Basic Data Processing and Visualization

Python data products are powering the AI revolution. Top companies like Google, Facebook, and Netflix use predictive analytics to improve the products and services we use every day. Take your Python skills to the next level and learn to make accurate predictions with data-driven systems and deploy machine learning models with this four-course Specialization from UC San Diego.This Specialization is for learners who are proficient with the basics of Python. You'll start by creating your first data strategy. You'll also develop statistical models, devise data-driven workflows, and learn to make m

( 13 Reviews )

Created by: Julian McAuley

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Overall Score : 82 / 100

Course Description

This is the first course in the four-course specialization Python Data Products for Predictive Analytics, introducing the basics of reading and manipulating datasets in Python. In this course, you will learn what a data product is and go through several Python libraries to perform data retrieval, processing, and visualization. This course will introduce you to the field of data science and prepare you for the next three courses in the Specialization: Design Thinking and Predictive Analytics for Data Products, Meaningful Predictive Modeling, and Deploying Machine Learning Models. At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization.

Time: 14h

Instructor Details

Julian McAuley

4.1 Rating
13 Reviews

McAuley has been an Assistant Professor in the Computer Science Department at the University of California, San Diego since 2014. Previously he was a postdoctoral scholar at Stanford University after receiving his PhD from the Australian National University in 2011. His research is concerned with developing predictive models of human behavior using large volumes of online activity data.

Reviews

4.1

13 total reviews

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By Paul E J on 3-Jul-19

This is not a Python introduction, but the authors approach it as if it were. Even the most basic data scientist will not calculate averages in the way described here. We'd use pandas or similar to get not just means, but other summary stats as well. For a Python course, I could understand doing it the way shown here. But not for data science.

By Davide C on 18-Jun-19

The test scripts make no sense.

By Ioana B on 11-Oct-19

The information learned in this course is very useful, for a beginner in data science. It is a very good introduction in working with python, extracting data-sets, defining features and plotting graphics. What I didn't like at all is the engagement. Finishing the course was not satisfactory at all for me - even if I submitted my project on time, I didn't receive 3 reviews and I found the grading system very subjective. Knowing this, I would think twice about paying for this experience - what I learned can be found in free tutorials too, and only for the interaction with other users I don't think it is worth the price.

By Jonas J T on 23-Aug-19

Quick intro to data processing. More material on numpy and pandas would have been nice. Im still trying to figure out why the specialization mentions "Design Thinking". At least in this course...not a single design thinking concept was mentioned.

By Sebastian S on 22-Jun-19

The positives: I liked the design of the final project, and how users were encouraged to 'get out there' and find some interesting open source data sets. The lectures were well structured with good narratives and good examples. The negatives: I would have liked a bit more focus on actual visualization libraries like matplotlib and maybe seaborn. When covering the data types (date, string, boolean etc.), it might be worth adding an extra week or so were these things are done with the help of the standard library pandas. I feel like this is what people will end up doing anyway bc there are so little alternatives in python to do processing, so a course on data processing should ideally cover that library.

By Tiago F on 11-Nov-19

Very Good to start learning Python

By Clarence E Y on 24-Aug-19

This course enables students to learn intermediate level skills in data wrangling, data exploration, and visualization. The final project requires selecting a topic of personal interest and constructing a complete project work flow. By doing this, areas of weakness in data wrangling, cleaning/QA, data exploration, and visualization may to uncovered and addressed. The result is to build greater skills and confidence.

By umair on 24-Aug-19