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Summary of "Merging DataFrames with pandas

Lead by Timo Grossenbacher, Project Lead Automated Journalism at Tamedia

You can model different phenomena in your data, as well as the relationships between them. This gives your data structure and consistency, which results in better data quality. In this course, you'll experience this firsthand by working with a real-life dataset that was used to investigate questionable university affiliations. You'll learn how to create tables and specify their relationships, as well as how to enforce data integrity. You'll also discover other unique features of database systems, such as constraints.

Your first database

Create your very first database with a set of simple SQL commands. Next, migrate data from existing flat tables into that database. Learn how meta-information about a datab

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Introduction to SQL

This course teaches syntax in SQL shared by many types of databases, such as PostgreSQL, MySQL, SQL Server, and Oracle. This course teaches you everything you need to know to begin working with databases today!

Lead by Nick Carchedi, Product Manager at DataCamp

Selecting columns

Brief introduction to working with relational databases. Learn about their structure, how to talk about them using database lingo, and how to begin an analysis using simple SQL commands to select and summarize columns from database tables.

misho-kr

Analyzing Police Activity with pandas

You will explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior. Practice cleaning messy data, creating visualizations, combining and reshaping datasets, and manipulating time series data. Analyzing Police Activity with pandas will give you valuable experience analyzing a dataset.

Lead by Kevin Markham Founder of Data School

Preparing the data for analysis

Examine and clean the dataset, to make working with it a more efficient process. Fix data types, handling missing values, and dropping columns and rows while learning about the Stanford Open Policing Project dataset.

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Statistical Thinking in Python (Part 1)

After acquiring data and getting them into a form you can work with, you want to make clear, succinct conclusions from them. This crucial last step of a data analysis pipeline hinges on the principles of statistical inference. You will start building the foundation to think statistically, speak the language of your data, and understand what your data is telling you. Get up-to-speed and begin thinking statistically.

By Justin Bois Lecturer at the California Institute of Technology

Graphical exploratory data analysis

Before diving into sophisticated statistical inference techniques, you should first explore your data by plotting them and computing simple summary statistics. This process, called exploratory data analysis, is a crucial first step in statistical analysis of data.

misho-kr

Increasing http.Server boilerplate

So, how does a well configured server look according to Cloudflare?

func NewServer(addr string, handler http.Handler) *http.Server {
	return &http.Server{
		Addr:    addr,
		Handler: handler,

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Intermediate Python for Finance

Learn how to use Python data structures, execution control statements, and DataFrames to manipulate financial data. Work with pandas, using data from the Federal Reserve Bank, to explore national economic trends—an essential part of understanding investment strategies. Calculate risk based on stock price data, and display this data in easy to read plots.

By Kennedy Behrman, Data Engineer, Author, Founder

Datetimes and Dictionaries

Create and manipulate Python datetime objects to help you identify key financial events, such as Black Friday. Store and efficiently look up items using Python dictionaries.

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Merging DataFrames with pandas

The data you need is not in a single file. It may be spread across a number of text files, spreadsheets, or databases. Import the data you’re interested in as a collection of DataFrames and combine them to answer your central questions. This course is all about the act of combining — or merging — DataFrames. Organize, reshape, and aggregate multiple datasets to answer your specific questions.

Lead by Team Anaconda, Data Science Training

Preparing data

Different techniques to import multiple files into DataFrames. Share information between DataFrames using their indexes. How indexes work is essential to merging DataFrames.

misho-kr

Introduction to Airflow in Python

A guide to the basic concepts of Airflow and how to implement data engineering workflows in production

By Mike Metzger, Data Engineer Consultant @ Flexible Creations

Intro to Airflow

An introduction to the components of Apache Airflow and why to use them

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Loan Amortization in Spreadsheets

Course Description

A loan amortization schedule sounds like something that's only used by bankers and financial traders, right?

Wrong! In this course, we'll be looking at the key financial formulas in Google Sheets that you can use to investigate your own loans, like student loans, car loans, and mortgages. We'll build up a dashboard in Google Sheets which uses visualizations and conditional formulas to produce presentation-ready spreadsheets which will impress any finance manager!

By Brent Allen, Financial Spreadsheets Specialist

misho-kr

Introduction to Jenkins

Instructor

Deepika Gautam

• https://www.jenkins.io/doc/book/using/using-credentials/
• https://bmuschko.com/blog/jenkins-build-pipeline/
• http://jenkinsci.wonder.land:8080/ci/