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@misho-kr
misho-kr / Importing Data in Python (Part 2).md
Created Dec 8, 2019
Summary of "Importing Data in Python (Part 2)" course on Datacamp
View Importing Data in Python (Part 2).md

Importing Data in Python (Part 2)

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.

@misho-kr
misho-kr / Importing Data in Python (Part 1).md
Last active Dec 11, 2019
Summary of "Importing Data in Python (Part 1)" course on Datacamp
View Importing Data in Python (Part 1).md

Importing Data in Python (Part 1)

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 import flat

@misho-kr
misho-kr / Python Data Science Toolbox (Part 2).md
Last active Nov 2, 2019
Summary of "Python Data Science Toolbox (Part 2)" course on Datacamp
View Python Data Science Toolbox (Part 2).md

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()
@misho-kr
misho-kr / Python Data Science Toolbox (Part 1).md
Last active Oct 27, 2019
Summary of "Python Data Science Toolbox (Part 1)" course on Datacamp
View Python Data Science Toolbox (Part 1).md

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

@misho-kr
misho-kr / Intermediate Python for Data Science.md
Last active Oct 27, 2019
Summary of "Intermediate Python for Data Science" course on DataCamp
View Intermediate Python for Data Science.md

Intermediate Python for Data Science

Intermediate Python for Data Science is crucial for any aspiring data science practitioner learning Python. Learn to visualize real data with Matplotlib's functions and get acquainted with data structures such as the dictionary and the pandas DataFrame. After covering key concepts such as boolean logic, control flow, and loops in Python, you'll be ready to blend together everything you've learned to solve a case study using hacker statistics.

Lead by Filip Schouwenaars, Data Science Instructor at DataCamp

Matplotlib

Data visualization is a key skill for aspiring data scientists. Matplotlib makes it easy to create meaningful and insightful plots. In this chapter, you’ll learn how to build various types of plots, and customize them to be more visually appealing and interpretable.

@misho-kr
misho-kr / Introduction to Python.md
Last active Oct 22, 2019
Summary of "Introduction to Python" course on DataCamp
View Introduction to Python.md

Introduction to Python

Python is a general-purpose programming language that is becoming ever more popular for data science. Companies worldwide are using Python to harvest insights from their data and gain a competitive edge. Unlike other Python tutorials, this course focuses on Python specifically for data science. In our Introduction to Python course, you’ll learn about powerful ways to store and manipulate data, and helpful data science tools to begin conducting your own analyses. Start DataCamp’s online Python curriculum now.

Lead by Hugo Bowne-Anderson, Data Scientist at DataCamp

Python Basics

An introduction to the basic concepts of Python. Learn how to use Python interactively and by using a script. Create your first variables and acquaint yourself with Python's basic data types.

@misho-kr
misho-kr / DataCamp.md
Last active Dec 8, 2019
DataCamp Courses and Career Tracks
View DataCamp.md

DataCamp Courses and Career Tracks

Data Scientist with Python

A Data Scientist combines statistical and machine learning techniques with Python programming to analyze and interpret complex data

Python | 100 Hours | 26 Courses

@misho-kr
misho-kr / Getting Started with Google Kubernetes Engine.md
Last active Oct 31, 2019
Summary of "Getting Started with Google Kubernetes Engine"
View Getting Started with Google Kubernetes Engine.md

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:
@misho-kr
misho-kr / IBM Cloud -- Deploying Microservices with Kubernetes.md
Last active Oct 11, 2019
Summary of "IBM Cloud: Deploying Microservices with Kubernetes" course on Coursera.Org
View IBM Cloud -- Deploying Microservices with Kubernetes.md

IBM Cloud: Deploying Microservices with Kubernetes

In this course, you learn how to deploy and manage Microservices applications with Kubernetes. You also learn about securing and managing a Kubernetes cluster, and how to plan your Kubernetes cluster for deployment to IBM Cloud.

Taught by

Megan Irvine, Technical Enablement Specialist

Week 1: Introduction to Kubernetes

@misho-kr
misho-kr / Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning.md
Last active Aug 26, 2019
Summary of "Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning" course on Coursera.Org
View Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning.md

Introduction to TensorFlow for AI, ML, and DL

The "Machine Learning" course and "Deep Learning" Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems.

Taught by

Laurence Moroney, AI Advocate, Google Brain

Week 1: A New Programming Paradigm

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