What is Data Science
Updated on 22nd Feb, 24 9.6K Views

In an emerging digital economy, data science is creating a buzz in each and every domain that one can think of. With a consistent flow of information in the form of unstructured data, the need to use data science to convert the same into actionable insights is more prominent than ever before.

In this article, we will learn about Data Science in its entirety to understand how one can create a roadmap to excelling in their career in this field.

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What is Data Science?

Data Science is the process of getting useful insights from raw data. It includes statistical analysis, data analysis, machine learning, data modeling, and data preprocessing.

To put it in Layman’s terms – Let’s consider an example. A case study that also went on to become a Hollywood feature film, “Moneyball.”

In the movie, they show how an underdog team went on to compete at the highest level of the baseball tournament by analyzing the statistical data points of each player and quantifying their performances to win the game. It can be aligned with how data science actually works.

Another example would be how search engines gather user data, and based on their choices(data points), recommendations are put forward for them. Organizations use recommendation engines made using various machine learning algorithms on streaming websites to predict recommendations that best serve the user’s history. 

Data science is the domain of study where data is processed through advanced statistical and mathematical concepts using machine learning techniques to gather actionable insights to cater to problem statements or business problems.

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Why Data Science?

Data science is all about using information to help us make better choices and solve problems. It is like having a superpower for decision-making.

What is Data Science
  • Smart Decisions: With data science, we can analyze data to make smart decisions in business, health, and many other areas.
  • Solving Problems : It helps us solve tricky problems. For example, it can help doctors find better ways to treat diseases.
  • Discovering Patterns : Data science helps us find patterns in data that we might not see on our own. It’s like finding hidden treasures.
  • Saving Time and Money: By using data, we can save time and money. For businesses, this means more profit.
  • Endless Possibilities : Data science is a powerful tool with endless possibilities. It’s like having a magic wand for understanding the world.

In a nutshell, data science is all about using data to make life better, easier, and more exciting.

Data Science Path

Look at the following infographic to better understand the scope of data science.

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 and Artificial Intelligence these days, it offers one of the best salary packages to its data science employees.

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Data Science Life Cycle

The Data Science lifecycle comprises the following:

1. Problem Definition: This is where you start by clearly defining the problem you want to solve using data. It’s crucial to have a well-defined goal. For example, you might want to predict customer churn for a business or detect fraud in financial transactions.

2. Data Collection: Once you know the problem, you gather the data required to address it. This data can come from various sources, such as databases, sensors, or online sources. It’s like assembling the tools and equipment you need for your journey.

3. Data Cleaning: Real-world data can be messy. In this step, you clean the data, removing errors, duplicates, and irrelevant information. It’s akin to clearing the path for your journey so that you don’t stumble on obstacles.

4. Exploratory Data Analysis: Here, you dig into the data to understand its characteristics. You might create visualizations or perform statistical analyses to get insights. It’s like studying a map to know the terrain you’re going to traverse.

5. Data Preprocessing: You prepare the data for analysis. This may involve scaling, normalizing, or encoding the data, making it suitable for modeling. It’s similar to packing your bags with the essentials you need for the journey.

6. Model Building: Now, you create models using various algorithms to analyze the data. Depending on the problem, you might use regression, classification, clustering, or deep learning models. It’s like using tools to explore the terrain you’re traveling through.

7. Model Evaluation: After building models, you assess their performance. You use metrics to see how well they solve the problem. It’s like checking your progress and making sure you’re on the right path during your journey.

8. Deployment: When you are satisfied with a model’s performance, you deploy it to work in the real world. For instance, if you built a recommendation system, it starts making recommendations to users. It’s like reaching your destination and putting your skills to practical use.

9. Feedback and Improvement: The journey doesn’t end with deployment. You continually monitor the models and gather feedback. If they’re not performing as expected, you make improvements. It’s similar to learning from your journey’s experiences and getting better for the next one.

The Data Science Life Cycle is a structured approach that ensures you go through these steps in a systematic manner, maximizing your chances of successfully using data to solve real-world problems. It’s an iterative process, meaning you may revisit some steps as you learn and improve along the way.

Learn the essential skills for a career in data science with our comprehensive Data Science Learning Path blog

Prerequisites for Data Science

There are several prerequisites that must be fulfilled in order to efficiently drive data science solutions in an organization. Here are some technical terms you should know before learning what is data science are as follow:

Programming Knowledge

For the statistical analysis and computations that are required for the Data Science processes, it is necessary for the professionals to be familiar with Programming languages such as Python or R programming. The library support and scripting knowledge helps you create machine-learning models from scratch with ease. Scikit-learn, Tensorflow, Pandas, Matplotlib, Seaborn, Scipy, Numpy, etc., are some of the inbuilt Python programming libraries that can be used for Data Science using Python.

Statistics, Probability, And Linear Algebra

The knowledge of descriptive statistics and inferential statistics is a must if you really want to make a career in data science. With the help of statistical analysis, you are able to create various inferences and understand the data at hand. One example would be how we discussed performing hypothesis testing to test whether a time series is stationary or not.

Probability and linear algebra also play an important role in shaping the understanding of complex machine-learning algorithms. If you’re familiar with these concepts, it will be easier for you to understand the internal functioning of various machine learning algorithms.

Check out this blog to learn more about time series analysis!

SQL, Excel, And Visualization Tools

The visualization tools such as PowerBI, Tableau, etc., can provide a great interactive interface to represent various data points, which can help in performing initial analysis or just understanding the data.

SQL and Excel, on the other hand, can help you in understanding the representation of data in tabular format or data frames that help in data manipulation, wrangling, etc.

Big Data And Cloud

A machine learning model deployed at scale is where the cloud comes into the picture, to be able to magnify the learnings and outcomes for any business problem we use machine learning on the cloud. And big data gives a better perspective on how to handle large and complex data for our business problems and for creating data pipelines for continuous development and training of various machine learning models at scale.

Get to understand the concept of data visualization in python!

What is Data Science Process?

Let’s understand what is data science process with an example:

Step 1: Gathering Raw Data

Let’s say a company wants to understand public sentiment toward its brand on social media. They decide to gather data from the Twitter API, which provides a stream of tweets related to their brand.

Step 2: Data Modeling

Using statistical analysis and machine learning approaches, the data scientists preprocess and clean the Twitter data. They extract relevant features such as sentiment scores, user demographics, and engagement metrics. The data is then transformed into a structured format suitable for analysis.

Step 3: Actionable Insights

The data scientists analyze the structured data to derive insights. They identify patterns, trends, and correlations within the Twitter data. For example, they may discover that positive sentiment is higher among younger demographics and during certain events. These insights provide the company with actionable information on how to improve its brand perception and engagement strategies.

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Importance of Data Science

Data is a highly valuable asset for a wide range of industries, serving as a cornerstone for making well-considered and informed business decisions. Data science, as a field, possesses the remarkable capability to transform raw data into meaningful and actionable insights.

A data scientist holds the expertise to extract valuable information from whatever data they have access to. Their proficiency lies in the art of converting numbers, statistics, and data points into practical recommendations. Instead of merely working with data, they sculpt it into a compass, guiding organizations along the path of data-driven success.

Data scientists serve as trusted navigators, steering companies through the constantly changing data landscape. They ensure that every decision and suggestion is firmly anchored in robust and insightful data analysis. This, in turn, empowers businesses to remain agile, seize opportunities, and maintain their competitive edge in an increasingly data-centric world.

Uncover the distinctions between BI & Data Science to optimize your business strategy.

Now that you know what is data science, let’s focus on the data science life cycle.

Check out our Data Science Tutorial to learn more about Data Science.

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Applications of Data Science

The below listed applications showcase how data science drives operational efficiency, cost savings, and improved decision-making across diverse sectors.

Below-mentioned is 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. Recently Data Science approaches were brought in to combat the COVID-19 pandemic. Data Scientists helped in digital contact tracing, diagnosis, risk assessment, resource allocation, estimating epidemiological parameters, drug development, social media analytics, etc.

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 targeted advertising is based on a user’s past behavior with the help of data science algorithms.

Recommendation systems

Major online companies, along with countless other businesses, have enthusiastically adopted recommendation engines. These engines are all about providing personalized suggestions to users based on their past interests and searches. It’s like offering users ideas and products they might like. This not only keeps users engaged but also creates a more tailored and enjoyable experience for customers.

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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.

Augmented reality (AR)

Augmented Reality (AR) is a technology that combines digital information or virtual elements with the real world, typically viewed through a device like a smartphone or AR glasses. It enhances your perception of reality by adding computer-generated images, sounds, or other data to what you see, creating an interactive and enriched experience.

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Examples of Data Science

Let’s take a look at some Data Science examples:

  • Amazon: Amazon uses a personalized recommendation system to improve customer satisfaction. This is majorly dependent on predictive analytics. Amazon analyzes the user’s purchase history to recommend more products.
  • Spotify: Spotify utilizes Data Science to offer personalized music recommendations to users. In 2013, Spotify made predictions about the Grammy Award Winners by analyzing what music its users listen to. Out of the 6 predictions, 4 came true.
  • Uber: Uber utilizes big data to gain better insights and provide better service to the users. With its huge database of drivers, it can suggest to users the most suitable one. Uber charges customers based on the time it takes to get to their destination. This prediction is helped by various algorithms.

Also, check out the blog on Top Data Science Companies in India.

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

What Does a Data Scientist Do?

The role and responsibilities of a data scientist can vary from organization to organization, based on this, we can segregate them in the following manner.

A data scientist’s role in any organization will involve the following:

  1. Data Extraction, Loading, Transformation
  2. Exploratory Data Analysis
  3. Data Manipulation
  4. Statistical Analysis
  5. Visualization
  6. Data Modeling
  7. Gathering Actionable Insights

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.

Since we have discussed what data scientists do, let us also discuss why becoming a data scientist is good for your career.

Also, check out the blog on Data Science Colleges in India.

Why Should You Become a Data Scientist?

  • High Demand: Data scientists are really wanted in many industries because companies are relying more and more on data to make choices. This means there are plenty of job options, and they often come with good pay.
  • Exciting Career Prospects: If you decide to be a data scientist, you’ll get to work with new and cool technologies, deal with tough problems, and bring out new ideas. You’ll always have chances to learn and grow in this field.
  • Meaningful Contributions: Data scientists can find important information in data that helps companies make smart decisions, improve how they work, and stay competitive. You’ll feel good about the impact you’re making on businesses and society.
  • Diverse Skill Set: Data science gives you a set of skills that you can use in many jobs. You’ll be good at things like looking at data, programming, and understanding different subjects. This means you can choose from a lot of different careers.
  • Learning All the Time: The world of data science is always changing. This means there’s always something new to learn and discover. You’ll have to use your brain a lot, and you’ll get to learn more throughout your career.
  • Working with Different People: Data scientists often work with people from different fields, like business, engineering, marketing, and healthcare. This mix of ideas makes work interesting and lets you see things from new angles.
  • A Career That’s Ready for the Future: As more and more companies use digital tools and data, data scientists will keep being in demand. This means that if you become a data scientist, you’ll have a job that’s safe and can change with the times.

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Top Companies Hiring Data Scientists

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

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 employees.

Amazon

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.

Visa

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 is 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 a 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 accommodating. An experienced data scientist in China can earn up to ¥350,000 p.a.
  • India: India has the 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.

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

Intellipaat provides many opportunities to 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.

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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.

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FAQs

What is the difference between data science, artificial intelligence, and machine learning?

With data science, you can analyze, visualize, and predict data using statistical techniques. Artificial Intelligence makes machines act like humans. The machine is made to imitate human behavior. Machine Learning is a part of AI that makes machines learn using the data provided.

What is Data Science in simple words?

Data science helps in finding meaningful insights from data using various techniques.

What does a Data Scientist do?

A Data scientist helps businesses by analyzing large amounts of data and extracting meaning from it.

What is Data Science with example?

Data science uses various tools and techniques to process and analyze data. For example, it can optimize road routes using traffic data and location data from various users. This can help in reducing fuel consumption.

What kinds of problems do Data Scientists solve?

Data scientists can solve issues like forecasting events, revamping search engines, predicting crime, traffic prediction, etc.

What is the Data Science course eligibility?

You can check out Intellipaat’s Data science course for more details.

Can I learn Data Science on my own?

Data science could be daunting to learn by oneself. It is recommended that you learn it with the help of a structured program.

Also, check out the list of Data Science Hackathons.

If you have any queries regarding this, ‘What is data science blog’, please put it the comments section of the blog.

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