A data science career integrates various areas, which include data analysis, statistics, computer science, and machine learning. If you just finished your data science course or you are new to the job, you will undoubtedly find it really intimidating. However, you should remember that companies that are looking for data scientists are in search of specific skills and roles, so you don’t have to be proficient in all fields.

An essential tip for those who are looking for a job as a data scientist is to read the job descriptions carefully. This will allow you to apply to the jobs that you know you are qualified for, or you can find time to hone certain skills that the job needs for you to continue pursuing the job. Here are four types of data science jobs that you can choose from.

Data Science Generalists. Generalists are typically what most companies look for. If you’re a generalist, it’s possible that you won’t be working in a data company but in a business that needs someone to gather data plus other skills like touch production coding, analysis, and visualizing data, among others. Data generalists are sought-of by companies that deal with big data at the same time dealing with confusing and disorganized data sets.

Data Analysts. These specific individuals are most often confused with being THE true data scientists. They are tasked to produce raw data visualizations, report dashboards, pull data from SQL databases, and are expected to be Tableau or Excel masters. Some companies may ask analysts to explore and evaluate A/B test results. Better yet, they may be hired to become the head of their company’s Analytics account.

Machine Learning Engineers. In a lot of companies, the data analysis program is their product or what they are selling. If this is the case, data analysis or machine learning in the company is quite concentrated. This entails the data scientist to be specifically proficient in mathematics, physics, or statistics. Machine learning engineers are tasked to produce large amounts of data-driven products, and they are not so much into responding to operational queries. The usual employers of machine learning engineers are consumer-facing businesses or those that provide data-based services.

Data Engineers. They are most wanted in companies that have a heavy load of data and a lot of traffic. Data engineers set up this data into a specific structure that the company needs to progress. They also provide more analysis and less machine learning and statistic skills. If you’re interested in this job, you’ll want to look for posting under both ‘data engineers’ and ‘data scientists.’ In companies looking for data engineers, there are less training opportunities for first-level data scientists, so you’ll have the chance to grow through trial by fire.

Which Category Do You Think Will You Shine?

So where do you think are you most qualified to work in? Have you decided?

Hopefully, the article has given you an insight into the scope of the data scientist. When you are looking for the best type of job that will match your skillset, don’t forget to read and understand the job description and make sure you have what it takes to apply for the specific job. Who knows? You might be more than qualified to take on the job that you really are built for!

Fraud Analytics Defined

It is a system or process wherein illegal transactions can be possibly apprehended while they are happening, and only the legitimate transactions are allowed to go through. These programs are developed after observing and assessing the regular pattern of such technical details, activities, and doubtful indicators.

Fraud analytics has been increasingly used in digital as well as other businesses; it has become a necessity in financial establishments, banks, healthcare, government, and insurance companies.

The Increasing Need Of Fraud Analytics

There have been some existing fraud detection tools that are utilized today, but fraud analysts are still sought-after because of these main reasons:

• The detection tools are operated manually, although with limitations. The previous systems are suitable for only a few bytes of information and records. But we know that in reality, daily information and the corresponding problems can reach up to terabytes. Fraud analysts are trained and skilled to do this task proficiently.

• Previous programs can only detect false transactions by following a set of guidelines, which have been prepared ahead. Fraud analysts can do this too, and they can also tackle inconsistent behavioral configurations. More and more criminals have learned to hack into the fixed system, thus making it no longer very reliable.

• Analytics has made it possible for analysts to identify frauds that are done real-time so that they can be avoided. Insurance companies, for instance, can scan for fraud or banks have a system of detecting a scan in the system before a claim or withdrawal is approved. In manual mechanisms, the process of detecting frauds are done and found after the transactions have been made.

• Some fraudulent situations are very evident, while sometimes they can be hard to recognize. In these kinds of situations where the pattern is complicated, companies turn to machine learning. The data scientist can create an algorithm that can identify real-time as well as future fraudulent occurrences.

The Role Of The Fraud Analytics Team

What does a team of skilled fraud analysts do? The primary role that they do is to reconstruct the fraud recovery procedures and augment error and fraud prevention opportunities. They look closely at research materials and analyze specific elements like the IP addresses, differences in shipping addresses, recognizing proxy servers, and creating and improving fraud-detection algorithms. Most terms may be foreign to us, and that is understandable. Fraud analytics is a complex field that needs to be studied to be understood and mastered.

Promising Prospects For Fraud Analysts

There are three vital qualities that fraud analysts must possess so that he will stay on top of the others in his field. First, they need to, of course, have extensive knowledge and comprehension of fraud analytics. With this, they can develop better models and learn to prioritize the larger business objectives. The second quality that they must have is superb communication and visualization skills. They must know how to embody complex analytical patterns in the most simplified ways. The key is to convey the appropriate amount of data at the right time.

Lastly, fraud analysts should be highly adaptable. Fraudsters are inclined to change their operations continually, especially since Big Data and Analytics is an equally evolving field. So you will need to be able to adapt to the vibrant nature of analytics itself.

Final Thoughts

As a proficient fraud analyst, you must possess an open mind to be able to understand the data in front of you, seemingly telling you a different story every time. You should have the skills and the patience to dig into the specific information, chop it into segments, and analyze it in different angles. These and everything detailed above will put you on top and apart from the rest of the fraud analysts in the world.

Today, the terms data science and big data are no longer limited to the tech-savvy. Even the business world knows of these terminologies, as they have become increasingly become useful for businesses to grow and improve a hundredfold. Big data has been predicted to generate approximately a 60% increase globally. For the European government, this means that the administrators can save almost $150 billion just by utilizing big data.

This technology can extend to various other industries. Thus, for big and small-scale entrepreneurs, knowledge of how to use data science will keep you ahead of or at par with the competitive business world.

Data Science Defined

Data is just mere information that is spread through the Internet. The technical use of this data implies that it is collected in large amounts, integrated, and then classified according to the specific information that a person wants. It is the data scientists who gather these large sets of data, using their expert skills and algorithms to obtain the particular data for the client. This big data is usually utilized by large businesses that have the money to hire data scientists to perform the task of meticulously pulling out information for their enterprise.

However, as technological advances have become more accessible and affordable, there are tools that medium and small-sized businesses can now use to collect big data and equally provide them with the same opportunity of growing their business.

To elaborate on the explanation, here is an example.

Say you were a bank that wanted to create loan products that your clients are going to like, products and packages that can, of course, compete with the rival banks. You will utilize the data science to collect the information that you need – kinds of loans, loans that are popular to consumers, time of the year that loans are common, best loan qualities, and the classification of consumers who are more inclined to get a loan. You use this information to create or improve your loan products, and you promote them to specific groups. This helps your lending department and ultimately, your whole business to grow.

The Role Of The Data Scientist

Frequently, data scientists possess advanced skills and training in math, statistics, and computer science. They are also experienced and very competent data miners, data visualizers, and information managers. Lastly, they have a strong background in cloud computing, data warehousing, and infrastructure design.

These are the tasks of data scientists that make hiring them worth your time, risk, and money.

• They deliver products that are vital and relatable to consumers. Organizations can conveniently find where and when their products are most sellable. This makes it easier for companies to determine the right time to provide the appropriate products, thus helping businesses formulate new products or improve previous products to meet the consumer’s needs.
• They alleviate or reduce fraud. Data scientists are tasked to recognize relevant and crucial data that will attract the consumer’s attention. They generate networks and big data approaches that detect fraud, and they create alert systems that warn consumers when unfamiliar information is identified.
• They provide customized consumer experience. This is one of the most popular roles that make data scientists sought-after by businesses. The marketing and sales team is trained to understand their consumer on a more profound level. With this, the business undoubtedly has twice the edge over its competition.

Conclusion

Data scientists are today’s biggest heroes in the technological era. Businesses would be wise to take a risk and invest in data science for their enterprise to not only equal their rival but to rise above them.

Mental health professionals and neuroscientists across the globe are now utilizing machine learning to help create better treatment plans for their patients and to recognize essential markers for mental health problems even before they develop. Diane Dreher, Ph.D. says, “The past few years have witnessed an escalation in teen suicides and anxious, depressed, and suicidal students crowding college counseling centers.” That is probably one of the biggest reasons why this form of data science has increasingly risen to popularity – it’s capacity to assist clinicians in predicting individuals who have a higher likelihood of developing a mental health disorder.

There are so many sets of information available to us on mental health, yet we can gather all of this data in a way that mental health professionals can perform their jobs more efficiently. Decades ago, a diagnosis had to be based on statistics, and the overall average population who had the same medical problem. Today, because of machine learning, doctors and other clinicians are capable of personalizing their diagnosis. One example of this is online therapy apps such as BetterHelp. Many patients benefit from this flexible form of treatment made possible by technology.

Machine learning has paved the way for changing the system of mental health through recognizing biomarkers, creating treatment plans, and predicting a crisis.

Recognizing Biomarkers And Creating Treatment Plans

Currently, a trial and error method is used when clinicians diagnose patients with a mental health condition. They need to do this to establish the proper dosage of medicine and to come up with the appropriate treatment plan. This trial and error shouldn’t exist, but then the truth is that one patient’s symptoms may not be present in another patient. Symptoms are almost always different for each patient.

The body does have not only physical biomarkers but also behavioral biomarkers as well for mental issues such as depression and anxiety, among others. And machine learning systems could help recognize these behavioral biomarkers to assist doctors and therapists in determining whether or not a patient is at risk of developing a mental health disorder. The system also helps track the potency of a particular treatment plan.

Thus, it is safe to say that each patient has his biological makeup, reactions, and triggers to stress and other conditions like anxiety or panic. A lot of symptoms for mental health overlap each other, and though some of the key markers are already known, a trial and error regimen shouldn’t be acceptable. Matthew D. Jacofsky, Psy.D. wrote, “To complicate things further, sometimes two separate disorders may be present at the same time. Thus, it is quite possible to have both an eating disorder and an anxiety disorder.”

This is where machine learning comes in. It offers a ready opportunity for mental health professionals to recognize the subcategories of various illnesses and create a more customized treatment plan, including the dosages of the patient’s medications.

Predicting A Crisis

People need to get a good grasp of the reality that those who have specific mental conditions are naturally more inclined to having a crisis like psychosis, panic attacks, or manic episodes. Barbara Markway, Ph.D. explains, “Briefly, a panic attack is a sudden rush of acute fear and anxiety accompanied by physical symptoms such as shortness of breath, dizziness, tightness in the chest, tingling, nausea, and other stomach distress, shaking, and sweating.” Patients who have been diagnosed with a mental health illness are supervised to help them manage their activities of daily living. However, specific conditions like bipolar disorder or schizophrenia have a much higher likelihood of developing a crisis, and the mental healthcare team is accountable for reducing the risk of a crisis from happening.

Machine learning systems can help in this area by combining passive information derived from the media or smartphones and self-provided information to establish if the patient has a forthcoming attack or episode.  Crises such as these can be predicted if clinicians have observed the patient experiencing stress or being exposed to his specific triggers. Professionals are also using online platforms to help ease these triggers.

Conclusion

The bottom line is that there are vivid markers of a forthcoming crisis or episode, and whether or not the patient has a confirmed diagnosis. Each of us has specific triggers as well as coping strategies, and crises can be inevitable. Through clearer biomarkers and more structured treatment plans made possible by machine learning, mental health professionals have a better way of helping their patients recover quickly and effectively.