Welcome to Demystifying Artificial Intelligence, a Movius blog post series where we look at Artificial Intelligence through the lens of practical business applications. We discussed Machine Learning in the last post. Today, we discuss Data Science.
What is Data science?
Data science includes analytic techniques for interpreting data to explain phenomena, predict the outcome of decisions, and solve problems. Organizations have always used data and statistics to solve problems. However, because we have so much more data now generated by the Internet, data is playing a much more significant role. New techniques, such as developed in machine learning, are also needed to work with such huge amounts of data.
Data scientists are experts in extracting insights and meaning from data, using a variety of tools, algorithms, and techniques. They are responsible for collecting, cleaning, and preparing data. They also develop and test the algorithms and models produced by other team members.
What kind of problems are best to solve with machine learning and data science?
You need a sufficiently complex and constantly changing problem. If the problem is too simple, or too static, then you can probably apply traditional techniques to solve the problem.
You also need enough data. There is no set answer to how much data you need, as it’s highly contextual. But to get the most out of machine learning and data science, you need “big data”, a high volume of data with sufficient variety.
How do data scientists work with other members of the organization?
Data science teams must operate cross-functionally to contribute important insights needed by the organization.
How do data scientists work with machine learning engineers?
Data scientists and machine learning engineers work together in the development and implementation of AI algorithms and systems. Data scientists prepare and analyze the data that trains machine learning models, while machine learning engineers design and implement the models.
Once the model is trained, the data scientist and machine learning engineer work together to evaluate the performance of the model, looking at its accuracy, precision, and recall. They then work together to improve the model.
How do data scientists work with the analytics team?
Data scientists and analytics teams work together to establish success metrics and reporting metrics. We’ll discuss more about the analytics team in a later post.
How do data scientists work with the design team?
Data scientists must work with design teams to ensure the best user experience. Data scientists often identify patterns that design teams can use to improve the product. These teams will also collaborate on usability testing.
How does a business collect data?
There are many ways businesses can collect data:
- Web and social media, such as site traffic and customer interactions
- Sales and customer data, such as purchase history and demographics
- Sensors and IoT data
- 3rd party sources, such as market research firms and government agencies
- User generated data, such as surveys and feedback forms
Businesses must consider the quality, relevance, and reliability of the data they collect to ensure that it is suitable for AI applications.
Where can I learn more?
Satish Medapati is the technical reviewer for Capitalizing Data Science: A Guide to Unlocking the Power of Data for Your Business and Products by Mathrangi Sri Ramachandran. Mathrangi has built data science teams for CitiBank, HSBC, GE, and startups. This highly valuable text simplifies data science and helps founders understand what problems it solves and how to set up data science teams.
We hope this helped you understand the role Data Science plays in Artificial Intelligence. Come back after the holidays for the next one. We will discuss the role of Natural Language Processing in Artificial Intelligence. If you want to be notified, please subscribe for updates! Learn more about our products at https://www.movius.ai/.
Satish Medapati – Head of AI and Data Solutions
Melanie Allen – Product Marketing Content Writer