We often hear data science mentioned in the same breath as machine learning. While they are distinct, the two fields are also closely intertwined.
This can make it confusing to those on the periphery of the field, leading to uncertainty or hesitation that can inadvertently stymie the work of data science teams. So, how is data science related to machine learning?
Understanding the difference
Data science is the study of data and entails data professionals extracting and analyzing data to solve specific problems or to predict trends. It is a broad discipline interconnected with data analytics, data mining, and other fields such as machine learning. In the latter, data scientists draw inferences from data using fundamental disciplines such as statistics and mathematics for informed decision-making.
It must be pointed out that data science encompasses a rich variety of techniques and methods. For instance, extracting insights and knowledge from data often necessitate tasks such as data cleaning, exploration, and visualization, while the application of statistical and machine learning models are required to make predictions or identify patterns.
Machine learning, on the other hand, is a subfield of artificial intelligence and computer science that involves the development of algorithms and models that can learn from and make predictions without explicit instructions. In a nutshell, while data science often involves the use of machine learning, it also encompasses a wide range of other methods and techniques.
Data science in action
Some simple use of data might revolve around simple data analysis using off-the-shelf tools such as Excel or Tableau, to complex root cause analysis to identify the causes of faults or problems. Generally, businesses turn to data science because they want to prioritize data-driven decisions within their organization.
For instance, data scientists might conduct “A/B” experiments to compare different versions of a product or service to determine which performs better. Or perhaps data from suppliers and customers might be analyzed to improve the flow of logistics, reduce costs, or improve efficiency.
Another example might be the analysis of data to better understand customers’ needs, preferences, and buying patterns to improve customer service or create targeted marketing campaigns. Alternatively, data scientists might analyze customer data to identify different segments of the market so that they can better tailor their products or services to specific groups of customers.
Examples of machine learning models
For its part, machine learning revolves around the application of an algorithm that learns from data to create models that can discern trends and solve problems. This model relies on patterns and relationships from the data to make predictions or decisions without being explicitly programmed to do so.
There are several types of machine learning training methods, which include supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning, among others. One of them, transfer learning, is notable for how a model trained on one task can be used as the starting point for a second, related task – allowing the model to learn more quickly and with less data.
Two well-known machine learning tasks would undoubtedly be computer vision and natural language processing. The former involves the use of machine learning models to analyze images to detect objects and for facial recognition, while the latter entails the training of machine learning models to understand natural language text and speech.
Other uses of machine learning include:
- Anomaly detection: In which a model is trained to highlight unusual or abnormal data points to potentially identify fraudulent transactions.
- Classification: Where a model is trained to classify data into the correct group, such as the ability to differentiate animals such as a dog from a cat.
- Recommender system: Training a model to make personalized recommendations for items or movies, much like what we see with Amazon.com or Netflix.
- Predictive maintenance: Predict when a machine or equipment is likely to fail using ancillary or external data. This allows for the scheduling of preventive maintenance, reducing disruptive outages.
Conclusion
With the increasing amount of data being generated, traditional methods of data analysis are becoming less effective. Machine learning gives us a way to quickly identify patterns and insights in data that would be difficult or impossible to detect by other means. I suppose this makes machine learning an indispensable aspect of data science.
Paul Mah is the editor of DSAITrends. A former system administrator, programmer, and IT lecturer, he enjoys writing both code and prose. You can reach him at [email protected].
Image credit: iStockphoto/Ayman-Alakhras