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blog / Data Science and Analytics What is Applied Data Science? How Does it Harness Insights for Innovation

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The following blog discusses what applied data science is, how it differs from traditional data science, and why it is gaining momentum.

What is Applied Data Science?

Applied data science uses data analysis tools and techniques to solve real-world problems and make data-driven decisions. Furthermore, it also involves extracting, cleaning, and analyzing data to gain insights and inform decision-making. Given its vast potential, applied data science is essential to diverse industries such as healthcare, finance, marketing, and more.

How is Applied Data Science Different From Data Science?

Applied data science and data science are closely related but have distinct differences in focus and scope. Hence, here’s an explanation of how they differ. 


Data science has a broader focus and encompasses the entire data analysis and modeling process, including data exploration, cleaning, and algorithm development. Applied data science narrows the focus to applying data science techniques to solve specific problems in a particular domain or industry.

Practical Application Domain Expertise

Applied data scientists often have a deeper understanding of their specific domain or industry. Furthermore, unlike traditional data scientists, they combine their data science skills with subject-matter expertise. This allows them to develop effective solutions tailored to the unique challenges of that domain.

Implementation and Deployment

Applied data science focuses on implementing and deploying data-driven solutions in a production environment. It also involves integrating data science models and insights into existing systems or workflows to drive decision-making and generate business value.

What is the Role of Applied Data Science in the Business World? Decision-Making

Applied data science helps businesses make informed as well as evidence-based decisions. Businesses can also gain insights into customer behavior, market trends, and operational patterns by analyzing and interpreting large volumes of data. 

Predictive Analytics

Businesses can seek help from data science to make predictions and forecasts based on historical data. Such organizations can anticipate customer behavior, demand patterns, and market trends by developing predictive models. 

Customer Insights

Applied data science also helps businesses understand their customers better. For instance, businesses can analyze customer data and identify patterns, preferences, and segments for better sales and revenue. 

Risk Management

Businesses can analyze historical data to detect patterns that indicate fraudulent activities, credit default risks, or cybersecurity threats. They can use applied data science by leveraging predictive models and anomaly detection techniques.

What are the Skills Required for a Successful Career in Applied Data Science?

A successful career in applied data science requires combining technical skills, domain expertise, and soft skills. Let’s now look at some key skills typically sought in the field.

Strong statistical as well as mathematical background with proficiency in probability theory, hypothesis testing, regression analysis, as well as other statistical techniques

Programming skills with proficiency in programming languages commonly used in data science, such as Python or R

Data visualization, manipulation, and analysis skills, including data preprocessing, cleaning, and transformation abilities

A solid understanding of machine learning techniques as well as algorithms and practical experience in building and fine-tuning machine learning models 

Finally, domain expertise in the industry or domain of application to formulate questions and also identify variables 

What Tools and Techniques are Essential for Data Analysis in Applied Data Science?

Applied data science involves the use of various essential tools as well as techniques for better data analysis, modeling, and decision-making. Some of them are mentioned below:  

Programming languages such as Python and R for extensive support for data manipulation, statistical analysis, and machine learning

Data manipulation and analysis tools like Pandas in Python and dplyr in R are essential for data manipulation tasks such as filtering, merging, grouping, as well as transforming data

Statistical analysis libraries like SciPy in Python and the stats package in R to perform statistical tests, estimate parameters, as well as analyze relationships between variables

Machine learning algorithms for predictive modeling and classification of data

Data mining and text analytics techniques, such as data mining and text analytics to extract insights from large and unstructured data sets

Lastly, big data analytics tools like Apache Hadoop and Apache Spark for distributed processing and analysis of big data

What Job Opportunities are Available in the Field of Applied Data Science?

Applied data science also offers various job opportunities across multiple industries. Here are some common job roles in applied data science.

Applied Data Scientist

Applied data scientists analyze complex data sets, develop statistical models and apply machine learning techniques. They also aim to extract insights and solve business problems through model building and evaluation. 

Data Analyst

Data analysts collect, clean, and analyze data to identify trends, patterns, and insights. Moreover, they perform exploratory data analysis, create visualizations, and generate reports to support decision-making processes.

Machine Learning Engineer

Machine learning engineers develop and deploy machine learning models in production environments. Furthermore, they are responsible for building scalable and efficient algorithms, optimizing model performance, and integrating models into software systems.

Data Engineer

Data engineers build and maintain the data storage, retrieval, and processing infrastructure. In addition, they also develop and optimize data pipelines, implement data integration solutions, and ensure data quality and reliability.

What is the Average Salary for an Applied Data Scientist

An applied data scientist earns an average salary of $127,044 annually in the U.S. as of May 2023. Furthermore, the base pay is around $104,029 per year. 

ALSO READ: How to Become a Data Scientist

What are Some Trends in the Field of Applied Data Science?

The field of applied data science is dynamic and constantly evolving. We take a look at some current trends that are shaping the field.

Explainable AI

Artificial Intelligence (AI) and Machine Learning (ML) models are becoming increasingly complex. This means there is a greater need for interpretability and transparency. Explainable AI aims to develop techniques to explain the predictions and decisions made by AI models. 

Auto ML and Automated Data Science

AutoML (Automated Machine Learning) and automated data science tools are gaining popularity because they automate various stages of the data science workflow. This includes data preprocessing, feature engineering, model selection, and hyperparameter tuning. 

Edge Computing and IoT Analytics

The proliferation of Internet of Things (IoT) devices and the need for real-time applied data science analytics are driving the adoption of edge computing. This involves processing data on local devices or edge servers closer to the data source rather than sending it to the cloud. 

Augmented Analytics

Augmented analytics combines machine learning and natural language processing with traditional analytics tools. Besides, it automates data preparation, analysis, and visualization. 

Learn More About Applied Data Science with Emeritus

Applied data science is essential for businesses because it enables them to uncover valuable insights that help decision-making. That is why companies actively seek professionals well-versed in this field. It is, therefore, essential to stay updated and skill up. A good way to do so is by signing up for Emeritus’ data science courses because they provide comprehensive knowledge and practical skills needed to thrive in this dynamic space. These courses equip professionals with industry-relevant knowledge, hands-on project experience, and guidance from experienced instructors.

By Apsara Raj

Write to us at [email protected]

You're reading Applied Data Science: How To Harness Insights For Innovation

How To Build A Career In Data Science

Is there a sector with better job prospects than Data Science? It’s unlikely. Virtually every company now relies heavily on data analytics software – which requires data pros to use most effectively.

In this Webinar, we’ll discuss:

How to get started in Data Science — without a four year college degree.

Some of the most meaningful and lucrative career paths in Data Science.

General tips on building a career in Data Science.

The remarkable future of Data Science as a career option.

Please join this wide-ranging discussion with a top leader in the Data Science sector, Kirk Borne, Principal Data Scientist, Booz Allen Hamilton


You don’t need that, actually. I think I have to focus on a two-stage process here. The first step is just getting your foot in the door, which starts by just learning the skills of data science, the coding, the algorithms, the techniques, the methods, the process, all these things.

But if you’re really going to have a long-standing career, I always say that a degree gives you that extra career of padding, that is when the organizations look to promote people, maybe to leadership positions or whatever, it’s not just that you happen to know some coding skills, there’s a lot more that goes into that, and that comes with the things you learn in formal education programs, which are outside of the sciences, right?

The PhD is a research degree. Okay, so if you want to be a research scientist, that is where you want to go, but most data scientists aren’t gonna be research scientists, and by that I mean, you’re actually publishing papers in research journals, peer-reviewed journals, peer-reviewed conferences, probably at an academic institution trying to get tenure.

But if you want to have a successful data science analytics career, I’d say having a master’s degree teaches those professional skills of communication, leadership, collaboration, the things that go beyond just the academic stuff you learn in bachelor’s and beyond, and different from the research things you learn in a PhD program.

So I say, yeah, you can get into this field right away without a degree, but for the long-term career success, think about the collegiate education as well.


The number of jobs opening is far and excess than the number of people available. Once you’ve certified in any of those things, you’re gonna get a job.

I know a number of data scientists who’ve gone on to become founders of companies, so they’re sort of managing a company now. So, a master’s in business analytics is a pretty impressive thing to have under your belt as well, because then that business analytics gives you both the analytics and the business experience.

But I do wanna say yes, there are certification programs. [There’s the] certified analytics professional, the CAP certification, but there’s also lots of boot camps. So boot camps can teach you skills like in 12 weeks or 16 weeks that will get you the job.

There’s also master’s, there’s a lot of master’s degree programs that are basically 11-month programs, so you get the full master’s degree, but it’s a full-time job, you can’t have a job or a life, pretty much for 11 months.

And master’s programs are different from certifications in that any college degree programmers require state accreditation, and they have to meet certain minimum standards, like 30 credit hours, certain number of courses. And whereas at boot camp, you just take a boot camp in Python and you can get their Python certification and go get a Python job, there’s no sort of state university regulation over a boot camp.


So anyway, so I think in terms of specific jobs, the AI engineer, machine learning engineer, cloud engineer, they surpass data scientists in terms of lucrative sort of salary. and the reason I say that is because these are the people who actually have to build it out, deploy it, put it in their production, keep it running. Data scientists are also well paid and you can have a really nice satisfying job as a data scientist, but most of the time you’re building models, you’re playing with data, tweaking with data, exploring data, finding the right algorithms. And that’s fine, that’s great, and that’s sort of what gets the foot in the door towards business value creation from the data, which is really what my message always is, is focus on the business value creation.

When it’s deployed, put into production and maintained, and that’s where the AI engineer, machine learning engineer, cloud engineer, is gonna be the person or team of people who is going to accomplish that. So everyone has a value in the chain, but that engineer who’s gonna deploy, build, and keep it in production is the one who can say, “I’ll take any salary I want.” [laughter]

So if you’re gonna build that [extensive deployment], you have to have a way more capability than your traditional data scientist, but, nevertheless, people are being hired as AI engineers and machine learning engineers because they’re being hired to do what data scientist’s job, which is to explore the data and build models from the data. And the job title doesn’t really match what I would call the data scientist job, and vice versa.


A few years ago, I starting thinking of sort of the key skills or soft skills… I should say aptitudes, not really a skill, soft skill of a successful data scientist, and things like being curious and creative and critical thinking, collaborator, communication. I started thinking, “Oh, those things all start with the letter C.”

But I think for sure, being a curious person, I mean, I can just say, for example, we had students in our PhD program at the university who, in some cases, were not curious people. I can just say it bluntly. That is, they just… When they put together a proposal to do a doctoral dissertation, it was really, “I wanted to build this software system to do data science.”

But the one we’ve already hit upon is this continuous lifelong learning, I mean for me that’s super-duper important. But another big important one, which you may not think of, is number 10 on this list, which says “consultative.” If you’re doing data science for a company, an employer, a stakeholder, whoever, you have to be able to communicate. Not just communicate, but listen to what they’re saying and ask the right questions to make sure you build the right system, so that’s really a business focus.

The principal of system engineering is, there’s a difference between building the system right and building the right system. So they followed the letter of the law and the requirements document, and they built the system right, but it was completely not functional for science and the research needed. It didn’t build the system that scientists would want to use.

It’s hard to even say exactly why. My first day on the job, I worked at the Hubble project 10 years, and it was in my seventh year that I got appointed to be this NASA project scientist for the data archive. And my first day on the job, the previous guy who was the archive project scientist handed a big box to me, about the size of a typical Xerox box full of reams of paper. Literally thousands of pages. There was a lot of discussion on the system requirements and the functional requirements, but if you know anything about user experience and design thinking, no one was talking user experience and design thinking 30 years ago. [chuckle]

Oh, that’s another one of the Cs on my list there, was that compassion. Again, the forced letter C there, meaning more like empathy, that is being able to understand that you’re dealing with users of this thing you’re building, and if it’s opaque and not understandable and uses complex terminology to explain it, you’re not being very empathetic with your end user. [chuckle]



Yeah, I’m looking for my crystal ball right now, let’s see…[laughter]. I think as time goes on, we’re just seeing the data science is more being blended into organizations. There was a time where it was sort of like a side project or the team was off to the side, “Here’s our data science team.” But for one thing, I think there’s gonna be some data democratization that has to happen. [There are ] two aspects of the culture. One is a culture of experimentation, that is being able to test data for patterns that might give business insight for better actions and decisions, so culture of experimentation. And the other is a culture of, if you see something, say something. So where have we heard that before, right? [chuckle]

If you’ve ever been in the New York City subway, you see the signs everywhere, “If you see something, say something.” And the same thing with data. If you see something, it’s “Oh it was not my job. It’s someone else’s job”. No, we are… If we’re a digital organization, if we are undergoing digital transformation, then we all need to be empowered to work with and learn from and take actions from digital data.

Anyway, so I think data science, the future of is we’ll see less of it emphasized as data science and more in terms of its other dimensions, which the applications, like machine learning and AI. Well, AI being the application, machine learning being the technology for the actual implementations, which include cloud and other things. So we’ll start seeing more focus on those, but we’ll still be doing data science. We just may not use that word to describe the job title.


So essentially becoming immersed in data first. At that point you sort of… If you don’t catch the bug there, you’re not gonna catch it at all, ’cause once you get immersed in data, you realize there’s power, there’s patterns and trends, and correlations in data. So once you get that experience, then I’d say first thing to look at is unsupervised learning, because unsupervised learning is basically just finding the patterns in the data without any regard to any preconceived notion of what you’re looking for. Now, supervised learning is specifically designing algorithms that can diagnose or predict an outcome based upon training data. So you could start there too. A lot of people do start there because they feel like, “Hey, I can predict the future.” [chuckle] So supervised learning, it gives you a rush because you’re actually predicting something pretty cool.


Yeah, yeah, it’s not really easy to answer that because there’s so many thousands out there. There are websites that do a compilations of surveys of what data scientists recommend, and so… KD Nuggets. If people are not familiar with KD Nuggets, they should check that, chúng tôi has been around, Greg Shapiro started that you know in 1980s.


Yeah. Absolutely. I’m actually a keynote speaker for a conference in Peru at the end of July. I’m giving two keynote talks, one on AI and one on data business analytics basically. So Peru is really ramping up. I know that in Africa, it’s an enormous activity going on right now, a lot of activity in Nigeria. So even a few years ago, I was invited to the South African Embassy in Washington DC, which is a very moving experience because it was the week that Nelson Mandela died. It was really an emotional experience, but they’re talking about the importance of data analytics to basically rise up the whole South African continent in terms of agriculture, economics, and business, and healthcare, and medicine, and so on, and so on. Just how the power of data to inform, to inspire, for innovation and insights… It was just really impressive, and so I don’t think anyone is gonna be immune from the benefits of this if you just go after it.

Data Science Resume: How To Make It More Appealing?

Know the best way to write your Data Science Resume and make a statement to top-tier tech companies.

The concept of life is simple, you need oxygen to live and a resume to get a job. It is essential to write an eye-catching resume to be first in a race, especially if you are applying for a

data science

job. Even if you are not a fan of writing resumes, you cannot ignore the fact that most companies require a resume in order to apply to any of their open jobs, and it is often the first layer of the interview process.

So does it matter how you write personal, educational, and professional qualifications and experience details in a resume? Yes, it does, and here are some tips about how to make your resume more appealing that will catch the eye of a recruiter or interviewer.

1. Always write a resume in brief

Rule number 1, always keep your resume short and engaging. Try to get all your details on one page because recruiters receive thousands of resumes every day and have a minute to look over someone’s resume and make a decision. Therefore make sure your resume speaks on your behalf and makes an impression.

2. Customize your resume according to the job description

While you unquestionably can make a solitary

resume and send that to each job you apply for, it would be a smart move to attempt to add customized changes depending upon the job description would positively intrigue the recruiter.

This doesn’t mean you have to do rework and upgrade your resume each time you go after a position. However, if you notice significant skills mentioned in the work posting (for example, skills like

Data Visualization

or Data Analysis) you should be certain about the resume you’re sending focuses on those skills and increase your chances of getting that job.

3. Pick a right layout

While each resume will consistently incorporate data like past work insight, abilities, contact data, and all, you ought to have a resume that is remarkable to you. That starts with the visual look of the resume, and there are various approaches to achieve a one-of-a-kind design.

Remember that the type of resume layout you pick is also significant. In case you’re applying to

with a more customary feel attempt to focus on a more traditional, curbed style of resume. In case you’re focusing on an organization with more of a startup vibe, you can pick a layout or make a resume with more colors and graphics.

4. Contact Details

After the selection of your resume’s layout next step is to add contact detail. Here are some important things you need to remember about your contact details and what to put there in the context of a data science resume specifically:

If you are applying for a job in a different city and don’t want to relocate it is better not to add your entire physical address, only put in your city and state you live.

The headline underneath your name: reflects the job you’re looking to get rather than the job you currently have. If you’re trying to become a data researcher, your headline should say “Data researcher” even if you’re currently working as an event manager.

5. Data Science Projects/Publications area

Quickly following your name, feature, and contact data ought to be your Projects/Publications area. In any resume, particularly in the technology business, you should focus on highlighting the things you have created.

For a data science resume, this may incorporate machine learning projects, AI projects, data analysis projects, and more. Hiring organizations need to perceive what you can do with your mentioned skills. This is the segment where you can flaunt.

6. Highlight your skills

At the point when you portray each project, be pretty specific about your abilities, tools, and innovations you utilized, how you made the project. Indicate the coding language, any libraries you utilized, and more. The more talk about your skills and key tools the better.

7. Professional Experience 8. About Education

If you have relevant work experience to showcase, it is better to add your educational details closer to the bottom. But if you are fresher and applying for your first job then, in that case, you have to highlight your qualification.

9. Last thing to do

While you unquestionably can make a solitary data researcher Remember that the type of resume layout you pick is also significant. In case you’re applying to tech companies

Learnbay: Most Acknowledged Data Science Institute Offering Comprehensive Data Science Courses

Data has become an important part of everybody’s life. Without data there is nothing. Data mining for digging insights has marked the demand for gaining knowledge of using data for business strategies. Data science is not limited to only consumer goods or tech or healthcare. There is a high demand to optimize business processes using data science from banking and transport to manufacturing. Therefore, the field of data science is growing with increasing demand.

Developing Analytical and Technical Skills

The institute aims at securing working professionals' careers by assisting them in developing analytical and technical skills. This will enable them to make a transition into high-growth analytical job roles by leveraging their own domain knowledge and work experience at an affordable cost.  

Data Science Courses

Presently, Learnbay is offering six different data science courses as follows: • Business Analytics and Data Analytics Programs for working professionals with 1+ years of experience in any domain. Course duration: 5 months with 200+ hours of classes. Project: 1 Capstone project and more than 7 real-time projects. Course Fee: 50,000 INR • Data Science and AI Certification for working professionals with 1+ years working experience in any domain Course duration: 7 months with 200+ hours of classes. Project: 2 Capstone projects and more than 12 real-time projects. Course Fee: 59,000 INR • AI and ML Certification to Become AI Expert in Product Based MNCs for working professionals with 4+ years of working experience in the technical domain Course duration: 9 months with 260+ hours of classes. Project: 2 Capstone projects and more than 12 real-time projects. Course Fee: 75,000 INR • Data Science and AI Certification for Managers and Leaders with 8 to 15 years of working experience in any domain Course duration: 11 months with 300+ hours of classes. Project: 3 Capstone projects and more than 15 real-time projects. Course Fee: 75,000 INR Course duration: 9 months with 300+ hours of classes. Project: 2 Capstone projects and more than 12 real-time projects. Course Fee: 95,000 INR. • Industrial Training in AI and Data Science for Fresh Graduates Course duration: 4 months with 200+ hours of classes. Project: 1 Capstone project and more than 7 real-time projects. Besides, there is a 6-month internship program. Course Fee: 39,999 INR.  

Key Features of Learnbay Courses

• 1to1 learning supports via complete live interactive classes, additional discussion sessions, etc. • 24/7 instant tech support. • Regularly updated learning modules. • Flexible installment options for course fees. • Lifetime free access to the premium learning materials and recorded videos of the attended classes. • Hands-on live industrial project-based learning.  

About the Initiator

Krishna Kumar, the Founder of Learnbay, has observed the data-related job market for different industries as well as the data science training platforms very closely. He founded Learnbay in 2023. Although he started his journey with Learnbay as a founder, he worked for the institute from the very grassroots. To understand students' expectations, he took classes, conducted career counseling sessions, and provided personalized doubt clearance assistance by himself. During that time, he worked with the motto of staying connected with his students directly and revealed many of the hidden facts of the data science training business/ teaching platforms. He found that most of his students having doubts concerning the efficacy of data science courses available in the market from the perspective of learning support- even after paying a fair amount of course fees. They came to the institute with the hope of a complete industry-grade data science learning experience with dedicated learning support at affordable prices. He started focusing on the efficacy of learning assistance and placement support of his institute's courses from that time. Within one year, the institute got many impressive responses from the students. Even though he has plenty of expert faculties (trainers, counselors, project organizers, etc) today, still at some level, he maintains direct interaction with each of the Learnbay students. Based on their feedback, he keeps updating, altering, and upgrading the institute's learning modules, teaching approaches, and learning supports.  

Personalized Data Science Career Counselling

The edge of the institute’s Analytics and Data Science Program over other institutes in the industry is owing to the following factors: • Instead of a generalized course, the institute offers different courses according to Student’stheir personal career needs. • It offers personalized data science career counseling to help a student in investing in the course that best his present working experience and future growth. • Its placement assistance helps in securing a student’s first data science job. • It offers the flexibility of attending multiple sessions of the same modules instructed by different instructors for better understanding.  

Internships and Placements Training on Analytical Tools Notable Awards and Achievements

This year (2023), Learnbay is stepping into the successful journey of a total of 5 years. Within these five years, it has grown a lot and currently holds an excellent reliability percentage from data science aspirants/training seekers. In the last five years, the institute has earned highly positive responses and feedback from students, professionals, and new data science aspirants. Leranbay has already achieved industrial collaboration from the IT giant, IBM. It got placed in the top seven data science institutes listed by chúng tôi The institute ranked 3 for Bangalore and 1 for Chennai locations. Even being a Bangalore-based organization, it got massive recognition across the different metro cities of India, like Hyderabad, Kolkata, Mumbai, Delhi, Mumbai, etc. The course review of Learnbay is 4.8 on Google.  

Foreign University Certification, a Key Challenge

Initially, the students were showing more interest infor foreign university certification tag, even if they had to pay three times more than the actual fees. But as mentioned earlier, the key mission of the institute is to offer the appropriate learning guide to the career transformation seeker and not to confuse or divert the students with decorative and eye-catching staff. In the real-world data science job market, what matters only is the hands-on experience and project work. Recruiters are not even interested in the student certification tag. So, the institute keeps enriching the course efficacy from the hands-on learning perspective and project works without focusing on the certification tag. Its efforts got rewarded with the IBM collaboration. From last year, the scenario has entirely changed. The continuous success of the students and plenty of available data science job market analytical insight now support its training approach. Although plenty of competitors started providing such university tags, still it's not effective for its colossal student base.  

The Future of Big Data Analytics

Top 7 Free Datasets Sources To Use For Data Science Projects

Free datasets sources for data science enthusiasts 

Data is preliminary for companies and corporations to analyze and obtain business intelligence. It helps in finding the correlations between the data and the unique insights for a better decision-making process. And for these  

Google Cloud Public Dataset

Most of us think that Google is just a search engine, right? But it is way beyond. Several datasets can be accessed through the Google cloud and analyzed to fetch new insights from the data. Google cloud has more than hundreds of

Amazon Web Services Open Data Registry

Amazon Web Services has the largest number of

The US government is also keen on data science, as most of the tech companies are located in Silicon Valley. chúng tôi is the main repository of the US government’s open datasets that can be used for research, developing data visualizations, mobile applications, and creating the web. This is an attempt of the government to become more transparent in terms of access without registering. But some of the datasets need permissions before downloading them. chúng tôi has diverse varieties of


Kaggle has more than 23,000 public datasets that can be downloaded for free. You can easily search for the dataset that you’re looking for and find them hassle-free ranging from health to cartoons. The platform also allows you to create new public datasets and can also earn medals along with the titles such as Expert, Master, and Grandmaster. The competitive Kaggle datasets are more detailed than the public datasets. Kaggle is the perfect place for data science lovers.  

UCI Machine Learning Repository

If you are looking for interesting datasets then UCI Machine Learning Repository is a great place for you. It is one of the first and oldest data sources that are available on the internet since 1987. The datasets of the UCI are great for machine learning with their easy access and download options. Most of the datasets of UCI are contributed by different users so the data cleanliness is a little low. But UCI maintains the datasets for using them for ML algorithms.  

Global Health Observatory

If you are from a medical background then Global Health Observatory is a great option for creating projects on global health systems and diseases. The WHO has made all their data public on this platform. This is for the good quality health information available worldwide. The health data is characterized according to various communicable and noncommunicable diseases, mental health, morality, medicines for better access.  


Data is preliminary for companies and corporations to analyze and obtain business intelligence. It helps in finding the correlations between the data and the unique insights for a better decision-making process. And for these datasets sources are important to help you with your data science projects . But luckily there are many online data sources to fetch you free datasets to help with your projects by just downloading them absolutely free. Let’s learn more about the top 7 free dataset sources to use for data science projects in this chúng tôi of us think that Google is just a search engine, right? But it is way beyond. Several datasets can be accessed through the Google cloud and analyzed to fetch new insights from the data. Google cloud has more than hundreds of datasets that are hosted by BigQuery and cloud storage. Google’s machine learning can be helpful in analyzing datasets such as BigQuery ML, Vision AI, Cloud AutoML, etc. Also, Google’s Data Studio can be used to create data visualization and dashboards for better insights. These datasets have data from various sources such as GitHub, United States Census Bureau, NASA, and BitCoin, and many more. You can access these datasets free of cost.Amazon Web Services has the largest number of datasets on their registry. It is very easy to download these datasets and use them to analyze the data on the Amazon Elastic Compute Cloud. It also employs various tools such as Apache Spark, Apache Hive, and more. The Amazon Web Services is an open data registry that is part of the AWS Public Dataset Program that focuses on democratizing the access of data so that it is available to everybody. AWS open data registry is free but allows you to own a free AWS chúng tôi US government is also keen on data science, as most of the tech companies are located in Silicon Valley. chúng tôi is the main repository of the US government’s open datasets that can be used for research, developing data visualizations, mobile applications, and creating the web. This is an attempt of the government to become more transparent in terms of access without registering. But some of the datasets need permissions before downloading them. chúng tôi has diverse varieties of datasets relating to climate, agriculture, energy, oceans, and ecosystems.Kaggle has more than 23,000 public datasets that can be downloaded for free. You can easily search for the dataset that you’re looking for and find them hassle-free ranging from health to cartoons. The platform also allows you to create new public datasets and can also earn medals along with the titles such as Expert, Master, and Grandmaster. The competitive Kaggle datasets are more detailed than the public datasets. Kaggle is the perfect place for data science chúng tôi you are looking for interesting datasets then UCI Machine Learning Repository is a great place for you. It is one of the first and oldest data sources that are available on the internet since 1987. The datasets of the UCI are great for machine learning with their easy access and download options. Most of the datasets of UCI are contributed by different users so the data cleanliness is a little low. But UCI maintains the datasets for using them for ML chúng tôi you are from a medical background then Global Health Observatory is a great option for creating projects on global health systems and diseases. The WHO has made all their data public on this platform. This is for the good quality health information available worldwide. The health data is characterized according to various communicable and noncommunicable diseases, mental health, morality, medicines for better chúng tôi you are looking for data related to Earth or Space then, Earthdata is your place. This is created by NASA to provide datasets based on Earth’s atmosphere, oceans, cryosphere, solar flares, and tectonics. It is a part of the Earth Observing System Data and Information System that helps in collecting and processing the data from various NASA satellites, aircraft, and fields. Earthdata also has tools for handling, ordering, searching, mapping, and visualizing the data.

Entropy – A Key Concept For All Data Science Beginners

This article was published as a part of the Data Science Blogathon.


The focus of this article is to understand the working of entropy by exploring the underlying concept of probability theory, how the formula works, its significance, and why it is important for the Decision Tree algorithm.

But, then what is Entropy?

The Origin of Entropy

The term entropy was first coined by the German physicist and mathematician Rudolf Clausius and was used in the field of thermodynamics.

In 1948, Claude E. Shannon, mathematician, and electrical engineer, published a paper on A Mathematical Theory of Communication, in which he had addressed the issues of measure of information, choice, and uncertainty. Shannon was also known as the ‘father of information theory’ as he had invented the field of information theory.

“Information theory is a mathematical approach to the study of coding of information along with the quantification, storage, and communication of information.”

In his paper, he had set out to mathematically measure the statistical nature of “lost information” in phone-line signals. The work was aimed at the problem of how best to encode the information a sender wants to transmit. For this purpose, information entropy was developed as a way to estimate the information content in a message that is a measure of uncertainty reduced by the message.

So, we know that the primary measure in information theory is entropy. The English meaning of the word entropy is: it is a state of disorder, confusion, and disorganization. Let’s look at this concept in depth.

But first things first, what is this information? What ‘information’ am I referring to?

In simple words, we know that information is some facts learned about something or someone. Notionally, we can understand that information is something that can be stored in, transferred, or passed-on as variables, which can further take different values. In other words, a variable is nothing but a unit of storage. So, we get information from a variable by seeing its value, in the same manner as we get details (or information) from a message or letter by reading its content.

The entropy measures the “amount of information” present in a variable. Now, this amount is estimated not only based on the number of different values that are present in the variable but also by the amount of surprise that this value of the variable holds. Allow me to explain what I mean by the amount of surprise. 

Let’s say, you have received a message, which is a repeat of an earlier text then this message is not at all informative. However, if the message discloses the results of the cliff-hanger US elections, then this is certainly highly informative. This tells us that the amount of information in a message or text is directly proportional to the amount of surprise available in the message.

In information theory, the entropy of a random variable is the average level of “information“, “surprise”, or “uncertainty” inherent in the variable’s possible outcomes.

That is, the more certain or the more deterministic an event is, the less information it will contain. In a nutshell, the information is an increase in uncertainty or entropy.

All this theory is good but how is it helpful for us? How do we apply this in our day-to-day machine learning models?

To understand this, first lets’ quickly see what a Decision Tree is and how it works.

Walkthrough of a Decision Tree

Decision Tree, a supervised learning technique, is a hierarchical if-else statement which is nothing but a collection of rules or is also known as the splitting criteria that are based on comparison operators on the features.

A decision tree algorithm, which is a very widely used model and has a vast variety of applications, can be used for both regression and classification problems. An example of a binary classification categorizing a car type as a sedan or sports truck follows as below. The algorithm finds the relationship between the response variable and the predictors and expresses this relation in the form of a tree-structure.

This flow-chart consists of the Root node, the Branch nodes, and the Leaf nodes. The root node is the original data, branch nodes are the decision rules whereas the leaf nodes are the output of the decisions and these nodes cannot be further divided into branches.


Hence, it is a graphical depiction of all the possible outcomes to a problem based on certain conditions or as said rules. The model is trained by creating a top-down tree and then this trained decision tree is used to test the new or the unseen data to classify these cases into a category.

It is important to note that by design the decision tree algorithm tries to build the tree where the smallest leaf nodes are homogenous in the dependent variable. Homogeneity in the target variable means that there is a record of only one type in the outcome i.e. in the leaf node, which conveys the car type is either sedan or sports truck. At times, the challenge is that the tree is restricted meaning it is forced to stop growing or the features are exhausted to use to break the branch into smaller leaf nodes, in such a scenario the objective variable is not homogenous and the outcome is still a mix of the car types.

How does a decision tree algorithm select the feature and what is the threshold or the juncture within that feature to build the tree? To answer this, we need to dig into the evergreen concept of any machine learning algorithm, yes…you guessed it right! It’s the loss function, indeed!

Cost Function in a Decision Tree

The decision tree algorithm learns that it creates the tree from the dataset via the optimization of the cost function. In the case of classification problems, the cost or the loss function is a measure of impurity in the target column of nodes belonging to a root node.

The impurity is nothing but the surprise or the uncertainty available in the information that we had discussed above. At a given node, the impurity is a measure of a mixture of different classes or in our case a mix of different car types in the Y variable. Hence, the impurity is also referred to as heterogeneity present in the information or at every node.

The goal is to minimize this impurity as much as possible at the leaf (or the end-outcome) nodes. It means the objective function is to decrease the impurity (i.e. uncertainty or surprise) of the target column or in other words, to increase the homogeneity of the Y variable at every split of the given data.

To understand the objective function, we need to understand how the impurity or the heterogeneity of the target column is computed. There are two metrics to estimate this impurity: Entropy and Gini. In addition to this, to answer the previous question on how the decision tree chooses the attributes, there are various splitting methods including Chi-square, Gini-index, and Entropy however, the focus here is on Entropy and we will further explore how it helps to create the tree.

Now, it’s been a while since I have been talking about a lot of theory stuff. Let’s do one thing: I offer you coffee and we perform an experiment. I have a box full of an equal number of coffee pouches of two flavors: Caramel Latte and the regular, Cappuccino. You may choose either of the flavors but with eyes closed. The fun part is: in case you get the caramel latte pouch then you are free to stop reading this article 🙂 or if you get the cappuccino pouch then you would have to read the article till the end 🙂

This predicament where you would have to decide and this decision of yours that can lead to results with equal probability is nothing else but said to be the state of maximum uncertainty. In case, I had only caramel latte coffee pouches or cappuccino pouches then we know what the outcome would have been and hence the uncertainty (or surprise) will be zero.

The probability of getting each outcome of a caramel latte pouch or cappuccino pouch is:

P(Coffee pouch == Caramel Latte) = 0.50

P(Coffee pouch == Cappuccino) = 1 – 0.50 = 0.50

When we have only one result either caramel latte or cappuccino pouch, then in the absence of uncertainty, the probability of the event is:

P(Coffee pouch == Caramel Latte) = 1

P(Coffee pouch == Cappuccino) = 1 – 1 = 0

There is a relationship between heterogeneity and uncertainty; the more heterogeneous the event the more uncertainty. On the other hand, the less heterogeneous, or so to say, the more homogeneous the event, the lesser is the uncertainty. The uncertainty is expressed as Gini or Entropy.

How does Entropy actually Work?

Claude E. Shannon had expressed this relationship between the probability and the heterogeneity or impurity in the mathematical form with the help of the following equation:

H(X) = – Σ (pi * log2 pi)

The uncertainty or the impurity is represented as the log to base 2 of the probability of a category (pi). The index (i) refers to the number of possible categories. Here, i = 2 as our problem is a binary classification.

This equation is graphically depicted by a symmetric curve as shown below. On the x-axis is the probability of the event and the y-axis indicates the heterogeneity or the impurity denoted by H(X). We will explore how the curve works in detail and then shall illustrate the calculation of entropy for our coffee flavor experiment.

Source: Slideplayer

The log2 pi has a very unique property that is when there are only two outcomes say probability of the event = pi is either 1 or 0.50 then in such scenario log2 pi takes the following values (ignoring the negative term):

  pi  = 1 pi = 0.50

log2 pi log2 (1) = 0 log2 (0.50) = 1

Now, the above values of the probability and log2 pi are depicted in the following manner:

The catch is when the probability, pi becomes 0, then the value of log2 p0 moves towards infinity and the curve changes its shape to:

The entropy or the impurity measure can only take value from 0 to 1 as the probability ranges from 0 to 1 and hence, we do not want the above situation. So, to make the curve and the value of  log2 pi back to zero, we multiply log2 pi with the probability i.e. with pi itself.

Therefore, the expression becomes (pi*  log2 pi) and  log2 pi returns a negative value and to remove this negativity effect, we multiply the resultant with a negative sign and the equation finally becomes:

H(X) = – Σ (pi * log2 pi)

Now, this expression can be used to show how the uncertainty changes depending on the likelihood of an event.

The curve finally becomes and holds the following values:

This scale of entropy from 0 to 1 is for binary classification problems. For a multiple classification problem, the above relationship holds, however, the scale may change.

Calculation of Entropy in Python

We shall estimate the entropy for three different scenarios. The event Y is getting a caramel latte coffee pouch. The heterogeneity or the impurity formula for two different classes is as follows:

H(X) = – [(pi * log2 pi) + (qi * log2 qi)]


pi = Probability of Y = 1 i.e. probability of success of the event

qi = Probability of Y = 0 i.e. probability of failure of the event

Case 1:

Coffee flavor Quantity of Pouches     Probability

Caramel Latte 7 0.7

Cappuccino 3 0.3

Total 10 1

H(X) = – [(0.70 * log2 (0.70)) + (0.30 * log2 (0.30))] = 0.88129089

This value 0.88129089 is the measurement of uncertainty when given the box full of coffee pouches and asked to pull out one of the pouches when there are seven pouches of caramel latte flavor and three pouches of cappuccino flavor.

Case 2:

Coffee flavor Quantity of Pouches     Probability

Caramel Latte 5 0.5

Cappuccino 5 0.5

Total 10 1

H(X) = – [(0.50 * log2 (0.50)) + (0.50 * log2 (0.50))] = 1

Case 3:

Coffee flavor Quantity of Pouches     Probability

Caramel Latte 10 1

Cappuccino 0 0

Total 10 1

H(X) = – [(1.0 * log2 (1.0) + (0 * log2 (0)] ~= 0

In scenarios 2 and 3, can see that the entropy is 1 and 0, respectively. In scenario 3, when we have only one flavor of the coffee pouch, caramel latte, and have removed all the pouches of cappuccino flavor, then the uncertainty or the surprise is also completely removed and the aforementioned entropy is zero. We can then conclude that the information is 100% present.

Python Code:

So, in this way, we can measure the uncertainty available when choosing between any one of the coffee pouches from the box. Now, how does the decision tree algorithm use this measurement of impurity to build the tree?

Use of Entropy in Decision Tree

As we have seen above, in decision trees the cost function is to minimize the heterogeneity in the leaf nodes. Therefore, the aim is to find out the attributes and within those attributes the threshold such that when the data is split into two, we achieve the maximum possible homogeneity or in other words, results in the maximum drop in the entropy within the two tree levels.

At the root level, the entropy of the target column is estimated via the formula proposed by Shannon for entropy. At every branch, the entropy computed for the target column is the weighted entropy. The weighted entropy means taking the weights of each attribute. The weights are the probability of each of the classes. The more the decrease in the entropy, the more is the information gained.

Information Gain is the pattern observed in the data and is the reduction in entropy. It can also be seen as the entropy of the parent node minus the entropy of the child node. It is calculated as 1 – entropy. The entropy and information gain for the above three scenarios is as follows:

  Entropy Information Gain

Case 1 0.88129089 0.11870911

Case 2 1 0

Case 3 0 1

The estimation of Entropy and Information Gain at the node level:

We have the following tree with a total of four values at the root node that is split into the first level having one value in one branch (say, Branch 1) and three values in the other branch (Branch 2). The entropy at the root node is 1.

Source: GeeksforGeeks

Now, to compute the entropy at the child node 1, the weights are taken as ⅓ for Branch 1 and ⅔ for Branch 2 and are calculated using Shannon’s entropy formula. As we had seen above, the entropy for child node 2 is zero because there is only one value in that child node meaning there is no uncertainty and hence, the heterogeneity is not present.

H(X) = – [(1/3 * log2 (1/3)) + (2/3 * log2 (2/3))] = 0.9184

The information gain for the above tree is the reduction in the weighted average of the entropy.

Information Gain = 1 – ( ¾ * 0.9184) – (¼ *0) = 0.3112

Frequently Asked Questions

Information Entropy or Shannon’s entropy quantifies the amount of uncertainty (or surprise) involved in the value of a random variable or the outcome of a random process. Its significance in the decision tree is that it allows us to estimate the impurity or heterogeneity of the target variable.

Subsequently, to achieve the maximum level of homogeneity in the response variable, the child nodes are created in such a way that the total entropy of these child nodes must be less than the entropy of the parent node.



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