sets.Application of above mentioned techiques for better strategic decision-making.
  • Nowadays the kind of raw data has become more heterogeneous and voluminous than ever before which cannot be accommodated on a single computer
  • All the analog information is being converted into digital ones to grab maximum insights possible from them
  • One of the concepts that deal with this irregular and unorganized data is the domain of data science.
  • By 2020, fifty times more data will be generated than what was available back in 2011.

Required Data Science skills

It is a field which requires a specific skill set comprising of expertise in following:

Required skills Data Science

Mathematics – In order to understand a complex web of unstructured data requires a combination of heuristics and quantitative analysis to provide solutions to the prevailing problems. Many a times the business problems require analytical models to be prepared in order to resolve them and knowledge of data analysis is a must. Nowadays advanced analytics tools like SAS are being extensively deployed for getting increased insights. However there is a misconception among people about data science that it is purely related to statistics. But it is not true as statistics is one of the pillars that support data science.

Technology and hacking – A data scientist is supposed to have a solid technical knowledge about breaking up and solving problems by creating complex and solvable Data Science algorithms. Data science requires the expert to think like a data analyst through the high-dimensional data and data control flows.

Here by stating Hacking, should not be taken as literal hacking the computers and unethical intrusions. But, the reference is about bringing creativity and innovation into the process in order to come up with unique techniques to solve the existing problems.

Business or strategy acumen – One of the important pillars of data science is business or strategic insights. Along with an expertise in mathematics and technological skills, a clever and deep insight in business is also required to become a complete package for data scientist’s job. A sharp vision that could predict the future trend and prepare the strategy to deal with it beforehand is what companies want now a days to survive in this cut-throat competition. Hence you can say that data science is a perfect amalgamation of technological and strategic proficiency which is a must to align the business requirements with tactical knowledge.

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Why should we use Data Science?

While the companies were going clueless about how to deal with this massive data and how to put it into use, data science evolved as a revolutionary concept and changed the entire game. Gradually, it has become a necessity for the companies to utilize the power of data science to harness the opportunities hidden in the complex business trends. What made data science to gain huge popularity in such a short span of time? Let’s have a look into it:

Download Data Science Interview questions asked by top MNCs in 2018 ?

Accurate answers – Business strategies are better formulated when these are backed by accurate predictions and logic which is possible only through data science algorithms. A growing number of companies have realized the applications of data science and are investing in implementing this concept to serve their customers in a better way.

Better decision-making ability – According to a study conducted by Harvard Business Review revealed that the companies which are data-driven perform better in objective financial and operational measurements. Moreover these companies earned 6% more profit than their rivals.

Gone are the days when businesses relied upon the experience and past trends when it came to decision-making. Nowadays big data analytics has changed the present scenario and now the companies take the help of data science and dig deep into the data to find the logic and reason before making an important business move.

Finds important business trends – Big Data scientists look into the data, find out the pattern and forecast on the basis of specific trends way before it is visible to the other subject matter experts. Going forward towards fulfilling the organizational goals, data science taps into the existing information and finds out significant trends for which the organization needs to be prepared with alternative strategies. Acting as a competitive advantage, it is beneficial to a great extent for the industry players in standing out from the rat race.

Command high-paying analytics job with these Top Data Science Interview Questions!

Data Science Scope

Data Science Scope

 

If the growth of Data science is mapped based on its interest and time on two different axes, it becomes clearly visible that it has created a strong impact over the years and is supposed to do the same in future as well. As per the trends reported by Google, following details can be interpreted:

  • Despite struggling through the late adoptions and resistance in implementation, data science has managed to grow tremendously over past years, i.e., 2011-2016.
  • The concept of data science is being widely accepted across the globe, especially in the developing countries like India, Nigeria, China, etc.
  • More and more industries are hiring data scientists in the countries at an increasing rate like Singapore at 91%, Nigeria at 84%, United Stated at 71% and Hong Kong at 55% per se.

A study by IBM’s Business Tech Trend says “Nearly 70% of leading companies say analytics are integral to how their organizations make decisions.”

A report by McKinsey predicts “by 2020, there will be 40,000 exabytes of data collected. Someone has to do something with that data.”

Imagine the kind of data revolution coming at an unimaginable speed that is forcing the companies to focus more on implementing data-driven strategies instead of relying purely on experience.

Why do we need Data Science?

Every phenomenon has a reason behind its occurrence. So has data science. Therefore it would be interesting to know the emerging trends that give data science the utmost importance to keep pace with the changing scenario:

Why do we need Data Science

Evolution of digital advertising- With the advent of digital advertisement, it has become essential for the companies to adopt data science techniques. And surprisingly, These data science algorithms are being implemented in many steps starting from display banners to digital billboards, which increase the CTR on the advertisements which was not possible for traditional advertisements.

Facilitates better data interpretation– Analyzing facts statistically allows the marketers to interpret it in a better way which ultimately simplifies formulating strategies. Data science applications help the companies target different segments more effectively.

Speeds-up the performance– The companies do not tend to make moves based on anticipation anymore, but everything is preplanned and a properly strategized activity. Data Science plays a pivotal role in fulfilling this necessity as it provides sufficient insights that are required for planning and execution, helping in speeding up the process in effect.

Allows real-time experimentation- The one who is able to please the customers is the winner in today’s competition. Data science facilitates the companies with the information about the tastes and preferences of the customers, which helps in understanding customers more deeply which allows companies to experiment in real-time rather than trying and testing back-stage.

Not only this, but internet search and recommender systems are also implementing data science to gear up the performances. Having said this, it is clear that big data analytics has become one of the key ingredients to reap both short and long-run benefits.

Want to know more? Read this extensive Data Science Tutorial!

Who is the right audience for learning Data Science technologies?

The field of Data Science is not limited to technology experts but statisticians and Information architects can also grow big having in-depth understanding about data science technologies.

However this field is no less than a holy grail for aspirants who wish to build a career in:

  • Data Science
  • Machine Learning
  • Data Mining
  • Data visualization
  • Business Intelligence
  • Big Data
  • Business Analysis

How this technology will help you in career growth?

It is clear that the job of data science is going to reach its heights in future. However, a clearer picure of the opportunities provided by data science will drive you toward this direction:

Attractive Package – Data scientists have become one of the hottest commodities around the industries.

By 2017 the U.S. could face a shortage of almost 200,000 people with “deep analytical skills.”-  McKinsey

Whether its is a start-up or a Fortune 500 company, data scientists are always in a demand than any other professionals and are getting eye-popping salaries which can reach an average of $120,000.

Combination of knowledge and money- Data science is not all about money, but allows you to gain immense knowledge also. 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.

With such high demand for professionals who could deal with technologies as well as with strategic concepts, data science has proven to be a multidisciplinary field inculcating both the requirements. It 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 will be the best career move you will ever make Intellipaat offers a wide range of courses dedicated to provide you end-to-end knowledge about the trending and highly in-demand Data science skills in this domain.

Conclusion

It is not for nothing that the Harvard Business Review has mentioned 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 to make sense of their customers, markets and business as a whole. So it is just the beginning of the rise of the Data Scientist role in today’s world and things can only get better with time for Data Science and Data Scientists. If you want to become a Google Data Scientist at the best salaries 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 your incredible data science journey.

Check the Intellipaat Data Science Training to get ahead in your career!

 

 

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