Certificate in Data Science Program offered by University of Washington
- Public University
1 Campus- Estd. 1861
Certificate in Data Science Program at University of Washington Overview
Duration | 8 months |
Total fee | ₹3.36 Lakh |
Mode of learning | Online |
Course Level | UG Certificate |
Certificate in Data Science Program at University of Washington Highlights
- Earn a certificate from University of Washington
- Get eligible to apply for a UW Certificate Scholarship
- Get hand on experience
- Top Employers: Microsoft, Amazon, Boeing, T-Mobile, Starbucks, Facebook
- Complete quizzes as well as both structured and open-ended assignments to demonstrate data science skills
Certificate in Data Science Program at University of Washington Course details
- For technical professionals with data analysis and programming experience who want to learn how to apply statistical techniques and machine learning in their areas of expertise
- What the data science process looks like for business, scientific research and other fields
- How to apply the data science process to different problems
- How to use statistics and visualization to explore data sets
- How to weigh the pros and cons of the most common machine learning algorithms
- Use the Python programming language to summarize and visualize data
- Use the Python programming language to pre-process data and to train and evaluate machine learning algorithms
- Organizations of all kinds accumulate huge amounts of data, but they often struggle to make sense of it
- Data scientists turn data into meaningful and actionable insights, which is increasingly crucial as organizations strive to stay ahead of the competition
- Discover how to apply standard tools and processes to extract insights from data
- Use statistical analysis and machine learning algorithms to analyze data sets and help make informed decisions
Certificate in Data Science Program at University of Washington Curriculum
Data science: Process and Tools
How to apply the data science process and methodology to different use cases
How to formulate a question as a data science problem and present the results to stakeholders
Basics of supervised and unsupervised learning
Methods for data analysis
Common statistical measures and plots to describe data and results
Statistical inference using both the Bayesian and frequentist approaches
Common statistical pitfalls and how to avoid them
Machine learning techniques
How to train and evaluate machine learning models for classification and regression
The pros and cons of common machine learning algorithms
Deep learning and how it differs from traditional machine learning
Common techniques for explaining complex machine learning models
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