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Machine Learning vs. Data Science – Key Differences and Similarities You Should Know

As organizations strive to make the most out of the data they collect, they turn to different techniques for generating insights from vast data sets. The market for data science and analytics is on the rise – according to IMB, the number of jobs for all US data professionals will increase by 364,000 openings to reach smashing 2,720,000 by 2020!

Machine Learning and Data Science are two tech buzzwords that have everyone's attention today. But when does each of these approaches bring best results? Which projects stand to benefit most from Machine Learning (ML) and which ones from Data Science techniques?

Read on to find out what the key differences and similarities between ML and Data Science are and why it's a good idea to pick ML for your project.

What is Data Science?

Data Science is an umbrella term for various techniques and methods that help data scientists transform data into actionable insights, which may have a massive impact on the bottom line of organizations. To extract valuable information from data and solve complex analytical problems, Data Science approaches combine data analysis, algorithmic development, and different data analytics techniques and technologies. The primary objective of Data Science is generating information that brings organizations tangible business value.

What is Machine Learning?

Machine Learning is a collection of methods whose purpose is to provide software with the ability to learn. Machine Learning deals with massive sets of data by developing algorithms that can learn by themselves, without being programmed at each and every step of the way. ML algorithms generate actionable insights on their own, responding to changes reflected in their data sets (for example, the changing patterns in consumer choices).

What do Data Science and Machine Learning have in common?

Machine Learning is a part of Data Science, an area that deals with statistics, algorithmics, and similar scientific methods used for knowledge extraction. Each of these Data Science techniques has something else to offer in bringing organizations insights.

The advantage of Machine Learning is that engineers can apply it in the Data Mining (analysis) stage, as well as in the production environment; for example, in an automated decision-making system or a recommendation engine that brings organizations value.

Machine Learning vs. Data Science

Engineers can use ML models to replace complex, explicitly-coded decision-making processes by providing equivalent or similar procedures learned in an automated manner from data. ML offers smart solutions for organizations that want to implement decision processes that are just too complex to be manually coded.

Moreover, ML models have specific characteristics that help provide useful insights into the collected data. For example, organizations that implement ML models can get their hands on insights that focus on similarities between data entries (for example, people with similar names), the relative importance of particular type of information (such as specifying whether a person's surname is vital in their identification), or even the quality of the data with the aim building better models.

Choose the right technology

Data Science and its many techniques aren't going anywhere. Organizations are looking to support their decision-making processes with data and measure the results of the decisions they make to learn what worked best. They can't do that without sophisticated data analytics software that incorporates valuable Data Science technologies such as Machine Learning.

Are you interested in implementing Machine Learning in your project but don't know where to start? Reach out to our team of consultants; we can help you choose from a broad range of Data Science techniques to make your project a success.

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