Data Science vs Machine Learning

The terms "data science" and "machine learning" are closely related. Machine learning is in fact a part of data science. So it makes perfect sense that anyone who's unfamiliar with data science might confuse the two.

But if you're pursuing a career in data, you need to start with a strong understanding of what data science is, and what machine learning refers to specifically.

In this article, we’ll learn more about data science and machine learning, including their differences, job prospects, and the skills required to become a professional in either discipline.

What Is Data Science?

Data science is the processing, extraction, and analysis of unstructured data generated by a business in order to help develop insights to inform decision-making in businesses of all sizes. If you’re shopping online on Amazon or Walmart, for example, a data scientist could uncover a pattern in your choices using big data, which would help them to better understand overall customer behavior. This helps companies to develop recommendation engines, tagged with the lines like “you may also like” or “since you were viewing this product, you may also be interested in”. This is only possible when the company has enough data to apply algorithms to analyze and extract information.

In simpler terms, data science is a blend of technology, decision-making, and management. It strives to obtain accurate data from sets of unstructured data. Data scientists prepare and analyze data by incorporating skills from computer science, statistics, and database management.

Techniques and technologies used in data science include:

• Clustering
• Dimensional reduction
• Machine learning
• Programming languages like Python and R
• Frameworks like TensorFlow and Pytorch
• Web interfaces like Jupyter Notebook
• Data visualization tools like Tableau
• Software frameworks like Apache Hadoop

What Is Machine Learning?

Machine learning is an element of data science and the study of algorithms. It is seen as an indispensable part of data science. Machine learning allows computers to learn from data so that they can carry out certain tasks. It is used to process data sets autonomously without human interference. Based on the algorithms, it works on the data extracted from different sources with the help of data science.

Data science has enabled the generation of data in large amounts, which means it has now become difficult for data scientists to manage it manually. This is where machine learning comes in. Machine learning makes it easier for data scientists to manage the data without any external advice or input.

This is achieved through techniques like:

• Genetic algorithms
• Federated learning
• Bayesian Networks
• Regression analysis
• Artificial neural networks
• Decision trees
• Robot learning

Skills Needed for Data Science and Machine Learning

While there's plenty of overlap, there are some specific skills you need to specialize in machine learning. And if you choose to become a more generalized data scientist, there are skills you'll end up learning that apply to other areas of the discipline.

Here are some of the most important skills you'll need to perfect if you want to pursue data science, compared to the skills specific to machine learning expertise.

Data Science

• Statistics
• Data visualization
• Unstructured data management techniques and methods
• Programming languages such as R, Python, and Java
• Data mining and data cleansing
• Understand SQL databases
• Big data tools like Pig

Machine Learning

• Computer science fundamentals
• Understanding of algorithms
• Natural language processing (NLP)
• Statistical modeling
• Data architecture plan
• Text representation methods

Data Science Careers and Salaries

Listed below is a selection of job titles within the data science field, along with the typical annual salary each role attracts.

• Data Scientist: Data scientists are capable of handling large amounts of data to extract and analyze structured data, which helps in advising on the business models of organizations of all sizes. The average annual salary of a data scientist is $116,654.
• Application Architect: An application architect keeps track of the behavior of applications used in organizations. The average annual salary of an applications architect is $129,101.
• Enterprise Architect: An enterprise architect applies architecture principles and practices to guide organizations through the business, information, process, and technology changes necessary to execute their strategies. The average annual salary of an enterprise architect is $146,366.
• Statistician: Statisticians analyze and collect data to track patterns and trends between users and stakeholders. The average annual salary of a statistician is $96,844.
• Data Analyst: Data analysts help make sense of large data sets in order to help inform business decision-making processes. The average annual salary of a data analyst is $66,570.

Machine Learning Careers and Salaries

• Machine Learning Engineer: These are programmers who develop systems and machines that can learn and apply learned knowledge based on user behavior. Machine learning experts essentially deal with artificial intelligence. The average annual salary of a machine learning expert is $140,250.
• Natural Learning Process Scientist: Natural language processors (NLPs) enable machines to talk to humans and understand their queries. Siri, Cortana, and Google Assistant are a few examples of NLPs. An NLP scientist needs to be fluent and grammatically correct so that the machine acquires the same skills. The average annual salary of an NLP Scientist is $123,974.
• Business Intelligence Developer: A business intelligence developer uses machine learning to collect and produce required data for an organization. The average annual salary of a business intelligence developer is $94,368.
• Human-Centered Machine Learning Designer: This role is related to machine learning algorithms that are focused on humans and their behavior. An example of this is the suggestion that pops up after you finish watching a series or film on Netflix. It reads the pattern and suggests a similar series or film. The average annual salary of a Human-Centered Machine Learning Designer is $116,668.

Next Steps

If you’re motivated to switch careers, we’re here to help with all the tools, skills, and knowledge you’ll need to become a professional data scientist. Our full-time Data Science program offers an accelerated online program of classes, mentorship, and professional guidance designed to get you a career in data science, fast. If you need to keep working while you learn, try our part-time Data Science bootcamp.