Updated on 26th Jul, 21 19505 Views

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. Now, organizations can analyze this data to understand global market trends and enable their business to thrive. Data Science can also predict future events based on the present and past data. 

So, in this blog, will converting the following topics:

What is Data Science?

Data Science is an interdisciplinary field of Computer Science that involves creating algorithms and models to extract, process, visualize and find hidden patterns from the raw information. Data Extraction and Transformation, Statistical Analysis, Data Manipulation, visualization, Machine Learning, and predictive Modeling, are some of the most popular fields in Computer Science that utilize Data Science techniques. 

Data Scientists come from a diverse range of expertise and educational backgrounds, so they must be strong in the following areas:

  • Domain Knowledge: The main aim of a data scientist is to get useful information out of raw data that benefits a company’s business. As a Data Scientist, you should know about the business model of the company and ask the right questions to produce valuable results.
  • Math Skills: Linear Algebra, Calculus, and other concepts of mathematics help us to understand the complex behavior of Machine Learning algorithms and discover hidden patterns. In data analysis, probability and statistics are mainly used in predictive modeling and clustering, Therefore, you should have a good knowledge of the mathematical concepts. 
  • Computer Science: It’s not possible to implement Data Science techniques without knowing programming languages like Python, R, SQL, Scala, Julia, JavaScript, etc. As a Data Scientist, you’ll be dealing with varied databases and loud networks to process the data. So, you should be comfortable with basic programming languages, structures, and algorithms, relational and non-relational databases, Distributed Computing, and Machine Learning.
  • Communication Skills: While working on a project, it’s necessary to have good communication with other team members. As a Data Scientist, you have to draw conclusions from the data analysis and present them in front of your team, boss, or stakeholders. 

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Why Data Science?

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 can process it and derive valuable insights out of it.

data science

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?’

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In recent years, there is a huge growth in the field of the 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 on 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.

Data Science Path

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

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.

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Data Science

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.

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

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.

Google

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.

Amazon

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.

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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 Science

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.

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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.

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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.

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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.

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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.

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.

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Business Intelligence vs. Data Science

Below are the key differences between Business Intelligence and Data Science:

FactorsBusiness Intelligence Data Science
ConceptIt’s a collection of processes, tools, and technologies that helps a business with data analysisConsist mathematical and statistical models used for processing the data, discovering hidden patterns, and predicting future actions based on those patterns.
DataDeals mainly with the structured dataAccept both structured and unstructured data
FlexibilityFor BI, the data sources should be planned before the visualization. Data Sources can be added anytime based on the requirements. 
ApproachHave both a statistical and visual approach towards data analysis.Statistical, graph analysis, NLP, Machine Learning, Neural Networks, and other methods can be used to process the data.
Expertise Made for business users to visualize raw business information without any technical knowledge. Its expertise is data scientist, which means you should have sound knowledge of data analysis and programming. 
ComplexityCompared to data science, BI is much simpler to use and visualize data on a single user.  More complex as compared to Business Intelligence.
ToolsBI tools include MS Excel, Power BI, SAS BI, MicroStrategy, IBM Cognos, throughput, and more.Some of the most popular Data Science tools are Python, Hadoop, Spark, R, TensorFlow, BigML, MATLAB, Excel, and more.

Learn more about the differences between Data Science and Artificial Intelligence in our comparison blog on Data Science vs Artificial Intelligence.

What is a Data Scientist?

Data Scientists are IT professionals whose main role in an organization is to perform data wrangling on a large volume of data—structured and unstructured—after gathering and analyzing it. They need this voluminous data for multiple reasons, including building hypotheses, analyzing market and customer patterns, and making inferences.

Their role requires a combination of mathematical, statistical, and computer science knowledge for analyzing, processing, and modeling data. This modified data is further used for the prediction of results that can help organizations come up with efficient plans that need to be executed for the company’s welfare.

These experts use their skills and techniques to extract and manage data for boosting business efficiency. They make use of their experience, contextual knowledge, current market trends, and their assumptions made on the existing data to find solutions to the current challenges that the organization is facing. To do so, they must use predictive analysis, Machine Learning (ML) algorithms, and other advanced-level analytical technologies.

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Let’s briefly try to gather some knowledge of what these professionals do and what their responsibilities are.

What does a Data Scientist do: Role and Responsibilities?

A Data Scientist must assume many roles while working in an organization, including that of an analyst, mathematician, computer scientist, and trendspotter. To fulfill these many roles daily, they have several responsibilities in the organization. Let’s take a look at some of the most common and significant ones:

  • Collect large volumes of quantitative and qualitative data and transform it into a readable and usable format
  • Use data-driven methods to resolve business issues
  • Work with Python, SAS, R, and other programming languages
  • Apply several distribution methods and statistical tests
  • Make use of Deep Learning, ML, and analytical techniques
  • Analyze patterns and trends in data to help in building business efficiency

The overall life cycle of these professionals is mentioned below:

Step 1: Discover data

Step 2: Perform ETL (extract, transform, and load) for data preparation

Step 3: Use visualization tools to apply Exploratory Data Analytics (EDA) for planning the model

Step 4: Use necessary tools to build the model

Step 5: Deliver the results using the data visualization tools

How to Become a Data Scientist?

You have read about Data Scientists in terms of who they are and what they do. Now, you may wonder what steps need to be followed to become one. What are the skills and qualifications required? And so on. Let’s discuss how to become Data Scientist and the criteria that you need to meet.

Following are the steps that will lead you to become a Data Scientist:

  • Get a bachelor’s degree in statistics, mathematics, or computer science
  • Acquire the necessary skills expected from a Data Scientist
  • Gain practical experience as a Data Scientist
  • Enroll in a training course

Moreover, you will have to work on numerous industry-specified projects that will provide you hands-on experience. Our training program offers ample opportunity to explore Data Science projects in various industries to enhance your learning experience.

If you are a student, then you must choose the right degree in the mentioned fields, complete it, and then take up our online training program. However, if you are a professional from a different background and are looking for a career transition to Data Science, Intellipaat’s course on Data Science is the best for you.

Skills Required to Become a Data Scientist

Below are some of the most important skills you should learn to become a successful Data Scientist:

  • Probability and Statistics:  These concepts are like the grammar of Machine Learning, even basic operations like Linear regression use probability functions to find the gradient and deviation of each point from the regression line. 

Moreover, descriptive statistics like mean, median, variance, and standard deviation are used to find the relation between different constraints in the given information. You should learn and practice these mathematical concepts in the first place. Otherwise, you have to come back later in your mid-career and learn them. 

  • Programming skills: Programming languages like R and Python are extensively used in Machine Learning Big and Data Analysis. Python has multiple libraries available for Machine Learning and Data Science. Some of them are NumPy, SciPy, TensorFlow, and Matplotlib. Whereas, R language is mainly used for visualizing the data and performing statistical analysis. So, should have a sound knowledge of different programming languages and algorithms.
  • Machine Learning: Machine Learning algorithms and Deep Neural Networks are used to train various models that can be deployed to process the data in real-time. It’s one of the core skills a Data Scientist should be proficient in. ML and Deep Learning algorithms are extensively used to predict the future sales, requirements, and growth of an enterprise in the market. 
  • Data Visualization: Another important skill for a Data Scientist to know is Data Visualization. Converting the raw data into visually attractive charts, graphs, and maps helps non-technical people to understand the current trends in the industry.
  • Big Data: At present, we are generating quintillions of data per day in various forms. With the rise in the internet, IOTs, Social Media networks, Cloud services, and other factors lead industries to use Big Data technologies to properly store this data and efficiently retrieve it when needed. 

You should know the frameworks like Hadoop, Spark, Flink, Apache, and Hive to retrieve the data to understand the structure of different databases. 

  • Software Engineering: Data Scientists work in teams of people from a different domain, where each member is assigned a specific task. Therefore, a Data Scientist should be able to write clean codes and have knowledge of software engineering subjects like compilers, basic lifecycle models, SR’s documentations, time-space complexity, and more. 
  • Communication Skills: You should be able to communicate with your team members and effectively deliver the presentations in front of the Team leaders, managers, and stakeholders. Good storytelling skills will always help you to explain the in and outs of the visualizations you made.

Salary and Jobs Available in different Countries

Data Science is expanding at a mind-blowing rate, resulting in increased demand for skilled Data Scientists around the globe. According to PayScale, the average salary of a skilled Data Scientist is US$94,491. However, it may differ based on your location and the experience you bring to the table. 

Below is the list of five countries with the most opportunities for Data Scientists:

  • United States:  The US has the highest demand for skilled Data scientists where companies spent more than a billion dollars to acquire from different countries. The average salary of an entry-level job in the US will pay you around US$85,000, but your can up to US$136,000 per annum if you’re an expert and have years of experience in this field. 
  • Europe: A skilled Data Scientist in Europe can easily grab up to  C$93,000 per annum. With the rise in demand for Data science professionals, European countries are recruiting skilled scientists from different countries and offering various benefits so that they can fill the growing demand for Data Scientists. The average salary for a fresher is C$70,000, for people with 5 years of experience is C$93,000, and C$108,000 for people with more than 10 years of experience in the industry.
  • United Kingdom: Similar to Europe and the US, various industries in the United Kingdom are now hiring skilled professionals to manage, maintain, and analyze large amounts of data they’re in real-time. If you’re from the UK or planning to go there for a job as a Data Scientist, you can earn up to £50,000 per annum. 
  • China: China is the leading country in Asia thanks to the cheap labor and electronics they provide. It’s planning to lead the world in the AI sector by the year 2030 by investing in IT industries and improvising government policies. Plus, companies like Tencent are becoming one of the biggest gaming enterprises in the world. An experienced Data Scientist can earn up to ¥350,000 per annum.
  • India: India has one of the fastest-growing industries from different sectors like healthcare, defense, logistics, and AI. Although India has the youngest population compared to any other country in the world, it’s facing acute challenges in finding skilled Data Scientists. So, if you have the right skills and experience as a Data Scientist, you can earn up to ₹1,000,000 per year.

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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 with end-to-end knowledge about the trending and highly in-demand Data Science skills in this domain.

Conclusion

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 and the scope of Data Science, then Intellipaat is just the right place to start with your incredible Data Science journey.

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