Introduction to Data Science and Machine Learning

Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge. It uses analytics and machine learning to help users make predictions, enhance optimization, and improve operations and decision-making.

The goal of “Python Programming for Data Science” is to help you learn the most important tools in Python that will allow you to do data science. As you progress through this course, you’ll learn how to approach a variety of data science challenges, using the best parts of Python.

Data is one of the important assets in every organization because it helps business leaders make decisions based on facts, statistical numbers and trends. The importance of data science is based on the ability to take existing data that is not necessarily useful on its own and combine it with other data points to generate insights an organization can use to learn more about its customers and audience.

Today’s data science teams are expected to answer many questions. Business demands better prediction and optimization based on real-time insights.

With the volume and variety of social, mobile and device data, along with new technologies and tools, data science today plays a broader role than ever before. Business considers data science and AI to be a technology-enabled strategy.

The short answer is yes —and the opportunities extend well beyond the job title “Data Scientist.”

According to the U.S. Bureau of Labor Statistics (BLS), employment of Data Scientists is projected to grow 35% from 2025 to 2035, compared with 3% for all occupations. BLS projects approximately 24,800 openings for Data Scientists each year, on average, during that period. The median annual wage for Data Scientists was $120,230 in May 2025.

The growth is closely connected to organizations’ increasing use of data analytics, artificial intelligence, and machine learning.

Data science is one of the fastest growing fields today and is expected to continue into the next decade. As most of the fields are emerging continuously, the importance of data science is increasing rapidly. Data science has influenced various areas. Its effect can be observed in multiple sectors such as the retail industry, healthcare, government, financial and education. Current job listings include positions such as:

• Data Scientist
• Data Scientist, Product Analytics
• Business Data Scientist
• Data Scientist, Machine Learning
• Data Scientist, Fraud Risk
• Data Science Analyst
• Machine Learning Engineer
• Data Analyst
• AI/Data Science roles

As the growth of data accelerates, so does the importance of data science and the teams of data scientists formed to turn this data into useful information, insight and knowledge.   While companies prepare for big data integration, business leaders need to adapt their roles as team leaders for their data science employees.  Your data science team should have the expertise to process data with freedom, but business leaders still need to understand the basic structures of what’s happening to create value from that data.

Put into context in today’s business environment, there’s no situation where it’s okay to say as the leader, “I don’t know what’s going on but my team does and that’s good enough”.  Yet many business leaders don’t know the most basic principles of data science.  Business leaders (managers, directors, executives, vice presidents, etc.) don’t need to know the intimate details of data science processes but as the line between big data and business operations disappear, it’s more important than ever for business leaders to speak (understand) a little data science.  This translates into having some basic foundational knowledge.

Data science can be good at storytelling, but it is still science.  Telling a story can often obscure the facts or make links where there aren’t any.  Having the foundational knowledge or basic proficiency can help you avoid:

• Getting taken – manipulating the data, not telling the whole story, targeting information gaps, all these things could make it easier to coerce or persuade you into a bad decision
• Asking the wrong questions – data pulls are only as good as the questions you’re asking. Data must be evaluated regularly and that requires starting with the right question(s).
• Replicating bias – data is neutral, but its aggregation and results are often the product of our preconceived ideas.  Understanding the basics of data science helps you sort out the messiness of data in the real world.

You don’t have to become a Data Scientist to benefit from learning Data Science.

Data skills are increasingly used across businesses, healthcare, finance, technology, marketing, government, and other industries. Depending on your background and the skills you develop, learning data science can lead toward careers such as Data Analyst, Business Intelligence Analyst, Data Scientist, Data Analytics Specialist, Machine Learning Engineer, or other AI-related roles.

An introductory course can be a starting point—not a guarantee of employment—and students can build from there by developing skills in Python, SQL, statistics, data visualization, machine learning and AI.