Data Science
Computational Thinking with Python
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We live in an era of unprecedented access to data. Understanding how to organize and leverage the vast amounts of information at our disposal are critical skills that allow us to infer upon the world and make informed decisions. This course will introduce you to such skills.
To work with large amounts of data, you will need to harness the power of computation through programming. This course teaches you basic programming skills for manipulating data. You will learn how to use Python to organize and manipulate data in tables, and to visualize data effectively. No prior experience with programming or Python is needed, nor is any statistics background necessary.
The examples given in the course involve real world data from diverse settings. Not all data is numerical – you will work with different types of data from a variety of domains. Though the term “data science” is relatively new, the fundamental ideas of data science are not. The course includes powerful examples that span the centuries from the Victorian era to the present day.
This course emphasizes learning through doing: you will work on large real-world data sets through interactive assignments to apply the skills you learn. Throughout, the underlying thread is that data science is a way of thinking, not just an assortment of methods. You will also hone your interpretation and communication skills, which are essential skills for data scientists.
What you'll learn
- Basics of the Python programming language, and how to use it as a tool for data analysis
- Tools widely used by industry and academic data scientists, such as Jupyter Notebooks
- How to use computation to help your data tell a story
- Fundamental principles and methods of visualization
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| Rating | 5.0★ based on 2 ratings |
|---|---|
| Length | 5 weeks |
| Effort | 4 - 6 hours per week |
| Starts | On Demand (Start anytime) |
| Cost | $199 |
| From | University of California, Berkeley, BerkeleyX via edX |
| Instructors | Ani Adhikari, John DeNero, David Wagner |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Programming Data Science |
| Tags | Computer Science Data Analysis & Statistics |
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What people are saying
industry standard libraries such
The good thing about the datascience library is that it's very easy to understand, the drawback is that you won't be introduced to industry standard libraries such as numpy or pandas.
things get more advanced
At first, the courses pace was a bit too slow for me, but later on I appreciated it, because as things get more advanced, the slower pace helps with understanding.
bit too slow
good thing about
no programming experience
Data 8.1 is a perfect course for people with no programming experience.
numpy or pandas
at first
great examples
Great examples.
wonderfully organized
Wonderfully organized.
entire toolset
The course introduces the entire toolset needed in a very intuitive way.
intuitive way
called `` datascience ''
Careers
An overview of related careers and their average salaries in the US. Bars indicate income percentile.
AD, Data Science $47k
Associate Data Science Supervisor $55k
Science writer / data analyst $63k
Genomic Data Science Programmer $75k
Volunteer Director of Data Science $78k
Expert Data Science Supervisor $79k
Supervisor 1 Data Science Supervisor $91k
Guest Director of Data Science $101k
Data Science Architect $105k
Head of Data Science $131k
Assistant Director 1 of Data Science $133k
Owner Director of Data Science $149k
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| Rating | 5.0★ based on 2 ratings |
|---|---|
| Length | 5 weeks |
| Effort | 4 - 6 hours per week |
| Starts | On Demand (Start anytime) |
| Cost | $199 |
| From | University of California, Berkeley, BerkeleyX via edX |
| Instructors | Ani Adhikari, John DeNero, David Wagner |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Programming Data Science |
| Tags | Computer Science Data Analysis & Statistics |
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