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Essential Data Science: Database and ETL With Python

 
Essential Data Science: Database and ETL With Python
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 25 lectures (1h 34m) | Size: 748.5 MB

Mastering database programming and ETL with Python.


Data Processing and Manipulation

Getting started with Python application development and database

Working on with file I/O, Text, CSV, Excel, JSON and XML

Reading Files from HTTP Website ( Included Web Authentication)

Reading Files from Server-Based S3 Protocol

Accessing Python applications to Multiple Databases such as SQLite, MySQL, SQL Server and PostgreSQL

Accessing Python applications to Multiple NoSQL Databases such as MongoDB, Redis and Apache Cassandra

Building ETL applications with various scenario

Having a basic knowledge of Python programming

A computer with internet accesses

Extract, Transform, Load (ETL) is a process to process various data sources to be targeted data sources. ETL is one of required skill in data science to implement pre-processing and/or post-processing. This workshop is designed for anyone who wants to improve ETL skills.

The workshop will focus on the following data sources

Files

RDBMS databases

NoSQL databases

We start to learn for basic I/O files and directories. We can copy and delete files or directories. Next, we explore how to access various file types such as Text, CSV, JSON, and XML. In addition, we access remote data source over website and server-based S3 protocol.

We learn how to work with RDBMS database with Python. We use RBDMS database ees such as SQLite, MySQL, SQL Server and PostgreSQL. We perform CRUD (Create, Read, Update, Delete). We also access database table from Python Pandas. Then, we can convert Python Pandas Dataframe into database table.

We can leverage ETL with NoSQL database ees. We will work with MongoDB, Redis and Apache Cassandra. We perform CRUD (Create, Read, Update, Delete) on these NoSQL database ees. We also access NoSQL database from Python Pandas. Then, we can convert Python Pandas Dataframe into NoSQL database.

Last, we implement ETL Python program. We have three case studies to show how ETL work with Python.

This workshop needs a basic Python programming to follow all hands-on-labs. Internet access is needed when we're installing additional Python libraries.

Student and professional developers

Any developer who wants to learn Python and database

Any developer who wants to learn ETL with Database



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