Data science platform marketis estimated to rise with a CAGR of 31.1% by generating a revenue of $224.3 billion by 2026. Asia-Pacific holds the highest growth rate, expecting to reach $80.3 billion during the forecast period.

Data science is the preparation, extraction, visualization, and maintenance of information. Data science uses scientific methods and processes to draw the outcomes from the data. With the help of data science tools and practices one can recognize the data patterns. The person dealing with data science tools and practices uses meaningful insights from the data to assist the companies to take the necessary decision. Basically, data science helps the system to function smarter and can take autonomous decisions based on historical data.

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Many companies have a large set of data that are not being utilized. Data science is majorly used as a method to find specific information from a large set of unstructured and structured data. Concisely, data science is a vast and new field which helps to build, asses and control the data by the user. These analytical tools help in assessing business strategies and taking decisions. The rising use of data analytics tools in data science is considered to be major driving factor for the data science platform market.

Data science is mostly used to find hidden information from the data so that business decisions and strategies can be conceived. If the data prediction goes wrong, business has to face a lot of consequences. Therefore, professional expertise are required to handle the data carefully. But as the data science platform is new, the availability of the workforce with relevant experience is considered to be the biggest threat to the market.

Service type is predicted to have the maximum growth rate in the estimated period. Service segment is projected to grow at a CAGR of 32.0% by generating a revenue of $76.0 billion by 2026. Increasing difficulties in terms of operational work in many companies and rising use of Business Intelligence (BI) tools are predicted to be major drivers for the service type segment.

Manufacturing is predicted to have the highest growth rate in the forecast period. Data scientists have acquired a key position in the manufacturing industries. Data science is being broadly used for increasing production, reducing the cost of production and boosting profit in manufacturing area. Data science has also helped the companies to predict potential problems, monitor the work and analyze the flow of work in the manufacturing work area. Manufacturing segment is expected to grow at a CAGR of 31.9% and is predicted to generate a revenue of $43.28 billion by 2026.

North Americas has the largest market size in 2018. North America market is predicted to grow at a CAGR of 30.1% by generating a revenue of $80.3 billion by 2026. The presence of large number of multinational companies and rising use of data with the help of analytical tools in these companies gives a boost to the market in this region. Asia-Pacific region is predicted to grow at a CAGR of 31.9% by generating a revenue of $48.0 billion by 2026. Asia-Pacific is accounted to have the highest growth due to increasing investments by companies and the increased use of artificial intelligence, cloud, and machine learning.

The major key players in the market are Microsoft Corporation, Altair Engineering, Inc., IBM Corporation, Anaconda, Inc., Cloudera, Inc., Civis Analytics, Dataiku, Domino Data Lab, Inc., Alphabet Inc. (Google), and Databricks among others.

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What will be the growth scenario of Data Science Platform Market? by 2020-2026? SoccerNurds - SoccerNurds

When I started my pediatric genetic practice over 20 years ago, I was frustrated by constantly having to tell families and patients that I couldnt answer many of their questions about autism and what the future held for them. What were the causes of their childs particular behavioral and medical challenges? Would their child talk? Have seizures? What I did know was that research was the key to unlocking the mysteries of a remarkably heterogeneous disorder that affects more than five million Americans and has no FDA-approved treatments. Now, thanks in large part to the impact of genetic research, those answers are starting to come into focus.

Five years ago we launched SPARK ( Simons Foundation Powering Autism Research for Knowledge) to harness the power of big data by engaging hundreds of thousands of individuals with autism and their family members to participate in research. The more people who participate, the deeper and richer these data sets become, catalyzing research that is expanding our knowledge of both biology and behavior to develop more precise approaches to medical and behavioral issues.

SPARK is the worlds largest autism research study to date with over 250,000 participants, more than 100,000 of whom have provided DNA samples through the simple act of spitting in a tube. We have generated genomic data that have been de-identified and made available to qualified researchers. SPARK has itself been able to analyze 19,000 genes to find possible connections to autism; worked with 31 of the nations leading medical schools and autism research centers; and helped thousands of participating families enroll in nearly 100 additional autism research studies.

Genetic research has taught us that what we commonly call autism is actually a spectrum of hundreds of conditions that vary widely among adults and children. Across this spectrum, individuals share core symptoms and challenges with social interaction, restricted interests and/or repetitive behaviors.

We now know that genes play a central role in the causes of these autisms, which are the result of genetic changes in combination with other causes including prenatal factors. To date, research employing data science and machine learning has identified approximately 150 genes related to autism, but suggests there may be as many as 500 or more. Finding additional genes and commonalities among individuals who share similar genetic differences is crucial to advancing autism research and developing improved supports and treatments. Essentially, we will take a page from the playbook that oncologists use to treat certain types of cancer based upon their genetic signatures and apply targeted therapeutic strategies to help people with autism.

But in order to get answers faster and be certain of these results, SPARK and our research partners need a huge sample size: bigger data. To ensure an accurate inventory of all the major genetic contributors, and learn if and how different genetic variants contribute to autistic behaviors, we need not only the largest but also the most diverse group of participants.

The genetic, medical and behavioral data SPARK collects from people with autism and their families is rich in detail and can be leveraged by many different investigators. Access to rich data sets draws talented scientists to the field of autism science to develop new methods of finding patterns in the data, better predicting associated behavioral and medical issues, and, perhaps, identifying more effective supports and treatments.

Genetic research is already providing answers and insights about prognosis. For example, one SPARK familys genetic result is strongly associated with a lack of spoken language but an ability to understand language. Armed with this information, the medical team provided the child with an assistive communication device that decreased tantrums that arose from the childs frustration at being unable to express himself. An adult who was diagnosed at age 11 with a form of autism that used to be known as Aspergers syndrome recently learned that the cause of her autism is KMT2C-related syndrome, a rare genetic disorder caused by changes in the gene KMT2C.

Some genetic syndromes associated with autism also confer cancer risks, so receiving these results is particularlyimportant. We have returned genetic results to families with mutations in PTEN, which is associated with a higher risk of breast, thyroid, kidney and uterine cancer. A genetic diagnosis means that they can now be screened earlier and more frequently for specific cancers.

In other cases, SPARK has identified genetic causes of autism that can be treated. Through whole exome sequencing, SPARK identified a case of phenylketonuria (PKU) that was missed during newborn screening. This inherited disorder causes a buildup of amino acid in the blood, which can cause behavior and movement problems, seizures and developmental disabilities. With this knowledge, the family started their child on treatment with a specialized diet including low levels of phenylalanine.

Today, thanks to a growing community of families affected by autism who, literally, give a part of themselves to help understand the vast complexities of autism, I can tell about 10 percent of parents what genetic change caused their childs autism.

We know that big data, with each person representing their unique profile of someone impacted by autism, will lead to many of the answers we seek. Better genetic insights, gleaned through complex analysis of rich data, will help provide the means to support individualschildren and adults across the spectrumthrough early intervention, assistive communication, tailored education and, someday, genetically-based treatments. We strive to enable every person with autism to be the best possible version of themselves.

This is an opinion and analysis article.

Read more:

How Big Data Are Unlocking the Mysteries of Autism - Scientific American

Today, most drug discovery programmes begin with the identification and validation of disease modifying biological targets. The primary way to uncover these targets is through searching and reviewing published scientific literature. Eric Gilbert explores how data science techniques like text mining are speeding up research into areas such as pancreatic cancer.

The increasing rate of scientific publishing only made more apparent in 2020 with the flood of important and necessary COVID-19 research has made the task of sifting through literature extremely difficult and time consuming.

Between just February and May 2020, submissions to Elseviers journals alone went up by 58%compared to the same period in 2019. As a result, many researchers and organisations are relying more on digital technologies and data science techniques to help break down and digest the information available. Within this area, text mining is of particular interest because it enables researchers to retrieve highly specified information from unstructured content which means they can more quickly find meaningful answers to complex research questions.

Text mining can aid the search for new and innovative therapies for many unmet needs where there are large volumes of published literature for example, oncology. Pancreatic cancer is the third leading cause of cancer death in the United States and the five-year survival rate differs dramatically depending on the stage of the cancer at diagnosis. As such, it is a disease where we urgently need new and innovative therapies to not only treat the cancer but to improve early diagnosis rates.

Text mining was also able to highlight new insights into the mechanisms of pancreatic cancers immune evasion, which refers to cancer cells ability to evade an immune response

To explore how data science techniques like text mining can accelerate research into an area like pancreatic cancer, Elsevier developed a new research report using text mining to identify the emerging trends in pancreatic cancer literature over the last two years. The analysis has uncovered a number of research trends and points towards potential new research areas for the development of therapeutics.

Understanding more about the disease and what determines poor patient outcomes

The results of the text mining report bring light to recently trending protein and genomic terms with a semantic relationship to pancreatic cancer, showing the emergence in research in both X-inactive specific transcript (XIST) and lincoo511 (fig.1.). These are both long non-coding RNAs, which have attracted attention in the past two years due to their involvement in several cancers including pancreatic. They have the potential to be not only diagnostic/prognostic biomarkers but also targets of pharmaceutical intervention.

Figure 1: Trending Protein and Genomic terms with a semantic relationship to pancreatic cancer

Text mining was also able to highlight new insights into the mechanisms of pancreatic cancers immune evasion, which refers to cancer cells ability to evade an immune response. Currently, while progress has been made with the discovery of checkpoint inhibitors, clinical results for immune monotherapies have been disappointing for pancreatic cancer. This is in part due to the immunosuppressive nature of the tumours, however, recent revelations in the mechanism of immune evasion could provide disease-modifying therapeutic targets.

Potential areas for further research

Within the analysis of trending terms, ferroptosis was also highlighted in relation to biological functions. The induction of ferroptosis, a type of regulated cell death, is being looked at as a new strategy for pancreatic cancer treatment. This was only discovered in 2012 and is a currently active area of research within pancreatic cancer.

Finally, there is increasing evidence that propofol has biological effects that may alter the progression of pancreatic cancer. Propofol has been used for more than 30 years as an anaesthetic during medical procedures; more recently it has been found to interact with non-coding RNAs and to modulate immune function and a number of signaling pathways. Furthermore, propofol has been shown to suppress autophagy and enhance the activation of T helper cells. While propofol is unlikely to be used therapeutically, it is helping our understanding of pancreatic cancer and cancer in general.

How data science and analysis of research can progress future innovations

We have seen data science techniques applied to many other industries to accelerate innovation and find faster answers. For example, in financial services to help sift through regulatory reports and company filings; in retail to identify trends and analyse consumer decision making; and in entertainment to analyse customer preferences and to make recommendations. Life sciences is considerably more complex, but it can follow suit to help researchers stay current with the ever-expanding scientific literature. It is essential to help equip the R&D sector with a greater depth of information to help battle diseases and tackle unmet needs.

With COVID-19 still impacting the capacity of laboratories, being able to maximise the knowledge in already existing research is more important than ever. It also prevents work being duplicated and empowers future R&D decisions. In terms of pancreatic cancer, current research trends show hope for the development of new treatments and diagnostics. Ferroptosis and immune evasion are highly relevant to pancreatic cancer and remain areas where further research is required. Data science techniques, including text mining, will have an important role to play in further breakthroughs.

About the author

Eric Gilbert is a life sciences consultant at Elsevier. He is an accomplished medicinal chemistry research scientist with over 15 years of experience in drug discovery at Pfizer, Schering-Plough, and Merck. He is author or coauthor of 16 publications and an inventor for 22 issued US patents. Eric possesses a unique combination of synthesis and drug discovery experience along with an extensive data science skill set.

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The role of data science and text mining in the search for new therapies - - pharmaphorum

A data scientist collects and cleans large amounts of data, maintains dashboards, interprets data to solve problems, run experiments, build algorithms and presents visualized data to stakeholders. If all that interests you, heres some news for you, you can become a data scientist without experience.

Although most of the job postings that you will come across will mention a masters degree or a Ph.D. in engineering, computer science, mathematics, or statistics, its possible to land a job without any of that. There are plenty of online courses and certification programs that can give you the knowledge.

If you have a quantitative background, the switch from your old job to data science should be easy. But before jumping on to high-tech tools, getting the basics right, like plotting data points on graphs and finding correlations, is important. As a checklist, these are the things you should build a solid base on:

To be a data scientist, its important to know and master the necessary skills rather than getting a shiny degree from a university. The interview process is skill-based and these are the languages you need to master:

Companies look for people with practical experience. Once you have the basic knowledge puting that to work in real-life and dealing with work problems will make your case stronger and impress recruiters with real-time skills. These internships are easy to find as the criteria for internships start with no-basic experience.

Firstly, a data scientist and a data analyst are two different professions. Data analysts manage data collection and identify data trends, while data scientists also interpret data along with using coding and mathematical modeling. Hence, a data analyst role is the best way to launch yourself in the field.

Data science is a booming field and many might be having the idea to switch due to lucrative job roles. However, you need to be able to explain your career transition. Mention your past roles in such a way that you highlight the common aspects of the field. If you are a pro at using Microsoft Excel or developed business, communication, and collaborative skills, mention those skills and explain how you have improved on them to apply in this job.

With all these in mind, you can become a data scientist without experience. Another important thing to keep in mind is to network with people who can influence your position in this field. The more you network, the more opportunities will knock your door.

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This Is the Only Way to Become A Data Scientist Without Any Experience - Analytics Insight