Organizations need to find ways to extract and analyze all sources of data in order to make informed decisions. This course provides an overview of the practice of data science with an emphasis on data cleaning, the data life cycle and data processing. Real world and fragmented data will be examined. Participants will learn how to use statistical methods and R programing language to analyze data sets. Experiential learning activities combined with hands-on learning practice sessions will provide participants with practical skills in computer programming and data cleaning.
Professionals working with data from various sectors in healthcare, finance, insurance and marketing.
Upon successful completion of the course, students will be able to:
- Define and describe the concepts of Data Science.
- Explain the role of data scientist in the real-world environment.
- Explore the various phases of data analysis – cleaning, transforming and analyzing data.
- Demonstrate how to clean data and improve data quality for reporting and analytics.
- Differentiate between supervised and unsupervised approaches to statistical learning
- Demonstrate the use of linear regression
- Apply statistical methods such as cross-validation and bootstrap.
- Apply model selection and regularization to data sets.
- Program using R programming language.
Additional RequirementsAdmission to the program requires an Undergraduate degree or College Diploma plus the completion of a Basic Statistics course.
Applies Towards the Following Certificates
- Data Science Certificate : Required Courses