Data science vs machine learning – what is the difference?


Data science vs machine learning – what is the difference?

Data science and machine learning are commonly used terms, but do you know the difference? What ML engineer and data scientist do? Read in this article.

Data science and machine learning are terms that are gaining in popularity during the beginning of an AI era, but do you know their actual meaning and the differences between them? What skills do you need to become a Machine Learning Engineer or Data Scientist? If you thought those terms could be used interchangeably or have any doubts in that matter, this article will clear things up for you. Have a read and never confuse these terms again.

What is data science?

Data science is a multidisciplinary approach to extracting utility and insights from vast amounts of data collected and created by modern organisations.

It includes preparing data for processing as well as performing advanced data analysis. The crucial step is the proficient presentation of results and occurring patterns to show the analysed data's business value.

What does a Data Scientist do?

The main responsibility of a Data Scientist is to prepare data for a specific type of processing by, for example, cleaning, aggregating and manipulating it in any other way. 

The next step is an in-depth analysis, which requires appropriate development, algorithms, analytics and advanced artificial intelligence models. 

The software also plays a significant role in the work of a Data Scientist, combing through data to find patterns and turning them into predictions that support business decisions. Of course, the accuracy of these predictions must be verified through correctly designed tests and experiments, which are also the responsibility of the data science team.

Afterwars, the obtained results should be made available through data visualisation tools that allow anyone to understand and see clearly occuring patterns.

What skills are crucial to be a Data Scientist?

Now we understand Data Scientist's scope of work. But what skills are necessary to be one? Below you can find the most important skills you need to work as a Data Scientist:

  • basics of programming languages such as Python or R,
  • ability to explore and clean data,
  • knowledge of relevant tools and techniques for data evaluation and preparation,
  • very good understanding of SQL and NoSQL databases,
  • ability to conclude data using predictive analytics and artificial intelligence, including machine learning and deep learning models,
  • ability to develop applications that automate data processing and computation,
  • knowledge of big data tools such as Hadoop, Hive, and Pig,
  • ability to use unstructured data management techniques,
  • knowledge of data visualisation tools such as Tableau and Microsoft Power BI (which allows to present even extremely complex data in a more accessible way).

What is machine learning?

Machine learning is a field of artificial intelligence that uses data and algorithms to teach machines to replicate the way humans learn. It focuses on developing computer programs that can access data and learn basing on that data. What's more, ML systems are constantly learning – the more data they receive and process, the greater accuracy they have.

What does a Machine Learning Engineer do?

In this profession one must combine the experience of a software engineer with data science skills.

While a Data Scientist analyses a set of data to draw appropriate conclusions, a Machine Learning Engineer designs software that is able to learn and function on its own without direct human assistance. To create that, he uses data and creates and automates appropriate predictive models.  In other words, a Machine Learning Engineer is the link between data and software.

The responsibilities of a Machine Learning Engineer vary depending on the specifics, size and, industry of the company he or she works for. However, the main job here is always to combine data science with computer science fundamentals to design, build and maintain machine learning systems.

What skills are necessary to be a Machine Learning Engineer

The most important skills a Machine learning Engineer must have include:

  • programming skills in languages such as Python or R,
  • knowledge of probability and statistics,
  • ability to model and evaluate data,
  • ability to statistically model data,
  • knowledge of machine learning algorithms and libraries such as Scikit-Learn, Keras, TensorFlow, PyTorch or MLlib, among others
  • ability to work on neural networks,
  • knowledge of natural language processing (NLP)
  • data architecture design know-how,
  • depending on the company's preferences: knowledge of tools such as Spark & Hadoo, Apache Kafka or Google Cloud ML Engine.

Data science and machine learning – fields that overlap

Data science is a vast field that uses massive amounts of data and computing power to gain knowledge. While machine learning is a way to make appropriate use of that data through computers that learn from the patterns provided. So, it is often just one of many tools where data can be used.

Machine learning is dependent on data science: it requires adequate preparation of data and correct application of tools to make the most of the collected information.

In short, data science is the study, building and interpretation of a model, while machine learning is the production of that model. 

While there is some overlap in the required skills of a Data Scientist and Machine Learning Engineer, the main difference is that the former focuses on statistics, model building and interpretation of results, while the latter uses the resulting models to scale them up and put them into production.

Conclusion: Data science vs machine learning – what is the difference?

Understanding the differences between the responsibilities and required skill set of Data Scientists and Machine Learning Engineers makes it simple to understand the difference between the two terms.

To sum up, data science is a field that is concerned with processes and systems for extracting, analysing and then visualising and drawing conclusions from huge data sets. In contrast, machine learning mainly involves programming computers to take action based on data.

However, the terms are intertwined, and Data Scientists and Machine Learning Engineers usually work together in teams developing artificial intelligence-based solutions.

How can we help you?

Are you looking for a Machine Learning Engineer or Data Scientist position? Check out the current job offers at Websensa. If you won't find anything suitable for you – write to us anyway – maybe we don't know yet we need you in our team. ;)

Or do you need a company to develop an advanced machine learning and data science project for you? Contact our expert. We will be happy to talk about your needs.

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