Tag Archives: Big Data

Role of data science in the healthcare revolution

The healthcare industry is playing a crucial role in fighting COVID-19 Pandemic. This unprecedented crisis has accelerated the implementation of many technological solutions in healthcare that had been long struggling to prove their value. Only those countries that have readily adopted these technological advancements in healthcare are the countries that are doing well in the current pandemic situation. One such technology that has helped the healthcare industry is data science. 

So, how crucial is the role of data science in the healthcare revolution? Here is how data science is shaping the future of the healthcare industry.

Accelerating drug discovery

The process of drug discovery takes years and costs billions before it gets approved. The drug needs to pass through millions of testing procedures over the years until it gets approved. But in most cases, even after investing so much time, money, and effort, the drug may get rejected. But this process can be shortened and made more efficient with the help of data science. Data science and Machine learning algorithms can successfully predict the response and reaction of a certain drug to the body and help the scientists to improve it. Thus, data science in the healthcare sector has proven to be of great help in finding a vaccine for the COVID-19 pandemic. 

Improving diagnostic accuracy and efficiency

Despite having huge amounts of health data at hand, the healthcare industry still suffers from high diagnostic failure rates. Each year, millions of patients are misdiagnosed and are given the wrong treatment. This is where data science comes in. With the help of a deep learning algorithm that can read imaging data such as x-rays, CT scans, etc, data scientists analyze and check the results against an extensive database of clinical reports and studies to deliver more accurate results faster. This is how data science in healthcare helps improve diagnostic accuracy and efficiency.

Optimizing hospital performance

This is another major role of data science in the healthcare industry. Data science combined with predictive analytics is a valuable tool that can help optimize the way in which the hospital or a clinic operates. It can optimize hospital staff scheduling, manage supplies, accounting, and can even build efficient action plans for pandemic outbreaks. Thus, data science helps hospitals make better sense of their data and improves business performance.

Virtual assistance

There can be no better example to explain the role of data science in the healthcare industry than virtual assistance. Data scientists have built digital platforms for patients that give them a personalized experience. These are basically medical applications that identify the patient’s disease by analyzing the data that the patient enters on the application. Based on the symptoms and data, the application will predict the disease, condition of the patient, suggest medication, treatment, and precautions required as per the condition of the patient.

The healthcare industry is swimming in data and data is the new oil. However, in reality, whatever data you have in your hand, it is worth very little if you don’t have highly skilled professionals like data scientists who can derive actionable insights from it. This is why the role of data science in the healthcare industry is crucial. With the world going more digital day by day, the world is in need of data scientists who can study and gain useful information from the trillions of gigabytes of data that are produced each day. This way they could not only help the healthcare industry but also all the other sectors in helping them serve the people better. As a premier business school in India, Praxis offers a 9-month full-time postgraduate program in Data Science. With our vast experience in business education, we offer students both the time to understand the complex theory and practice of data science concepts and the guidance from knowledgeable faculty who are available on campus for mentoring. We also have a well-structured campus placement program that ensures interview opportunities with the most significant companies in the field.

Four Analytics Trends to Keep an Eye On In 2018

Where is Analytics heading in 2018? Does the prominence of AI in our day to day lives, democratization of data and advanced analytics keep you excited? Last year was quite an eventful year, with the rise of self-service analytics, IoT analytics and of course chatbots becoming smarter. Having sensed these developments, 2018 should become another year of accelerated innovation in analytics industry – with some expected and unexpected disruptions of course! Excited? Here is our take on the top four analytics trends to watch out for in 2018!

AI chatbots newbies no more! Soon to become major drivers of all operations! 

“Siri, which movie should I watch tonight? Or ‘’Google, show me the best route to reach office’’ Familiar with these everyday conversations? Just imagine your life without them! Can you? Considering their impact on our busy lives!

In 2017, there was so much noise around smart recommendation, with AI chatbots identifying our emotions and responding to them accordingly. Not limited to weather updates or traffic congestion information, chatbots will evolve and possibly also help in scouring financial operational metrics as well as getting answers to ‘why’ and ‘what if’ questions, thereby enabling the transformation of business as well as consumer space. Although this might take a couple of years to mature, we can anticipate the beginning of the success stories in 2018.

Augmented Reality, from reel to real! Augmented Reality is and will be changing the world around us

Remember in July 2016, how millions of people trampled through parks, walked over graves and entered churches to hunt for augmented-reality versions of Pokémon characters? Although the Pokemon frenzy has faded, the frenzy surrounding augmented reality has not and we can hope to see some more advanced and dynamic mode of AR in 2018. The human-machine interaction will boost as businesses are already employing AR to enhance manufacturing and research processes or to offer new customer experiences. But, why does it matter to analytics industry? According to Gartner’s VP David Cleary, “Augmented analytics is a particularly strategic growing area that uses machine learning for automating data preparation, insight discovery and insight sharing for a broad range of business users, operational workers, and citizen data scientists.” So, yes in few years all the resource draining and time-sensitive analysis will become significantly easier and smoother with augmented analytics!

IoT AnalyticsA silver bullet for every industry, in 2018?

2017, was a year of huge gains in ‘’connectivity’’. There were a lot of investments and adoptions around IoT, despite many security issues. How about 2018? Will it be as exciting as 2017 for IoT analytics? IoT will continue to expand this year too, with more and more devices being connected, almost every second. Although retailhealthcare,and industrial/supply chain industries have been using IoT to boost ROI, this year we can see an increasing number of companies use IoT for more personalized marketing efforts. Additionally, Business Insider predicts business spending on IoT solutions will hit $6 trillion by 2021. Going by these predictions, we will see many venture capitalists continuing to pour funds into the promise of IoT. Thus underscoring its potential to improve customer experience in almost every industry!

Block Chain Technology: Enabling new forms of data monetization

2017 was a year of tremendous growth for block chain. Many believe we are already in the “early majority” phase of adoption, and that we are aligned towards full adoption of blockchain. And as with any new technology, the importance of data keeps growing. This year we might see blockchain going more mainstream with sectors such as healthcare & retail. May also start using blockchain to handle data and to prevent hacking & data leaks. According to Bill Schmarzo, CTO of Dell EMC Services, blockchain technology also “has the potential to democratize the sharing and monetization of data and analytics by removing the middleman from facilitating transactions.” So, yes organizations will accelerate their data analysis process on these virtual currencies to unmask strong trends, frauds, and insights and make informed decisions!

If you want to learn about virtual currency  in details, then read on!

Though it is hard to say how fast these analytics trends will manifest in our lives, we are confident that 2018 will yet again be another eventful year. There will be issues around security, governance and most importantly, the consumers’ ability to accept and adapt these innovations and changes. The only assurance, that the future this year is going to be different but promising and to see how it actually turns out, stay tuned!

Originally Published at Towards Data Science.com

9 Must-Have Skills You Need to Become a Data Scientist

This blog by Mirko Krivanek can can be found here.

Usually I tend to criticize this type of articles, but in this case I agree pretty much agree with BurtchWorks, the author of this article, even though the article is more than 6 months old. Note that BurtchWorks is a recruiting firm that recently posted interesting salary surveys for data scientists.

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Below is the skills list they recommend:

Technical Skills: Analytics

1. Education – Data scientists are highly educated – 88% have at least a Master’s degree and 46% have PhDs – and while there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist. Their most common fields of study are Mathematics and Statistics (32%), followed by Computer Science (19%) and Engineering (16%).

2. SAS and/or R – In-depth knowledge of at least one of these analytical tools, for data science R is generally preferred.

Technical Skills: Computer Science

3. Python Coding – Python is the most common coding language I typically see required in data science roles, along with Java, Perl, or C/C++.

4. Hadoop Platform – Although this isn’t always a requirement, it is heavily preferred in many cases. Having experience with Hive or Pig is also a strong selling point. Familiarity with cloud tools such as Amazon S3 can also be beneficial.

5. SQL Database/Coding – Even though NoSQL and Hadoop have become a large component of data science, it is still expected that a candidate will be able to write and execute complex queries in SQL.

6. Unstructured data – It is critical that a data scientist be able to work with unstructured data, whether it is from social media, video feeds or audio.

Non-Technical Skills

7. Intellectual curiosity – No doubt you’ve seen this phrase everywhere lately, especially as it relates to data scientists. Frank Lo describes what it means, and talks about other necessary “soft skills” in his guest blog posted a few months ago.

8. Business acumen – To be a data scientist you’ll need a solid understanding of the industry you’re working in, and know what business problems your company is trying to solve. In terms of data science, being able to discern which problems are important to solve for the business is critical, in addition to identifying new ways the business should be leveraging its data.

9. Communication skills – Companies searching for a strong data scientist are looking for someone who can clearly and fluently translate their technical findings to a non-technical team, such as the Marketing or Sales departments. A data scientist must enable the business to make decisions by arming them with quantified insights, in addition to understanding the needs of their non-technical colleagues in order to wrangle the data appropriately. Check out our recent flash survey for more information on communication skills for quantitative professionals.

Doing a quick search for becoming a data scientist will provide tons of additional valuable information.

17 Predictions About The Future Of Big Data Everyone Should Read

This article by Bernard Marr was published on www.forbes.com on March 15, 2016.

Almost everyone can agree that big data has taken the business world by storm, but what’s next?  Will data continue to grow?  What technologies will develop around it? Or will big data become a relic as quickly as the next trend — cognitive technology? fast data? — appears on the horizon.

Let’s look at some of the predictions from the foremost experts in the field, and how likely they are to come to pass.

  1. Data volumes will continue to grow. There’s absolutely no question that we will continue generating larger and larger volumes of data, especially considering that the number of handheld devices and Internet-connected devices is expected to grow exponentially.
  2. Ways to analyse data will improve. While SQL is still the standard, Spark is emerging as a complementary tool for analysis and will continue to grow, according to Ovum.
  3. More tools for analysis (without the analyst) will emerge.  Microsoft and Salesforce both recently announced features to let non-coders create apps to view business data.
  4. Prescriptive analytics will be built in to business analytics software. IDC predicts that half of all business analytics software will include the intelligence where it’s needed by 2020.
  5. In addition, real-time streaming insights into data will be the hallmarks of data winners going forward, according to Forrester. Users will want to be able to use data to make decisions in real time with programs like Kafka and Spark.
  6. Machine learning is a top strategic trend for 2016, according to Gartner. And Ovum predicts that machine learning will be a necessary element for data preparation and predictive analysis in businesses moving forward.
  7. Big data will face huge challenges around privacy, especially with the new privacy regulation by the European Union. Companies will be forced to address the ‘elephant in the room’ around their privacy controls and procedures. Gartner predicts that by 2018, 50% of business ethics violations will be related to data.
  8. More companies will appoint a chief data officer. Forrester believes the CDO will see a rise in prominence — in the short term. But certain types of businesses and even generational differences will see less need for them in the future.
  9. “Autonomous agents and things” will continue to be a huge trend, according to Gartner, including robots, autonomous vehicles, virtual personal assistants, and smart advisers.
  10. Big data staffing shortages will expand from analysts and scientists to include architects and experts in data management according to IDC.
  11. But the big data talent crunch may ease as companies employ new tactics. The International Institute for Analytics predicts that companies will use recruiting and internal training to get their personnel problems solved.
  12. The data-as-a-service business model is on the horizon. Forrester suggests that after IBM ‘s acquisition of The Weather Channel, more businesses will attempt to monetize their data.
  13. Algorithm markets will also emerge. Forrester surmises that businesses will quickly learn that they can purchase algorithms rather than program them and add their own data. Existing services like Algorithmia, Data Xu, and Kaggle can be expected to grow and multiply.
  14. Cognitive technology will be the new buzzword. For many businesses, the link between cognitive computing and analytics will become synonymous in much the same way that businesses now see similarities between analytics and big data.
  15. All companies are data businesses now,” according to Forrester.  More companies will attempt to drive value and revenue from their data.
  16. Businesses using data will see $430 billion in productivity benefits over their competition not using data by 2020, according to International Institute for Analytics.
  17. “Fast data” and “actionable data” will replace big data, according to some experts. The argument is that big isn’t necessarily better when it comes to data, and that businesses don’t use a fraction of the data they have access too. Instead, the idea suggests companies should focus on asking the right questions and making use of the data they have — big or otherwise.

Only time will tell which of these predictions will come to pass and which will merely pass into obscurity. But the important takeaway, I believe, is that big data is only going to get bigger, and those companies that ignore it will be left further and further behind.

Pic Credit: Predicting the future of big data (Source: Shutterstock)