How mask and robotics are used in order to fightback COVID-19 effectively

The coronavirus pandemic has shut the world down like nothing we’ve ever seen before. With the medical community completely shifting their focus and effort on fighting back against this worldwide pandemic by innovating different technologies in face mask and using robots to treat patients, it’s left many without work or anything productive to do.

robotics and mask designing and testing

The coronavirus pandemic has shut the world down like nothing we’ve ever seen before. With the medical community completely shifting their focus and effort on fighting back against this worldwide pandemic by innovating different technologies in face mask and using robots to treat patients, it’s left many without work or anything productive to do.

Around the world, there are a plethora of engineers, physicists, scientists, and otherwise just normal people making superhuman efforts at fighting back against COVID-19. There are thousands of collaborative engineering efforts against COVID-19 taking place each and every day. From 3D printed mask to mechanical ventilators, the STEAM community is putting up a solid fight back against the coronavirus. 

To aid in giving the projects and people visibility as well as highlighting how engineering is being used to combat the coronavirus, this is an engineering coronavirus site here created by interesting engineering. You’ll find a graphical interface to browse through submitted projects and news stories and get a live look at how the virus is spreading. It’s an engineering-centric COVID-19 hub.

3D printed mask solution for coronavirus 

With 3D printing practically in the mainstream, it’s been a primary tool for engineering to fight against the coronavirus. One notable project is that of the NonoHack Mask. While there have been a number of 3D printed masks, this mask design offers up versatility in just what you use for the air filtering portion.

Designed specifically for use with a polypropylene filter material to fit in the bottom, it can provide filtration for up to 96.4% of microorganisms the size of one micron and 89.5% of microorganisms of .02 microns.

3d printed design of a mask

Notably though, due to the way that the interface of the mask was designed, it allows for you to replace the filter material with any other found material if you don’t have access to the specific filter required.

The team behind the mask references research indicates that if you don’t have a polypropylene filter, you might be able to use a modified vacuum cleaner bag, tea towel, or cotton mix as close alternatives. While they won’t be as good as the specially designed filter, the design of the mask allows these other materials to be easily inserted and used.

different types of mask table

Robotic solutions for COVID-19

While there have been a plethora of companies and individuals that have hacked robots to create ventilators for seriously ill patients, we’re going to focus on another robotic innovation helping patients’ well-being: Robot doctors.

Researchers at Chulalongkorn University have rolled out three new telemedicine robots that can aid the doctor-patient relationship while sparing the regular human interaction. The robots can easily be used by hospital staff to communicate with COVID-19 patients remotely.

Specifically, the robots created by the university are going to be used at the Bamrasnaradura Infectious Diseases Institute.

The robots were initially designed by the university team to help care for patients that were recovering from strokes, but they are now being repurposed to supply world-class leading medical care during a time when intense quarantine and isolation is needed.

These robots not only maintain a strict barrier between doctor and patient, but they also help one doctor quickly and easily talk with multiple patients. Seeing multiple patients after one another in hospitals often requires stripping and reapplying medical garb, whereas tele medicine robots can easily avoid that.

robotics used in covid fightback

The robots are capable of assessing the patients’ conditions as well as helping the medical staff to easily track the patients’ symptoms. Read more about the project in the press release from the university here.

You can also take a look at a variety of other robotics solutions for the coronavirus submitted on our engineering COVID-19 page here.

Coronavirus inspired innovations in sanitation and mask

Sanitation has become of a big concern in the overcrowded medical systems where coronavirus outbreaks are peaking. In many places, there is a serious deficit in medical supplies that is forcing doctors and nurses to reuse their surgical mask.

This presents a need for a device that can quickly and easily disinfect surgical mask with a 100% success rate. That is exactly what Prescientx, a company located in Ontario, Canada, has tried to create.

They have engineered a device that can disinfect N95 mask utilizing ultraviolet, or UV light. The device is situated overtop of the masks and a UV-C light is shone on the mask at different angles for differing amounts of time. That said, it doesn’t take very long to disinfect just one mask. In fact, the device, called the Terminator CoV, can disinfect up to 500 masks per hour. This can be life-changing for medical staff across the world as they battle the need for safe and clean protective gear.

The machine isn’t just specific to one kind of N95 mask, either. Thanks to the way that it is built, it works practically universally with a variety of mask types and sizes. The masks are driven through a reflective aluminum tunnel for disinfection. While in this tunnel the UV-C light is shone, being sure to hit the masks at all angles, as UV light rays cannot pass through the N95 grade mask material.

The speed of the conveyor on which the masks are taken through the disinfected tunnel and the height of that tunnel can be adjusted with ease, making the device practically universal. Take a look at a small demo of the device in the video below.

How you can get involved

It doesn’t take much to start getting involved in the engineering fight against the novel coronavirus. If you have a project that you’ve worked on or know of a project someone else worked on, submit it to COVID-19 engineering site so the rest of the world can learn about all of the advancements being made.

Source: Interesting Engineering

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Online Course learning: acquiring new skills during lockdown

Devoting some of our quarantine time to self-education makes sense. Besides helping to bolster your career during this economic uncertainty, learning a new skill can give you a sense of control that will help cope with anxiety engendered by the epidemic.

a girl learning a course from home

For many of us in self-isolation, it can feel like the coronavirus has put the world on hold as we wait for release from our temporary imprisonment. But increasing numbers of people are using the time to build their skillset, with an upsurge in enrollments on online course learning platforms such as edX, FutureLearn and Coursera, which offer “massive open online courses” – or Moocs.

Coursera, for instance, has seen an eightfold increase in enrolments for social science, personal development, arts and humanities courses since the start of the coronavirus outbreak. “It’s unprecedented,” says the company’s chief product officer, Shravan Goli. (In late March, its Science of Well Being course saw 500,000 new enrolments in a single weekend.)

Devoting some of our quarantine time to self-education makes sense. Besides helping to bolster your career during this economic uncertainty, learning a new skill can give you a sense of control that will help cope with anxiety engendered by the epidemic.

As James Wallman says in his book Time and How to Spend It, personal growth is central to many psychological theories of long-term happiness. So although an hour listening to a lecture may not be as enticing as the instant gratification of reality TV or social media, it will lead to greater life-satisfaction in the long term. “You could say that humans are like bicycles: if you’re not heading towards something you fall over,” Wallman says. And when we are social distancing, online courses are one of the best ways to do that.

What does a course involve?

The specifics vary from platform to platform, though many follow the same basic model. With the larger platforms such as edX, Coursera, and FutureLearn, you can choose university-affiliated courses – so you know you are being taught by experts in the field. The courses are of varying lengths – from a few hours to a regular, weekly commitment over several months – and typically involve video lectures, reading texts and regular tests to check your memory and understanding of the syllabus.

In many cases enrolment is free, but may have to pay to get a certificate verifying that you have completed the course.

What should I look for in a course?

You might be tempted to sign up to the courses with the most prestigious instructors, but that would be a mistake, says James Murphy, who used Moocs to prepare a master’s degree while he was housebound with an illness. “Institutional affiliations aren’t always a good guide to quality,” he says.

Many of the platforms offer user reviews where you can gauge other learners’ enjoyment and satisfaction with the course, but nothing beats trying it for yourself, says Murphy, who is now an associate lecturer at the Open University. “I think the best advice is to sign up and see if you like it – there’s no reason to stick with one you dislike if the delivery isn’t engaging. You can usually tell in the first hour if you’ll enjoy the course or not.”

If you are hoping for professional development and considering the cost of the certificate, you might want to check whether employers recognise the qualification. Coursera’s Goli points out that about 30 companies now accept the Google-affiliated course on IT management, for instance. The reviews can guide you on this, as can the course descriptions, which sometimes include statistics from student surveys about the professional benefits that came from the experience.

It’s also important to pick a course of the right difficulty – something just beyond your comfort-zone – engaging enough to occupy your mind, but not so ambitious that it’s frustrating. That way, you’ll achieve the “flow state”. “You’ll lose track of time,” says Wallman – and the deep concentration will feel much more rewarding than simply scrolling through social media.

How can I stay motivated?

Even if you have chosen a course that is perfectly suited to your goals, you may find your initial enthusiasm evaporates and your discipline trails off. “Lack of routine and time is often the biggest hurdle,” says writer and regular Mooc user Bianca Barratt. Her advice is to try to set up a schedule and “treat it like a physical class you’ve signed up for. Show up when you say you will, make an effort with the class exercises and homework and complete the course in full.”

Another good strategy, according to Anant Agarwal, the founder and CEO of edX, is to find a “study buddy or form a bigger learning group, so that they can motivate each other and enjoy the course together”. You might make a pact with people you already know, or you could connect with people from the discussion forums that accompany the course. Like your classmates at a traditional school or university, you can help each other to understand the difficult material, and the feeling of accountability might spur you on when you find distractions drawing you away from your goal.

What do I do after completing the course?

For some, this may be just the start of the journey – furnishing you with a greater confidence to learn and the motivation to take it further. If you find that you’re hooked, many of the platforms also provide accredited bachelors and master’s degrees from selected universities, though this will be more expensive.

For others, the completion of a single course will be enough. But whatever your goals, the quest to learn a new skill or discipline may be the perfect distraction from the frustrations of self-isolation – allowing you to connect with new people and transforming this period into a time of enlightenment and self-discovery.

The next step – seven Moocs to expand your mind

Learning How to Learn
McMaster University, University of California San Diego, via Coursera
With more than 2 million enrolments, this short course is a natural place to start your journey, offering the mental tools for you to master any new subject.

From the Big Bang to Dark Energy
University of Tokyo, via Coursera
Physicist Hitoshi Murayama examines the biggest question of all – the origins of the universe.

Rhetoric: The Art of Persuasive Writing and Public Speaking
Harvard University, via edX
Professor James Engell will help you to polish your communication skills in this eight-week course.

Science and Cooking
Harvard University, via edX
Physicists, chemists and restaurant chefs explore the transformation of food in the kitchen.

An Introduction to Screenwriting
University of East Anglia, via FutureLearn
If your mind is fizzing with inspiration for a Netflix mini-series, this course – from UEA’s prestigious creative writing programme – will help you translate it to the screen.

Introduction to Mathematical Thinking
Stanford University, via Coursera
Not for the fainthearted but more than 200,000 learners have taken this journey through the basics of mathematical logic and proof.

Buddhism and Modern Psychology
Princeton University, via Coursera
Bestselling author Robert Wright examines what modern psychology can learn from ancient teachings.

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What is Data Science ? A Student’s Guide to Data Science

As the world entered the era of big data, the need for its storage also grew. It was the main challenge and concern for the enterprise industries until 2010. The main focus was on building framework and solutions to store data.

As the world entered the era of big data, the need for its storage also grew. It was the main challenge and concern for the enterprise industries until 2010. The main focus was on building framework and solutions to store data. Now when Hadoop and other frameworks have successfully solved the problem of storage, the focus has shifted to the processing of this data. Data Science is the secret sauce here. All the ideas which you see in Hollywood sci-fi movies can actually turn into reality by Data Science. Data Science is the future of Artificial Intelligence. Therefore, it is very important to understand what is Data Science and how can it add value to your business.Edureka 2019 Tech Career Guide is out! Hottest job roles, precise learning paths, industry outlook & more in the guide. Download now.

In this blog, I will be covering the following topics.

  • The need for Data Science.
  • What is Data Science?
  • How is it different from Business Intelligence (BI) and Data Analysis?
  • The lifecycle of Data Science with the help of a use case.

By the end of this blog, you will be able to understand what is Data Science and its role in extracting meaningful insights from the complex and large sets of data all around us. To get in-depth knowledge on Data Science, you can enroll for live Data Science online course by Edureka with 24/7 support and lifetime access.

Let’s Understand Why We Need Data Science

  • Traditionally, the data that we had was mostly structured and small in size, which could be analyzed by using the simple BI tools. Unlike data in the traditional systems which was mostly structured, today most of the data is unstructured or semi-structured. Let’s have a look at the data trends in the image given below which shows that by 2020, more than 80 % of the data will be unstructured.

    This data is generated from different sources like financial logs, text files, multimedia forms, sensors, and instruments. Simple BI tools are not capable of processing this huge volume and variety of data. This is why we need more complex and advanced analytical tools and algorithms for processing, analyzing and drawing meaningful insights out of it.

This is not the only reason why Data Science has become so popular. Let’s dig deeper and see how Data Science is being used in various domains.

  • How about if you could understand the precise requirements of your customers from the existing data like the customer’s past browsing history, purchase history, age and income. No doubt you had all this data earlier too, but now with the vast amount and variety of data, you can train models more effectively and recommend the product to your customers with more precision. Wouldn’t it be amazing as it will bring more business to your organization?
  • Let’s take a different scenario to understand the role of Data Science in decision making. How about if your car had the intelligence to drive you home? The self-driving cars collect live data from sensors, including radars, cameras and lasers to create a map of its surroundings. Based on this data, it takes decisions like when to speed up, when to speed down, when to overtake, where to take a turn – making use of advanced machine learning algorithms.
  • Let’s see how Data Science can be used in predictive analytics. Let’s take weather forecasting as an example. Data from ships, aircrafts, radars, satellites can be collected and analyzed to build models. These models will not only forecast the weather but also help in predicting the occurrence of any natural calamities. It will help you to take appropriate measures beforehand and save many precious lives.

Let’s have a look at the below infographic to see all the domains where Data Science is creating its impression.

Data Science Use Cases - Edureka

Now that you have understood the need of Data Science, let’s understand what is Data Science.

What is Data Science?

Use of the term Data Science is increasingly common, but what does it exactly mean? What skills do you need to become Data Scientist? What is the difference between BI and Data Science? How are decisions and predictions made in Data Science? These are some of the questions that will be answered further.

First, let’s see what is Data Science. Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. How is this different from what statisticians have been doing for years?

The answer lies in the difference between explaining and predicting. 

Data Analyst v/s Data Science - Edureka

As you can see from the above image, a Data Analyst usually explains what is going on by processing history of the data. On the other hand, Data Scientist not only does the exploratory analysis to discover insights from it, but also uses various advanced machine learning algorithms to identify the occurrence of a particular event in the future. A Data Scientist will look at the data from many angles, sometimes angles not known earlier.

So, Data Science is primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics (predictive plus decision science) and machine learning.

  • Predictive causal analytics – If you want a model which can predict the possibilities of a particular event in the future, you need to apply predictive causal analytics. Say, if you are providing money on credit, then the probability of customers making future credit payments on time is a matter of concern for you. Here, you can build a model which can perform predictive analytics on the payment history of the customer to predict if the future payments will be on time or not.
  • Prescriptive analytics: If you want a model which has the intelligence of taking its own decisions and the ability to modify it with dynamic parameters, you certainly need prescriptive analytics for it. This relatively new field is all about providing advice. In other terms, it not only predicts but suggests a range of prescribed actions and associated outcomes.
    The best example for this is Google’s self-driving car which I had discussed earlier too. The data gathered by vehicles can be used to train self-driving cars. You can run algorithms on this data to bring intelligence to it. This will enable your car to take decisions like when to turn, which path to take, when to slow down or speed up.
  • Machine learning for making predictions — If you have transactional data of a finance company and need to build a model to determine the future trend, then machine learning algorithms are the best bet. This falls under the paradigm of supervised learning. It is called supervised because you already have the data based on which you can train your machines. For example, a fraud detection model can be trained using a historical record of fraudulent purchases.
  • Machine learning for pattern discovery — If you don’t have the parameters based on which you can make predictions, then you need to find out the hidden patterns within the dataset to be able to make meaningful predictions. This is nothing but the unsupervised model as you don’t have any predefined labels for grouping. The most common algorithm used for pattern discovery is Clustering.
    Let’s say you are working in a telephone company and you need to establish a network by putting towers in a region. Then, you can use the clustering technique to find those tower locations which will ensure that all the users receive optimum signal strength.

Let’s see how the proportion of above-described approaches differ for Data Analysis as well as Data Science. As you can see in the image below, Data Analysis includes descriptive analytics and prediction to a certain extent. On the other hand, Data Science is more about Predictive Causal Analytics and Machine Learning.Powered by Edureka

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Data Science Analytics - Edureka

I am sure you might have heard of Business Intelligence (BI) too. Often Data Science is confused with BI. I will state some concise and clear contrasts between the two which will help you in getting a better understanding. Let’s have a look.

Business Intelligence (BI) vs. Data Science

  • BI basically analyzes the previous data to find hindsight and insight to describe the business trends. BI enables you to take data from external and internal sources, prepare it, run queries on it and create dashboards to answer the questions like quarterly revenue analysis or business problems. BI can evaluate the impact of certain events in the near future.
  • Data Science is a more forward-looking approach, an exploratory way with the focus on analyzing the past or current data and predicting the future outcomes with the aim of making informed decisions. It answers the open-ended questions as to “what” and “how” events occur.

Let’s have a look at some contrasting features.

FeaturesBusiness Intelligence (BI)Data Science
Data Sources Structured
(Usually SQL, often Data Warehouse)
 Both Structured and Unstructured( logs, cloud data, SQL, NoSQL, text)
ApproachStatistics and VisualizationStatistics, Machine Learning, Graph Analysis, Neuro- linguistic Programming (NLP)
FocusPast and PresentPresent and Future
ToolsPentaho, Microsoft BI, QlikView, RRapidMiner, BigML, Weka, R

This was all about what is Data Science, now let’s understand the lifecycle of Data Science.

A common mistake made in Data Science projects is rushing into data collection and analysis, without understanding the requirements or even framing the business problem properly. Therefore, it is very important for you to follow all the phases throughout the lifecycle of Data Science to ensure the smooth functioning of the project.

Lifecycle of Data Science

Here is a brief overview of the main phases of the Data Science Lifecycle:

Lifecycle of Data Science - Edureka
Discovery of Data Science - Edureka

Phase 1—Discovery: 
Before you begin the project, it is important to understand the various specifications, requirements, priorities and required budget. You must possess the ability to ask the right questions. Here, you assess if you have the required resources present in terms of people, technology, time and data to support the project. In this phase, you also need to frame the business problem and formulate initial hypotheses (IH) to test.

Data Science data preparation - Edureka

Phase 2—Data preparation:In this phase, you require analytical sandbox in which you can perform analytics for the entire duration of the project. You need to explore, preprocess and condition data prior to modeling. Further, you will perform ETLT (extract, transform, load and transform) to get data into the sandbox. Let’s have a look at the Statistical Analysis flow below.

You can use R for data cleaning, transformation, and visualization. This will help you to spot the outliers and establish a relationship between the variables. Once you have cleaned and prepared the data, it’s time to do exploratory analytics on it. Let’s see how you can achieve that.

Data Science model planning - Edureka

Phase 3—Model planning:Here, you will determine the methods and techniques to draw the relationships between variables. These relationships will set the base for the algorithms which you will implement in the next phase. You will apply Exploratory Data Analytics (EDA) using various statistical formulas and visualization tools.

  Let’s have a look at various model planning tools.

Model planning tools in Data Science - Edureka
  1. R has a complete set of modeling capabilities and provides a good environment for building interpretive models.
  2. SQL Analysis services can perform in-database analytics using common data mining functions and basic predictive models.
  3. SAS/ACCESS  can be used to access data from Hadoop and is used for creating repeatable and reusable model flow diagrams.

Although, many tools are present in the market but R is the most commonly used tool.

Now that you have got insights into the nature of your data and have decided the algorithms to be used. In the next stage, you will apply the algorithm and build up a model.Powered by Edureka

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Data Science model building - Edureka

Phase 4—Model building: In this phase, you will develop datasets for training and testing purposes. You will consider whether your existing tools will suffice for running the models or it will need a more robust environment (like fast and parallel processing). You will analyze various learning techniques like classification, association and clustering to build the model.

You can achieve model building through the following tools.

Model building tools in Data Science
Data Science operationalize - Edureka

Phase 5—Operationalize:In this phase, you deliver final reports, briefings, code and technical documents. In addition, sometimes a pilot project is also implemented in a real-time production environment. This will provide you a clear picture of the performance and other related constraints on a small scale before full deployment.

Communication in Data Science - Edureka

Phase 6—Communicate results: 
Now it is important to evaluate if you have been able to achieve your goal that you had planned in the first phase. So, in the last phase, you identify all the key findings, communicate to the stakeholders and determine if the results of the project are a success or a failure based on the criteria developed in Phase 1.

Case Study: Diabetes Prevention

What if we could predict the occurrence of diabetes and take appropriate measures beforehand to prevent it?
In this use case, we will predict the occurrence of diabetes making use of the entire lifecycle that we discussed earlier. Let’s go through the various steps.

Step 1:

  • First, we will collect the data based on the medical history of the patient as discussed in Phase 1. You can refer to the sample data below.
Data Science sample data - Edureka
  • As you can see, we have the various attributes as mentioned below.


  1. npreg     –   Number of times pregnant
  2. glucose   –   Plasma glucose concentration
  3. bp          –   Blood pressure
  4. skin        –   Triceps skinfold thickness
  5. bmi        –   Body mass index
  6. ped        –   Diabetes pedigree function
  7. age        –   Age
  8. income   –   Income

Step 2:

  • Now, once we have the data, we need to clean and prepare the data for data analysis.
  • This data has a lot of inconsistencies like missing values, blank columns, abrupt values and incorrect data format which need to be cleaned.
  • Here, we have organized the data into a single table under different attributes – making it look more structured.
  • Let’s have a look at the sample data below.
Data Science inconsistent data - Edureka

This data has a lot of inconsistencies.

  1. In the column npreg, “one” is written in words, whereas it should be in the numeric form like 1.
  2. In column bp one of the values is 6600 which is impossible (at least for humans) as bp cannot go up to such huge value.
  3. As you can see the Income column is blank and also makes no sense in predicting diabetes. Therefore, it is redundant to have it here and should be removed from the table.
  • So, we will clean and pre-process this data by removing the outliers, filling up the null values and normalizing the data type. If you remember, this is our second phase which is data pre-processing.
  • Finally, we get the clean data as shown below which can be used for analysis.
Data Science consistent data - Edureka

Step 3:

Now let’s do some analysis as discussed earlier in Phase 3.

  • First, we will load the data into the analytical sandbox and apply various statistical functions on it. For example, R has functions like describe which gives us the number of missing values and unique values. We can also use the summary function which will give us statistical information like mean, median, range, min and max values.
  • Then, we use visualization techniques like histograms, line graphs, box plots to get a fair idea of the distribution of data.
Data Science visualization - Edureka

Step 4:

Now, based on insights derived from the previous step, the best fit for this kind of problem is the decision tree. Let’s see how?

  • Since, we already have the major attributes for analysis like npreg, bmi, etc., so we will use supervised learning technique to build a model here.
  • Further, we have particularly used decision tree because it takes all attributes into consideration in one go, like the ones which have a linear relationship as well as those which have a non-linear relationship. In our case, we have a linear relationship between npreg and age, whereas the nonlinear relationship between npreg and ped.
  • Decision tree models are also very robust as we can use the different combination of attributes to make various trees and then finally implement the one with the maximum efficiency.

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Here, the most important parameter is the level of glucose, so it is our root node. Now, the current node and its value determine the next important parameter to be taken. It goes on until we get the result in terms of pos or neg. Pos means the tendency of having diabetes is positive and neg means the tendency of having diabetes is negative.

If you want to learn more about the implementation of the decision tree, refer this blog How To Create A Perfect Decision Tree

Step 5:

In this phase, we will run a small pilot project to check if our results are appropriate. We will also look for performance constraints if any. If the results are not accurate, then we need to replan and rebuild the model.

Step 6:

Once we have executed the project successfully, we will share the output for full deployment.

Being a Data Scientist is easier said than done. So, let’s see what all you need to be a Data Scientist.  A Data Scientist requires skills basically from three major areas as shown below.

Data Science skills - Edureka

As you can see in the above image, you need to acquire various hard skills and soft skills. You need to be good at statistics and mathematics to analyze and visualize data. Needless to say, Machine Learning forms the heart of Data Science and requires you to be good at it. Also, you need to have a solid understanding of the domain you are working in to understand the business problems clearly. Your task does not end here. You should be capable of implementing various algorithms which require good coding skills. Finally, once you have made certain key decisions, it is important for you to deliver them to the stakeholders. So, good communication will definitely add brownie points to your skills.

l hope you enjoyed reading this blog and understood what is Data Science. Check out this Data Science certification training here, that comes with instructor-led live training and real-life project experience.

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