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Applied Data Science with Python

$44.00

(3 customer reviews)
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Description

Prices differ based on Coursera’s programmes. 

Coursera might get your enrollment for free, you only pay for the certificate

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistically, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.

Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.

WHAT YOU WILL LEARN

  • Analyze the connectivity of a social network

  • Conduct an inferential statistical analysis

  • Discern whether a data visualization is good or bad

  • Enhance a data analysis with applied machine learning

3 reviews for Applied Data Science with Python

  1. Lingjun L

    Excellent material. Admittedly I can see why there are so many negative reviews about the ambiguity of the assessed tasks. It won’t be an easy course for anyone who is unfamiliar with programming. However, if you do have programming experience under your belt, you’ll likely find this course strikes an excellent balance in terms of conciseness, practice, and theory. Each lecture is crafted carefully to teach you about some nuance of pandas or numpy, and the programming assignments are packed with coding questions that will help you revise what you have learned, in a very efficient way. There is very little “fluff” in this course, which is a major weakness I’ve seen in similar courses of its kind. Too much spoon feeding often does not challenge or engage the learner. The course is very direct about what it expects of its students. Every week there is a comment “This week’s assignment requires more self-learning than the last”. And true to its word, there is less and less hand-holding as you go further into the course. I thoroughly enjoyed the material and probably learned the most out of this course than any other course I’ve taken on Coursera, taking in to account its length.

  2. Georg S

    This course was fast paced but the material was interesting and not to complex. I can only recommend this course to anyone interested in Data Science and who already has a basic knowledge of Python.

  3. Asmund S

    I have been doing some coding in different languages and this is my first time in Python. I would say I am on intermediate level (7 years practice), but this course made me spend a lot of time on learning only a tidy bit. The course fails in the basic educational purpose: Provide theoretical lectures and apply it with practical experience. There was little to none connection between these two aspects. The exercises were also way too difficult vs the lectures. I would never recommend this course.

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