Sollers College

Tag: Clinical data science

  • Do you know why Data Science is the next big thing in technology?

    Do you know why Data Science is the next big thing in technology?

    • Despite increasing digitization and smart technologies, data has emerged as a critical business asset. Companies must leverage massive amounts of data, use analytics, and develop cutting-edge technologies to remain competitive.
    • Data science has the potential to transform the healthcare sector in numerous ways. 
    • Data analysis supports a value-based, data-driven approach in everything from health tracking to scheduling nursing shifts. 
    • This, in turn, optimizes the workforce and throughput, improves care recipient satisfaction, and balances the supply. 
    • Furthermore, by implementing the proper use of data science in healthcare, medical organizations can drastically reduce costs and readmissions.
    • Data can transform businesses. It’s no surprise that the global market for Big Data as a Service is expanding. 

    Why data science will continue to be a prominent part of the technology landscape are as follows:

    The massive increase in data:

    •  In recent years, the amount of data generated by individuals, businesses, and various digital devices has increased exponentially. 
    • This data is a valuable resource that, when properly utilized, can provide valuable insights, improve decision-making, and drive innovation across a variety of industries.

    BI and Making the Right Decisions:

    • With data science, organizations can find patterns, trends, and associations by analyzing large quantities of data, which allows them to make wise business decisions by analyzing trends and associations.
    • Increasing the competitive edge of a company can be achieved by making wise decisions and optimizing the company’s operations.
    •  These types of statistics are extremely helpful in helping organizations achieve this goal.

    Data Science and Artificial Intelligence: 

    • Inextricably linked together are data science, artificial intelligence (AI), and machine learning (ML). By using data in a machine learning algorithm, you can gain insight into your experience and improve your performance over time.
    •  Increasingly, AI is being employed in several varied fields, and data science plays a valuable role in both training and refining AI models as they become more prevalent.

    Personalization and Customer Experience: 

    • Data science enables businesses to personalize products, services, and user experiences based on individual preferences and behaviors.
    •  Personalization increases customer engagement and loyalty.

    Automation and efficiency: 

    • Using data science techniques, businesses can automate repetitive tasks, optimize processes, and improve overall efficiency, resulting in cost savings and increased productivity.

    Healthcare and Biotechnology:

    •  Data science is transforming medical research, drug development, and patient care in the healthcare industry.
    •  Large-scale dataset analysis helps discover disease patterns, potential treatments, and personalized medicine options.

    Internet of Things (IoT): 

    • The proliferation of IoT devices has resulted in massive data generation. 
    • Data science is critical for gaining meaningful insights from this data and making IoT devices smarter and more useful.

    Predictive Analytics: 

    • Data science enables predictive analytics, which helps businesses anticipate future trends, customer behavior, and potential risks. 
    • This foresight aids in risk mitigation and proactive decision-making.

    Financial Analysis and Counterfeiting Detection: 

    • Data science is critical for analyzing financial data, spotting illicit behavior, and determining optimum investment approaches

    Social networking consequences: 

    • Data science has the potential to address societal challenges such as poverty, climate change, and healthcare disparities by analyzing data to discover problems and propose data-driven solutions.

    Given the ongoing advancement of technology and the ever-increasing volume of data generated, data science is likely to remain a significant and evolving field.

     Its application spans multiple domains, and its impact on businesses and society is expected to last for many years.

    The Data Science programs at Sollers College were designed with the burgeoning need for trained data analysts in mind. Algorithms, mathematical concepts, statistics, programming in R, AWS, and Python, data visualization using Tableau, modeling and prediction, information and text analytics, machine learning, NLP, and deep learning using Python are the main topics of the programs.

    It is Sollers‘ goal to create programs that are tailored to the needs of the industry, and this is the biggest reason why students choose to study here. The career service advisors at the Data Science Center are industry professionals who assist students with resume writing and interview practice to prepare them for careers in data science.

  • Data Science: Gateway for the best-paying jobs of the near future

    Data Science: Gateway for the best-paying jobs of the near future

    In light of the proliferation of data, the necessity to study and analyze information to attain insights increases day by day. As a result of this, data science is essential to businesses since it helps individuals make decisions and solve problems.

    As technology develops, we create more and more data, rendering conventional business intelligence outdated. Machine learning and sophisticated analytics are the only ways to gain insights from data sets instead of traditional methods. The Internet has changed everyone’s daily lives, so has this technology. Data Science and artificial intelligence are ready to transform our unimaginable lives.

    What are the Benefits of Data Science for Businesses?

    Data science is becoming increasingly significant in the commercial sector. Although it is a relatively new business, comprehending its current relevance is becoming increasingly crucial. Data collection, storage, analysis, and interpretation are essential aspects of Data Science. Health care, business, social media marketing, and sports betting are just a few of the areas where it may be used.

    Data Science is widely accepted. With Big Data being incorporated in practically every aspect of our lives now and shortly, no business organization can afford to overlook the value of data science. There’s a reasonable risk they’ll fall behind their competitors if they do. Smaller businesses with robust data handling abilities will win over larger companies with insufficient data understanding and expertise. 

    Even start-ups aren’t passing up the opportunity to make data-driven judgments. The corporate sector has grasped the significance of data science in the current situation. Assume that this massive data set can be reviewed and computed using a scientific method. It may assist firms in reaching relevant conclusions, resulting in improved business decisions, more earnings, and a higher return on investment.

    What exactly does a Data Scientist perform for a company?

    Data collecting and analysis from numerous sources has decreased the need to take high-stakes bets since the introduction of data scientists. 

    Data scientists use current data to construct models that mimic many potential actions, allowing businesses to determine which path would yield the most significant outcomes. 

    Measuring and measuring essential metrics associated with critical developments pays reward.

    Most businesses collect consumer data from at least one source, whether Google Analytics or customer surveys. Data science’s capacity to connect current data, which may not be relevant on its own, with other data points to develop insights that a company can use to learn more about its audience and consumers is a vital component. A data scientist can accurately identify the main groups by examining diverse data sources. Tailoring services and goods may increase profit margins to specific consumer groups.

    Data science may assist any company capable of efficiently exploiting its data. 

    Any firm in any field may benefit from data science. 

    It’s relevant across the board and will define the future of enterprises, from assessing processes and employing new employees to assisting senior staff in making better-informed decisions.

    Despite being one of the fastest-growing disciplines for new graduates, demand for data science significantly outnumbers supply. Furthermore, business intelligence professionals and analysts are being investigated to democratize data science access and speed up recruiting through the solution. A certification in Clinical Data Science can speed your career growth. 

  • Why Machine Learning Is the Next Big Thing

    Why Machine Learning Is the Next Big Thing

    With the help of artificial intelligence (AI), computer programs may continuously improve their performance by learning from their experiences with data. By employing algorithms that learn incrementally, it automates analytics. Rather than relying on rule-based programming, it uses a basic self-learning approach instead. So this technology has become a part of our daily lives, whether it’s helping us find our way across town or advising us on how to make the most significant investments. As a result, machine learning is essential since it affects how easy it is to live or make decisions. It has become so ingrained in our everyday routines that you may not even realize it. 

    What are the benefits of using machine learning in a business?

    Machine learning may be used to solve problems and bring value to a company. Personalized suggestions may be made more effective by using this information in marketing and segmentation techniques. Conversion rates may be predicted with more accuracy using machine learning models built on various marketing indicators. 

    By grouping products, the unsupervised learning approach of a machine learning algorithm finds more purchase patterns. 

    A wide range of sectors and applications benefit from machine learning, which improves customer experience and increases the value of investments (ROI). ML may reduce downtime by creating predictions in online searches that provide sensible results.  

    The application of NLP algorithms in AI chatbots enables them to serve as very effective self-learning customer service representatives. In this way, resources may be used more efficiently, and a new channel for consumer analytics can be opened up.

    Trends and potential for a future dominated by machine learning

    Data flywheel, algorithm marketplace, and cloud-hosted intelligence are three themes that are likely to affect the future of machine learning. An algorithm marketplace has been formed by scaling up machine learning algorithms to profit from shared algorithmic knowledge. 

    Machine learning platforms deliver pre-trained models as a subscription service because of the economies of scale.

    Machine learning is projected to be widely used in marketing, finance, and healthcare in the following years.  Predictive suggestions and demand forecasting are made more accessible with this marketing information. Fraud detection and risk management will be made more accessible with the help of machine learning in the financial sector. Machine learning will find its most major use in healthcare, where the outcomes can alter people’s lives.

    Using Machine Learning and Data Science, make data-driven decisions. A certification from Sollers in data science and machine learning will provide you with the skills you need to succeed in any organization’s data science endeavors.

  • Big Data Analytics In Healthcare

    Big Data Analytics In Healthcare

    Data analytics are having a significant impact on the healthcare industry. As the world’s population lives longer on average, current treatment options face substantial hurdles in Clinical Data Science. In reality, healthcare analytics can minimize treatment costs, forecast epidemic breakouts, eliminate avoidable diseases and improve the overall quality of life.

     What Is Big Data In Healthcare?

     In healthcare, massive amounts of previously unmanageable data have been dubbed “big data.” It refers to the vast amounts of patient records and hospital performance data gathered through digital technologies previously uncontrollable by traditional technology.

     In healthcare, the use of big data analytics offers several benefits, including the potential to save lives. Big-style data refers to the massive amounts of data generated by digitalization, aggregated and evaluated by a particular technology. Use in healthcare might help avoid epidemics, treat sickness, or save expenses by using precise health data of a community (or a person).

     Treatment approaches have altered as people live longer, and many of these improvements have been driven by data. For this reason, doctors are always on the lookout for early warning symptoms of sickness to save money by treating it at its earliest possible stage rather than waiting until it is too late. Key performance indicators and healthcare data analytics may prevent illness rather than treat it. 

     Improvements in Staffing Predicted by Patients

     When it comes to implementing big data in healthcare, one of the most common issues shift managers confront is how many personnel to put on duty at any particular moment. If you hire too many people, you may end up paying for them in the long term. 

     EHRs (Electronic Health Records) 

    It’s the most common use of big data in the medical field. Digital records for each patient contain demographics, medical history, and allergies, as well as results of laboratory tests. Records are made available to public and private sector suppliers via secure information systems. Every patient’s medical history is made up of a single, editable file, allowing clinicians to make changes over time without the need for paperwork or the risk of data duplication.

    Alerting in Real-Time

    Real-time alerting is a crucial feature of other healthcare data analytics examples. Clinicians in hospitals can use Clinical Decision Support (CDS) software to help them make prescriptive judgments based on real-time data analysis.

    As a result, clinicians will examine this data in a socio-economic context and change their delivery tactics accordingly by accessing the general public’s health database. Institutions and care administrators will monitor and react to this vast data stream using advanced tools.

    Encouraging Patients to Participate

    Intelligent gadgets that record every step, heart rate, sleep pattern, etc., continuously are already popular with consumers and potential patients. Trackable data may be used to uncover possible health dangers hiding in the background.

     The Clinical Data Science Program will prepare you for the job market with the skills employers want. Obtain the skills employers want. This course aims to teach clinical supervisors how to apply effective learning methods in R, SAS, SQL, Machine Learning, and Tableau to clinical practice.

  • Data science revolutionizes healthcare, but how exactly?

    Data science revolutionizes healthcare, but how exactly?

    Data science in healthcare is no longer a distant prospect. Advances in internet-based medical devices and artificial intelligence are poised to revolutionize clinical research and services. By integrating connected medical devices, patients will aggregate, store, and utilize their data more effectively. Furthermore, AI has been proven to be a key to hospital workflow optimization, cost control, and disease prevention.

    The trend toward big data in medicine and the growing demand for data scientists in the healthcare industry make now a great time to get into data science.

    The following are use cases that have a significant impact on the industry.

    Medicine with precision

    Today, data science can completely transform the way diseases are diagnosed and treated. Here’s how. Information on genetics, healing treatments, and outcomes are included in this section.

    Thus, there will be less risk of harm to the patient or of using inappropriate treatment.

    Also, data regarding a patient’s lifestyle and environmental exposure can determine what triggers certain diseases and conditions. High-performance genome analytics platforms and tools have also revolutionized data science in healthcare.

    Genomics Application Platform will allow hospitals to perform genome sequencing to diagnose and treat genetic diseases and improve the diagnosis and treatment of newborns with complex disorders. That is undoubtedly a crucial step for improving the prevention of illness. Data science’s growing importance in healthcare is also evident from this.

    Optimizing clinic workflow and performance

    Remember the TV show ER from the 90s? As a result of a lack of workflow orchestration, tensions between physicians and staff were frequently high.

    Healthcare facilities often face dynamic and unpredictable conditions.

    Moreover, productivity is challenged by numerous factors. A hospital’s patients’ needs, staff coordination, and resource allocation are a few examples.

    Preventing and controlling healthcare-associated infections

    In addition to handling one of the world’s most serious public health problems, data science plays a critical role. The problem is healthcare-associated infection (HAI). Each year, 100,000 patients in the European Union contract healthcare-associated infections. It leads to thousands of deaths.

    This certification program will prepare you for the workforce with workforce-ready skills. Get the skills employers want. This course for clinical supervisors will examine the effective learning methods in R, SAS, SQL, Machine Learning, and Tableau and how to apply them appropriately in practice.

  • Benefits of Predictive Analysis in Healthcare Industry

    Benefits of Predictive Analysis in Healthcare Industry

    Almost all of the time, the healthcare business is under pressure to achieve greater results than previously. Doctors, nurses, workers, and others must be precise at all times. They are intended to be error-free, but we all know that is impossible.

    Both knowledge and experience have limits. However, artificial intelligence and machine learning in the clinical field (Clinical Data Science) may support and assist the healthcare business in being one step ahead of the competition at all times.

    Hospitals can improve their business operations and employee management. In the healthcare industry, predictive analytics assists clinicians in being proactive rather than reactive when a problem happens. The goal is to avoid or avert the catastrophe rather than to mitigate the harm after it occurs.

    Predictive Analytics:

    Artificial intelligence, machine learning, and the Internet of Things (IoT) can improve medical treatment and empower medical teams to provide extraordinary performance, which sounds exciting. But what is predictive analytics, exactly?

    As the name implies, predictive analytics is a field of advanced analytics that analyses previous data to anticipate future occurrences. To evaluate this past data and develop future insights, AI services, deep learning, machine learning algorithms, data mining, and statistical modeling are employed. For data processing and extraction, unstructured data is organized in an easy-to-understand manner.

    Choosing an Appropriate Location for New Clinics and Hospitals

    It takes a lot of effort to open a new clinic or medical institution. The first step is to choose the ideal location for the business. If management makes a mistake here, it might have ramifications throughout the company, resulting in losses. Predictive analytics may assist management in assessing potential sites based on a variety of characteristics. 

    Predictive analytics in healthcare can show you the benefits and drawbacks of opening a clinic in a specific area by looking at how rivals are performing and examining the site’s accessibility (among other factors).

    Improving Business Operations for Efficient Hospital Administration

    Hospital administration is possibly the most difficult of all. Even minor blunders and misunderstandings might result in life-threatening scenarios. Using sophisticated technology, however, is conceivable. Patients, hospitals, and insurance companies are working together to process claims and minimize issues thanks to predictive analytics in healthcare insurance.

    Identifying the Correct Target Audiences for Clinic Promotion

    As previously said, marketing the clinic is equally as crucial as providing high-quality treatments. The first step is to figure out who your target audiences are. 

    Healthcare firms use predictive analytics to rethink their marketing tactics to target families and audiences that are more likely to respond to commercials.

    Understanding Opportunities For The market growth

    In healthcare, real-time predictive analytics should not be confined to assisting doctors and experts. For a hospital or clinic to be successful, it must do far more than hire professionals. 

    It’s equally as crucial to promoting the healthcare center. How will people know they may go to your physicians for better treatment for their ailments?

    To provide the finest possible services, you must understand market trends, know which areas to invest in, how much to spend in, and how to maximize resource utilization. A certificate course in Clinical Data Science will help to achieve your career goals. 

  • Role of Big Data in Clinical Trials and Drug Safety?

    Role of Big Data in Clinical Trials and Drug Safety?

    Clinical data and its analysis are essential to clinical research. Securing the overall quality of clinical data is eminent to give quality care and relevant decision-making in the pharmaceutical and healthcare fields.

    What establishes clinical data, what should you look for in it, and what sources are there to maintain data and data analysis and ensure their quality?

    Let’s take a look.

    Key to this development transformation is the burgeoning use of big data, which, in simple terms, transfers to the enormous quantity of data gathered over the clinical trial method.

    These vast data come from several sources, together with biomarkers, claims data, pharmaceutical research, electronic health records, patient registries, payer records, genomic sequencing, wearables, medical devices, and clinical trial data. Pharmaceutical and biotech companies can provide this data to increase patient recruitment, clinical trial design, site selection, and overall decision-making.

    Let’s understand how big data can be leveraged to resolve significant difficulties in recruitment and drug development. 

    • Problems on Quantity of data
    • Managing big data to recruit patients
    • Operating data technologies to involve patients and better health outcomes
    • Lessening clinical trial dropout rates with the use of big data
    • Gaining big data available for analysis sooner to minimize costs
    • Knowing big data’s role in the expectation of healthcare

    The need for data in the health care sector is beyond conception. Be it genomics, medical records, imaging data, the list is infinite. The collection of more data leads to an exponential increase in cost. 

    Internet of Things (IoT) consists of a system of interrelated, internet-connected things that can receive and transfer data across a wireless network without human intervention 

    The days of cardiovascular therapies, beta-blockers, antibiotic drugs, and broad-spectrum are long gone. These Days, many pharma companies are looking for a niche market that benefits from very particular therapies. 

    These therapies remain to expand, big data is expected to rise in value with regards to recruitment. 

    Operating data collection technology for specific patient groups

    With the advancement in data collection technology, there is a chance to interact with patients who have particular conditions & diseases on a direct basis. We can describe drugs and therapies which cure a patient’s disease and improves the quality of life. 

    Reducing clinical trial dropout rates

    Reduction in dropout rates in clinical trials has been a big issue. The thing is, the practice itself may not be effective for the patient. Finding the right patient for a fair trial is half the battle. The power of big data can ensure patients are matched with the appropriate test to fit their requirements.

    Obtaining big data ready for analysis sooner to decrease costs

    Do you know most pharma companies are wasting their money by not identifying proper patients at the start of a trial? Moreover, the time it takes to recruit a patient is a long, complicated method. 

    Knowing big data’s role in the future of healthcare

    The ability to leverage and interpret big data will be developed in a workforce of hungry and technologically competent individuals. This method will take time to create the proper foundation to increase big data to its full potential. 

    Sollers designs certificate programs for aspiring students and the people who want to switch their career in the health care sectors.

  • How Artificial Intelligence transforms the Healthcare Industry?

    How Artificial Intelligence transforms the Healthcare Industry?

    In the era of digital innovation, big data, value-based healthcare, and clinical decision support systems have become indispensable for organizations to advance in the Healthcare sector. Data standards have embraced the paradigm shift, but we’re still playing around with how to structure clinical data in the life sciences industry.

    One of the most inspiring things about modern data science in medicine development is Artificial Intelligence. It is making its way to the realm of all applications from scratch to surface. Future of Clinical Data Science is on the verge of a significant transformation due to the convergence of Artificial Intelligence.

    These days, we are generating more clinical records and data than ever before. But the value of this data can only be apprehended if we can analyze, interpret, and apply it. We harness technology and data to enhance time for the discovery and delivery of potential new medicines. Embedding Artificial Intelligence (AI) in Clinical Data Science across our R&D helps our scientists push scientific boundaries and deliver life-changing medicines.

     AI help us decipher this abundance of information with the object of:

    • Obtaining a better knowledge of the diseases we want to treat
    • Recognizing new targets for novel medicines
    • Design and recruiting for better clinical trials
    • Managing personalized medicine strategies
    • Expediting up the way we design, develop and make new drugs 

    But there is a vast scale of data explosion in bioinformatics. To bring such a data crisis to the foreground, many pharmaceutical companies are inclining towards Artificial Intelligence. Clinical Data Science and AI are at work, assisting in analyzing and interpreting medical data more quickly and accurately. With AI, it is easy to identify patterns for creating dynamic patient cohorts of studies. AI is designed to tackle the complex data that has generated from modern clinical care.

    Through Clinical Data Science and AI, it is possible to uncover new biological insights to increase productivity. At the core of many of these improved CDS tools are technologies that have long occupied the minds of healthcare tech enthusiasts is artificial intelligence.  

    The increasing demand for medications is forcing pharmaceutical research and development organizations to adopt computational tools to make research processes more efficient. Get Certified in Clinical Data Science, if you are looking to get into this lucrative field or stand out against it.

    Sollers College is the flagship institution of Life Sciences & IT certificate programs to excel in Clinical Data Science and other courses. Sollers’s mission is to empower students with all the essential skills to turn as working professionals in various disciplines to shape tomorrow’s economy.

  • Why should you opt for a career in the Life Science sector?

    Why should you opt for a career in the Life Science sector?

    Research and development in the pharmaceutical field have always been treated as the heart of the business. There is no surprising factor that the pharmaceutical industry’s roles related to clinical research, drug safety, clinical trial, and data management are evolving and moving as the industry endure to embrace radical transformations. A solid understanding of these fields may put you ahead of the race.

    Need for Clinical talent:

    Are you in the clinical trial field looking for a break? It is worthy to assess the required skills to be on-trend. Optimize your probability of grabbing and sustaining employment in the fundamental and exciting department of work 

    There are specific skills that go with the territory of being in a pharmaceutical role such as deductive reasoning, critical thinking, attentive to detail. With the advent of technological advancements, companies restructure, and business practices, few desirable skills have been made into many employers’ wish lists.

     Is a Clinical Research Associate job for you?

    The primary responsibility of a Clinical Research Associate (CRA) is to monitor clinical trials’ progress. They usually involve visiting sites to ensure that protocols are being conducted in compliance with useful clinical practice guidelines.

    CRAs have highly diverse, visible positions. Mature interpersonal skills are essential to enable them to work well with the staff at the various sites they visit. Being friendly, professional, knowledgeable, punctual, and accessible, combined with a ‘checklist’ mentality, are good fit attributes. As CRAs principally work from home, evidence of being a motivated self-starter may attract employers’ attention.

    With regards to working in Drug Safety:

    At the grassroots level, drug safety experts are liable for the fielding and processing of adverse event records for drugs in the marketplace. It is their responsibility to recognize the risks associated with medicines and to make this information generally available. Like the RA professional, drug safety experts need to understand legislation and regulatory standards while overseeing pharmacovigilance (PV) processes. 

     How about Clinical Data Management?

    Clinical data managers are liable for assuring that clinical trials’ statistical information and results are recorded precisely and maintain complex data systems’ integrity and security. Expertise with IT, software systems, and generally managing vast volumes of data is therefore imperative.

    If you’re keen to seek a career in clinical data science, clinical research, and drug safety and pharmacovigilance but lack specific skills, it would pay to work towards obtaining or acquiring them. Providentially, many skills are transferable, so you don’t necessarily have to gain them in a clinical research setting. Sollers offers certificate and master programs to give you a leading-edge. 

  • The Emphasis of Clinical Data Science in Healthcare sector

    The Emphasis of Clinical Data Science in Healthcare sector

    COVID-19 pandemic is undoubtedly producing a disruptive effect across the world. The healthcare field is at the epicenter of this unprecedented global pandemic confrontation, and the epidemic is likely to cost huge to the health sector. In an era where artificial intelligence and big data play an essential role in healthcare, data sharing has enormous potential for global health improvement and patient safety. It is especially pertinent during the COVID-19 pandemic.

    As the influence of data becomes more intricately tied to the growth of healthcare each year, many pharmaceutical and healthcare companies face a vast range of data management challenges coupled with healthcare and with the opportunities needed to reduce operating expenses and enhance efficiency and performance.  

    The advance of electronic health records (EHR), digital medical imagery, and wearables are contributing to this data explosion. There are several healthcare fields, such as drug discovery, medical imaging, predictive diagnosis, genetics, and several others that make use of data science. The prime and foremost use of data science in the health industry is through medical imaging. With the advent of comprehensive learning technologies in data science, it is feasible to find such microscopic deformities in the scanned images. 

    The rising need for improved and cost-effective healthcare is increasing the demand for clinical data Science. As the security of digital channels and platforms continues to grow, the adoption of clinical data science will continue to gain momentum over the projected period, thus augmenting the growth of this market. A growing focus on cost-cutting and implementing practices to ensure profitability has made Clinical Data Science is highly significant.

    With the help of Clinical Data Science, one can 

    •  Automate the acquisition & streamline and, specification, analysis, and implementation of clinical data.
    • Render end to end support for clinical procedures, from protocol planning to specification to post product launch analysis.
    • Timely, accurate, and authoritative integration and deployment of master data management data in a firm with incompatible systems, procedures, and processes.
    • The ability to blend data standards seamlessly includes metadata regulations into the firm’s firm’s service-oriented architecture layer.
    • Sustain and create a state–of–art MDM architecture that supports not only future growth but also the landscape changes without effective changes, overhead, and that all in a persistent and determined way.

    The life science sector is growing at a fast pace. While Data science has been successfully deployed across multiple industries, pharma is gradually picking up on its transformative potential. A Certification in Clinical Data Science or other life science courses from Sollers enables one to hone practical aspects and acquire research skills to lead a multi-disciplinary clinical research team, for individuals choosing a career in clinical research or already working in the field. Shape your future of healthcare and enhance patient outcomes through a career in Clinical data science.

    Obtain workforce-ready skills with the Sollers Clinical Data Science certification program. Acquire the skills that employers want. Explore the effective learning methods in R, SAS, SQL, Machine Learning, and Tableau and use them adequately in practice with this course for clinical supervisors.

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