About the course
Develop, deploy, and streamline your data science projects with the most popular end-to-end platform: Anaconda
Anaconda is an open-source platform that brings together the best tools for data science professionals with more than 100 popular packages supporting the Python, Scala, and R languages. Hands-On Data Science with Anaconda gets you started with Anaconda and demonstrates how you can use it to perform data science operations in the real world with ease
Throughout this course, you will learn how to use different packages, with Anaconda to get the best results. You will learn how to efficiently use Conda — the package, dependency, and environment manager for Anaconda. You will also be introduced to several powerful features of Anaconda. You will learn how to build scalable and functionally efficient packages, and how to perform heterogeneous data exploration, distributed computing, and more. You will learn to discover and share packages, notebooks, and environments to increase productivity. You will also learn about Anaconda Accelerate, a feature that can help you to achieve SLAs easily and optimize computational power
The code bundle for this video course is available at - https://github.com/PacktPublishing/Hands-On-Data-Science-with-Anaconda-Video-
Style and Approach
This course is your step-by-step guide, full of use cases, examples, and illustrations to help you master Anaconda concepts.
What You Will Learn
- Perform cleaning, sorting, classification, clustering, regression, and dataset modeling using Anaconda
- Use the Conda package manager and discover, install, and use functionally efficient and scalable packages
- Get comfortable with heterogeneous data exploration using multiple languages within a project
- Perform distributed computing and use Anaconda Accelerate to optimize computational power
- Discover and share packages, notebooks, and environments, and use shared project drives on Anaconda Cloud
- Tackle advanced data prediction problems
Dr. Yuxing Yan
Dr. Yuxing Yan graduated from McGill University with a PhD in Finance. He has taught various finance courses at eight universities in Canada, Singapore, and the U.S. He has published 23 research and teaching-related papers and is the author of six books. Two of his recent publications are Python for Finance and Financial Modeling Using R. He is well-versed in R, Python, SAS, MATLAB, Octave, and C. In addition, he is an expert on financial data analytics.
James Yan is an undergraduate student at the University of Toronto (UofT), currently double-majoring in computer science and statistics. He has hands-on knowledge of Python, R, Java, MATLAB, and SQL. During his study at UofT, he has taken many related courses, such as Methods of Data Analysis I and II, Methods of Applied Statistics, Introduction to Databases, Introduction to Artificial Intelligence, and Numerical Methods, including a capstone course on AI in clinical medicine.
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