Intro to Data Science

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6/10 stars
based on  7 reviews
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Cost FREE
Start Date On demand

Course Details

Cost

FREE

Upcoming Schedule

  • On demand

Course Provider

Udacity online courses
Udacity gives students the opportunity to create hands-on projects that can be put into their portfolios and used to demonstrate their skills to future employers. You'll have a personal coach who helps provide feedback on your assignments and projects to assist you in reaching your goals and staying on track in your online classes. Throughout your education experience, you'll be able to track your development, complete in-class projects, have access to interactive exercises and videos and ...
Udacity gives students the opportunity to create hands-on projects that can be put into their portfolios and used to demonstrate their skills to future employers. You'll have a personal coach who helps provide feedback on your assignments and projects to assist you in reaching your goals and staying on track in your online classes. Throughout your education experience, you'll be able to track your development, complete in-class projects, have access to interactive exercises and videos and earn a verified certificate at the end of the course as proof of all that you've learned. You'll be learning from knowledgeable professors across various schools and parts of the globe. Learn about computer science from Dave Evans, an instructor at the University of Virginia, or delve into app development with Samantha Ready, a Developer Evangelist at Salesforce.com.

Provider Subject Specialization
Sciences & Technology
102 reviews

Course Description

The Introduction to Data Science class will survey the foundational topics in data science, namely: * Data Manipulation * Data Analysis with Statistics and Machine Learning * Data Communication with Information Visualization * Data at Scale -- Working with Big Data The class will focus on breadth and present the topics briefly instead of focusing on a single topic in depth. This will give you the opportunity to sample and apply the basic techniques of data science. This course is also a part of our Data Analyst Nanodegree.
Reviews 6/10 stars
7 Reviews for Intro to Data Science

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Greg Hamel profile image
Greg Hamel profile image
8/10 starsCompleted
  • 116 reviews
  • 107 completed
5 years, 4 months ago
Intro to data science is an intermediate level course that assumes basic Python programming skills and knowledge of statistics. The course focuses on gathering, manipulating, analyzing and visualizing data using Python and various Python packages such as numpy, scipy and pandas. One of the best parts about this course was getting some exposure to some Python packages in the scipy stack, although I wish more time was devoted to explaining what the various modules in the scipy stack do, how to set them up at home and when to use them. The first lesson is a fairly gentle introduction with an interesting homework project dealing with data from the Titanic disaster. Lesson 2 goes into more detail about gathering and cleaning data using Pandas and an additional module that lets you make SQL-lite queries to extract data from Pandas data frames. Lesson 3 jumps into data analysis with a T test and linear regression using gradient descent. Goi... Intro to data science is an intermediate level course that assumes basic Python programming skills and knowledge of statistics. The course focuses on gathering, manipulating, analyzing and visualizing data using Python and various Python packages such as numpy, scipy and pandas. One of the best parts about this course was getting some exposure to some Python packages in the scipy stack, although I wish more time was devoted to explaining what the various modules in the scipy stack do, how to set them up at home and when to use them. The first lesson is a fairly gentle introduction with an interesting homework project dealing with data from the Titanic disaster. Lesson 2 goes into more detail about gathering and cleaning data using Pandas and an additional module that lets you make SQL-lite queries to extract data from Pandas data frames. Lesson 3 jumps into data analysis with a T test and linear regression using gradient descent. Going from basic data manipulation into these topics was a bit jarring in terms of difficulty and more time could have been spent explaining how the functions worked. I left without a great appreciation of what gradient descent is really doing. Lesson 4 is focused on making visualizations using a module that attempts to port the functionality R language’s ggplot2 plotting package. Finally, lesson 5 introduces the concept of big data and MapReduce as a solution to deal with large data sets. Each homework assignment after the first has students dealing with New York subway turnstile data, which allows you to get some level of familiarity with the data throughout the course. This was a very good decision, since it lets you focus on learning new concepts rather than spending time familiarizing yourself with new data sets over and over again. Intro to data science introduces some major topics in data science and does a pretty good job given the amount of content it offers, but coverage of the topics is too brief. Hopefully the forthcoming Udacity courses Exploratory Data Analysis and Data Wrangling with MongoDB will build on the foundation provided by this course and give students a bit more depth.
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6/10 starsCompleted
3 years, 4 months ago
The course has 5 sections, each covering a major area of data science( Intro to Numpy/Pandas, data wrangling, data analysis, data visualization, and MapReduce. The course is pretty broad and is a good primer to take courses in each of the above topics. Since it covers so many topics, it has to do so at a broad level and thus what you learn in this course isn't enough to really understand data science. The sections themselves were kind of a mixed bag. Sections 1 & 2 (The Intro. and data wrangling) were very well done. Good clear explanations, an example for each concept covered. Definitely a nice chunk of material squeezed into two lessons. However, after this, the course gets a bit more spotty. Section 3 (data analysis) was essentially divided into two parts - t-tests and regression with gradient descent. The t-test portion was pretty good. You definitely should be familiar with t-tests going in; but the review is brief but go... The course has 5 sections, each covering a major area of data science( Intro to Numpy/Pandas, data wrangling, data analysis, data visualization, and MapReduce. The course is pretty broad and is a good primer to take courses in each of the above topics. Since it covers so many topics, it has to do so at a broad level and thus what you learn in this course isn't enough to really understand data science. The sections themselves were kind of a mixed bag. Sections 1 & 2 (The Intro. and data wrangling) were very well done. Good clear explanations, an example for each concept covered. Definitely a nice chunk of material squeezed into two lessons. However, after this, the course gets a bit more spotty. Section 3 (data analysis) was essentially divided into two parts - t-tests and regression with gradient descent. The t-test portion was pretty good. You definitely should be familiar with t-tests going in; but the review is brief but good. Only complaint was that certain tests were described but there were no Python exercises for them. The regression portion was not the best. The explanation of regression was passable if you just needed a quick refresher. The Python exercises were pretty bad. No Python explanation was given - just launched straight from the concept to "now you do a regression in Python." This is probably because the solutions they were going for were basically straight up writing the equations in Python and writing the numbers in (I'm sorry we are essentially using Python as a glorified calculator here). No built-in regression functions or anything - not practical at all. Gradient descent seemed very advanced for a course that was teaching t-tests 5 slides ago. Once again you were just expected to manually type out whole equation - the whole time I was thinking there was a SciPy stats function I forgot they covered. Section 4 on Visualization was okay as a very very brief intro to the topic. However there were only two coding exercises in the whole section and both were line charts. I feel like more info and exercises could have been added to beef up the section (lets at least learn to code bar charts, c'mon). As it stands now, it was not very useful from a programming standpoint. Section 5 was a very brief MapReduce intro. In my opinion, it was too brief and was significantly shorter than the other sections. Overall, if you are trying to get started in Data Science and NumPy/Pandas this is a decent first course. Just realize that you need to follow up with a course about each of the sections to really gain any sort of skillset or knowledge.
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Student

2/10 starsTaking Now
3 years, 11 months ago
this course assumes more python knowledge than the intro to python class actually provides. do not assume that the "pre-reqs" will prepare you for this course, even if you are familiar with other programming languages. the quiz grader that is used in the course is horrible. it does not accept answers that are outlined as as answers in the videos, and it will often return a long list of bugs on code that runs perfectly in ipython. it's super buggy and makes the course painful.
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4/10 starsDropped
4 years, 3 months ago
I paid to do this course but asked for a refund. I didnt complete it. It was half good half incomplete. Often explanations were good, in other cases were vague and ambigious. I have to use the Machine Learning Courera course + other maths websites to understand what was going on in the Udacity Data Science linear regression. I do like Udacity I just felt this course needed much more robustness and better clearer explanations.. so many topic eg plotting charts were explained badly
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brian c profile image
brian c profile image
5/10 starsCompleted
  • 3 reviews
  • 3 completed
4 years, 6 months ago
The course starts off really great giving you the tools to use Pandas and a little exposure to SQL. The problem is that this course gives a high level overview of five different topics--sometimes these overviews have so much breadth they become pointless. I scratched the surface of data visualization, map reduce, and machine learning, but don't know nearly enough about these topics for them to be useful to me at all. There's a lot of good information in the course, but it fails because of its overly ambitious curriculum. Perhaps taking the courses about each topic would be better.
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caroline m profile image
caroline m profile image
2/10 starsCompleted
  • 2 reviews
  • 2 completed
4 years, 9 months ago
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Tomas Kazemekas profile image
Tomas Kazemekas profile image
10/10 starsCompleted
  • 2 reviews
  • 2 completed
5 years, 2 months ago
As an intro to Data Science it was a good course. During the course we were given a principal overview of the emerging discipline of data science. The thing I liked about the course most was that the Python packages that were introduced and used for the assignments are really some of the most progressive ones available. Especially Pandas for data manipulation and ggplot for visualization. Overall the course provides good guidelines for further exploration of data science.
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