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Spreadsheet Basics

Spreadsheet software is one of the most popular and powerful tools in data analysis. Millions of people use tools like Google Sheets or Microsoft Excel on a daily basis. Even the most experienced data scientists often started their careers with spreadsheets and still use it to test assumptions or to look at data for the first time. In this course, you will learn the basics of spreadsheets by working with rows, columns, addresses, and ranges. You will create your own formulas and learn how to use references.

Lecturers

Vincent Vankrunkelsven, Spreadsheet instructor at DataCamp

1.1 Getting started

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Fedora 28 Installation Notes

Steps to install and set up Fedora Workstation on my desktop machine.

Download and other links

• Notes from the previous Fedora 20 Installation

Preparations

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Conda Essentials

Summary

Conda packages are files containing a bundle of resources: usually libraries and executables, but not always. In principle, Conda packages can include data, images, notebooks, or other assets.

One of the powerful aspects of conda, both the tool and the package format, is that dependencies are taken care of. That is, when you install any Conda package, any other packages needed get installed automatically.

A Conda package, then, is a file containing all files needed to make a given program execute correctly on a given system.

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Conda for Building & Distributing Packages

Summary

In the Conda Essentials course you learned how use the Conda package manager to create and share reproducible environments for data science development.

Anaconda Projects

In this chapter you'll create an Anaconda Project, which is a data science asset that specifies package installs, file downloads, and executable commands. Anaconda projects can be used to run Jupyter notebooks, Bokeh server apps, REST APIs, and command line tools on Windows, Mac OSX, and Linux platforms making deployment easy.

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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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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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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

• Introduction to Python

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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.

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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.

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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