Python for Data Science
In the information age, data is all around us. Within this data are answers to compelling questions across many societal domains (politics, business, science, etc.). But if you had access to a large dataset, would you be able to find the answers you seek?
This course, part of the Data Science MicroMasters program, will introduce you to a collection of powerful, open-source, tools needed to analyze data and to conduct data science. Specifically, you'll learn how to use:
python
jupyter notebooks
pandas
numpy
matplotlib
git
and many other tools.
You will learn these tools all within the context of solving compelling data science problems.
After completing this course, you'll be able to find answers within large datasets by using python tools to import data, explore it, analyze it, learn from it, visualize it, and ultimately generate easily sharable reports.
By learning these skills, you'll also become a member of a world-wide community which seeks to build data science tools, explore public datasets, and discuss evidence-based findings. Last but not least, this course will provide you with the foundation you need to succeed in later courses in the Data Science MicroMasters program.
What you'll learn
- Basic process of data science
- Python and Jupyter notebooks
- An applied understanding of how to manipulate and analyze uncurated datasets
- Basic statistical analysis and machine learning methods
- How to effectively visualize results
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| Rating | 4.3★ based on 46 ratings |
|---|---|
| Length | 10 weeks |
| Effort | 10 weeks, 8–10 hours per week |
| Starts | On Demand (Start anytime) |
| Cost | $350 |
| From | UCSanDiegoX, The University of California, San Diego via edX |
| Instructors | Ilkay Altintas, Leo Porter |
| 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
for data science
Practical introductory course for data science using python and its major data science libraries (numpy, pandas, matplotlib, ...).
One of the best course on Python for data science.
It's a really good introduction (and more) to Python tools necessary for data science.
Overall the course is good and would recommend anyone wanting to pursue python for Data Science.
Excellent course to get in touch with Python and Jupyter notebooks for data science.
This is the best introductory course for data science.
Key python skills + tons of well-organized information for data science.
This course set me on the right path for Data Science.
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jupyter notebook
The best thing about the course are the jupyter notebook notes and exercises.
Good work-through several tools and python libraries using jupyter notebook that gets you started right away.
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jupyter notebooks
The jupyter notebooks are great and I reference them even after completing the course.
An extremely good introduction to jupyter notebooks, pandas, numpy and others.
The use of jupyter notebooks encourages to dive into it and explore in further detail.
This course gave clear instructions on how to get started in making data science projects with Python and Jupyter Notebooks.
Unlike some other courses, the walkthroughs were prepared as Jupyter Notebooks which saved me from having to stop the videos every two seconds to type out notes.
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easy to follow
Lectures were easy to follow and the assignments were fun.
Useful lectures were easy to follow and not too long.
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introduction to python
Great course, gave me a solid introduction to Python, Jupyter Notebooks and the fundamentals of data analysis.
Great overview of the data science process and great introduction to Python tools such as Pandas and Matplotlib.
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machine learning
I didn't rate this 5 stars because there was at least one time where I asked a forum question to the course staff, which was also echoed by another classmate regarding potential erratum in the machine learning section that wasn't major, but had me wondering if I had misunderstood some concepts of parallel plots but the edx staff never responded to the question.
The later weeks do throw a lot of material at you (machine learning, natural language processing, databases) but don't dive into it deeply.
The python notebooks were excellent starting points to play around with the different data processing and machine learning techniques.
It includes machine learning and linux aspects too.
As an example, the download for Week 7 Machine Learning should have a file named "minute_weather" with over 1,000,000 record of weather data used in the lecture.
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other courses
I hope to continue with the other courses in this series!
This course is better combined with other courses.
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data analysis
Great tool for sharing data analysis and powerful material to get done fast and useful data analysis including the basics regarding graphics for information presentation.
peer review
One thing I wish the instructors did was actually grade the projects instead of leaving it to peer review.
Peer review is arbitrary and most of the time my peers did not really understand my project mostly because I used statistics which is out of the scope of the course, but I put extra time into figuring stuff out and wish that I could receive better feedback on that aspect.
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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 | 4.3★ based on 46 ratings |
|---|---|
| Length | 10 weeks |
| Effort | 10 weeks, 8–10 hours per week |
| Starts | On Demand (Start anytime) |
| Cost | $350 |
| From | UCSanDiegoX, The University of California, San Diego via edX |
| Instructors | Ilkay Altintas, Leo Porter |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Programming Data Science |
| Tags | Computer Science Data Analysis & Statistics |
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