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Here is the major difference between programming language vs scripting language

Many individuals are unaware of the distinctions between scripting languages and programming languages, and they frequently use the phrases interchangeably. They may sound similar, yet they are extremely different. Anyone interested in entering the realm of software development must understand the distinctions between scripting language and programming language. Recent innovations in the programming world, however, have blurred the boundary between them.

Both languages are utilised in the development of software. All scripting languages may be used as programming languages, but not the other way around. The main distinction is that scripting languages are interpreted rather than compiled. Before the introduction of scripting languages, programming languages were used to create software such as Microsoft PowerPoint, Microsoft Excel, Internet Explorer, and so on. However, there was a need for languages to have new functions, which led to the development of scripting languages. Let us now examine the distinctions between scripting languages and programming languages in further depth. Here we will explore the difference between programming language vs scripting language.

Programming Language

A programming language is used to communicate with computers to create desktop software, internet, and mobile apps. It is a set of instructions intended to achieve a certain aim. Programming languages include C, C++, Java, and Python, to name a few. Programming languages typically include two components: syntax (form) and semantics (meaning).

Key Features of Programming Language

Simplicity: Most current languages, such as Python, have a straightforward learning curve. There is generally a compromise between a language’s simplicity and its speed and abstraction.

Structure: Every programming language has a present structure, such as syntax, semantics, a set of rules, and so on.

Abstraction: It refers to the programming language’s ability to conceal intricate features that may be superfluous for consumers. It is one of the most significant and necessary characteristics of object-oriented programming languages.

Efficiency: Programming languages are translated and executed effectively to prevent wasting too much memory or taking too long.

Portability: Because programming languages are portable, they should be easy to transfer from one machine to another.

Scripting Language

A scripting language is a programming language that is specially designed for use in runtime settings. It automates work completion. They are employed in system administration, web development, gaming, and the creation of plugins and extensions. These are interpretive languages. Scripting languages are generally open-source languages that are supported by practically every platform, which implies that no special software is necessary to run them because they are a series of instructions that are executed without the aid of a compiler.

Key Features of Scripting Language

Easy to learn and use: They are simple to learn and apply. JavaScript and PHP are two of the most user-friendly scripting languages.

Open-source and free: All they have to do now is research them and incorporate them into their current system. They’re all open-source, which means that anybody on the planet can help shape them.

Powerful and extensible: enough so that the relevant tasks may be completed using the scripts. Scripting languages are also quite adaptable.

Lighter memory requirements: They are interpreted rather than compiled, unlike programming languages. As a result, they demand less memory from the computers that operate them.

Runtime Execution: A system that allows code to be run during runtime allows an application to be configured and adjusted while it is running. In reality, this capability is the most crucial characteristic that makes scripting languages so useful in most applications.

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Data Scientist Vs Data Analyst: Key Differences Explained

In the world of data-driven decisions, two prominent roles have emerged: data analysts and data scientists. These professionals play a crucial role in helping organizations harness the power of data, but their responsibilities and skill sets are quite different.

Data analysts focus on using data visualization and statistical analysis to understand data and identify patterns. They are usually required to have at least a bachelor’s degree in a relevant field like mathematics, statistics, computer science, or finance.

Broadly speaking, both professions involve extracting valuable insights from data; however, their approaches and skill sets do vary.

In this article, we will explore the differences between data scientists and data analysts and highlight the unique skills and responsibilities required for each role.

Let’s dive in.

While data scientists and data analysts both work with data, they have distinct roles and responsibilities.

Understanding the differences between these two roles is important for organizations seeking to build an effective data team. Also, it is crucial for those that would like a career in data to understand.

In this section, we will explore the key differences between data scientists and data analysts, including their educational backgrounds, technical skills, and the types of problems they are typically tasked with solving.

The table below gives a quick overview of the differences between the two roles:

Education/BackgroundData ScientistData AnalystDegreeBachelor’s degree in business, economics, statistics, or a related fieldBachelor’s degree in business, economics, statistics, or related fieldProgramming skillsProficient in languages such as Python, R, and SQLProficient in Excel, SQL, and basic scripting languagesMathematics skillsStrong mathematical skills, including linear algebra, calculus, and statisticsStrong statistical skills, including regression analysis and hypothesis testingWork experienceExperience with big data technologies, machine learning, and data visualizationExperience with statistical analysis, data modeling, and reporting

Joining a boot camp, using tutorials, or completing online courses or certificate programs may not cut it.

Data scientists should have a strong foundation in mathematics, statistics, and computer science, as well as hands-on experience with programming languages such as Python, R, and SQL.

Other data analyst skills include working with databases and having basic scripting language skills.

The job involves working on large data sets, developing predictive models, and extracting insights from data. Like data analysts, it also requires soft skills like communication and collaboration since you often need to work with different teams.

Data analysts: Very simply, a data analyst’s job involves analyzing and interpreting data to provide insights and recommendations to stakeholders.

You may be tasked with working with different data sources to identify trends and patterns that can inform business decisions.

Some specific responsibilities of data analysts can include:

Collecting, cleaning, and organizing data from various sources

Conducting statistical analysis to identify trends and patterns in data using software like Tableau

Creating reports and dashboards to visualize data and communicate insights to stakeholders

Identifying areas for process improvement and making data-driven recommendations to stakeholders

Developing and maintaining databases and data systems to support data analysis

Keeping up-to-date with the latest trends and developments in data analysis and visualization.

Now, things get a little more complex.

Data scientists: Being a data scientist involves analyzing complex data sets, developing predictive models, and extracting insights from data.

They work closely with stakeholders across different departments to provide insights and recommendations based on their data analysis.

Some specific responsibilities of data scientists include:

Conducting exploratory data analysis to identify patterns and trends in data

Developing predictive models using statistical and machine learning techniques

Building and testing machine learning models to improve predictive accuracy

Using problem-solving skills and business intelligence to come up with data-driven solutions to business problems

Communicating complex findings and recommendations to non-technical stakeholders

Collaborating with data engineers and software developers to build and deploy data-driven solutions

In the next two sections, we’ll take a look at the future job prospects and salary expectations for the two professions.

The job outlook for data scientists in 2023 is very promising as organizations across industries continue to collect and analyze increasing amounts of data.

According to the U.S. Bureau of Labor Statistics (BLS), employment of data scientists is projected to grow by 36% from 2023 to 2031, which is much faster than the average when compared to other occupations. Job opportunities in the field are driven by the increasing use of data and analytics to drive decision-making in organizations of all sizes.

According to Glassdoor, the national average salary for data scientists in the United States is around $103,000 per year. Many organizations also offer various additional forms of compensation for data scientists, such as bonuses, equity, and other benefits like medical insurance and paid time off.

Please note that compensation can vary widely depending on location, industry, and years of experience.

According to the BLS, employment of management analysts (which includes data analyst careers) is projected to grow by 11% from 2023 to 2030. Like data scientists, the job outlook for data analysts is very positive for the foreseeable future.

Compensation for data analysts may vary based on factors such as experience, industry, and location. Entry-level data analysts typically earn lower salaries, they can expect their pay to increase as their skills and expertise develop over time.

In terms of salary, the national average for data analyst positions in the United States is around $65,850 per year, according to Glassdoor.

The job prospects and compensation for both data scientists and data analysts are very promising, but how can you decide which career is right for you? We’re going to take a look at factors to consider in the next section.

Deciding which career path is right for you can feel daunting, but think of it as an exciting opportunity to explore this wonderful world of data!

The two fields may seem similar at first glance, and in a way, they are, but they require different skill sets and offer unique career paths.

With the right information and guidance, you can choose the path that is best suited for your skills, interests, and career goals.

In this section, we’ll provide some tips and insights to help you navigate this decision and choose the right path for you.

When considering a career in data science or data analysis, it’s important to think about your skills, interests, and career goals.

Here are some specific factors to consider:

Roles and responsibilities: Data scientists are often responsible for more strategic and complex initiatives, such as developing predictive models or creating machine learning algorithms. Data analyst roles focus more on day-to-day operations and providing insights to stakeholders.

Job outlook and salary: Both data scientists and data analysts have strong job prospects and competitive salaries, but the specific job outlook and salary can vary depending on the industry, location, and years of experience.

Ultimately, the right path for you will come down to your individual goals and aspirations.

Now one great thing about data skills is that they can be applied in most industries, let check them out.

The field of data science and data analytics is in high demand across a wide range of industries and company types.

Here are some examples of industries that both commonly employ data scientists and data analysts:

Finance and Banking: The finance and banking industry relies heavily on data analytics to identify trends, assess risk, and make informed business decisions. Business analysts are in high demand.

Healthcare: Healthcare organizations use data science and data analytics to improve patient outcomes, manage resources, and drive innovation in medical research.

E-commerce: E-commerce companies use data analytics to better understand their customer’s behavior, preferences, and purchasing habits in order to improve marketing and sales strategies.

Technology: Technology companies use data science and data analytics to develop new products and services, improve user experiences, come up with real-world solutions, and identify areas for innovation and growth.

There are employment opportunities across different company types, including startups, large corporations, consulting firms, and government agencies.

Understanding the diverse range of industries and company types that rely on data professionals is crucial for individuals looking to build successful careers in these fields.

It’s also important to note that both fields are evolving, and there are emerging trends that are worth considering.

In addition to industry types, consider emerging trends in data science and data analytics that are changing the landscape of the two fields.

Here are some current trends that are shaping the future of data science and data analytics:

Artificial intelligence and machine learning: AI and machine learning are increasingly being used in data science and data analytics to automate data processing, identify patterns, and make predictions. These technologies have the potential to revolutionize industries from healthcare to finance to marketing.

Cloud computing: Cloud computing has made it easier and more cost-effective to store, manage, and analyze large amounts of data. As cloud infrastructure and technology continue to improve, it’s expected that cloud-based data analytics and machine learning will become more widespread.

Data ethics and privacy: As more and more data is collected and analyzed, concerns about data ethics and privacy have come to the forefront. Data scientists and analysts are being called upon to ensure that data is being used ethically and responsibly and to implement measures to protect sensitive data.

Internet of things (IoT): The IoT refers to the network of interconnected devices and sensors that collect and share data. With the increasing adoption of IoT technology, there is a growing need for data scientists and analysts who can manage and analyze the vast amounts of data generated by these devices.

In the world of data, both data scientists and data analysts play important full-time roles in a business. While there are similarities between the two, they possess distinct differences in terms of responsibilities and required skills.

Data analysts primarily focus on working with structured data to solve tangible business problems using SQL, R, or Python programming languages, data visualization tools, and statistical analysis. They help organizations identify trends and derive insights from data.

On the other hand, data scientists are more involved in programming machines, optimizing systems, and creating frameworks and algorithms for collecting usable data. Their primary duties lie in collecting data and designing robust data-driven solutions.

While both job descriptions work within the realm of big data, identifying the right path depends on your interests, skills, and career goals. Whichever path you choose, both data scientists and data analysts are in-demand careers, making them an exciting and rewarding choices for those interested in working with data.

12 Body Language Tips For Business Meetings

You may be handsome, reasonably tall, well-dressed, and good at talking, but will it be sufficient to impress others?  According to new research, body language is important for making a good impression in job interviews and business meetings. It communicates your confidence, thoughts, and attitude in a non-verbal way and often decides your fate or outcome. Our subconscious mind controls them like breathing, heartbeat, and other involuntary bodily movements.

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Even before you shake hands with someone or fold your hands in Namaste, the other person(s) you will meet have already made their first assessment.

12 Body Language Tips For Having A Perfect Business Meeting

Here are body language tips to help you make a good impression while attending interviews, important business meetings, or discussions.

1. Walk upright, don’t bend or have drooping shoulders

Nothing communicates your energy and attitude as much as the way you walk. When walking into an interview room, a first impression is formed, and sometimes even a hiring judgment is made within the first ten seconds. That is when you should be seen at your best. It would be best if you neither walked slowly nor too fast.

Each stride should be one to two feet wide; Pull your shoulders back and elongate your neck. Your feet should be firmly on the ground and there is a scientific reason for it- it helps to be rational, creative, and have a great presence of mind, according to experts.

If you are asked to wait in the reception area or a waiting room, you must ensure your posture is upright and sit confidently, waiting for your turn.  Some companies may have installed CCTV cameras to record your movements and also seek the receptionist or front office manager’s opinion regarding body language and arrival performance.

While walking into the room, smile and have direct eye contact with the person you will meet, apart from having a fleeting glimpse of others present.

2. Don’t lean backward or too forward

Once you are told to sit, gently adjust the chair if required without making a sound and sit comfortably. Avoid hunching your shoulders or getting your chin tucked into your chest. If you lean back, interviewers may judge you as lazy while sitting on the edge of the seat, and leaning forward may denote aggression – both are not desirable in business meetings or interviews.  Ideal posture would be back straight and chest open with expansive shoulders.

Appear at ease but not lazy, but the posture should not be too stiff to denote a lack of confidence. It is good to maintain eye contact but don’t stare directly into the eye of the interviewer or chairman; instead, maintain face contact. Avoid getting distracted by anything on the walls or letting your eyes wander, as it denotes a lack of focus and attention.

It is better to follow the interviewers’ body language- if they are leaning forward, you can also lean forward a bit. It will build better connections and rapport.

3. Hand gestures are important

When gesturing, keep your hands above the desk but below the collarbone so you don’t appear aggressive or frantic. If you are positioned a foot away from the table, you must be comfortable with your hand gestures and leg movements.

4. Control your breathing

You can appear tensed or relaxed depending on your breathing patterns. According to experts, the candidate has to focus on breathing throughout the session to give a good impression to the interviewer. It is better to inhale while listening to a question and exhale while talking. There is also a physiological reason for breath control during interviews and meetings. It will enable you to lower the heart rate, blood pressure and reduces stress associated with the event. It is better to take ten deep diaphragmatic breaths before the interview, which should make you comfortable.

5. Nodding your head often

When an interviewer says something, if it is about the job profile, the company, and any other information related to the job, then it is better to nod a few times when an important message is conveyed. Nodding your head too often may denote that you agree with everything, sometimes not listening correctly or being a ‘Yes’ person.

6. Don’t appear to be in a hurry

You might have taken leave or arrived early to ensure you were on time for the interview. You might have traveled some distance to attend it and waited half an hour or more for your turn to be called. And once you attend the interview, don’t appear bored or in a hurry. After all, you need the job. Avoid peeping at the clock or wrist watch which shows you are not serious about the job. If the interview drags for a long time, they are likely taking more time to assess you and possibly decide on your appointment than itself.

7. The art of exiting 8. Avoid undesirable body movements and actions

According to experts, some people have the habit of adjusting the collar and smiling too often to please; this should be avoided.

9. The best dress to wear for the interview

It is quite usual for some companies or organizations to suggest a dress code for business meetings or interviews. Unless specified otherwise, a suit is best for interviews, while women can wear a pantsuit. Experts say a suit denotes respect toward the company and the interviewing panel. Unpolished shoes denote a lack of attention to detail and grooming. A suit is not a must for technology jobs, but candidates can wear formal shirts and trousers.  It is better to appear formal for government jobs, avoiding a flashy style. Sales, marketing, and management cadres require suits, while automotive jobs may be quite harsh and greasy, but for interviews, they can appear in formal clothes.

10. Understand color psychology

You knowingly or unknowingly communicate through the choice of colors for your dress, watch, tie, trousers, shoes, etc.  The color of the interview choice is blue, which denotes stability, truth, calmness, security, and confidence. Grey is also a formal color and less distracting for both men and women. It gives a sophisticated look and is quite pleasing too.  As it is not distracting, interviewers will focus on what you say rather than your appearance. Trousers may be suited for black, but shirts or churidars are not.

Although it represents authority and formality, it should be used cautiously as too much black with a lighter contrasting color, especially for suits, could give a mourning feeling. During interviews, it is best to avoid wearing red as it may be associated with aggression, violence, desire, power, and greed. On the other hand, white is a color that represents peace and purity and is suitable for both men and women. Shirts and blouses in white can be safely used as they also suggest simplicity and goodness. Some companies, especially airlines have white and navy blue, while some have white and black; white-grey are all good combinations.

Some colors are quite gaudy and worn during social meetings, parties, and functions. They are green, yellow, brown, orange, pink, violet, and other tones. As a general rule, use formal colors and avoid flashy colors. It may be true that posture, mannerisms, hand, facial and body gestures are more important than color, but color reinforces a good impression formed and could help clinch a job.

11. Tone of your voice

Along with body movements and gestures, the tone of your voice has a significant bearing on your success.  High-pitched voices and low-pitched voices are not desirable. To get an optimal tone one can practice saying ‘Um hum, um, hum, um hum to relax voice. In telephonic interviews, the tone of your voice sound matters.

12. Wear a smile

Research shows that a smile can increase attractiveness in interviews and meetings. It gives a sense of well-being and gives a feeling of warmth, trust, and friendliness. Duke University researchers found that smiling faces are more recalled by people. Researchers conducted an MRI on individuals who recalled smiling people, revealing high activity in the brain’s orbitofrontal cortices or reward centers. Smiling is infectious, and people reciprocate creating good well and warmth. It also tells about the emotional state of the candidate.


Body language has become an essential indicator of behavior as, most often in interviews and meetings, the interviewer has only a short period to judge or evaluate the candidate’s personality. Even in routine life, we come across many people, and it is impossible to judge them all by talking and analyzing. Policemen, doctors, psychologists, shopkeepers, government officials, and managers all use their power to judge people by body language to help assess somebody where quickly. Although body language is not an exact science, it goes by some human traits, customs, beliefs, and traditions acquired over the years and classified as desirable and non-desirable.

It is quite natural that those with desirable traits succeed in careers and life. It also has an essential bearing on the right selection of employees and workers, leading to increased productivity.

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We hope that this EDUCBA information on “12 Body Language Tips for Business Meetings” was beneficial to you. You can view EDUCBA’s recommended articles for more information.

How To Develop An Effective Leadership Language

In communications with your employees, the right language can be the difference between a happy, high-functioning workplace and a negative environment with high turnover.

Frame performance conversations to focus on the employee and their career goals.

If you want to improve your communication skills, start by improving your listening skills.

This article is for business owners and managers who want to improve morale in their company and mentor highly effective teams.

Common wisdom about leadership often favors leading by example, so you might not think too much about how your team interprets what you say. But the truth is, your words and phrases enormously impact your team’s morale and productivity.

“Words are important,” said Isaac Oates, CEO of Justworks, an HR, benefits and payroll platform. “It’s through our words that we communicate our intentions.”

How you speak to your team – whether making a statement or responding to a question – impacts them. Effective communication is essential for success, allowing you and your team to establish trust and create better long-term outcomes. Your leadership development goals should include learning to use your words carefully, eliminating jargon to avoid confusion, and focusing on the end goals when communicating.

Areas where language matters

There are several critical areas where the language you use as a leader affects morale, operations and even employee retention:

Performance management

Hiring and onboarding

Disciplining employees

Motivational leadership

Performance management

The way you discuss an employee’s performance and engagement is critical, according to Vip Sandhir, founder and CEO of employee engagement platform HighGround. These discussions can impact the way your employee views the company, your leadership, and their role on your team. Your direct communication affects them, and so do your reactions or responses to their questions or concerns.

“Performance management is going through a renaissance,” Sandhir told Business News Daily. “The importance of that conversation and how it’s done [is changing]. It was typically one-sided, judging individuals based on numbers. But neuroscience research on how the brain reacts to conversations shows that [this communication style] can trigger a threat response.”

If, for example, you start a performance discussion by telling an employee they are a 3 or 4 out of 5 – or by threatening the employee’s status at the company – they will perceive it as unfair and judgmental, Sandhir said. The conversation will then head in a hostile or defensive direction.


Frame performance discussions to focus on the employee and their career goals to show that you value working together and want to help them.

Hiring and onboarding

Managers often see hiring and onboarding as simple processes to bring new employees into the company and set them up with their team. However, these processes are also an excellent opportunity to show new hires what to expect from you as their leader, based on how you communicate their role, your expectations for employees, company values and who their team members will be.

Onboarding is a pivotal time to ensure employees feel welcomed into the company and receive foundational knowledge. When speaking to a new employee during a thoughtful onboarding process, understand that they don’t know everything yet, and explain any concepts or vernacular they may need to use later. Share the company’s values and commitment to inclusivity, and let them know you value their feedback by giving them space to communicate with you directly.

Did You Know?

The consequences of poor onboarding include lower productivity, greater inefficiency and higher employee turnover.

Disciplining employees

Effective leaders must be clear from the start about company policies, including disciplinary policies, so employees understand what they can or can’t do – and what consequences will occur if they break the rules.

When an employee violates a policy, talk to them about what the policy states. Explain why their behavior or action wasn’t acceptable and can’t continue to happen.

However, remember that the way you speak to them about their violation is critical and must convey that you care about them as a person and want them to succeed. To do this, offer them a chance to talk about what happened in their own words and listen as they explain their side of the story.


A comprehensive employee handbook is an excellent tool for sharing company policies and keeping everyone on the same page.

Motivational leadership

Every employee is different and may respond best to a specific type of motivational language. Stacey Philpot, head of succession and leadership development practice at Deloitte, said it’s essential to plan your words and phrases to connect with your employees meaningfully.

“The most impactful leaders are the ones who think about how they will energize their people,” she said. “They know what makes their people feel confident and likewise what drains their energy. Rather than talking about plans or tactical objectives, they are able to link their employees’ current circumstances with some kind of opportunity or outcome that they will care about.”

Oates, who has a military background, noted that straightforward, action-oriented phrases related to your company’s core values could be motivational if you have a strong company culture.

“Some of our core company values are ‘grit’ and ‘simplicity,’ [so] I use phrases without a lot of fluff to motivate team members – phrases like, ‘Let’s do this,’ ‘Keep doing what you’re doing,’ and ‘We are laser-focused on XYZ,’” he said.

But there’s no single magic phrase that will continuously inspire your team to achieve its best; motivational leadership comes from an authentic emotional connection with your team, explained James Rohrbach, president and chairman of language school Fluent City.

“Look your colleagues in the eye [and] ask them how they are,” he said. “Really listen to the answers, and tell them regularly what you are grateful for in their work and why.”

To that end, it helps to include employees in the ongoing conversation about the company’s mission and how their work aligns with it, said Shaun Ritchie, CEO of EventBoard, a provider of meeting tools and workforce analytics.

“Check in on progress through a regularly scheduled, preferably face-to-face meeting to align on progress and build trust,” he said. “If you’re doing that at appropriate intervals, you’ll have the confidence that the right things are being worked on, that issues are addressed before they become problems, [that] your team is held accountable, and that you have the information you need to make decisions. Using encouraging but knowledgeable language helps to implement objectives and key results at all levels in our organization.”

“I like to let everyone know that their work is important, and I appreciate the effort they put into all assignments, no matter how small,” added Kim Paone, senior vice president of Highwire Public Relations. “I think encouraging people to take on projects they have an interest in makes them work harder and, overall, produces better results.”


Ways to prevent workplace alienation include transparent communication, an open-door policy and employee recognition programs.

Learning the language of leadership

Even if you manage a global team with different linguistic and cultural backgrounds, it’s still important to master the “language of leadership,” said Ray Carvey, executive vice president of corporate learning at Harvard Business Publishing.

“We’re connected by so many shared human experiences that enable us to live, grow and interact in universal ways,” he said. “Whatever our industry, whatever our country [or] language, we all have to deal with the same business basics in order to run our companies successfully. It’s these common business situations and concerns that unite and move us forward.”

Despite these commonalities, remember that cultural differences might affect the way others interpret your words. Richard Stevenson, head of corporate communications at cloud-based e-commerce platform ePages, noted that a clear, universal sense of mission is essential, but international staffers may expect and value differing communication styles.

“I find that American and British talent thrive on very open and personalized feedback and an emphasis on development needs, while Central European staff tend to relax more when there’s a structure to feedback, numeric inputs, and reference to agreed goals and KPIs,” Stevenson told Business News Daily. “Be prepared to wear different hats day to day and do experiments in order to bring out the best in each of them.”

Philpot reminded leaders that motivating employees takes dedication and time. A one-off message of encouragement or the occasional pat on the back won’t be enough; you need to keep working at it and refining your message.

“It can be like tossing a balloon into the air – with time, it is bound to descend,” Philpot said. “Sincerity, repetition and consistency of communication over time is what really makes the difference.”

Sean Peek contributed to the writing and reporting in this article. Source interviews were conducted for a previous version of this article.

How Natural Language Processing In Healthcare Is Used?

Natural Language Processing in Healthcare: Enhancing Patient Care and Clinical Operations INTRO

Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) that focuses on the interaction between computers and humans through natural language. It has numerous applications in different industries, including healthcare. The healthcare industry generates a vast amount of data that can be challenging to process and analyze without the assistance of technology. NLP has the potential to revolutionize the healthcare industry by improving the quality of care, reducing costs, and increasing efficiency. In this article, we will explore how NLP is used in healthcare.

Improving Patient Care

One of the primary benefits of NLP in healthcare is its ability to improve patient care. Healthcare professionals can use NLP to extract relevant information from patient records, such as medical history, medication allergies, and previous diagnoses. This information can be used to develop personalized treatment plans for patients. NLP can also help identify patients who are at high risk of developing certain conditions, allowing healthcare professionals to intervene early and prevent the development of the disease.

Enhancing Medical Research

NLP can also be used to analyze vast amounts of medical data to identify patterns and trends. This can help researchers develop new treatments and therapies. For example, NLP can be used to analyze patient data to determine which treatments are most effective for certain conditions. It can also help identify the side effects of different medications, allowing researchers to develop safer and more effective treatments.

Improving Clinical Trials

NLP can also be used to improve clinical trials by making the recruitment process more efficient. Clinical trials require a large number of participants and finding suitable candidates can be time-consuming and expensive. NLP can be used to analyze patient data to identify suitable candidates for clinical trials, reducing the time and cost required to recruit participants.

Improving the recruitment process is just one of the ways NLP can benefit clinical trials. By analyzing patient data, NLP can help identify patients who meet the specific inclusion criteria for a clinical trial. This process can be time-consuming and labor-intensive if done manually, but NLP can speed up the process significantly.

NLP algorithms can sift through a large amount of data and extract information relevant to the clinical trial. This information can include medical history, previous diagnoses, medication usage, and other factors that might make a patient suitable for a particular trial. By automating this process, researchers can save time and money while increasing the likelihood of finding suitable participants.

Enhancing Electronic Health Records (EHRs)

NLP can also be used to improve the accuracy and completeness of electronic health records (EHRs). EHRs are digital versions of patient medical records that contain information about a patient’s medical history, diagnosis, and treatment plan. NLP can help healthcare professionals extract relevant information from these records, ensuring that they are accurate and up-to-date. This can help improve patient care by providing healthcare professionals with the information they need to make informed decisions about a patient’s treatment plan.

Assisting Healthcare Professionals

NLP can also be used to assist healthcare professionals in their day-to-day tasks. For example, it can be used to transcribe physician notes, allowing them to focus on patient care instead of documentation. It can also be used to identify potential drug interactions and side effects, allowing healthcare professionals to adjust a patient’s treatment plan accordingly.

NLP has the potential to assist healthcare professionals in a wide range of day-to-day tasks. Here are some of the most significant examples:

Transcribing physician notes:

NLP can be used to transcribe physician notes, which is a time-consuming and often error-prone task. By using NLP to transcribe notes, healthcare professionals can save time and reduce errors, allowing them to focus on providing patient care instead of documentation.

Extracting information from medical literature:

Build A Natural Language Generation (Nlg) System Using Pytorch


Introduction to Natural Language Generation (NLG) and related things-

Data Preparation

Training Neural Language Models

Build a Natural Language Generation System using PyTorch


Hence, to capture the sequential information present in the text, recurrent neural networks are used in NLP. In this article, we will see how we can use a recurrent neural network (LSTM), using PyTorch for Natural Language Generation.

If you need a quick refresher on PyTorch then you can go through the article below:

And if you are new to NLP and wish to learn it from scratch, then check out our course:

Table of Contents

A Brief Overview of Natural Language Generation (NLG)

Text Generation using Neural Language Modeling

– Text Generation

Natural Language Generation using PyTorch

A Brief Overview of Natural Language Generation

Natural Language Generation (NLG) is a subfield of Natural Language Processing (NLP) that is concerned with the automatic generation of human-readable text by a computer. NLG is used across a wide range of NLP tasks such as Machine Translation, Speech-to-text, chatbots, text auto-correct, or text auto-completion.

We can model NLG with the help of Language Modeling. Let me explain the concept of language models – A language model learns to predict the probability of a sequence of words. For example, consider the sentences below:

We can see that the first sentence, “the cat is small”, is more probable than the second sentence, “small the is cat”, because we know that the sequence of the words in the second sentence is not correct. This is the fundamental concept behind language modeling. A language model should be able to distinguish between a more probable and a less probable sequence of words (or tokens).

Types of Language Models

The following are the two types of Language Models:

Statistical Language Models: These models use traditional statistical techniques like N-grams, Hidden Markov Models (HMM), and certain linguistic rules to learn the probability distribution of words.

Neural Language Models: These models have surpassed the statistical language models in their effectiveness. They use different kinds of Neural Networks to model language.

In this article, we will focus on RNN/LSTM based neural language models. If you want a quick refresher on RNN or LSTM then please check out these articles:

Text Generation using Neural Language Modeling Text Generation using Statistical Language Models

First of all let’s see how we can generate text with the help of a statistical model, like an N-Gram model. To understand how an N-Gram language model works then do check out the first half of the below article:

Suppose we have to generate the next word for the below sentence:

However, there are certain drawbacks of using such statistical models that use the immediate previous words as context to predict the next word. Let me give you some extra context.

Now we have some more information about what’s going on. The term “sandcastle” is very likely as the next word because it has a strong dependency on the term “beach” because people build sandcastles on beaches mostly right. So, the point is that “sandcastle” does not depend on the immediate context (“she built a”) as much as it depends on “beach”.

Text Generation using Neural Language Models

To capture such unbounded dependencies among the tokens of a sequence we can use an RNN/LSTM based language model. The following is a minimalistic representation of the language model that we will use for NLG:

x1, x2, and x3 are the inputs word embeddings at timestep 1, timestep 2, and timestep 3 respectively

ŷ1, ŷ2, and ŷ3 are the probability distribution of all the distinct tokens in the training dataset

y1, y2, and y3 are the ground truth values

U, V, and W are the weight matrices

and H0, H1, H2, and H3 are the hidden states

We will cover the working of this neural language model in the next section.

Understanding the Functioning of Neural Language Models

We will try to understand the functioning of a neural language model in three phases:

Data Preparation

Model Training

Text Generation

1. Data Preparation

Let’s assume that we will use the sentences below as our training data.

  ‘what is the price difference’]

The first sentence has 4 tokens, the second has 3 and the third has 5 tokens. So, all these sentences have varying lengths in terms of tokens. An LSTM model accepts sequences of the same length only as inputs. Therefore, we have to make the sequences in the training data have the same length.

There are multiple techniques to make sequences of equal length.

One technique is padding. We can pad the sequences with padding tokens wherever required. However, if we use this technique then we will have to deal with the padding tokens during loss calculation and text generation.

So, we will use another technique that involves splitting a sequence into multiple sequences of equal length without using any padding token. This technique also increases the size of the training data. Let me apply it to our training data.

Let’s say we want our sequences to have exactly three tokens. Then the first sequence will be split into the following sequences:

‘that is perfect’ ]

The second sequence is of length three only so it will not be split. However, the third sequence of the training data has five tokens and it will be broken down into multiple sequences of tokens:

‘the price difference’ ]

Now the new dataset will look something like this:

‘the price difference’ ]

2. Model Training

Since we want to solve the next word generation problem, the target should be the next word to the input word. For example, consider the first text sequence “alright that is”.

As you can see, with respect to the first sequence of our training data, the inputs to the model are “alright” and that”, and the corresponding target tokens are “that” and “is”. Hence, before starting the training process, we will have to split all the sequences in the dataset to inputs and targets as shown below:

So, these pairs of sequences under Input and Target are the training examples that will be passed to the model, and the loss for a training example will be the mean of losses at each timestep.

Let’s see how this model can then be used for text generation.

3. Text Generation

Once our language model is trained, we can then use it for NLG. The idea is to pass a text string as input along with a number of tokens you the model to generate after the input text string. For example, if the user passes “what is” as the input text and specifies that the model should generate 2 tokens, then the model might generate “what is going on” or “what is your name” or any other sequence.

Let me show you how it happens with the help of some illustrations:

n = 2

Step 1 – The first token (“what”) of the input text is passed to the trained LSTM model. It generates an output ŷ1 which we will ignore because we already know the second token (“is”).  The model also generates the hidden state H1 that will be passed to the next timestep.

Step 2 – Then the second token (“is”) is passed to the model at timestep 2 along with H1. The output at this timestep is a probability distribution in which the token “going” has the maximum value. So, we will consider it as the first generated or predicted token by our model. Now we have one more token left to generate.

Step 3 – In order to generate the next token we need to pass an input token to the model at timestep 3. However, we have run out of the input tokens, “is” was the last token that generated “going”. So, what do we pass next as input? In such a case we will pass the previously generated token as the input token.

The final output of the model would be “what is going on”. That is the text generation strategy that we will use to perform NLG. Next, we will train our own language model on a dataset of movie plot summaries.

Natural Language Generation using PyTorch

Now that we know how a neural language model functions and what kind of data preprocessing it requires, let’s train an LSTM language model to perform Natural Language Generation using PyTorch. I have implemented the entire code on Google Colab, so I suggest you should use it too.

Let’s quickly import the necessary libraries.

View the code on Gist.

1. Load Dataset

We will work with a sample of the CMU Movie Summary Corpus. You can download the pickle file of the sample data from this link.

View the code on Gist.

You can use the code below to print five summaries, sampled randomly.

# sample random summaries random.sample(movie_plots, 5) 2. Data Preparation

First of all, we will clean our text a bit. We will keep only the alphabets and the apostrophe punctuation mark and remove the rest of the other elements from the text.

# clean text movie_plots = [re.sub("[^a-z' ]", "", i) for i in movie_plots]

It is not mandatory to perform this step. It is just that I want my model to focus only on the alphabet and not worry about punctuation marks or numbers or other symbols.

Next, we will define a function to prepare fixed-length sequences from our dataset. I have specified the length of the sequence as five. It is a hyperparameter, you can change it if you want.

View the code on Gist.

So, we will pass the movie plot summaries to this function and it will return a list of fixed-length sequences for each input.

View the code on Gist.

Output: 152644

Once we have the same length sequences ready, we can split them further into input and target sequences.

View the code on Gist.

Now we have to convert these sequences (x and y) into integer sequences, but before that, we will have to map each distinct word in the dataset to an integer value. So, we will create a token to integer dictionary and an integer to the token dictionary as well.

View the code on Gist.

Output: (14271, ‘the’)

# set vocabulary size vocab_size = len(int2token) vocab_size

Output: 16592

The size of the vocabulary is 16,592, i.e., there are over 16,000 distinct tokens in our dataset.

Once we have the token to integer mapping in place then we can convert the text sequences to integer sequences.

View the code on Gist.

3. Model Building

We will pass batches of the input and target sequences to the model as it is better to train batch-wise rather than passing the entire data to the model at once. The following function will create batches from the input data.

View the code on Gist.

Now we will define the architecture of our language model.

View the code on Gist.

The input sequences will first pass through an embedding layer, then through an LSTM layer. The LSTM layer will give a set of outputs equal to the sequence length, and each of these outputs will be passed to a linear (dense) layer on which softmax will be applied.

View the code on Gist.



Let’s now define a function that will be used to train the model.

View the code on Gist.

# train the model train(net, batch_size = 32, epochs=20, print_every=256)

I have specified the batch size of 32 and will train the model for 20 epochs. The training might take a while.

4. Text Generation

Once the model is trained, we can use it for text generation. Please note that this model can generate one word at a time along with a hidden state. So, to generate the next word we will have to use this generated word and the hidden state.

View the code on Gist.

The function sample( ) takes in an input text string (“prime”) from the user and a number (“size”) that specifies the number of tokens to generate. sample( ) uses the predict( ) function to predict the next word given an input word and a hidden state. Given below are a few text sequences generated by the model.

sample(net, 15) Output:

‘it is now responsible by the temple where they have the new gospels him and is held’

sample(net, 15, prime = "one of the") Output:

‘one of the team are waiting by his rejection and throws him into his rejection of the sannokai’

sample(net, 15, prime = "as soon as") Output:

‘as soon as he is sent not to be the normal warrior caused on his mouth he has’

sample(net, 15, prime = "they") Output:

‘they find that their way into the ship in his way to be thrown in the’

End Notes

Natural Language Generation is a rapidly maturing field and increasingly active field of research. The methods used for NLG have also come a long way from N-Gram models to RNN/LSTM models and now transformer-based models are the new state-of-the-art models in this field.

To summarize, in this tutorial, we covered a lot of things related to NLG such as dataset preparation, how a neural language model is trained, and finally Natural Language Generation process in PyTorch. I suggest you try to build a language model on a bigger dataset and see what kind of text it generates.

In case you are looking for a roadmap to becoming an expert in NLP read the following article-


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