Data Analysis: Statistical Modeling and Computation in Applications
This course is the first of a two-course sequence: Introduction to Computer Science and Programming Using Python, and Introduction to Computational Thinking and Data Science. Together, they are designed to help people with no prior exposure to computer science or programming learn to think computationally and write programs to tackle useful problems. Some of the people taking the two courses will use them as a stepping stone to more advanced computer science courses, but for many it will be their first and last computer science courses. This run features lecture videos, lecture exercises, and problem sets using Python 3.5. Even if you previously took the course with Python 2.7, you will be able to easily transition to Python 3.5 in future courses, or enroll now to refresh your learning.
What you’ll learn
- Model, form hypotheses, perform statistical analysis on real data
- Use dimension reduction techniques such as principal component analysis to visualize high-dimensional data and apply this to genomics data
- Analyze networks (e.g. social networks) and use centrality measures to describe the importance of nodes, and apply this to criminal networks
- Model time series using moving average, autoregressive and other stationary models for forecasting with financial data
- Use Gaussian processes to model environmental data and make predictions
- Communicate analysis results effectively
How to Enroll:
- Choose your desired certificate program on the MIT website.
- Create an Amazon if you don’t have one.
- Select specific courses within your chosen program.
- Enroll in courses, and pay if necessary.
- Access course materials and complete requirements.
- Prepare for and take certification exams if required.
- Earn your certificate upon successful completion.
- Be aware of maintenance or renewal requirements, if applicable.
Enroll Now
Thanks for Visit GrabAjobs.co
Best Of LUCK : )
2 mins to read