If you’ve been to the blog before you know that I often mention data science, machine learning, artificial intelligence, and advanced analytics. Although I group these fields together, there are a few subtle and not so subtle differences between them. Here is a description of each for you to use to help determine where you want to focus your job search.

1. Data Science

Data scientists are data professionals who specialize in data-driven technologies and applications to analyze data and recommend meaningful long-term solutions. They use their data science skills to identify trends, forecasts the future, assess data reliability, and advise on data governance issues. Data scientists have backgrounds in mathematics, programming, technology, statistics, economics, or engineering that they bring to the table when dealing with data analysis problems. It's an evolving field of work filled with learning opportunities as data science continues to open many possibilities for new angles of data analysis.

SKILLS COMMONLY USED: Python, R, SaS, SQL

2. Machine Learning

Machine learning professionals explore how computers can be used to process vast amounts of data and identify patterns within them. Additionally, they develop complex algorithms and build intelligent tools that can analyze large sets of data with great accuracy. These abilities give organizations the insights they need from their data so that they can make data-driven decisions and increase their operational efficiency.

ALGORITHMS COMMONLY USED: Naïve Bayes Classifier, K Means Clustering, Support Vector Machine

3. Artificial Intelligence

Working with artificial intelligence involves using a range of programming languages and AI technologies to develop cutting-edge solutions to difficult problems. A typical day can include developing algorithms, building neural networks, utilizing natural language processing, and experimenting with machine learning to create exciting new opportunities.

SKILLS COMMONLY USED: Python, R, JAVA

4. Advanced Analytics

An advanced analytics professional is responsible for providing strategic guidance to organizations who are looking to leverage data to make informed decisions. They use a combination of powerful modeling techniques and data-driven insights to help build predictive and prescriptive models that enable organizations to gain greater understanding of their customers, operations, financial performance, strategy, and more. Overall, they strive to improve decision making capabilities by helping organizations identify hidden value in structured and unstructured data.

SKILLS COMMONLY USED: Python, R, SQL, knowledge of machine learning algorithms

After reading this post, you should have a better understanding of the differences between data science, machine learning, artificial intelligence, and advanced analytics. If you’re interested in pursuing a career in one of these fields, be sure to do your research so that you know which path is the best fit for you. And if you’re not sure which field is right for you, don’t hesitate to reach out to us – we’d be happy to chat with you about your options and help point you in the right direction.