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Data Scientists and Machine Learning Algorithms for the Data-Driven World

By   /  January 11, 2018  /  No Comments

machine learning algorithmsArtificial Intelligence (AI) and Machine Learning are projected to become mainstream technologies in the coming years, and are clearly already having a significant impact across many industries. How exactly is this happening? How are Data Scientists using their skills to develop better Machine Learning algorithms? Where are these innovative technologies going in the future?

With the rise in the implementation and usage of once revolutionary technologies/trends like Big Data, the Internet of Things (IoT), or the Cloud, Machine Learning (ML) and now Deep Learning (DL) are gradually moving into mainstream business corridors. Traditional business graduates can now think of becoming a Data Scientists as well, since many University programs are offering these courses as part of their business curriculums. The modern Data Scientist, armed with the power of an open source, algorithm economy, are becoming important parts of numerous organizations around the world.

The Harvard Business Review article titled How AI Fits into Your Data Science Team claims that Artificial Intelligence and Machine Learning will soon take the status of the ICE engine by bringing in sweeping changes to our everyday lives. The transformative power of AI and ML have already been perceived in customer service (digital assistants), in telemedicine (assisted patient care), in banking and finance (robot sales representatives), or in manufacturing (robot assembly-line workers).

So, who are these great scientists and engineers building these fascinating human machines and how exactly are they shaping the algorithms running these machines?

Moving into the Business Office

As AI technologies gradually started moving toward statistics-enriched solutions, the biggest stumbling block that surfaced was limited data. The recent emergence and rise of Big Data, IoT, and Data-Technologies-as-a-Service all jointly contributed to the meteoric rise of Artificial Intelligence and a widespread, packaged Machine Learning algorithm culture.

The biggest beneficiaries of this culture are mainstream business users, who can now begin to accomplish their tasks without the help of a Data Center, and in some cases even Data Scientists. However, the last statement in no way indicates that Data Scientists will soon become redundant. In fact, Data Scientists will be required to intervene when advanced data solutions or unique data solutions must be designed to accomplish complex business goals.

The Forbes blog post titled The Rise of AI Will Force a New Breed of Data Scientists  suggests that the new role of the Data Scientist will be more of an facilitator, rather than that of a data cruncher. The Data Scientist may evolve into Machine Learning expert, stepping in when packaged models fails to deliver.

The Modern Data Scientist’s Arsenal of Skills

Today, it is difficult to think of a Data Scientist without thinking of Machine Learning. In fact, a modern Data Scientist will not even be considered to be a “qualified field specialist” unless he or she is adequately trained in Machine Learning.

The Udacity blog post titled 5 Skills You Need to Become a Machine Learning Engineer lists a number of skills for ML experts. According to this analysis, computer science, software programming, statistics, Data Modeling, using ML libraries, and system design have been identified as core skills for preparing to be an ML expert.

KD Nugget’s post titled 10 Algorithms Every Machine Learning Engineer Should Know discusses many different algorithms important for the Data Scientists to know. Most people know about not necessarily how evolving Machine Learning science has shaped the powerful algorithms running these products behind the scenes.

ML Resources for Data Scientists and Business Users

The article Types of Machine Learning Algorithms You Should Know goes into further depth on more algorithms. This recent article takes the mystery out of learning models in ML, and explains “supervised,” “unsupervised,” and “reinforced” learning in layman’s terms. Any beginner in Machine Learning will find the explanations useful.

In Introduction to Machine Learning Algorithms, discusses introductory discourse on ML and how Machine Learning algorithms aspire to emulate the human brain functions by closely studying available data patterns. The ultimate goal of these smart machines is to explore and discover how the human brain studies, organizes, and interprets information to arrive at conclusions or make future predictions.  The article indicates that the choice of an ML algorithm to apply largely depends on the user’s domain knowledge, available data, and the desired results.

Microsoft’s Azure Machine Learning Platform

On Azure, modern Data Scientists or even business users can gain an understanding of the choice of algorithms available for Advanced Analytics. This platform is somewhat limiting as it contains mainly supervised learning algorithms, but the training sets are easy to follow. A newcomer to the field of Machine Learning will surely enjoy exploring the Azure Machine Learning Studio.

Machine Learning Algorithms: The Smart Choice

Big Data has transformed the role of the ML expert to a large degree. The blog post titled Machine Learning Strategy 7 Steps  suggests that perhaps Data Scientists and ML experts need to work together to complete the entire model building exercise from data acquisition/cleaning to finishing the model.

A large sample of available literature suggests that modern Data Scientists and ML Engineers need to be as technically qualified as business savvy to succeed in designing real-world solutions for the business world. The DATAVERSITY® article Machine Learning Algorithms Today: Usage Results, demonstrates that Machine Learning algorithms, by virtue of their ability to learn from data, can significantly improve on the traditional Analytics and forecasting capabilities.

Roles of ML and DL in Shaping Algorithms

As the broad objective of AI has been to make machines as smart as humans, Machine Learning and Deep Learning have played a major role in achieving those objectives. With the mainstreaming of Big Data and rise of sensor-aided streaming data, the sheer volume of available data has helped ML algorithms to prosper and continuously improve on the existing models. Here is an involved discussion on algorithms for ML engineers, which every aspiring ML engineer or Data Scientist should read. The article also guides an engineer on when to select a Decision Tree, a Random Forest, or a Cluster Analysis.

The Limitation of Machine Learning Algorithms

No matter how advanced or smart the ML algorithms become over time, these machines will still need human intervention in particular situations. The open sourcing of algorithms by the largest players in this space has brought Machine Learning to mainstream businesses. However, the accuracy of results and the quality of the training data still depend on the human interface, which is hard to ignore.

 

Photo Credit: Khakimullin Aleksandr/Shutterstock.com

 

About the author

Paramita Ghosh has over two and a half decades of business writing experience, much of which has been writing for technology and business domains. She has written extensively for a broad range of industries, including but not limited to data management and data technologies. Paramita has also contributed to blended learning projects. She received her M.A. degree in English Literature in 1984 from Jadavpur University in India, and embarked on her career in the United States in 1989 after completing professional coursework. Having ghostwritten and authored hundreds of articles, blog posts, white papers, case studies, marketing content, and learning modules, Paramita has included authorship of one or two books on the business of business writing as part of her post-retirement projects. She thinks her professional strength is “lifelong learning.”

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