Why Choose MTSS Education?
The mission of MTSS Education is to prepare adult learners for successful careers in the field of information technology. We seek to offer educational programs that provide real-world technical skills that match employer needs.
Our objective is to encourage students to think scientifically and help them develop a scientific mindset. This will help them achieve more professional growth and a uniqueness that will be their key to getting hired.
- To employ a highly qualified and immensely experienced faculty.
- To ensure students have access to a high-quality learning environment with up-to-date tools and technologies.
- To provide our students with practical experience and emphasize learning methods that can be applied in the real world.
- To offer comprehensive, technology training programs which are designed to meet job market demands.
- To adapt to new challenges in the information technology field.
Program Overview:
This Data Scientist Master’s program, in collaboration with IBM, helps you master the concepts of data science. You will learn key technologies including R, Python, Tableau, Hadoop, and Spark. You will also gain an understanding of supervised and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, KNN, and pipelines required to start your career as a data scientist.
Program Highlights
- 40s0+ hours of applied learning
- 15+ real-life projects providing hands-on industry training
- Industry-recognized certificates from IBM (for IBM courses)
- 30+ in-demand skills
- Portfolio-worthy capstone demonstrating mastered concepts
Course Delivery Method:
Online Bootcamp: Online self-paced video-based learning and live virtual classroom conducted by the industry’s leading coaches
Prerequisites:
To take this Data Scientist Master’s program you should have a basic understanding of statistics and any programming language.
Skills Covered:
- Exploratory data analysis
- Descriptive statistics
- Inferential statistics
- Model building and fine-tuning
- Supervised and unsupervised learning
- Natural language processing
- Ensemble learning
Tools covered
Skills Covered
- Exploratory data analysis
- Descriptive statistics
- Inferential statistics
- Model building and fine-tuning
- Supervised and unsupervised learning
- Natural language processing
- Ensemble learning