Trending December 2023 # Top 5 Career Benefits Of A Data Analyst Training Program # Suggested January 2024 # Top 13 Popular

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blog / Data Science and Analytics 5 Ways to Boost Your Data Analyst Career With a Training Program

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The global big data analytics market is expected to reach $745.15 billion by 2030. That would be a massive increase from the $307.52 billion that it is currently. This exponential growth can be attributed to businesses’ increased use of digital solutions for marketing opportunities, growth in databases, and the rise in edge computing technology. Consequently, as the market grows, the need for data analysis professionals is also expected to rise significantly. Hence, now is the best time to enter the field of data analytics. This blog discusses how to transition into a data analytics career and the benefits of a data analyst training program in making that transition smooth and easy.

Benefits of Transitioning to a Career in Data Analytics

Before discussing how to grow a career in data analytics, let’s see the relevance of a data analytics career today. The following are some benefits of transitioning to a career in data analytics.

1. A Future-Proof Career

According to the World Economic Forum’s Future of Jobs Report 2023, 80% of organizations are likely to adopt big data analytics technology by 2027. This will make data analytics the third most popular technology in the digital world. Hence, transitioning a career to data analytics offers job security and a secured career path due to many opportunities.

2. Increasing Options 3. High Earning Opportunities

Data analytics is also among the highest-paying tech jobs globally. Beginner data analysts in the U.S. earn an average salary of $83,067 per year. Moreover, experienced data analysts can earn an annual average salary of $126,442.

4. A Challenging Work Environment

Collecting, processing, and analyzing large volumes of data to extract valuable insights for business is a tough job. Therefore, being a data analyst is undoubtedly a challenging role. However, it also helps businesses succeed through innovation and strategic decision-making. Thus, a career in data analytics is best for people who love to take on new challenges and seek career satisfaction.

How Can an Online Data Analyst Training Program Help Professionals Make a Career Switch?

Before transitioning to a career in data analytics, it is essential to understand that there is a significant gap between academia and the job market in the data analytics industry. This is because of the interdisciplinary nature of data science and data analytics, which makes it difficult to create a uniform skill set. Data science professionals have a broader knowledge of data analytics and other skills. However, data analytics is a whole skill within the umbrella of data science that requires expert training. A data analyst training program provides this expert training.

Let’s understand how an online data analyst training program helps transition careers:

1. Skill Development

Having the most coveted skills is crucial when switching careers to data analytics. It makes navigating a new field easier, and these skills can also make up for the lack of experience in data analytics. Online data analyst training programs teach various skills such as data manipulation, statistical analysis, data visualization, programming languages (Python or R), SQL (Structured Query Language), and more.

2. Flexibility

Switching to data analytics from other professions requires strategic career planning. Therefore, gaining data analytics skills based on career goals is necessary before making a switch. An online program can help professionals learn data analyst skills while pursuing their jobs. It also offers the flexibility to learn about data science without displaying a career gap on your CV.

3. Learning From Leading Global Institutions

Data analyst training programs are offered in partnership with leading universities or colleges offering data analytics courses. These courses offer insights into the global data science and analytics industry. A program thus prepares learners eager to join a global workforce.

4. Certifications 5. Guidance

Most data analyst training programs have a dedicated mentor or career guide who helps learners smoothly switch to a career in data science. They offer job assistance and help with interview preparations too.

ALSO READ: How a Data Analytics Course Can Change How You Work Forever!

What Skills do Professionals Need to Succeed in Data Analytics?

Here are 10 popular skills one needs to build a successful career in data analytics:

Data analysis to gain insights

Probability and statistics to collect, analyze, and interpret data to find the probability of future events or forecast future trends

Data management, which involves collecting, storing, processing, and interpreting data through secured means

Machine learning to develop models that can draw insights from data patterns

Mining to help find hidden or uncovered data patterns from large databases

Data visualization, which is representing data insights into a visual format for a better understanding

Learning SQL, a programming language used for processing large databases

Data modeling involves creating data models or diagrams to define data flow. This is used for data formatting

Statistical programming to create

statistical programs or models for interpreting data

Finally, cultivating business intelligence, which means analyzing data and interpreting it to make sound business decisions

ALSO READ: How to Build a Successful Career in Data Science and Analytics?

How Long do Online Data Analytics Training Programs Typically Take to Complete? Why are Emeritus Data Science Courses in Demand for Career Transitioning?

Career transitioning has become a common phenomenon in the past few years. Some of the main reasons for switching careers are better-paying opportunities, satisfaction, intellectual growth, and finding jobs that align with one’s values. Consequently, more people are exploring different occupations, labor markets, and function areas for a fulfilling career.

Nevertheless, despite the increase in career transition, it has various challenges or limitations—such as a lack of resources, guidance, and skill gaps. Emeritus’ online data analyst training program helps professionals overcome these challenges in the following ways:

Skill-Based Learning

Emeritus’ courses focus on outcome-oriented training. That means they teach practical skills like business intelligence, data visualization, and data analysis.

Networking

Since industry experts teach our data analyst training programs, they help learners build a professional network. Learners can seek guidance from the network to grow their careers in data analytics.

Practical Projects

Emeritus’ data analyst training program also requires learners to apply their data analyst skills to real-life projects and case studies.

By Sneha Chugh

Write to us at [email protected]

You're reading Top 5 Career Benefits Of A Data Analyst Training Program

Top 5 Benefits Of Automated Provisioning In Iam In 2023

The provisioning of identity and access management (IAM) starts with onboarding. It requires permission grants by IT staff, steady updates, and consistent checks for each account created. However, the increasing number of employees in a business can turn IAM into a challenge.

Handling the provisioning process manually can delay business processes. Manual provisioning of IAM is prone to errors, subject to security risks, and time-consuming.

Like other IT processes, identity and access management can also benefit from automation to overcome the inefficiency of manual provisioning. Automated provisioning offers a lot of benefits to enterprises and streamlines IAM processes.

Investing in automated provisioning can minimize errors, eliminates security risks, and improves efficiency. This article focuses on five ways automated provisioning simplifies IAM processes.

What is automated provisioning?

Automated provisioning refers to the automated practices of managing identity and access to all resources of a business. Adding, changing, and removing access to the systems, data, and applications are adjusted automatically based on the level of role. Automated provisioning also removes manual approvals such as granting and deleting permissions. Overall, automating the IAM process lightens the workload on staff, preventing delays for new employees, and boosting productivity.

1. Simplifies the process of (de)provisioning of IAM

When new employees are hired, HR teams ask IT teams to create profiles for the new employees. They also follow other steps such as granting permissions to access the applications and systems and forwarding the user credentials to employees. Manual practices of the IT staff to create profiles and add responsibilities take time and cause delays.

Figure 1: Automated provisioning flow. Source: Google

Automated provisioning with an IAM platform (Figure 1) removes manual processes. It automates the provisioning and deprovisioning of IAM. When a new employee is hired or a new application is integrated into the system, identities are created and permissions are granted automatically without help from HR or IT teams.

2. Reduces errors 3. Minimizes security risks

Automated provisioning removes inefficient workflows and automates the onboarding and offboarding in the identity management process. It monitors who has access to which platforms, applications, and data. By using automated provisioning, IT staff can easily create, change, and remove accounts from the system and ensure that the right person has the right permissions.

For example, if an employee departs from a company and if IT staff do not remove the access of the departing employee from the company systems, the employee may still have access to the company resources, exposing a critical security risk to the company.

Automated provisioning helps IT teams remove the offboarded employee accounts from accessing company resources. As unused accounts are removed, security risks are minimized.

4. Saves time and money

Manual provisioning is costly and time-consuming. It requires IT teams to invest huge time and costs during IAM processes. Manual provisioning is especially a problem at scale.

Automated provisioning allows IT teams to manage the IAM process from a single point of control and eliminates manual processes. Operational costs and time that are invested in individual tasks can be cut. Besides, the time and costs can be saved and re-evaluated in other areas that enterprises are critical.

5. Helps onboarding employees to have a head start

Onboarding often takes a long time. Employees start work in actual terms after their user credentials are given and employee information is provisioned. Manually handling these tasks prolongs the process and delays new employees’ starting to work.

Automated provisioning helps IT teams handle provisioning fast and before an employee starts to work. Having everything ready from day one can help new employees to have a great start in their new job.

Should I Become A Data Scientist (Or A Business Analyst)?

Introduction

One of the common queries I come across repeatedly on several forums is “Should I become a data scientist (or an analyst)?” The query takes various forms and factors, but here is a common real-life anecdote:

“I have been doing Sales for multiple BFSI giants for last 3 years, but I have stopped enjoying my role. After reading about Business Analytics and machine learning, my interest in this area has grown. Should I make a switch and learn data science? If so, How do I do this?

When I reflect back on how I took the decision, I realized – I happened to be lucky! The decision was relatively easier for me. Why? I knew the industries/roles, I would not enjoy – these included roles in Sales, roles in Physical engineering, and a few others. I was open to roles in data science in retail banks and investment banks and luckily ended up with Capital One.

Today, after spending ~8 years in the industry, it is far easier for me to guide and mentor people on whether Analytics is the right role for them or not. So, I thought, I’ll try and put my thoughts in a framework and share it with the audience of this blog. The aim of this post is to help those people who are sitting on the fence and thinking about which job/role is right for them. So, if you are someone deliberating a move in data science or are wondering whether you are a right fit with this industry, here is a neat framework that might help.

The role of a mentor in building a career is priceless. Being from the industry, the mentor can help you navigate your learning path so that you don’t fall into traps. Certified AI & ML BlackBelt Plus Program comes with 100+ hours of live-course, 100+hours of self-paced video, 18+ real-life projects, and the most important – 1:1 mentorship so that you can focus on becoming an industry-ready professional with the relevant guidance. 🙂

Framework

I have put a framework in the form of a very simple test. This test is based on the attributes every analyst should possess. You should score yourself against each of the questions (out of the score mentioned after the question) and then add your scores. A good analyst should score more than 70 and anyone scoring below 50 should seriously re-consider a decision to be a data scientist.

Test Questions:

Do you love number crunching and logical problem solving – i.e. puzzles, probabilities, and statistics? (score out of 20)

By love I don’t mean like, I don’t mean you don’t mind numbers – I mean, do you have an obsession with numbers! Do you love doing guess-estimates at any time of the day – I have done those estimates while I am taking a shower, while I am driving, while I am watching a movie, or even when I am swimming (and lost my count of laps)! I know my friend Tavish does these calculations in his mind too – while he is driving or while he is playing badminton. If you want me to space out of a discussion, just ask me a really hard logical problem!

Key:

5 – dread mathematics & statistics, but can face to some extent

10 – Comfortable with mathematics and statistics, but need calculators and excel to work on problems. Don’t mind attempting puzzles

15 – Love crunching numbers and solving logical puzzles anywhere

20 – Can’t live without number crunching and logical puzzles – an obsession!

Do you enjoy working/handling unstructured problems? (score out of 20)

An analyst will inevitably be tested against unstructured and amorphous business problems. And it is how you solve these unstructured problems, that decides how good or bad an analyst you are. My first project in my first role stated: “In last few months, we have seen a high increase in high-risk customers of type X. You need to come up with a data-based strategy to measure, control, and improve this situation.“

Even the business did not have a clear definition of these customers. Can you handle this kind of ambiguity and provide a direction on your own? Do you enjoy these situations or you would rather be comfortable in a more defined role?

Key:

5 – Have tried these problems in past – but not my cup of tea!

10 – A score of 10 would mean, you like solving these problems once in a while (say 3 – 6 months)

15+ – You prefer unstructured problems over-structured. You don’t enjoy someone else structuring problems for you.

Do you enjoy deep research and can spend hours slicing and dicing data? (score out of 20)

Going back to the first project I faced, it took me 3 months to understand the business, have multiple discussions with stakeholders, brings them on the same page, and then mine the data to bring out solutions. You need an outlook of a researcher to be a good business analyst. When was the last time you spent hours and hours immersed in solving a problem? Can you do that again and again?

Key:

5 – You want a change every few hours. You can’t work on a single problem for the entire day

10 – You can work on a research problem – but need some side work to help you out of boredom

15 – You feel the side work is distracting you from making progress on the key problem you are working on. Would be happy if they are taken away

20 – Can’t stand distractions

Do you enjoy building and presenting evidence-based stories? (score out of 20)

A data scientist needs to be a fluid presenter. What is the use of all the hard work, if he is not able to influence his stakeholders? Communicating with data and presenting stories backed by data is one of the most important elements in the life of a data scientist. Imagine being part of companies like Google and Amazon – you have all the data you need (probably more than that) for the domain you are working on, but you need to convert it into a meaningful story, present it to the stakeholders and influence them to take the right decision!

Key:

5 – You struggle to communicate my mathematical thoughts to the audience

10 – You can manage telling stories with a lot of practice. Can’t think of doing this on the fly!

15+ – Any time, anywhere!

Do you always find yourself questioning people’s assumptions and are always curious to know ‘Why”? (score out of 10)

This is probably the best part and the most fun part! Here is a quote a read somewhere on Linkedin: Arguing with an Engineer is a lot like wrestling in the mud with a pig: After a few hours, you realize the pig likes it. Similarly, asking why comes naturally to a good data scientist. Some of the best data scientists would stop anyone and ask for a rationale if they are not clear – Why did you ask this question? What was your thought process? Why do you assume so? are just a few examples of these questions!

Key:

5 – You only ask questions when they are critical to be asked

8+ – You can’t stand the anxiety of not understanding something! Jumping to ask questions!

Do you enjoy problem-solving and thrive on intellectual challenges? (score out of 10)

Analysts require a knack for problem-solving. Most of the problems businesses would face would be unique to them and it would take a smart solver to solve them. Solutions, which work for one organization may not work for another – you need to be someone who quickly develops a deep understanding of a problem and then come out with innovative ways to solve these problems

Key:

3 – You don’t mind thinking about solving problems – but you struggle.

6 – You can solve problems at times

9 / 10 – You just love the process of intellectual thinking

End Notes:

What is my score? I would score somewhere between 80 – 85 on this test. It is your turn now. Do take the test and let me know, how much do you score? Also, do let me know if you think the test was helpful or otherwise.

Did you like this framework? We at Analytics Vidhya follow an analytical approach to problem-solving. If you want to become a data scientist with this analytical mindset, check out the Certified AI & ML BlackBelt Plus Program which offers 100+ hours of live-course, 100+hours of self-paced video, 18+ real-life projects, and the most important – 1:1 mentorship. The course is carefully crafted by experts so that you can become an industry-ready professional!

Now that you know that you can / can not become a data scientist, you might be asking “How do I become a data scientist?”. Here’s the Roadmap –

5 Steps To Prepare Ocr Training Data In 2023

The interest in optical character recognition (OCR) and intelligent character recognition (ICR) technology is falling (see figure 1) as companies switch to more automated solutions, such as machine learning-enabled data extraction. However, due to its various benefits, many companies still use1 or plan to use tools powered by OCR technology in their paper-based operations.

Whether you use OCR/ICR or data extraction tools enabled with machine learning, you require training data to develop robust models for such solutions. Preparing datasets to train such models can be challenging.

Therefore, this article explains how developers and business leaders can prepare effective datasets to streamline the development and implementation process of their intelligent document processing (IDP) solutions.

Figure 1. Interest in OCR and ICR technology

Source: Google trends

1. Define the purpose of the dataset

First establish the dataset’s purpose. This will make it easier to decide what kind of data needs to be gathered and how it should be presented.

For instance, if the dataset’s goal is to train an OCR system to recognize text in scanned paper-based or digital documents, the information gathered should include scanned images of text in a range of font sizes, styles, and arrangements. On the other hand, if the system needs to scan documents like invoices or bills, then the dataset should include images of numerical values, calculations, formulas, etc. (see image below).

The following image shows an example of an OCR system identifying numerical values in an invoice through bounding box tags:

2. Collect relevant data

Once the purpose of the dataset is understood, the next step is to collect the relevant data. This can be done by using the following data collection methods:

To learn more about these data collection methods, check out this article.

It’s critical to gather data that is representative of the kinds of documents the system will be handling. For instance, for an AI-powered resume screening system, you need to gather data that contains images of different types of resumes, such as:

Format (Chronological, functional, or combination)

Academic or professional

Field-specific resumes (For instance, a resume for a software developer will contain different terminologies as compared to a resume of a human resource candidate)

Similarly, images of handwritten text may be required for a system that will scan handwritten documents like letters or forms. The more diverse the dataset is in terms of variations in writing tools, content, writing styles, and other factors, the better the OCR system will function on new, unseen images.

Figure 2. Shows the process happening under the hood

In another example, a license plate recognition system also uses OCR technology. The data that is required to train such systems is usually blurry images of different types of license plates in different angles and different lighting scenarios. This is mainly because the system usually needs to scan fast-moving vehicles.

Our recommendations

If preparing your own dataset through in-house data collection does not suit your project timeline or budget, you can consider outsourcing or crowdsourcing the data.

3. Annotate the data accurately

Data annotation is a crucial step in preparing training data for any machine learning model, and so is the case for OCR processing. This involves modifying the data through labels and tags to make it easier for the system to recognize the text and the data that needs to be extracted.

Things to consider while annotating OCR and ICR systems:

In the case of an OCR system, the data should be labeled with the text that appears on the input image.

On the other hand, for an ICR system, the data should be annotated with the information that is attached to each unit of text/numerical value (e.g., date, amount, etc.).

For higher quality annotation, you can rely on a validator for important annotation work that will double check the annotation work done by the first annotator.

Data annotation can be done manually by human annotators or by using semi-automated tools.

Manual annotation

In manual annotation, human annotators can label the images with the corresponding text using tools like a text editor, a graphical user interface, or specialized annotation software. This process can be time-consuming and may require multiple annotators for large-scale datasets.

Semi-automated annotation

Leveraging semi-automated tools can fasten the annotation process by providing assistance by using OCR for handwriting recognition algorithms. These tools can automatically create text transcriptions, which can then be reviewed and corrected by human annotators. This human-in-the-loop approach can significantly reduce the amount of time required for manual annotation while ensuring the quality of the data.

Our recommendations

Regardless of the method applied, it is important to make sure that the annotated data is accurate and consistent throughout the dataset. If in-house data labeling and automated tools do not suit your project requirements, then you can work with a data labeling partner.

If you are having trouble finding the right data labeling service, check our data-driven guide to selecting the right data labeling partner for your project.

4. Split the dataset into training, validation, & test sets

Once the annotation is done, the dataset can now be divided into training, validation, and test sets.

The training set is used to train the model.

The validation set is used to evaluate the performance of the model during training.

The test set is used to evaluate the performance of the model after the training phase is complete (Metrics such as character error rate and word error rate are used with this subset of data to evaluate the output of the model).

It’s important to make sure that the 3 subsets accurately represent the data that the system will be processing. This can be done by randomly sampling the data to ensure that each set contains a similar distribution of data.

Check out this quick read to learn more about the AI training process.

5. Preprocess the data

The data should be pre-processed to ensure that it is in the correct format and has the desired quality for training before being fed into an OCR or ICR system. Pre-processing can help to reduce or eliminate noise sources, enhance the quality of the data as a whole, and boost the system’s accuracy.

For instance, consider a scenario where the OCR system is trained to recognize handwritten text. If the input data is not pre-processed, it may contain a lot of noise, such as smudges, creases, and distortions. This noise can make the recognition process more challenging. On the other hand, if the input data has been processed to eliminate noise and improve the quality, the OCR system will be more likely to recognize the text accurately.

To learn more, check out our article on the 5 steps to data preprocessing

Conclusion

Preparing an effective dataset is crucial for training any intelligent document processing system fitted with OCR/ICR technology. By considering the best practices outlined in this article, you can ensure that the dataset covers all the features of the model and helps the system achieve the desired results.

To learn more about training data collection for AI/ML models, download our free whitepaper:

You can also check our data-driven list of data collection/harvesting services to find the best option for your project’s data needs.

Sap Certification Training Program Online With Certification

Our SAP certification training starts from scratch and is apt for people who want to switch careers to IT. The topics covered will be of interest to consultants in the SAP world, regardless of their preferred module. Indeed, Advanced Business Application Programming (ABAP) is everywhere in this software, and acquiring experience and knowledge in this field will increase the learner’s competence.

This SAP training program will help you understand what is hidden behind the meanders of each functionality. A project manager will also find this training program useful. Regardless of the choice of career direction in this field, having a string to one’s bow that involves ABAP is a real asset in terms of value on the job market.

SAP Certification Training Overview

Start learning with SAP Basis, which refers to the administration of SAP systems. This includes activities like installation and configuration, load balancing, and performance of SAP applications running on Java stack and SAP ABAP. It also includes maintaining different services related to the database, operating system, application, and web servers in the SAP system landscape, and stopping and starting the system.

These video courses will walk you through the different features of SAP Basis. SAP is a career option in a broader sense and has various job hierarchies around the globe. This initiation course aims to take the basics, from the beginning, to allow the people who follow it to have all the cards in hand to apprehend more complex notions later on. Using good practices will help you acquire the basics necessary to learn the language.

What is SAP Certification?

SAP is an accounting regulation for the preparation of an insurance firm’s financial statements. Various SAP modules have been developed over time focused on different areas. With more than 400,000 customers worldwide, SAP has established itself over the years as a reference in terms of ERP. The powerful software adapts to all types of business. It allows a notable improvement of the processes and increases the performance of the companies. It especially increases the communication and the exchange of information between services with its single database.

Career Opportunities After Completing SAP

This training also includes but is not limited to training for SAP FICO Certification. Professionals will have the following career options:

SAP Network Specialists

SAP FI/CO courses for finance and accounting control

SAP Database Administrators

SAP Security consultants, etc.

Benefits of our SAP Certification Training

Training on SAP today is clearly an excellent choice for the future. The application areas are constantly expanding and the need for companies to improve productivity and quality is growing. It is a great option for those who want to change or diverge their careers to IT.

Scope

As of 2023, SAP is regarded as one of the best long-term career options to go with.

SAP career opportunities in finance, IT, sales, marketing, operations, HR, customer support, university, etc.

SAP is regarded as one of the highest-paid job pay scales in the world ranging from $120,000 to $130,000.

Goals

SAP FICO Controlling & Integration course, delivers excellent value to your resume.

Basics of SAP CO system with hands-on exercises

Learn CO to FI Module INTEGRATION

SAP: WM Module (Warehouse Management)

The use of the module but also the configuration

Presented in a dynamic format

Accompanied by examples and simplified sheets

Coaching via email from the trainer

SAP: ABAP (its history and use)

The basics of ABAP programming

The first code and debugging

Flow control Structure

The Runtime Error

Logical statements and modularization

Presented in a dynamic format

In a real SAP environment (S4/HANA)

Prerequisites

Why Data Science In Healthcare Is Valuable And Its Top 10 Benefits

blog / Data Science and Analytics 10 Ways in Which Data Science Can Make Healthcare Better

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Data science is a groundbreaking tool in the healthcare industry for improving patient outcomes, enhancing efficiency, and reducing costs. With the vast amounts of data generated from electronic health records, medical devices, clinical trials, and other sources, healthcare organizations can leverage data science to analyze and make sense of this data in ways that were previously impossible. These are some of the reasons that the demand for data scientists is surging. This is also supported by, well, data: According to the U.S. Bureau of Labor Statistics, nearly 13,500 openings for data scientists are predicted each year from 2023 to 2031. So, if you are looking to build a lucrative career in this space, then this blog is for you. Learning the fundamentals of data science in healthcare, its benefits, and its challenges will be a good start.

What is Data Science?

Simply put, data science is the study of massive amounts of data to extract meaningful insights to make informed decisions. It uses modern tools and techniques to identify patterns and derive meaningful information from raw, structured, and unstructured data. Data science today is implemented across a variety of industries such as healthcare, e-commerce, finance, transport, and gaming.

Applications of Data Science in Healthcare

The health industry generates massive amounts of data. Therefore, being able to handle, manage, and organize it to help in patient care, improve the efficiency of processes, etc, is very important. This is where data science in healthcare can make a difference. Here are 10 ways in which data science can make healthcare better.

Medical Image Analysis

The process of obtaining meaningful information from medical images is called medical image analysis. Imaging techniques include Magnetic Resonance Imaging (MRI) scans, Computed Tomography (CT) scans, and X-rays. The involvement of data science in these imaging techniques has revolutionized the healthcare industry as it helps extract complex information from numerous images in a short period. This, in turn, helps to save a lot of time and money.

Research and Development Improved Patient Outcomes

Today, people extensively use smartwatches and physical fitness monitors, all thanks to the Internet of Things (IoT). These gadgets can track and manage people’s health. They also generate a lot of data, which data scientists then scan to identify patterns and extract meaningful information. Doctors use this information to treat their patients more effectively.

Cost-Effectiveness

Data scientists can analyze Electronic Health Records (EHRs) to identify patients’ health patterns. Doing so can help prevent unnecessary treatment or hospitalization. Conversely, doctors can catch any change in the pattern in time and ensure people get treated on time.

Decoding Genomes

Before the arrival of data science tools and powerful computation, healthcare organizations spent a small fortune on genomics (the study of sequencing and interpretation of genomes). Now, data science makes it possible to derive insights from the gene at a much lower cost and in a much shorter period.

Undiagnosed Disease Discovery Predictive Analytics

The predictive analytics model uses data to analyze and search for patterns and correlations. For instance, experts can look at data gathered from multiple sources for patterns and correlations and can try to identify many things. These can be the symptoms of a disease, the extent of damage, and the stages of the disease among other things. The predictive analytics model uses all of this information to diagnose a patient’s condition and strategize the appropriate treatment.

Diagnostics

According to CNN, more than seven million patients are misdiagnosed annually in emergency rooms in the U.S. To fix such diagnostic failures, startups such as Enlitic and Bruxlab have started using data science to increase the efficiency and accuracy of diagnostics and create better patient outcomes.

Automated Pharmacies Virtual Assistance for Patients

Data scientists have developed AI platforms and chatbots to help people get virtual medical assistance during emergencies. Furthermore, people who suffer from mental illnesses such as depression, anxiety, and Alzheimer’s can leverage virtual applications to complete their daily tasks. Ada and Woebot are some of the most popular examples of virtual assistants.

ALSO READ: What is Data Science? Why is This Career Path in Demand? Find Out Now!

Challenges of Data Science in Healthcare

Although data science in healthcare has the potential to improve patient outcomes and reduce costs, it also has several challenges that need to be addressed. Some of the challenges are as follows:

Privacy and Security

Healthcare data can often be sensitive and subject to privacy regulations. Therefore, protecting such data from misuse is key to earning and maintaining patients’ trust. Data breaches in healthcare can also involve legal complications.

Translating Insights Into Clinical Practice

One of the main challenges of data science in the healthcare sector is translating findings into clinical practice. It needs to be integrated into clinical workflows to make sure the findings generated can be put into practice to improve patient outcomes.

Ethical Concerns

There are several ethical concerns when it comes to leveraging data science tools in healthcare. Patients’ consent, for example, is one of the most important ethical considerations to be aware of when using this technology in the healthcare industry.

Gain Insights on Data Science in Healthcare with Emeritus

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