Team Dec 29, 2022 No Comments
It is not surprising that Data Science is growing rapidly and is expected to reach a market worth USD 350 billion by the end of 2022. The hierarchy has been divided into many categories, such as Data Scientists, Data Analysts, Data engineers, etc., and they are monopolizing the IT sector as a result of the increasing demand and speed. This article will act as a roadmap for Data Analysts.
As per the recent survey, it was found that the market was unsuccessful in fulfilling the demand for Data Analysts for the past couple of years and is the reason people are shifting their careers into the Data Science niche because of the major attraction that this niche offers which is growth and salary opportunities.
These easy steps will help you in building your career in Data Science. Data Science is no rocket, you just need to follow a few steps and be dedicated to achieving what you wish. Then becoming a Data Analyst is just a matter of a few months. So without any further delay let us begin with the roadmap for Data Analyst.
It is very crucial to have your basics ready. The Data Science industry is all about understanding and if you have that, you are almost there. To create a solid foundation, the first step you will have to take is to enroll in a good data analytics course. Learning the basics will help you a lot in your career. Along with this if you get the chance to practice on some real-life industry-level projects then that will help you more. In this respect, we would take some time to tell you about Ivy Professional School. This is a data analytics institute that offers industry-relevant courses along with real-life projects and placement assistance. Ivy understands the importance and scope of Data Science in the present market scenario and creates its courses accordingly. Their courses are led by industry experts and help a lot in enriching your resume with real-life projects.
This is one of the most crucial steps in this roadmap for Data Analyst. As stated above, working on real-life projects will increase the weightage of your resume and will also help you in building confidence. There are a few ways to work on real-life projects:
Here are some of the examples that you can consider in creating your project.
Connect and network with like-minded people on Twitter, LinkedIn, or any other social media site you want. For instance, building relationships in this way should be your strategy if you want to improve as an analyst.
Let the Data Science industry know you. Showcase your projects on various social media handles. There are a few points to keep in mind while you share your projects.
When you have the above-mentioned steps, you can start to apply for jobs. The best thing about the Data Science field is that you can apply for jobs based on specific tools. So suppose you opt for Data Science With Vizualization certification course at Ivy, you can apply for the tools like Excel, or Power BI once they are completed individually. So this is the benefit that this industry offers.
There are various portals from where you can apply such as LinkedIn, Naukri, Indeed, and many more. But the best among them is LinkedIn. It offers wide exposure to all the spheres of recruitment. Here are some ways by which you can look for jobs in the Data Science domain:
With this, your roadmap for Data Analyst ends. Follow these steps with determination and smart work to achieve optimum results.
Today, billions of businesses produce data every day and use it to inform important business choices. It aids in determining their long-term objectives and establishing new benchmarks. In today’s world, data is the new fuel, and every industry needs Data Analysts to make it useful. The market share of Data Analysts is expected to increase by USD 650+ billion at a CAGR of around 13%, making it one of the most sought-after professions in the world. The more data, the greater the need. So if you are planning to make your career in data analytics then it is indeed the best choice. Hope this roadmap for Data Analyst helped you. You can also get free 1:1 career counseling to clear all your doubts regarding this industry. For.. more details visit Ivy’s official website.
Team Dec 26, 2022 No Comments
One of the most competitive and unpredictable business sectors is the insurance sector. It is highly related to risk. As a result, statistics have always been a factor. These days, data science has permanently altered this dependence. In this article, we will give you an insight into the various use cases of data science in insurance industry.
The information sources available to insurance firms for the necessary risk assessment have expanded. Big Data technologies are used to anticipate risks and claims, track and evaluate them, and design successful client acquisition and retention tactics. Without a doubt, data science and insurance easily compliment each other. In this article, we will have a look at the top 10 data science use cases in insurance.
Insurance companies are presently undergoing rapid digital transformation. With insurance digital transformation, a broader range of data is available to insurers. Data science in life insurance enables companies to assemble these data to effective use to drive more business and filter their product offerings. With that, let us have a look at the various applications of data science in insurance industry.
The first case among the various data science insurance use cases is the detection of fraudulent activities. Every year, insurance fraud costs insurance companies a great deal of money. It was able to identify fraudulent activities, suspicious relationships, and subtle behavioral patterns utilizing a variety of methods thanks to data science platforms and software.
A steady stream of data should be provided into the algorithm to enable this detection. Generally, insurance companies employ statistical frameworks for effective fraud detection. These frameworks depend on previous instances of fraudulent actions and use sampling methods to evaluate them. Along with that, predictive modeling techniques are used here for the purpose of analysis and also filtering of fraud scenarios. Evaluating connections between suspicious activities allows the company to identify fraud schemes that went unnoticed previously.
Customers are constantly eager to receive individualized services that completely suit their demands and way of life. In this regard, the insurance sector is hardly an exception. To satisfy these needs, insurers must ensure a digital connection with their clients.
With the aid of artificial intelligence and advanced analytics, which draw insights from a great quantity of demographic data, preferences, interactions, behavior, attitude, lifestyle information, interests, hobbies, etc., highly tailored and relevant insurance experiences are ensured. The majority of consumers like to find deals, policies, loyalty programs, recommendations, and solutions that are specifically tailored to them.
The platforms gather all relevant data in order to identify the primary client needs. Then, a prediction of what will or won’t work is made. Now it’s your turn to either create a proposal or select the one that will work best for the particular customer. This may be done with the aid of the selection and matching methods.
The personalization of policies, offers, pricing, messages, and recommendations along with a continuous loop of communication hugely contributes to the rates of the insurance company.
The idea of price optimization is complicated. As a result, it employs multiple combinations of different techniques and algorithms. Even though using this process for insurance is still up for debate, more and more insurance firms are starting to do so. This procedure entails merging data unrelated to predicted costs, risk characteristics, losses and expenses, as well as further analysis of that data. In other words, it considers the modifications compared to the prior year and policy. Price optimization and customer price sensitivity are so strongly related.
A qualitatively new level of product and service advertising has been reached thanks to modern technologies. Customers typically have different expectations for the insurance industry. Various strategies are used in insurance marketing to boost customer numbers and ensure targeted marketing campaigns. Customer segmentation emerges as a crucial technique in this regard.
According to factors like age, geography, financial sophistication, and others, the algorithms segment customers. In order to categorize all of the clients, coincidences in their attitudes, interests, behaviours or personal information are found. This categorization enables the development of solutions and attitudes especially relevant to the specific user.
CLV (Customer lifetime value) is a complicated factor portraying the value of the user to an entity in the form of the variation between the revenue gained and the expenses made projected into the whole future relationship with a user.
To estimate the CLV and forecast the client’s profitability for the insurer, consumer behaviour data is often used. The behaviour-based models are so frequently used to predict cross-selling and retention. Recency, the monetary value of a customer to a business, and frequency are seen to be crucial variables when estimating future earnings. To create the prediction, the algorithms compile and analyse all the data. This makes it possible to predict whether customers will maintain their policies or cancel them based on their behaviour and attitudes. The CLV forecast may also be helpful for developing marketing strategies because it puts customer insights at your disposal.
Healthcare insurance is a broad phenomenon across the globe. It generally implies the coverage of costs created by the accident, disease, disability, or death. In many nations, the policies of healthcare insurance are effectively supported by the governing bodies.
In this era of quick digital data flow, this niche cannot resist the influence of various data analytics applications. The global healthcare analytics market is constantly evolving. Insurance companies suffer from continuous pressure to offer better services and decrease their costs.
A broad range of data that includes insurance claims data, provider data and membership, medical records and benefits, case and customer data, internet data, and many more are assembled, framed, processed, and turned into valuable results for the healthcare insurance business. As a result, factors like cost savings, healthcare quality, fraud protection and detection, and consumer engagement may all greatly improve.
The future forecast piques the interest of insurance firms greatly. The potential to lessen the company’s financial loss is provided by accurate prediction.
For this, the insurers employ some complicated procedures. A decision tree, a random forest, a binary logistic regression, and a support vector machine are the main models. In order to reach all levels, the algorithms implement high dimensionality and incorporate the detection of missing observations as well as the discovery of relationships between claims. The portfolio for each customer is created in this way.
When talking about data science for insurance, we can conclude that modern technology is advancing quite quickly and entering many different industries. The insurance sector does not lag behind the others in this regard. Statistics have long been used in the insurance industry. Therefore, it is not unexpected that insurance companies are utilizing data science analytics in a big way.
The goal of using data science in insurance is much the same as it is in other industries: to improve the business, increase revenue, and lower expenses.
By now you must have understood the benefits data science has in the insurance sector. And not only in the insurance sector, but data science also finds relevance in almost all sectors of the world. So if you are aiming for a career as an insurance data scientist then this is the time. But before you enter this industry, it is important for you to grab a certificate in the same. Ivy Professional School offers great courses in data science and data engineering that you can enrol in. They offer expert-led courses along with complete placement assistance. Join Ivy and get to work on real-life insurance data science projects to make your resume more reachable to recruiters. For more details visit their website.
There are various applications of data science that include Claim Prediction, Healthcare Insurance, Lifetime Value Prediction, Customer Segmentation, Price Optimization, Personalised Marketing, and Fraud detection.
Using a plethora of data that is presently available, the insurance industry is seeing immense growth. Data anticipate how the industry will operate and also how its relation will be with its customers.
The job role of a data analyst in the insurance industry is to extract, convert, and summarise data as input for studies and reports, and data analysts design, alter, and run computer programmes. Examine the accuracy of the data that insurance firms offer, both in transactional detail and in aggregate, and assist the companies in fixing mistakes.
Team Dec 15, 2022 No Comments
The varied use of big data in all sectors of our life from transportation to commerce makes us realize how crucial it is in our daily lives. In the same way, data science is transforming the healthcare sector. In this article, we are going to have a look at how data science in healthcare can bring about a big and distinctive change.
Nearly 3.5 billion US dollars have been invested in digital health startups and in healthcare data science projects in 2017 enabling companies to meet their ambition of revolutionizing the general notion of healthcare that the world carries. If you are aiming to pursue a career in data science in the healthcare domain, then this is the ideal article for you as you will find many data science in healthcare jobs.
There are numerous factors that make data science crucial in healthcare in the present time, the most crucial of them being the competitive demand for important data in the healthcare niche. The collection of data from the patient via effective channels can help offer enhanced quality healthcare to users. From health insurance providers to doctors, all of them depend on the collection of factual data and its exact analysis to make effective decisions about the health situations of the patients.
Nowadays, diseases can be anticipated at the earliest stage with the help of data science in healthcare, that too remotely with innovative appliances boosted by ML (Machine Learning). Smart devices and mobile applications constantly assemble data about blood pressure, heartbeat rates, sugar, and so on transferring this data to the doctors as real-time updates, who can structurize treatments accordingly.
The significant contribution of data science in the pharmaceutical industry is to offer the groundwork for drug synthesis using AI. The metadata of the patient and mutation profiling is used for developing compounds that point towards the statistical correlation between the attributes.
Presently, AI platforms and chatboxes are structured by data scientists to allow people to get a better evaluation of their health by putting in several health data about themselves and getting a precise diagnosis. Along with that, these channels also assist users with health insurance policies and guide them to a better lifestyle.
The present-day scenario of the IoT (Internet of Things), which assures optimum connectivity is a blessing of data science. Presently, when this technology is applied to the medical arena, it can help supervise patient health. Presently, physical fitness supervises and smartwatches are used by people to manage and track their health. Along with that, these wearable sensor devices can be monitored by a doctor if they are given access and in chronicle cases, the doctor can remotely offer solutions to the patients.
Data scientists have developed wearable devices for public health that will allow doctors to collect most of the data such as sleep patterns, heart rates, stress levels, blood glucose, and even brain activity. With the help of various data science tools and also machine learning algorithms, doctors can track and detect common scenarios such as respiratory or cardiac diseases.
Data science technology can also anticipate the slightest alterations in the health indicators of the patients and anticipate possible disorders. Several wearables and also home devices as a part of an IoT network employ real-time analytics to anticipate if a patient will encounter any issue based on their current scenario.
A crucial part of medical services, diagnosis can be made more convenient and quicker by data science applications in the healthcare domain. Not only does the data analysis of the patient boosts early detection of health problems, but medical heatmaps pertaining to demographic patterns of issues can also be made.
A predictive analytics model uses historical data, evaluates patterns from the data, and offers precise predictions. The data could imply anything from the blood pressure and body temperature of the patient to the sugar level.
Predictive models in data analytics associate and correlates each data point to symptoms, diseases, and habits. This allows the identification of the stage of the disease, the extent of damage, and the appropriate treatment measure. Predictive analytics in the healthcare domain also helps:
Healthcare professionals seldom use several imaging technologies such as MRI, X-Ray, and CT Scan to visualize the internal system and organs of your body. Image recognition & deep learning technologies in health Data Science enable the detection of minute deformities in these scanned pictures, allowing doctors to plan an impactful treatment strategy.
Along with that, health data scientists are continuously working on the development of more advanced technologies to improve image analysis. For instance, the latest publication in Towards Data Science, the Azure Machine Learning channel can be used in training and optimizing a structure to detect the presence of three common brain tumors, Meningioma tumors, Glioma tumors, and Pituitary tumors.
As a data scientist in the healthcare and pharmaceutical industry, you will have to use your analytical skills to diagnose illness precisely and save lives. The huge amount of data that is sourced from the healthcare niche, from patient data to records kept by government authorities need a skilled analyst to handle it all.
The Covid-19 pandemic has lately shown how important data science in healthcare can be. Not only has data science enhanced the sampling and collection of data but also demonstrated global patterns in the spread of the infection, anticipating the next region where Covid would spread and how government policies can be structured to fight against the contagious disease effectively.
Regarding national-level healthcare, data scientists can help in monitoring the spread of the disease within the nation and coordinate in accordance with the authorities to send resources to the most affected areas.
In this section, we will outline the important responsibilities of a healthcare data scientist:
Evaluating the role of data science in healthcare is also an important responsibility for a data scientist in the healthcare domain. It includes modifying assembled data to align with the objectives and aims of the company.
Here are some of the top advantages of data science in healthcare that you can think of:
Perhaps the most crucial utilization of data science in healthcare is to decrease errors in the process of treatment via accurate anticipations and prescriptions. Since a substantial portion of data about the medical history of the patient is collected by the data scientists, that stored data can be employed for identifying symptoms of illness and offering a precise diagnosis. Mortality rates have significantly decreased since treatment options may now be tailored and care is given with better knowledge.
The development of medicine needs intensive research and time. However, both effort and time can be decreased by medical data science. Via the usage of case study reports, lab testing results, and previous medical and the impact of the drugs in clinical trials, machine learning algorithms can anticipate whether the drug is going to offer the desired impact on the human body.
In the case of quality treatment that needs to be taken care of, it is essential to create skill sets that can offer a precise diagnosis. Using predictive analytics, one can anticipate which patients are at greater risk and how to get in early to prevent serious damage. Along with that, the huge quantity of data requires to be managed skillfully to stop errors in administration, for which data science can be an ideal solution.
EHRs (Electronic Health Records) can be used by data science specialists in the medical arena to identify the health patterns of patients and stop unnecessary hospitalization or treatments, thus decreasing costs.
The 21st century is making lucrative use of data science in the healthcare niche to boost surgeries, operations, and patient recovery procedures. Apart from the developments in technology and the raised digitization of lifestyles, data science will also help in decreasing healthcare expenses, making quality medical amenities accessible to everyone.
We can conclude that there are various applications of data science in healthcare. The pharmaceutical and healthcare industry has heavily used data science for enhancing the lifestyles of patients and anticipating diseases at an early stage.
Along with that, with the advancements in medical image analysis, it is possible for doctors to find microscopic tumors that were previously difficult to find. Hence, it can be concluded that data science has revolutionized the healthcare sector and also the medical e
Now come to the section, where we can talk about how you can take your data science career to the next level. To establish your career in data science in the healthcare section you will have to have some sort of certification. The best institute for Data Science in this country is Ivy Professional School. Ivy offers a range of certifications that will help you in the future.
Team Dec 13, 2022 No Comments
AI is set to break all barriers. Now bots can answer all your questions in just a few seconds. If you are a tech-savvy person, by now you are aware of the latest ChatGPT 4 by OpenAi. ChatGPT is a newly launched application from OpenAI that is offering its users amazing answers to all their questions.
ChatGPT app is a highly advanced chatbot created by OpenAI. It is an AI research company that is behind products like the Dall E2 image generator and GPT3 which is the text model. This latest release which is in its beta version blew up the AI industry. People beyond this industry are actually talking about the ChatGPT 4. This AI bot has drawn interest while in its testing stage because of the high potential that it carries.
In 2020, OpenAI released ChatGPT3, which too gained a good push from the audience. But the GPT 4 is a bit different from its predecessor. It is more conversational in nature. The dialogue format makes it possible for ChatGPT 4 to follow up on questions, admit mistakes, challenge incorrect answers, and reject inappropriate requests. So, we can say that this is a sibling model to instruct GPT which is a subset of GPT 3.
In easy terms, ChatGPT is basically a chatbot where the users can ask questions and the chatbot uses AI (Artificial Intelligence) to give answers. OpenAI created this bot so that users can get both technical and non-technical responses.
ChatGPT as mentioned above is a chatbot that uses deep learning to offer text that resonates with a human and is created on GPT 3.5 language structure. This bot can respond to several questions in a natural way similar to a personal tutor, who is sound about all the subjects. But can this be called an alternative to Google, still remains a question n With that, let us have a look at some of the important features of ChatGPT 4?
This application which is trained by Artificial Intelligence and Machine Learning can offer information and answer questions via a conversation. If you are wondering how to use ChatGPT, below are the steps by which you can access ChatGPT.
ChatGPT is presently a prototype. At times it writes reasonable-sounding but factually incorrect answers. Along with that, ChatGPT seldom overuses several phrases like restating that it is a language structure trained by OpenAI. At times, it responds to harmful instructions, exhibits biased behavior, and also responds to inappropriate requests.
Recognizing the limitations in its present form, CEO of OpenAI Sam Altman posted on Twitter stating,
“…a lot of what people assume is us censoring ChatGPT is in fact us trying to stop it from making up random facts. Tricky to get the balance right with the current state of the tech. It will get better over time, and we will use your feedback to improve it.”
It was created by San Francisco-based OpenAI. A firm that also produced this year’s ground-breaking picture generator DALL-E 2 and tools like GPT-3.
A chatbot is a piece of software created to replicate human-like discussions in response to user input.
To test out ChatGPT, one can visit the OpenAI website and register. To use this service, you must register for an account with OpenAI. You might receive a notification stating that the beta is filled because the chatbot has already attracted one million users.