IBM Data Science Certification Program

Develop the skills and tools to gain a competitive edge as an entry-level data scientist with this IBM Data Science Professional Certificate. No prior knowledge of computer science or programming languages needed to enroll.

The program consists of nine of the best online data science courses that will provide you with current job-ready expertise, including relational database management system (RDBMS) tools.

This professional certificate offered by Alvernia has a strong emphasis on applied learning. Except for the first course, all others include a series of hands-on labs in the IBM Cloud enabling you to build a portfolio of data science projects applicable to any enter-level position.

Data Science Activities

Learn the methodology to think and work like a data scientist; develop skills using tools, languages, and libraries


Import and clean data sets, analyze and visualize data and evaluate machine learning models and pipelines using Python

Publish a Report

You'll develop the skills to apply various data science techniques to complete a project and publish a report

Professional Certificate Programs enable you to become empowered and successful in every phase of your job!

Dana Baker

Dana Baker, Executive Director of Regional Campuses

"We are committed to developing current and relevant coursework to help transform our next generation of leaders."

IBM Data Science Certification

100% Online

Learn on your own schedule

Flexible Schedule

Set and maintain flexible deadlines

Entry Level

No previous experience required

6-Months to Complete

Suggested pace of 10 hours/week; 8 Courses

IBM Data Science Certificate Courses

What is Data Science?

Studying data involves developing methods to record, store and analyze data effectively to extract useful information. The goal of the data scientist is to gain insights and knowledge from any type of data, both structured and unstructured. People working in data science have carved out a unique and distinct field for the work they do. In this course you will meet data science practitioners and learn what is current in the field.

By the end of this course, you will learn:

  • Data reporting
  • Regression and data analysis
  • Machine learning
  • Artificial neural networks
  • Data mining

Tools for Data Science

In this second course, you will be introduced to the most popular data science tools, their features and limitations, how to use them and the programming languages they can execute.

In this course, you’ll learn about:

  • Jupyter Notebooks
  • RStudio IDE
  • Apache Zeppelin and Data Science Experience

At the end of the course, you will:

  • Create a final project with a Jupyter Notebook on IBM Data Science Experience.
  • Demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers.

Data Science Methodology

This course provides a data-science methodology to ensure the data used in problem solving is relevant and properly manipulated to address the question at hand.

By the end of this course, you will learn:

  • The major steps involved in tackling a data science problem.
  • The major steps involved in practicing data science: forming a concrete business or research problem, collecting and analyzing data, building a model and understanding the feedback after model deployment.
  • How data scientists think

Python for Data Science and AI

This course is a beginner-friendly introduction to Python, one of the world’s most popular programming languages. Go from zero to programming in Python in a matter of hours with no prior programming experience. Completing this course will provide a strong foundation for more advanced learning in the field and develop skills to advance your career. 

By the end of this course, you will:

  • Learn data structures and data analysis.
  • Complete hands-on exercises throughout the course modules.
  • Create a final project to demonstrate your new skills.

By the end of this course, you’ll feel comfortable creating basic programs, working with data and solving real-world problems in Python. This course can be applied to multiple Specialization or Professional Certificate programs. Completing this course will count towards your learning in any of the following programs:

  • Applied AI Professional Certificate
  • Applied Data Science Specialization
  • IBM Data Science Professional Certificate

Databases and SQL for Data Science

Structured Query Language (SQL) is a powerful language used for communicating with and extracting data from databases. A working knowledge of databases and SQL is a must if you want to become a data scientist. This course introduces relational database concepts, helping you learn and apply foundational knowledge of the SQL language and enabling you to perform SQL access in a data science environment.

 Emphasizing hands-on, practical learning, in this course you will:

  • Work with real databases, real data science tools and real-world datasets.
  • Create a database instance in the Cloud.
  • Practice building and running SQL queries.
  • Learn to access databases from Jupyter notebooks using SQL and Python.

Data Analysis and Python

This course introduces you to the basics of Python and explores different types of data.

By the end of this course, you will learn to:

  • Prepare data for analysis.
  • Perform simple statistical analysis.
  • Create meaningful data visualizations.
  • Predict future trends from data.

Topics covered include:

  • Importing datasets.
  • Cleaning the data.
  • Data frame manipulation.
  • Summarizing the data.
  • Building machine learning regression models.
  • Building data pipelines.
  • Data analysis libraries – learn to use Pandas, Numpy and Scipy libraries to work with a sample dataset.

Data Visualization with Python

An important skill for a data scientist is the ability to tell a compelling story, visualizing data in an approachable and stimulating way. Learning how to leverage a software tool to visualize data will also help you extract information, better understand the data and make effective decisions.

By the end of this course, you will learn to:

  • Take unstructured, meaningless data and present it in a form that makes sense to people.
  • Use data-visualization libraries in Python that include Matplotlib, Seaborn, and Folium.

Machine Learning with Python

Learn the basics of machine learning using Python.  In this course, you will explore the purpose of machine learning and where and how it applies to the real world. You will gain an overview of machine learning topics that include supervised vs unsupervised learning, model evaluation and machine learning algorithms.

By the end of this course, you will learn to:

  • Practice with real-life examples of machine learning and see how it affects society in surprising ways.
  • Add new skills to your resume including regression, classification, clustering, sci-kit learn and SciPy.
  • Add new projects to your portfolio including cancer detection, predicting economic trends, predicting customer churn, recommendation engines and more.

Skills you will gain: