Introduction to Data Science

Find out what data science is, what data scientists do, and whether it's the right career for you. No experience required. Data science is one of the fastest-growing fields in the job market. The U.S. Bureau of Labor Statistics projects 34% employment growth for data scientists from 2024 to 2034, much faster than average, with a median annual wage of $112,590.¹ That works out to about 23,400 openings projected each year over the decade. The World Economic Forum's Future of Jobs Report 2025 also ranks big data specialists and AI and machine learning specialists among the fastest-growing jobs through 2030.² This course is where you get a clear picture of the field before you commit to a learning path. This IBM course gives you a practical foundation in how data science works and how organizations use it to make decisions. Across four modules, you'll learn what data science is, how big data and cloud computing power it, and how data scientists use machine learning, deep learning, and generative AI to find patterns and make predictions. You'll see how companies apply those methods to real problems, from improving efficiency to saving lives, and what it takes to get hired as a data scientist. You'll learn from people doing the work. Working data scientists share how they define the field, how they got started, and what they'd tell someone just beginning. You'll explore data in a hands-on IBM Cloud lab, follow a case study of one data scientist's journey, and complete a final project where you analyze a real data scientist job posting against what you've learned. By the end, you'll be able to explain what data science is, describe how data scientists work, and map out your next step with confidence. Whether you're considering a career change, exploring data science for the first time, or want to work more effectively with data teams in your current role, this course gives you the foundation to start. Skills you'll gain include: - Defining data science and explaining why it matters in a data-driven world - Describing how machine learning, deep learning, and generative AI fit into data science - Explaining how big data, data mining, and cloud computing support data-driven decision-making - Understanding how data is generated, stored, and organized, including databases, data lakes, and data pipelines - Identifying the skills, backgrounds, and career paths that lead to data science roles - Evaluating real job postings and positioning yourself for the role you want Join 1.3 million+ learners (4.7 stars from 77,000+ reviews, and 97% of learners liked this course). Finish with a shareable certificate you can add to your LinkedIn profile, resume, or CV. ¹ U.S. Bureau of Labor Statistics, Occupational Outlook Handbook: Data Scientists. Median wage as of May 2024; projected growth 2024 to 2034. This course provides an introduction to data science and is a first step toward data roles, not a guarantee of employment or salary. ² World Economic Forum, Future of Jobs Report 2025. Rankings reflect employer survey expectations for 2025 to 2030.

If there is a shortcut to becoming a Data Scientist, then learning to think and work like a successful Data Scientist is it. In this course, you will learn and then apply this methodology that you can use to tackle any Data Science scenario. You’ll explore two notable data science methodologies, Foundational Data Science Methodology, and the six-stage CRISP-DM data science methodology, and learn how to apply these data science methodologies. Most established data scientists follow these or similar methodologies for solving data science problems. Begin by learning about forming the business/research problem Learn how data scientists obtain, prepare, and analyze data. Discover how applying data science methodology practices helps ensure that the data used for problem-solving is relevant and properly manipulated to address the question. Next, learn about building the data model, deploying that model, data storytelling, and obtaining feedback You’ll think like a data scientist and develop your data science methodology skills using a real-world inspired scenario through progressive labs hosted within Jupyter Notebooks and using Python.

In order to be successful in Data Science, you need to be skilled with using tools that Data Science professionals employ as part of their jobs. This course teaches you about the popular tools in Data Science and how to use them. You will become familiar with the Data Scientist’s tool kit which includes: Libraries & Packages, Data Sets, Machine Learning Models, Kernels, as well as the various Open source, commercial, Big Data and Cloud-based tools. Work with Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. You will understand what each tool is used for, what programming languages they can execute, their features and limitations. This course gives plenty of hands-on experience in order to develop skills for working with these Data Science Tools. With the tools hosted in the cloud on Skills Network Labs, you will be able to test each tool and follow instructions to run simple code in Python, R, or Scala. Towards the end the course, you will create a final project with a Jupyter Notebook. You will demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers.