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Python Data Science Toolbox (Part 1)

It's time to push forward and develop your Python chops even further. There are tons of fantastic functions in Python and its library ecosystem. However, as a data scientist, you'll constantly need to write your own functions to solve problems that are dictated by your data. You will learn the art of function writing in this first Python Data Science Toolbox course. You'll come out of this course being able to write your very own custom functions, complete with multiple parameters and multiple return values, along with default arguments and variable-length arguments. You'll gain insight into scoping in Python and be able to write lambda functions and handle errors in your function writing practice. And you'll wrap up each chapter by using your new skills to write functions that analyze Twitter DataFrames.

Lead by [Hugo Bowne-Anderson](https://www.datacamp.com/ins

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Getting Started with Google Kubernetes Engine

This one-week, accelerated online class equips students to containerize workloads in Docker containers, deploy them to Kubernetes clusters provided by Google Kubernetes Engine, and scale those workloads to handle increased traffic.

Introduction to Containers and Docker

Acquaint yourself with containers, Docker, and the Google Container Registry.

• In this lab, you learn how to:

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Python Data Science Toolbox (Part 2)

In this second Python Data Science Toolbox course, you'll continue to build your Python data science skills. First, you'll learn about iterators, objects you have already encountered in the context of for loops. You'll then learn about list comprehensions, which are extremely handy tools for all data scientists working in Python. You'll end the course by working through a case study in which you'll apply all the techniques you learned in both parts of this course.

Using iterators in PythonLand

You'll learn all about iterators and iterables, which you have already worked with when writing for loops.

• Iterators and iterables, iter() and next()

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Launching into Machine Learning

Starting from a history of machine learning, we discuss why neural networks today perform so well in a variety of data science problems. We then discuss how to set up a supervised learning problem and find a good solution using gradient descent. This involves creating datasets that permit generalization; we talk about methods of doing so in a repeatable way that supports experimentation.

Objectives:

• Identify why deep learning is currently popular
• Optimize and evaluate models using loss functions and performance metrics
• Mitigate common problems that arise in machine learning

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Introduction to Importing Data in Python

As a data scientist, you will need to clean data, wrangle and munge it, visualize it, build predictive models, and interpret these models. Before you can do so, however, you will need to know how to get data into Python. In this course, you'll learn the many ways to import data into Python: from flat files such as .txt and .csv; from files native to other software such as Excel spreadsheets, Stata, SAS, and MATLAB files; and from relational databases such as SQLite and PostgreSQL.

Lead by Hugo Bowne-Anderson, Data Scientist at DataCamp

Introduction and flat files

In this chapter, you'll learn how to import data into Python from all types of flat files, which are a simple and prevalent form of data storage. You've previously learned how to use NumPy and pandas—you will learn how to use these packages to impor

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Intermediate Importing Data in Python

In this course, you'll extend this knowledge base by learning to import data from the web and by pulling data from Application Programming Interfaces— APIs—such as the Twitter streaming API, which allows us to stream real-time tweets.

Lead by Hugo Bowne-Anderson, Data Scientist at DataCamp

Importing data from the Internet

The web is a rich source of data from which you can extract various types of insights and findings. In this chapter, you will learn how to get data from the web, whether it is stored in files or in HTML. You'll also learn the basics of scraping and parsing web data.

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Cloud Computing Concepts

This course is at an undergraduate level, likely situated in third or fourth year. Students should feel programming language concepts, including recursion, as well as proof techniques, including induction.

1. Introduction: Clouds, MapReduce, Key-value stores
2. Classical Precursors: Peer-to-peer systems, Grids
3. Widely-used algorithms: Gossip, Membership, Paxos
4. Classical algorithms: Time and Ordering, Snapshots, Multicast
5. Fun: Interviews with leading managers and researchers, from both industry and academia

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Introduction to Linear Modeling in Python

Lecturer

Jason Vestuto, Data Scientist, University of Texas at Austin

Exploring Linear Trends

We start the course with an initial exploration of linear relationships, including some motivating examples of how linear models are used, and demonstrations of data visualization methods from matplotlib. We then use descriptive statistics to quantify the shape of our data and use correlation to quantify the strength of linear relationships between two variables.

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Introduction to Kubernetes

In this LinuxFoundationX's LFS158x course, we'll discuss some of Kubernetes' basic concepts and talk about the architecture of the system, the problems it solves, and the model that it uses to handle containerized deployments and scaling.

This course offers an introduction to Kubernetes and includes technical instructions on how to deploy a stand-alone and multi-tier application. You’ll learn about ConfigMaps and Secrets, and how to use Ingress.

Instructor: Neependra Khare

1. Container Orchestration

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Intro to TensorFlow

We introduce low-level TensorFlow and work our way through the necessary concepts and APIs so as to be able to write distributed machine learning models. Given a TensorFlow model, we explain how to scale out the training of that model and offer high-performance predictions using Cloud Machine Learning Engine.

Introduction

Course Objectives:

• Create machine learning models in TensorFlow