Updated on 25th Jan, 22 20103 Views

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 grow their businesses. Data Science can also predict future events based on the present and past data.

This blog will cover the following topics:

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Introduction to 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 raw information. Data Extraction and Data Transformation, Statistical Analysis, Data Manipulation, Data 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, and they must be strong in the following areas:

  • Domain knowledge: The primary aim of a data scientist is to get useful information, which benefits an organization’s business, out of raw data. 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 in understanding the complex behavior of Machine Learning algorithms and discovering hidden patterns. In data analysis, probability and statistics are primarily used for predictive modeling and clustering. Therefore, a data scientist should have good knowledge of mathematical concepts. 
  • Computer science: It is not possible to implement Data Science techniques without knowing programming languages such as Python, R, SQL, Scala, Julia, JavaScript, etc. A data scientist deals with varied databases and loud networks while processing data. So, a data scientist 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 is necessary to have good communication with other team members. A data scientist has to draw conclusions from data analysis and present them in front of their team, boss, or stakeholders.

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Components of Data Science 

Now, we will discuss some of the key components of Data Science. They are as follows:

  • Data (and Its Various Types)

The raw dataset is the foundation of Data Science. Data is primarily divided into two types—structured data, which is mostly in a tabular form, and unstructured data, which consists of images, videos, emails, PDF files, etc.

  • Programming (Python and R)

Data management and analysis is done by computer programming. Python and R are the two most popular programming languages.

  • Statistics and Probability

Data is manipulated to extract information out of it. The mathematical foundation of Data Science is statistics and probability. Without clear knowledge of statistics and probability, there is a high possibility of misinterpreting data and reaching inaccurate conclusions.

  • Machine Learning

A data scientist uses Machine Learning Algorithms, such as regression and classification methods, every day. It is very important for a data scientist to know machine learning as a part of their job so that they can gain valuable insights from the available data.

  • Big Data

In the current world, raw data is compared to crude oil. The way refined oil is extracted from crude oil, similarly valuable information can be extracted from raw data by applying data science. The different tools used by data scientists to process big data are Java, Hadoop, R, Pig, Apache Spark, etc.

  • Development Tools

Development tools, such as MongoDB, Apache Spark, Apache Kafka, pandas, ggplot2, Scikit-learn, etc., are used to develop and enhance data science functionalities such as data storage, data transformation, data modeling, and data visualization.

 

Data Science Examples

Today, the examples and applications of data science are widespread across many industries. For obvious reasons, some of the most important data science examples now are the use of data science in studying the coronavirus. 

Some examples of data science include fraud detection, healthcare recommendations, fake news detection, automation in customer care, e-commerce and entertainment recommendation systems, and more.

 

Why Data Science?

Currently, across industries, there is a huge need for skilled and certified data scientists. They are among the highest-paid professionals in the IT industry. According to Glassdoor, data scientist is the best job in America with an average annual salary of $110,000. Only a few people process the skills to derive valuable insights out of raw data.

data science

Furthermore, looking at the ever-increasing requirements, McKinsey has predicted that there will be a 50 percent gap in the demand and supply of data scientists in the upcoming years.

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In recent years, there has been huge growth in the field of the Internet of Things (IoT), which has led to the generation of 90 percent of data being generated today. Every day, 2.5 quintillion bytes of data is generated, and it is accelerated with the growth of IoT.

This data comes from all possible sources such as:

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

This data is known as big data.

Organizations and companies are flooded with tremendous amounts of data. Thus, it is very important to know what to do with this data and how to utilize it.

Data Science Path

The preceding picture represents the concept of Data Science. Data Science brings together a lot of skills such as statistics, mathematics, and business-domain knowledge, and helps organizations find ways to:

  • Reduce costs
  • Get into new markets
  • Tap in different demographics
  • Gauge the effectiveness of marketing campaigns
  • Launch new products or services

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 following infographic to better understand the scope of data science.

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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 salary packages to its data science employees.

 

Importance of Data Science

Data is a valuable asset for various industries to help make careful and sound business-related decisions. Data science has the ability to churn raw data into meaningful insights. 

An expert data scientist has the capability of digging out meaningful information from whatever data is available to them. They lead organizations to the right direction through sound data-driven decisions and suggestions.

 

Data Science Applications

Below-mentioned are some of the applications of Data Science:

  • Fraud and risk detection: Over the years, financial organizations have learned to analyze the probabilities of risks and defaults through customer profiling, past expenditures, and other variables available through data. 
  • Healthcare: Data science makes it possible to manage and analyze very large diverse datasets in healthcare systems, drug development, medical image analysis, and more.
  • Internet search: All search engines, including Google, use data science algorithms to deliver the best result for searched queries within seconds.
  • Targeted advertising: Digital ads have a higher call-through rate (CTR) than traditional ads because of targeted advertising based on a user’s past behavior with the help of data science algorithms.
  • Recommendation systems: Internet giants and as well as other businesses have fervidly made use of recommendation engines to promote their products based on users’ previous search results and their interests.
  • Advanced image, speech, or character recognition: Facial recognition algorithms on Facebook, speech recognition products, such as Siri, Cortana, Alexa, etc., and Google Lens are all perfect examples of data science applications in image, speech, and character recognition.
  • Gaming: Today, games use machine learning algorithms to improve or upgrade themselves as players move up to higher levels. In motion gaming, the opponent (computer) is able to analyze a player’s previous moves and accordingly shape up its game. This is all possible because of data science.
  • Augmented reality (AR): Augmented reality promises an exciting future through Data Science. A VR headset, for example, contains algorithms, data, and computing knowledge to offer the best viewing experience.
 

Data Science Life Cycle

Let us explore the life cycle of data science to better understand “what is data science?”. Suppose, Mr. X is the owner of a retail store and his goal is to improve the sales of his store by identifying the primary sales drivers. To accomplish his goal, Mr. X 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?

The answers to these questions would surely influence the outcome of the project. Hence, he appoints you as the data scientist. Let us solve this problem using the data science life cycle.

Data Science
  • Data Discovery

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

At this stage, as a data scientist, your objective is to boost the sales of Mr. X’s retail store. Some factors affecting the sales could be:

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

Keeping these factors in mind, you would develop clarity on the data and collect all data that pertains to the above-listed elements.

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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; it can also involve more complex methods such as identifying missing values by modeling and so on. Once 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, you would also try to explore and understand the datasets’ patterns and values.

  • Mathematical Models

Do you know, all Data Science projects have certain mathematical models driving them. These models are planned and built by data scientists in order to suit specific organizational needs. This might involve various mathematical concepts 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 such as Tableau and QlikView.

One model might not be enough to generate a satisfactory result. You might need to use two or more models. In this scenario, you, as a data scientist, will create a group of models. After measuring the models, you will revise the parameters and fine-tune them for the next modeling run. This process will continue until you are pretty sure that they have found the best model.

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At this stage, you will build mathematical models based on the business needs of Mr. X, i.e., based on if Product A or Product B is more profitable, whether the product placements are effectively working, 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 leading to a lot of troubleshooting; thus, the model might have to be tweaked. Here, model evaluation explains the performance of the model.

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At this stage, you 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. At this stage, you need to be a liaison between various teams and you should be able to seamlessly communicate your findings to key stakeholders and decision makers in the organization so that decisions can be made and actions can be taken based on your recommendations.

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

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

 

Who is a Data Scientist? What are their roles and responsibilities?

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. Data scientists need this voluminous data for multiple reasons including building hypotheses, analyzing market and customer patterns, and making inferences.

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

Data scientists 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 informed assumptions based on existing data to find solutions to the current challenges faced by the organizations. To do so, data scientists use predictive analysis, machine learning algorithms, and other advanced analytical technologies.

Let us briefly try to gather some knowledge on the responsibilities of data scientists.

A data scientist assumes many roles while working in an organization including that of an analyst, mathematician, computer scientist, and trendspotter. These many roles also come with several organizational responsibilities. Let us take a look at some of the most common and significant responsibilities of a data scientist:

  • 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, machine learning, and analytical techniques
  • Analyze patterns and trends in data to help build business efficiency

The overall life cycle of data scientists 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 by using data visualization tools

 

Business Intelligence vs Data Science

The following table states the key differences between business intelligence and data science:

FactorsBusiness Intelligence Data Science
ConceptIt is a collection of processes, tools, and technologies that help a business with data analysis.It consists of mathematical and statistical models used for processing the data, discovering hidden patterns, and predicting future actions based on those patterns.
DataIt deals mainly with structured data.It accepts both structured and unstructured data.
FlexibilityData sources should be planned before the visualization. Data Sources can be added anytime based on the requirements. 
ApproachIt has both statistical and visual approaches toward data analysis.Graph analysis, NLP, machine learning, neural networks, and other methods can be used to process the data.
Expertise It is made for business users to visualize raw business information without any technical knowledge. It requires sound knowledge of data analysis and programming. 
ComplexityFor a single user, compared to data science, business intelligence is much simpler to use and visualize data.  Data science is much more complex when compared to business intelligence.
ToolsBusiness intelligence 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.
 

How Do Top Industry Players Use Data Science?

In this section of the blog, we will look at how top industry players, such as Google, Amazon, and Visa, use 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. Some of the biggest companies that are hiring data scientists at competitive salaries are listed below:

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, it offers one of the best salary packages to its data science employees.

Google

Amazon is a global e-commerce and cloud computing giant that is hiring data scientists on a large scale. Amazon needs 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 millions in a single day. Due to this, the need for data scientists is huge at Visa to generate more revenue, check fraudulent transactions, customize products and services as per customer requirements, etc.

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Salaries 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 annual salary of a skilled data scientist is US$94,491. However, the salary offered may differ based on location and experience.

Below-mentioned are five countries with the most opportunities for data scientists:

  • United States (US): The US has the highest demand for skilled data scientists. In the US, companies have spent more than a billion dollars to acquire data scientists from different countries. The average annual salary of an entry-level data scientist in the US is US$85,000; the salary can go up to US$136,000 p.a. based on your expertise and experience in the field.
  • Germany: Data scientists in Germany can earn about €5,960 per month. The salary of a data scientist in Germany ranges from €2,740 per month to €9,470 per month. Germany offers the most lucrative salary packages for the role of data scientist.
  • United Kingdom (UK): Similar to Europe and the US, various industries in the UK are now hiring skilled professionals to manage, maintain, and analyze large amounts of data. A data scientist in the UK can earn up to £50,000 p.a.
  • China: China is planning to lead the world in artificial intelligence by the year 2030 by investing in IT industries and making government policies more accomodating. An experienced data scientist in China can earn up to ¥350,000 p.a.
  • India: India has fastest-growing industries in several sectors such as healthcare, defense, logistics, and artificial intelligence. Similar to the rest of the world, India too is 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 p.a.

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

Intellipaat provides many opportunities to aspirants or learners who are willing to establish themselves as all rounders in the domain of data science. Hence, getting trained in data science technologies through the courses offered by Intellipaat will be a great career move. Intellipaat offers a wide range of courses dedicated to providing you with end-to-end knowledge about trending and in-demand data science skills.

 

Conclusion

Today, if any digitally-driven organization is starved of data even for a short duration, then the organization loses its competitive edge. Data scientists help organizations make sense of their business, customers, and markets.

If you want to become a Google Data Scientist with 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 the right place to start your incredible Data Science journey.

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

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