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  • Others 
    freeCodeCamp - Data Analysis with Python

    freeCodeCamp - Data Analysis with Python

    In these comprehensive video courses, created by Santiago Basulto, you will learn the whole process of data analysis. You'll be reading data from multiple sources (CSV, SQL, Excel), process that data using NumPy and Pandas, and visualize it using Matplotlib and Seaborn, Additionally, we've included a thorough Jupyter Notebook course, and a quick Python reference to refresh your programming skills.

    Course related:
    AMA1600 Fundamentals of AI and Data Analytics and AMA1751 Linear Algebra
    Subjects:
    Mathematics and Statistics and Computing
    Keywords:
    Computer programming Computer science Python (Computer program language)
    Resource Type:
    Others
     
  • Others 
    Kaggle

    Kaggle

    Kaggle offers a no-setup, customizable, Jupyter Notebooks environment. Access GPUs at no cost to you and a huge repository of community published data & code. Inside Kaggle you’ll find all the code & data you need to do your data science work. Use over 50,000 public datasets and 400,000 public notebooks to conquer any analysis in no time.

    Subjects:
    Computing
    Keywords:
    Machine learning Artificial intelligence Big data
    Resource Type:
    Others
     
  • Video 
    StatQuest with Josh Starmer

    StatQuest with Josh Starmer

    Statistics, Machine Learning and Data Science can sometimes seem like very scary topics, but since each technique is really just a combination of small and simple steps, they are actually quite simple. My goal with StatQuest is to break down the major methodologies into easy to understand pieces. That said, I don't dumb down the material. Instead, I build up your understanding so that you are smarter.

    Course related:
    HTI34016 Introduction to Clinical Research
    Subjects:
    Computing and Mathematics and Statistics
    Keywords:
    Statistics Mathematical analysis Data mining Machine learning
    Resource Type:
    Video
     
  • MOOC 
    Machine Learning and Computational Statistics

    Machine Learning and Computational Statistics

    This course covers a wide variety of topics in machine learning and statistical modeling. While mathematical methods and theoretical aspects will be covered, the primary goal is to provide students with the tools and principles needed to solve the data science problems found in practice. This course also serves as a foundation on which more specialized courses and further independent study can build. This course was designed as part of the core curriculum for the Center for Data Science's Masters degree in Data Science. Other interested students who satisfy the prerequisites are welcome to take the class as well. Note that class is intended as a continuation of DS-GA-1001 Intro to Data Science, which covers some important, fundamental data science topics that may not be explicitly covered in this DS-GA class (e.g. data cleaning, cross-validation, and sampling bias).

    Course related:
    LGT6801 Guided Study in Logistics I
    Subjects:
    Computing and Mathematics and Statistics
    Keywords:
    Big data Data mining Machine learning Mathematical statistics -- Data processing
    Resource Type:
    MOOC