Excel in Data Science, one of the hottest fields in tech today. Learn how to gain new insights from big data by asking the right questions, manipulating data sets and visualizing your findings in compelling ways.
Read more
Excel in Data Science, one of the hottest fields in tech today. Learn how to gain new insights from big data by asking the right questions, manipulating data sets and visualizing your findings in compelling ways.
Excel in Data Science, one of the hottest fields in tech today. Learn how to gain new insights from big data by asking the right questions, manipulating data sets and visualizing your findings in compelling ways.
In this MicroMasters program, you will develop a well-rounded understanding of the mathematical and computational tools that form the basis of data science and how to use those tools to make data-driven business recommendations.
This MicroMasters program encompasses two sides of data science learning: the mathematical and the applied.
Mathematical courses cover probability, statistics, and machine learning. The applied courses cover the use of specific toolkit and languages such as Python, Numpy, Matplotlib, pandas and Scipy, the Jupyter notebook environment and Apache Spark to delve into real world data.
You will learn how to collect, clean and analyse big data using popular open source software will allow you to perform large-scale data analysis and present your findings in a convincing, visual way. When combined with expertise in a particular type of business, it will make you a highly desirable employee.
What's inside
Four courses
Python for Data Science
(90 hours)
Python for Data Science
(90 hours)
Probability and Statistics in Data Science using Python
(110 hours)
Probability and Statistics in Data Science using Python
(110 hours)
Machine Learning Fundamentals
(90 hours)
Machine Learning Fundamentals
(90 hours)
Big Data Analytics Using Spark
(105 hours)
Big Data Analytics Using Spark
(105 hours)Learning objectives
- How to load and clean real-world data
- How to make reliable statistical inferences from noisy data
- How to use machine learning to learn models for data
- How to visualize complex data
- How to use apache spark to analyze data that does not fit within the memory of a single computer
- How to load and clean real-world data
- How to make reliable statistical inferences from noisy data
- How to use machine learning to learn models for data
- How to visualize complex data
- How to use apache spark to analyze data that does not fit within the memory of a single computer
Save this collection
Save
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
