Renowned Data Science Personalities

With the advancement of big data and artificial intelligence, the need for its efficient and ethical usage also grew. Prior to the AI boom, the main focus of companies was to find solutions for data storage and management. With the advancement of various frameworks, the focus has shifted to data processing and analytics which require knowledge of programming, mathematics, and statistics. In more popular terms, this process today is known as Data Science. Few names stand out and have a separate base of importance when the name data science comes into the picture, largely due to their contributions to this field and who have devoted their life and study to reinvent the wheel. Let’s talk about some of the best data scientists in the world.


Andrew Ng

Andrew Ng is one of the most prominent names among leaders in the fields of AI and Data Science. He is counted among the best machine learning and artificial intelligence experts in the world.  He is an adjunct professor at Stanford University and also the co-founder of Coursera. Formerly, he was the head of the AI unit in Baidu. He is also an enthusiast researcher, having authored and co-authored around 100 research papers on machine learning, AI, deep learning, robotics, and many more relevant fields. He is highly appreciated in the group of new practitioners and researchers in the field of data science. He has also worked in close collaboration with Google on their Google Brain project. He is the most popular data scientist with a vast number of followers on social media and other channels.

DJ Patil

The Data Science Man, DJ Patil, needs no introduction. He is one of the most famous data scientists in the world. He is one of the influencing personalities, not just in Data Science but around the world in general. He was the co-coiner of the term Data Science. He was the former Chief Data Scientist at the White House. He was also honored by being the former Head of Data Products, Chief Scientist, and Chief Security Officer at LinkedIn. He was the former Director of Strategy, Analytics, and Product / Distinguished Research Scientist at eBay Inc. The list just goes on.

DJ Patil is inarguably one of the top data scientists around the world. He received his PhD in Applied Mathematics from the ‘University of Maryland College Park’.

Kirk Borne

Kirk Borne has been the chief data scientist and the leading executive advisor at Booz Allen Hamilton since 2015. Working as a former NASA astrophysicist, he was part of many major projects. At the time of crisis, he was also called upon by the former President of the US to analyze data post the 9/11 attack on the WTC in an attempt to prevent further attacks. He is one of the top data scientists to follow with over 250K followers on Twitter.

Geoffrey Hinton

He is known for his astonishing work on Artificial Neural Networks. Geoffrey was the brain behind the ‘Backpropagation’ algorithm which is used to train deep neural networks. Currently, he leads the AI team at Google and simultaneously finds time for the ‘Computer Science’ department at the ‘University of Toronto’. His research group has done some overwhelming work for the resurgence of neural networks and deep learning.

Geoff coined the term ‘Dark Knowledge’.

Yoshua Bengio

Having worked with AT&T & MIT as a machine learning expert, Yoshua holds a Ph.D. in Computer Science from McGill University, Montreal. He is currently the Head of the Montreal Institute for Learning Algorithms (MILA) and also has been a professor at Université de Montréal for the past 24yrs.

Yann LeCun

Director of AI Research at Facebook, Yann has 14 registered US patents. He is also the founding director of NYU Center for Data Science. Yann has a PhD in Computer Science from Pierre and Marie Curie University. He’s also a professor of Computer Science, Neural Science and the Founding Director of the Data Science Center at New York University.

Peter Norvig

Peter Norvig is a co-Author of ‘Artificial Intelligence: A Modern Approach’ and ‘Paradigms of AI Programming: Case Studies in Common Lisp’, some insightful books for programming and artificial intelligence. Peter has close to 45 publications under his name. Currently the ‘Engineering Director’ at ‘Google’, he has worked on various roles in Computational Sciences at NASA for three years. Peter received his Ph.D. from the ‘University of California’ in ‘Computer Science.’

Alex “Sandy” Pentland

Named the ‘World’s Most Powerful Data Scientist’ by Forbes, Alex has been a professor at MIT for the past 31 years. He has also been a chief advisor at Nissan and Telefonica. Alex has co-founded many companies over the years some of which include Home, Sense Networks, Cogito Corp, and many more. Currently, he is on the board of Directors of the UN Global Partnership for Sustainable Data Development.

These are some of the few leaders from a vast community of leaders. There are many unnamed leaders whose work is the reason why you have recommender systems, advanced neural networks, fraud detection algorithms, and many other intelligent systems that we seek help to fulfill our daily needs.

Data Science in the Chemical Industry

Data science and analytics is such an evergreen field that finds its use in every industry. Today the world is moving towards automation, and even the chemical industry is starting to adopt such practices and thus the use of data science in the chemical industry has increased significantly. Every experiment starts from a simulation of a process in the laboratory and data science and modeling helps in scaling it from the lab scale to a plant scale. So, let us dive deep into understanding how data science can be applied to chemical engineering.

For example, a lot of times, the chemical industry is full of recording errors. Error in recording parameters may hamper various simulations and processes. In such cases, data science and analytics in the chemical industry provides a significant advantage. A few major advantages of using industrial data science techniques are:

  • It helps in quickly identifying trends and patterns, which is an essential requirement for the chemical industry to recheck an observation.
  • It leads to reduced human effort, which means fewer chances of errors and reduced cost.
  • As data Science handles multi-dimensional and multi-variety data, things can be done in a dynamic and uncertain environment.
  • Observing calculations to estimating the number of chemicals required for a reaction, holds the capacity to benefit the industry.

Considering the above points in mind, we can clearly state that analytics can not only boost production but can also reduce and cut-off unprofitable production lines that are not of any use, helping in both – reduced energy consumption and reduced wastage of valuable resources like labor and time.

Stan Higgins, the retired CEO of the North East of England Process Industry Cluster (NEPIC), who currently is a non-executive director at the Industrial Technology Systems (ITS) and also a senior adviser to Tradebe, which is waste management and specialty chemical company, says that miracles can be done using analytics in chemical industry. He describes that his work accompanied by data analytics led him to win the Officer of the Order of the British Empire (OBE) for the work promoting the UK’s process manufacturing industry. He describes that in production, the challenges are never-ending.

 

The key to any successful venture is maintaining quality production and maximizing output within health, safety, and environmental goals. Every day, new chemicals, and intermediates are being developed in chemical industries, and it requires a lot of attention for a human being, considering all processes like cost, availability, quantity, and then being able to decide the most suitable chemical product and alternative on a daily basis. The chances of error are very high, and it can be crucial to the industry.

What are some of the other uses of data science and analytics in the chemical industry?

  • Use for checking the overall value of an alternative chemical, over the currently being used chemical.
  • It can help in determining precise and essential measurements for the reactivity of chemicals, checking for their optimum conditions that are favorable.
  • It can help in understanding the best reactivity of a catalyst for the different conditions of temperature, pressure, and other conditions.
  • It helps in guessing a pre-determined result after a reaction.

Concluding, it won’t be inappropriate to say that there isn’t a field where data science and analytics can’t find its application. For large industries, business intelligence plays a key role in promoting growth. So, analytics and BI in chemical industries can bring about huge improvements over a period of time.

Impact of AI in the Telecom Industry

THEN AND NOW

Gone are the days when the telecom industry used to be involved solely in providing basic phone and internet services. The telecom industry trends show that the future of the Telecom Industry is AI-driven. With the bloom of Artificial Intelligence, this particular industry has seen exceptional technological growth.  (Read – Telecoms have unique challenges in adopting AI). The use of AI in the Telecom industry is booming. And it is still growing, with experts expecting it to grow at least 42% by this year.

One move that could transform this sector would be to leverage AI’s ability to engage in active learning while analyzing very large amounts of data collected from their massive customer base.

Where does this data come from?

This data is culled from devices, networks, mobile applications, geolocations, detailed customer profiles, services usage, and billing data. Harnessing this Big data through AI could open up a wide range of uses across management and operations departments.

In a majority of companies, the role of AI in the telecom industry has been limited to chatbots that are automating customer service inquiries, routing customers to the proper agent, and routing prospects with buying intent directly to salespeople.

However, it is also possible to provide better customer experiences, improve operations, and increase revenue through new products and services by gaining actionable insights from data collected. Through the use of AI in the telecom sector, operators can improve network efficiency; lower operating costs, and improve both the quality of service and customer experience.

Tom Anderson, a Principal Technologist at Atis, writes that as operators transition their network architectures with software-defined networking and virtualization technologies that enable automation, AI will leverage these capabilities to self-diagnose, self-heal and self-orchestrate the network.

He says that through the use of algorithms that look for patterns, AI will be able to both detect and predict network anomalies, enabling operators to proactively fix problems before customers are impacted.  This pattern-recognition capability is particularly useful for network security as AI will be able to help identify suspicious activity related to potential security threats, allowing the network to “take-action” in real-time before it impacts network performance.

From a subscriber’s intelligence perspective, AI will allow operators to collect, store, and analyze data from across an operator’s entire customer base to achieve real-time behavioral insights. Their social media, brand coverage, customer sentiments, and other telecom industry benchmarks could also be analyzed to learn what drives customers to the service provider and what drives them to leave.

The information thus gained can be combined with machine learning algorithms to make personalized recommendations based on a user’s behavioral patterns and content preferences. Relevant up-sell and cross-sell offers to the right users at the right time can be made. Data could be analyzed and the call & data package that best suits different types of users can be offered, increasing the sales success rate. AI and machine learning could also be used in detecting and fixing potential issues for market customers of the telecom sector even before they’re apparent to the end-user.

Big data will be essential for operators to achieve better utilization of network resources, allowing the network to adjust services based on user needs, environmental conditions, and business goals resulting in better network optimization.

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