Before we discuss the marriage of Machine Learning and Data Science, we need to understand them as individuals. Historians of Machine Learning say:
“Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed.”
— Arthur Samuel, 1959
“A computer program is said to learn from experience (E) with respect to some task (T) and some performance measure (P), if its performance on (T), as measured by (P), improves with experience (E).”
— Tom Mitchell – Carnegie Mellon University, 1997
“The goal of Machine Learning is never to make ‘perfect’ guesses, because Machine Learning deals in domains where there is no such thing. The goal is to make guesses that are good enough to be useful.”
— George E. P. Box, 1987
Two of the most widely adopted machine learning methods are Supervised Learning and Unsupervised Learning. Supervised Learning algorithms are trained using labeled examples, such as an input where the desired output is known. The learning algorithm receives a set of inputs, along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors. The algorithm then modifies the model accordingly. Supervised learning is commonly used in applications where historical data predicts likely future events. Unsupervised learning, on the other hand, is used against data that has no historical labels. The system isn’t told the “right answer.” The algorithm must figure out what is being shown. The goal is to explore the data and find some structure.
Machine Learning is designed to solve real-world problems via complex statistics while factoring in predictor functions—sometimes called “the hypothesis.”
Here are some examples of Machine Learning by industry:
Increasing Customer Satisfaction in Financial Services
By analyzing user activity, “smart machines” can track spending patterns and customer behavior to offer tailored financial advice, and even spot a potential account closure before it occurs.
Improving Conversion Rates in Ecommerce
Retail prices fluctuate over time. “Smart machines” help ecommerce companies track fluctuation patterns and set prices according to demand. In addition, “smart machines” can design a better shopping experience by analyzing behaviors and actions that signify intent.
Improving Data Security in Government
New malware code tends to overlap 2 – 10% with previous versions. As a result, “smart machines” can predict malware with greater accuracy, find patterns in how cloud data is accessed, and report anomalies that could indicate security breaches. “Smart machines” can also help detect fraud and minimize identity theft.
Machine Learning has made a significant contribution to the enterprise in the just the last couple of years. In the past, we relied on statisticians to create a few new models per week. Today’s “smart machines” generate thousands of models weekly, dramatically accelerating the improvement of our businesses.
What is Data Science?
Now let’s look at Data Science. Data Science are methods, processes, algorithms and systems used to extract insights from either structured or unstructured data. The primary responsibilities of a Data Scientist involve data-cleansing: preparing and aligning data in order to extract insights and information.
In contrast to Machine Learning, Data Science requires human interaction and critical thinking in order to drive business decisions and effect business outcomes.
Data Science is founded upon the following principles:
- Business domain knowledge
- Statistics and algorithms
- Computer science and software programming
- Written and verbal communication skills
The typical goals and deliverables associated with data science initiatives address a specific goal and/or solve a specific problem. And while the data-science process varies based on complexity, resources and technology, the following steps provide a guideline towards a successful data science initiative at your company:
- Identify the specific business problem and/or business goal you’re trying to solve and define the deliverable(s) for this data-science initiative.
- Define your data requirements: acquisition, collection, integration and maintenance.
- Define the approach to maximize results, including the ability to write new algorithms and/or significantly modify existing ones.
- Perform initial queries to many databases and data sources (RDBMS, NoSQL, NewSQL), as well as integrate the data into an analytics-driven data source (e.g. multi-dimensional database, warehouse, data lake).
- Ensure data has high integrity (good data), quality (the right data), and is in optimal form and condition to guarantee accurate and reliable results.
- Select and implement the best tooling, algorithms, frameworks, languages, and technologies to maximize results and scale as needed.
- Work cross-functionally, effectively, and in collaboration with all departments and groups.
- Distinguish good from bad results, and highlight the potential risks and financial losses of subsequent decisions.
- Identify the required business decisions and/or process changes made based on the results.
- Deliver, communicate, and/or present final results.
The role and organizational maturity of data science and machine learning varies from one organization to the next. In this blog, we’ve highlighted how current industry trends work together to broaden insight into your business, advance the state of the art of computer science and provide unique benefits for society. Finally, Anexinet can help your organization develop a Machine Learning strategy aligned with your business drivers, and proven for success. Just give us a call. Our Machine Learning Strategy Kickstart may be just the boost your business has been looking for.
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