IBM Data Science
Professional Certificate

PROFESSIONAL & CONTINUING EDUCATION

Pursue a career in data science or machine learning by developing career-relevant skills and experience.

 

This Professional Certificate from IBM does not require any prior knowledge of computer science or programming languages. You'll develop the skills, tools, and portfolio to have a competitive edge in the job market as an entry level data scientist.

 

This program will provide you with the latest job-ready tools and skills, including open source tools and libraries, Python, databases, SQL, data visualization, data analysis, statistical analysis, predictive modeling, and machine learning algorithms. You’ll learn data science through hands-on practice in the IBM Cloud using real data science tools and real-world data sets.

 

Next Session Begins May 23, 2022

There are ten required courses in this Professional Certificate. The courses are offered in English, Arabic, Portuguese, and Spanish.

 

Course 1: What is Data Science?

The art of uncovering the insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and they accurately predicted the flooding of the Nile river every year. Since then, people working in data science have carved out a unique and distinct field for the work they do. This field is data science. In this course, we will meet some data science practitioners and we will get an overview of what data science is today.

Course 2: Tools for Data Science

What are some of the most popular data science tools, how do you use them, and what are their features? In this course, you'll learn about Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. You will learn about what each tool is used for, what programming languages they can execute, their features and limitations. 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. To end the course, you will create a final project with a Jupyter Notebook on IBM Watson Studio and demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers.

Course 3: Data Science Methodology

Despite the recent increase in computing power and access to data over the last couple of decades, our ability to use the data within the decision making process is either lost or not maximized at all too often, we don't have a solid understanding of the questions being asked and how to apply the data correctly to the problem at hand.

 

This course has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem solving is relevant and properly manipulated to address the question at hand. Accordingly, in this course, you will learn:

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

Course 4: Python for Data Science, AI & Development

Kickstart your learning of Python for data science, as well as programming in general, with this beginner-friendly introduction to Python. Python is one of the world’s most popular programming languages, and there has never been greater demand for professionals with the ability to apply Python fundamentals to drive business solutions across industries.

 

This course will take you from zero to programming in Python in a matter of hours—no prior programming experience necessary! You will learn Python fundamentals, including data structures and data analysis, complete hands-on exercises throughout the course modules, and 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. You’ll gain a strong foundation for more advanced learning in the field, and develop skills to help advance your career.

Course 5: Python Project for Data Science

This mini-course is intended to for you to demonstrate foundational Python skills for working with data. The completion of this course involves working on a hands-on project where you will develop a simple dashboard using Python.

Course 6: Databases and SQL for Data Science with Python

The purpose of this course is to introduce relational database concepts and help you learn and apply foundational knowledge of the SQL language. It is also intended to get you started with performing SQL access in a data science environment.

 

The emphasis in this course is on hands-on and practical learning. As such, you will work with real databases, real data science tools, and real-world datasets. You will create a database instance in the cloud. Through a series of hands-on labs you will practice building and running SQL queries. You will also learn how to access databases from Jupyter notebooks using SQL and Python.

Course 7: Data Analysis with Python

Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more!

 

Topics covered:

  1. Importing Datasets
  2. Cleaning the Data
  3. Data frame manipulation
  4. Summarizing the Data
  5. Building machine learning Regression models
  6. Building data pipelines

Course 8: Data Visualization with Python

One of the key skills of a data scientist is the ability to tell a compelling story, visualizing data and findings in an approachable and stimulating way. Learning how to leverage a software tool to visualize data will also enable you to extract information, better understand the data, and make more effective decisions.

 

The main goal of this Data Visualization with Python course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people. Various techniques have been developed for presenting data visually but in this course, we will be using several data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium.

Course 9: Machine Learning with Python

This course dives into the basics of machine learning using an approachable, and well-known programming language, Python.

In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.

 

In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed! By just putting in a few hours a week for the next few weeks, this is what you’ll get.

  1. New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy
  2. New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more.
  3. And a certificate in machine learning to prove your competency, and share it anywhere you like online or offline, such as LinkedIn profiles and social media.

Course 10: Applied Data Science Capstone

This capstone project course will give you a taste of what data scientists go through in real life when working with real datasets. You will assume the role of a Data Scientist working for a startup intending to compete with SpaceX, and in the process follow the Data Science methodology involving data collection, data wrangling, exploratory data analysis, data visualization, model development, model evaluation, and reporting your results to stakeholders.

 

You are tasked with predicting if the first stage of the SpaceX Falcon 9 rocket will land successfully. SpaceX advertises Falcon 9 rocket launches on its website, with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is because SpaceX can reuse the first stage.

 

Therefore if you can accurately predict the likelihood of the first stage rocket landing successfully, you can determine the cost of a launch. With the help of your Data Science findings and models, the competing startup you have been hired by can make more informed bids against SpaceX for a rocket launch.

 

Complete the certificate in 5 - 10 months, working at your own pace at approximately 8 hours per week.

 

An IBM Data Science Professional Certificate and badge will be issued by Coursera upon successfully completing all the required coursework of this non-credit bearing professional certificate.

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Registration Instructions

This is a professional certificate and is open to everyone. No minimum educational degree/background is required. 

To register, Create a Loras account and apply online. There is no application fee and you do not need to submit any transcripts.

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1. Create a Loras account

  • When first starting the application, select “Graduate and PCE Application” as the Application Type.
  • In the Student Profile section within the application itself, select Professional & Continuing Education as the Student Type, then select Coursera Professional Certificates as the Academic Program, then select your desired certificate from the dropdown menu:
    • Google IT Support
    • Google Project Management
    • Google UX Design
    • IBM Data Analyst
    • IBM Data Science
    • Meta Social Media Marketing
    • Salesforce Sales Development Representative
  • Please note that you do not have to upload anything to the Materials section of the application or submit any transcripts.

2. Start dates and application deadlines

  • Students may begin any of the certificate programs in either spring, summer, or fall. (The start dates align with the beginning of Loras’ spring, summer, and fall semesters each year. Please see the top of this webpage for the next upcoming start date).
  • Loras offers rolling admissions for this program, but the application should ideally be submitted at least one week prior to the start of your desired term.  

3. Enrollment process

  • After our receipt of your application, you will receive an email prompting you to log in to your Loras Applicant Status Portal to view your official acceptance information as well as details on how to make payment for the certificate.
  • Please note that payment is due in full before you may begin the program. (If your employer is paying for the certificate, please handle reimbursement with them directly). Federal financial aid is not available for this program. Students admitted into any of the Coursera Professional Certificate programs may receive a 100% payment refund up until two weeks (14 days) before the first day of the semester. Any payment received within two weeks of the start of the semester is non-refundable.
  • Once Loras receives full program payment, you will receive information on how to begin the coursework.

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Questions? Let’s get in touch.

 

Director of Graduate and Continuing Education Programs
Heidi M Nelson, PT, DSc, DPT, PCS
heidi.nelson@loras.edu
563.557.4002

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