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Application of Data Science in Healthcare

Application of Data Science in Healthcare

In today’s data-driven world,  it is hard to ignore the growing need for data science, as businesses are busy applying data to devise smarter marketing strategies and urging their employees to upgrade themselves. Data Science training is gaining ground as lucrative career opportunities are beckoning the younger generation.

So, it is not surprising that a crucial sector like healthcare would apply data science to upgrade their service. Health care is among one of the many sectors that have acknowledged the benefits of data science and adopted it.

The Healthcare industry is vast and it comprises many disciplines and branches that intercross generating a ton of unstructured data which if processed and analyzed could lead to revolutionary changes in the field.

Here is taking a look at how the industry can benefit by adopting data science techniques

Diagnostic error prevention

No matter what health issues one might have, accurate diagnosing is the first step that helps a physician prescribe treatment procedure. However, there have been multiple cases where a diagnostic error has led to even death. With the implementation of data science technology, it is now possible to increase the accuracy of the procedures as the algorithm sifts data to detect patterns and come up with accurate results.

Medical imaging procedures such as MRI, X-Ray can now detect even tiniest deformity in the organs which were erstwhile impossible, due to the application of deep learning technology.  Advanced models such as MapReduce is also being put to use to enhance the accuracy level.

Bioinformatics

 Genomics is an interesting field of research where researchers analyze your DNA to understand how it affects your health. As they go through genetic sequences to gain an insight into the correlation, they try to find how certain drugs might work on a specific health issue.

The purpose is to provide a more personalized treatment program. In order to process through the highly valuable genome data, data science tools such as SQL are being applied. This field has a vast scope of improvement and with more advanced research work being conducted in the field of Bioinformatics, we can hope for better results.  Researchers who have studied Data science using python training, would prove to be invaluable assets for this specific field.

Health monitoring with wearables

Healthcare is an ongoing process, if you fall ill, you get yourself diagnosed and then get treatment for the health condition you have. The story in most cases does not end there, with the number of patients with chronic health problems increasing, it is evident that constant monitoring of your health condition is required to prevent your health condition from taking a worse hit.  Data science comes into the picture with wearables and other forms of tracking devices that are programmed to keep your health condition in check. Be it your temperature or, heartbeat the sensors keep tracking even minute changes, the data is analyzed to enable the doctors take preventive measures, the GPS-enabled tracker by Propeller, is an excellent case in point.

Faster approval of new drugs

The application of data science is not restricted to only predicting, preventing, and monitoring patient health conditions. In fact, it has reached out to assist in the drug development process as well. Earlier it would take almost a decade for a drug to be accessible in the market thanks to the numerous testing, trial, and approval procedures.

But, now it is possible to shorten the duration thanks to advanced data science algorithms that enable the researchers to simulate the way a drug might react in the body. Different models are being used by the researchers to process clinical trial data, so, that they can work with different variables. Data Science course enables a professional to carry out research work in such a highly specialized field.

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In the context of Covid-19

With the entire world crippling under the unprecedented impact of COVID-19, it is needless to point out that the significance of data science in the healthcare sector is only going to increase. If you have been monitoring the social media platforms then you must have come across the #FlattenTheCurve.

The enormity of the situation and erroneous data collection both have caused issues, but, that hasn’t deterred the data scientists. Once, the dust settles they will have a mountainous task ahead of them to process through a massive amount of data the pandemic will have left behind, to offer insight that might help us take preventive measures in the future.

The field of data science has no doubt made considerable progress and so has the field of modern healthcare. Further research and collaboration would enable future data scientists to provide a better solution to bolster the healthcare sector.

 


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Top 7 Data Science Platforms to Choose From in 2020

Top 7 Data Science Platforms to Choose From in 2020

Lack of collaboration between team members could be a frustrating experience as could be spending time maintaining your models after deploying them.

These reasons among others could mean the need for adopting data science platforms and having to choose the right platform from a host of available packages in the market.

“Various organizations keep floating data science platforms to simplify machine learning workflows. However, in the ever-changing data science landscape, only a few draw the attention of practitioners,” says a report.

Here is a list of top 7 data science platforms available for use in 2020.

Databricks

“Built by the founder of Apache Spark, Databricks provides a unified analytics platform that allows data scientists to manage end-to-end machine learning workflows.

The one-size-fits-all platform not only enables practitioners to explore, visualize and build superior machine learning models, but also allows them to scale it quickly with the help of collaboration.”

DataRobot

DataRobotassists companies to automate the workflows of machine learning through its feature-rich solutions and it constantly strives to enhance its platform by either acquiring various companies, or by developing in-house solutions.

“Apart from assisting the regular analytics workflows”, DataRobot is among the best in the AutoML arena.

Apache Spark

“Apache Spark is an open-source unified analytics engine for large-scale data processing and analyzing. It is similar to HadoopMapReduce; it works on cluster computing, but due to exceptional speed – which is believed to be 100x faster in memory and 10x faster on disk than Hadoop – it has become popular among data scientists.”

Dataiku

This is yet another reputed enterprise AI and machine learning platform that “helps businesses in minimizing data processes to expedite the development of machine learning-based solutions”.

The platform helps companies in bringing together data analysts, engineers, and scientists to achieve shared goals through collaboration. “It also provides instant visual and statistical feedback on model performance to manage models’ lifecycle effectively”.

IBM Cloud Pak for Data

“Built on Red Hat OpenShift container platform, IBM Cloud Pak for Data is a fully-integrated AI platform to meet the changing needs of enterprises. It allows data scientists to unlock insights and eliminate data silos quickly.

The platform has a high degree of enterprise readiness and delivers business value by enabling practitioners to integrate with other platforms using APIs.”

Alteryx

“Alteryx is a self-service analytics platform that can be utilized across organizations to democratize data. The platform caters to every need of analytics professionals, such as business intelligence, data analyst, data scientist, and non-experts to assist them in quickly solving business problems. It supports analytics modelling without code and advanced modelling with algorithms.”

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TIBCO

TIBCO Software acts as a foundation for digital innovation for data-driven companies. “Integration among platforms has been one of the longest standing predicaments for organizations.”

“Thus, TIBCO offers a suite of products like Connect, API-Led Integration, Data Fabric, Unify, Data Science & Streaming, and more, to eliminate challenges for a streamlined data science workflow.”

For more on this do peruse the DexLab Analytics website today. DexLab Analytics offers the best Alteryx Training in Delhi NCR.

 


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Why Learning Python is Important for Data Scientists Today

Why Learning Python is Important for Data Scientists Today

Data Science is the new rage and if you are looking to make a career, you might as well choose to become a data scientist. Data Scientists work with large sets of data to draw valuable insights that can be worked upon. Businesses rely on data scientists to sieve through tonnes of data and mine out crucial information that becomes the bedrock of business decisions in the future.

With the growth of AI, machine learning and predictive analytics, data science has come to be one of the favoured career choices in the world today. It is imperative for a data scientist to know one of more programming languages from any of those available – Java, R, Python, Scala or MATLAB.

However, Data Scientists prefer Python to other programming languages because of a number of reasons. Here we delve into some of them.

Popular

Python is one of the most popular programming languages used today. This dynamic language is easy to pick up and learn and is the best option for beginners. Secondly, it interfaces with complex high performance algorithms written in Fortran or C. It is also used for web development, data mining and scientific computing, among others.

Preferred for Data Science

Python solves most of the daily tasks a data scientist is expected to perform. “For data scientists who need to incorporate statistical code into production databases or integrate data with web-based applications, Python is often the ideal choice. It is also ideal for implementing algorithms, which is something that data scientists need to do often,” says a report

Packages

Python has a number of very useful packages tailored for specific functions, including pandas, NumPy and SciPy. Data Scientists working on machine learning tasks find scikit-learn useful and Matplotlib is a perfect solution for graphical representation and data visualization in data science projects.

Easy to learn

It is easy to grasp and that is why not only beginners but busy professionals also choose to learn Python for their data science needs. Compared to R, this programming language shows a sharper learning curve for most people choosing to learn it.

Scalability

Unlike other programming languages, Python is highly scalable and perceptive to change. It is also faster than languages like MATLAB. It facilitates scale and gives data scientists multiple ways to approach a problem. This is one of the reasons why Youtube migrated to Python.

Libraries

Python offers access to a wide range of data science and data analysis libraries. These include pandas, NumPy, SciPy, StatsModels, and scikit-learn. And Python will keep building on these and adding to these.  These libraries have made many hitherto unsolvable problems seem easy to crack for data scientists.

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Python Community

Python has a very robust community and many data science professionals are willing to create new data science libraries for Python users. The Python community is tight-knit one and very active when it comes to finding a solution. Programmers can connect with community members over the Internet and Codementor or Stack Overflow.

So, that is why data scientists tend to opt for Python over other programming languages. This article was brought to you by DexLab Analytics. DexLab Analytics is premiere data science training institute in Gurgaon.

 


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Dexlab Analytics Starts National Level Training On Data Analysis Using OpenAir package of R

Dexlab Analytics Starts National Level Training On Data Analysis Using OpenAir package of R

From Saturday, 6th June 2020, a team of senior consultants at DexLab Analytics has been conducting a national level training for more than 40 participants who are research scholars, MPhil students and professors from colleges like IIT, CSIR, BHU and NIT, among others. This one of a kind, crowd-funded training is being conducted on “Environment Air pollution Data Analysis using OpenAir package of R”.

The training is a result of the lockdown wherein DexLab Analytics is working towards its upskilling initiatives for professionals and subject matter experts across India. The training is being conducted in DexLab Analytics’TraDigital format – real time, online, classroom styled, instructor-led training.

The attendees will be taking up these interactive classes from the safety and comfort of their homes. They will be getting assignments, learning material and recordings virtually.

The one-month-long training will be conducted in R Programming, Data Science and Machine Learning using R Programming from the perspective of Environmental Science. DexLab Analytics is conducting this training module in line with the tenets of ‘Atmanirbhar India’.

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DexLab Analytics is a leading data science training institute in India with a vast array of state-of-the-art analytics courses, attracting a large number of students nationwide. It offers high-in-demand professional courses like Big Data, R Programming, Python, Machine Learning, Deep Learning, Data Science, Alteryx, SQL, Business Analytics, Credit Risk modeling, Tableau, Excel etc. to help young minds be data-efficient. It has its headquarters in Gurgaon, NCR.

 

For more information, click here – 

www.prlog.org/12825521-dexlab-analytics-starts-national-level-training-on-data-analysis-using.html

 


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Mr Debuka is Key Speaker at EIILM’s Webinar

Mr Debuka is Key Speaker at EIILM’s Webinar

DexLab Analytics is proud to announce that its CMO, Vivek Debuka, was the Key Speaker at a webinar hosted by the Eastern Institute for Integrated Learning in Management (EIILM), Kolkata on “Changing Trend in Business in the Post COVID-19 World”.

The webinar was held on 30th May, 2020 from 5pm – 6pm. Students of the Eastern Institute for Integrated Learning in Management, the chairman and director of the EIILM Dr R P Banerjee said, were excited and eager to attend the webinar, especially because the topic was an emerging one and relevant to their corporate career goals.

On May 27, EIILM posted a Facebook post that read – “EIILM’s initiative for enriching young minds with post COVID-19 business trends!!!! The Covid era has brought about a lot of uncertainties that have resulted in a new thought process in the ever-changing world of business. To orient our budding managers with the dynamic business trends, EIILM – KOLKATA Family has scheduled a Webinar on 30 May 2020, from 5-6 pm under the title “Changing Trend in Business in the Post Covid 19 World”.

Data Science Machine Learning Certification

DexLab Analytics is a leading data science training institute in India with a vast array of state-of-the-art analytics courses, attracting a large number of students nationwide. It offers high-in-demand professional courses like Big Data, R Programming, Python, Machine Learning, Deep Learning, Data Science, Alteryx, SQL, Business Analytics, Credit Risk modeling, Tableau, Excel etc. to help young minds be data-efficient. It has its headquarters in Gurgaon, NCR.

 
For more information click on the link here www.prlog.org/12824488-dexlab-analytics-cmo-was-key-speaker-at-eiilm-webinar.html
 


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93% Indian Professionals Benefitting From E-Learning During Lockdown: Linkedin

93% Indian Professionals Benefitting From E-Learning During Lockdown: Linkedin

The Covid-19 pandemic has struck India like it has scores of countries across the world. As of May 27, over 1,51,000 Indians have been tested positive for the novel virus and over 4000 people have died due to the contagious disease. India has been under lockdown for over two months now in an attempt at abating the spread of the virus due to movement and contact.


 

With all offices closed and work from home decreed across numerous sectors of the economy, professionals have been forced to adapt to a new mode of work and training. With more time on hand since they are working from home, professionals are upgrading their skills by taking up online training modules and classes. A recent LinkedIn survey throws light on this phenomenon.

LinkedIn’s Work Force Confidence Index

India’s foremost social networking site that helps individuals network with professional peers and find jobs and appointments has conducted a survey called Work Force Confidence Index. As per the survey conducted between April 27 and May 3, “India’s professionals are logging learning hours for not just knowledge acquisition but also to increase productivity. About half of respondents from mid-market firms joined courses that help them manage time better, improve prioritisation or stay organised”.

93% Indian Professionals Benefitting From E-Learning During Lockdown: Linkedin

93% respondents to upskill online in next two weeks

According to LinkedIn News India, 1040 professionals were surveyed by LinkedIn and 93% of them said “their time spent on e-learning will either increase or remain the same over the next two weeks”. Moreover, 60% of the respondents of which 74% were from the engineering domain said e-learning was a conduit to furthering industry knowledge. “Advancing in one’s career was a driver for 57% of all respondents and 3 in 10 active job seekers undertook e-learning to make a career pivot,” said LinkedIn News India.

What respondents learnt

Of the respondents, 45% said they hoped to learn to collaborate with peers through online learning in lockdown. Also, 43% said they wished to learn to manage time and prioritise and stay organised. Moreover, 40% said they hoped to learn something unrelated to work through online platforms. Becoming a leader and managing personal finances were pegged at 37% and 32% respectively by the study as goals and 24% said e-learning could actually lead to a change in career paths for them.

Advantages of e-learning

Travelling to work and back is taxing and time consuming. When you are working from home, you save on energy and time that can be used for something productive like e-learning training modules. They are easy on the pocket, accessible from absolutely anywhere you are and convenient to absorb and retain information and new things learnt. Moreover, there is a large online community to help you out with study material and guidance.

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There are many popular e-learning courses in India, especially those around data science and artificial intelligence. DexLab Analytics is a premier credit risk modeling training institute that also trains professionals in artificial intelligence, machine learning and data science. This article was brought to you by DexLab Analytics.

 


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The Data Science Life Cycle

The Data Science Life Cycle

Data Science has undergone a tremendous change since the 1990s when the term was first coined. With data as its pivotal element, we need to ask valid questions like why we need data and what we can do with the data in hand.

The Data Scientist is supposed to ask these questions to determine how data can be useful in today’s world of change and flux. The steps taken to determine the outcome of processes applied to data is known as Data Science project lifecycle. These steps are enumerated here.

  • Business Understanding

Business Understanding is a key player in the success of any data science project. Despite the prevalence of technology in today’s scenario it can safely be said that the “success of any project depends on the quality of questions asked of the dataset.”One has to properly understand the business model he is working under to be able to effectively work on the obtained data.

  • Data Collection

Data is the raison detre of data science. It is the pivot on which data science functions. Data can be collected from numerous sources – logs from webservers, data from online repositories, data from databases, social media data, data in excel sheet format. Data is everywhere. If the right questions are asked of data in the first step of a project life cycle, then data collection will follow naturally.

  • Data Preparation

The available Data set might not be in the desired format and suitable enough to perform analysis upon readily. So the data set will have to be cleaned or scrubbed so to say before it can be analyzed. It will have to be structured in a format that can be analyzed scientifically. This process is also known as Data cleaning or data wrangling. As the case might be, data can be obtained from various sources but it will need to be combined so it can be analyzed.

For this, data structuring is required. Also, there might me some elements missing in the data set in which case model building becomes a problem. There are various methods to conduct missing value and duplicate value treatment.

“Exploratory Data Analysis (EDA) plays an important role at this stage as summarization of clean data helps in identifying the structure, outliers, anomalies and patterns in the data.

These insights could help in building the model.”

  • Data Modelling

This stage is the most, we can say, magical of all. But ensure you have thoroughly gone through the previous processes before you begin building your model. “Feature selection is one of the first things that you would like to do in this stage. Not all features might be essential for making the predictions. What needs to be done here is to reduce the dimensionality of the dataset. It should be done such that features contributing to the prediction results should be selected.”

“Based on the business problem models could be selected. It is essential to identify what is the task, is it a classification problem, regression or prediction problem, time series forecasting or a clustering problem.” Once problem type is sorted out the model can be implemented.

“After the modelling process, model performance measurement is required. For this precision, recall, F1-score for classification problem could be used. For regression problem R2, MAPE (Moving Average Percentage Error) or RMSE (Root Mean Square Error) could be used.”The model should be a robust one and not an overfitted model that will not be accurate.

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  • Interpreting Data

This is the last and most important step of any Data Science project. Execution of this step should be as good and robust as to produce what a layman can understand in terms of the outcome of the project.“The predictive power of the model lies in its ability to generalise.” 

 


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How Company Leaders and Data Scientists Work Together

How Company Leaders and Data Scientists Work Together

Business leaders across platforms are hungrily eyeing data-driven decision making for its ability to transform businesses. But what needs to be taken into account is the opinion of data scientists in the core company teams for they are the experts in the field and whatever they have to say regarding data driven decisions should be the final word in these matters.

“The ideal scenario is all parties in complete alignment. This can be envisioned as a perfect rectangle, with business leaders’ expectations at the top, fully supported by a foundation of data science capabilities — for example, when data science and AI can achieve management’s goal of reducing customer retention costs by automating identification and outreach to at-risk customers,”says a report.

The much sought after rectangle, however, is rarely achieved. “A more workable shape is the rhombus, depicting the push-and-pull of expectations and deliverables.”

Using the power of your company’s data.

Business leaders must have patience with developments on the part of data scientists for what they expect is usually not in sync with the deliverables on the ground.

“Over the last few years, an automaker, for example, dove into data science on leadership’s blind faith that analytics could revolutionize the driver experience. After much trial and error, the results fell far short of adding anything meaningful to what drivers found valuable behind the wheel of a car.”

Appreciate Small Improvements

Also, what must be appreciated are small improvements made impactful. For instance, “slight increases in profitability per customer or conversion rates” are things that should be taken into account despite the fact that they might be modest gains in comparison to what business leaders had invested in analytics. “Applied over a large population of customers, however, those small improvements can yield big results. Moreover, these improvements can lead to gains elsewhere, such as eliminating ineffective business initiatives.”

Healthy Competition

However, it is advisable for business leaders to constantly push their data scientists to strive for more deliverables and improve their tally with a framework of healthy competition in place. In fact, big companies form data science centers of excellence, “while also creating a healthy competitive atmosphere that encourages data scientists to push each other to find the best tools, strategies, and techniques for solving problems and implementing solutions.”

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Here are three ways to inspire data scientists

  1. Both sides must work togetherTake the example of a data science team with expertise in building models to improve customers’ shopping experiences. “Business leaders might assume that a natural next step is to use AI to enhance all customer service needs.”However, AI and machine learning cannot answer the ‘why’ or ‘how’ of the data insights. Human beings have to delve into those aspects by studying the AI output. And on the other hand, data scientists also must understand why business leaders expect so much from them and how to achieve a middle path with regard to expectations and deliverables.
  2. Gain from past successes and achievements – “There is value in small data projects to build capabilities and understanding and to help foster a data-driven culture.”The best policy for firms to follow is to initially keep modest expectations. After executing and implementing the analytics projects, they should conduct a brutally honest anatomy of the successes and failures, and then build business expectations at the same time as analytics investment.
  3. Let data scientists spell out the delivery of analytics results “Communication around what is reasonable and deliverable given current capabilities must come from the data scientists — not the frontline marketing person in an agency or the business unit leader.” Before signing any contract or deal with a client, it is advisable to allow the client to have a discussion with the data scientists so that there is no conflict of ideas between what the data science team spells out and what the marketing team has in mind. For this, data scientists will have to work on their soft skills and improve their ability to “speak business” regarding specific projects.


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The link between AI, ML and Data Science

The link between AI, ML and Data Science

The fields of Artificial Intelligence, Machine Learning and Data Science cover a vast area of study and they should not be confused with each other. They are distinct branches of computational sciences and technologies.

Artificial Intelligence

Artificial intelligence is an area of computer science wherein the computer systems are built such that they can perform tasks with the same agility as that done through human intelligence. These tasks range from speech recognition to image recognition and decision making systems among others.

This intelligence in computer systems is developed by human beings using technologies like Natural Processing Language (NLP) or computer vision among others. Data forms an important part of AI systems. Big Data, vast stashes of data generated for computer systems to analyze and study to find patterns in is imperative to Artificial Intelligence. 

Machine learning

Machine learning is a subset of artificial intelligence. Machine learning is used to predict future courses of action based on historical data. It is the computer system’s ability to learn from its environment and improve on its findings.

For instance, if you have marked an email as spam once, the computer system will automatically learn to mark as spam all future emails from that particular address. To construct these algorithms developers need large amounts of data. The larger the data sets, the better the predictions. A subset of Machine Learning is Deep Learning, modeled after the neural networks of the human brain.

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Data Science:

Data science is a field wherein data scientists derive valuable and actionable insights from large volumes of data. The science is based on tools developed with the knowledge of various subjects like mathematics, computer programming, statistical modeling and machine learning.

The insights derived by data scientists help companies and business organizations grow their business. Data science involves analysis of data and modelling of data among other techniques like data extraction, data exploration, data preparation and data visualization. As data volumes grow more and more vast, the scope of data science is also growing each passing day, data that needs to be analyzed to grow business.

Data Science, Machine Learning and Artificial Intelligence

Data Science, Artificial Intelligence and Machine Learning are all related in that they all rely on data. To process data for Machine Learning and Artificial Intelligence, you need a data scientist to cull out relevant information and process it before feeding it to predictive models used for Machine Learning. Machine Learning is the subset of Artificial Intelligence – which relies on computers understanding data, learning from it and making decisions based on their findings of patterns (virtually impossible for the human eye to detect manually) in data sets. Machine Learning is the link between Data Science and Artificial Intelligence. Artificial Intelligence uses Machine Learning to help Data Science get solutions to specific problems.

The three technological fields are thus, closely linked to each other. For more on this, do not forget to check-out the artificial intelligence certification in Delhi NCR from DexLab Analytics.


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