Description

Become a Data Engineer is a Big Data data engineering course published by Udacity Training Institute. The digital world is based on data, and without data there is virtually no digital world. Big Data and Data Engineering are two relatively new and potential occupations that are used in the development of data sharing systems and the construction of communication and information infrastructure. This course covers almost all topics related to data science and the most important topics can be designing information models, building data warehouses, data lake, data pipeline automation , Sorting, extracting and analyzing large datasets, etc. Data engineering is one of the most lucrative jobs this year and professionals in this field earn over 115 thousand dollars a year!

Information modeling and types of relational and non-relational information models is one of the most important topics in the field of information engineering. The user should be able to use appropriate and practical information models according to the needs of the project and customer requests. The use of ETLs in the process of building data warehouses is very common and helps you to collect data from several sources simultaneously and after refining and filtering additional data, the final information is stored in databases and data warehouses such as PostgreSQL and Apache Cassandra. Cloud and online data warehouses are common today, and during the training process you will learn the process of building and implementing data warehouses on the basis of Amazon cloud hosting (AWS). Learning the skills offered in this course can prepare the student for various occupations such as analytical engineer, big data engineer, information platform engineer, machine learning engineer and artificial intelligence.

What you will learn in Become a Data Engineer

• Familiarity with the job category of data engineer and other related fields
• Construction of information infrastructure and data warehouses
• Design of relational and non-relational information models
• Construction and implementation of data warehouses and data lakes
• Data Pipeline Automation
• Sorting, extracting and analyzing large datasets
• Receive data from several separate information sources and refine and store them
• Implement a data warehouse on Amazon cloud servers (AWS)
• Understand the Big Data ecosystem and related frameworks
• Plan, automate, and monitor Data Pipeline with Apache Airflow
• Perform data quality review and evaluation tests
• Review and track information sources

Course specifications

Publisher: Udacity
Instructors: Judit Lantos, David Drummond, Olli Iivonen, Sameh El-Ansary, Ben Goldberg, Amanda Moran and Juno Lee
Language: English
Level: Intermediate
Number of Lessons: 50
Duration: Approx. 5 Months

Course topics

Part 01 : Welcome to the Nanodegree Program

Module 01: Welcome

Module 02: Careers Services Orientation

Part 02 : Data Modeling

Module 01: Data Modeling Lessons and Projects

Part 03 : Cloud Data Warehouses

Module 01: Cloud Data Warehouses Lessons

Module 02: Project: Data Warehouse

Part 04 : Data Lakes with Spark

Module 01: Data Lakes Lessons

Module 02: Project: Data Lake

Module 03: Career Services: GitHub

Part 05 : Data Pipelines with Airflow

Module 01: Data Pipelines Lessons

Module 02: Project: Data Pipelines

Module 03: Career Services: LinkedIn

Part 06 : Capstone Project

Module 01: DEND Capstone

Part 07 (Elective): Intro to Python

Module 01: Lessons

Part 08 (Elective): SQL for Data Analysis

Module 01: Lessons

Part 09 (Elective): Command Line Essentials

Module 01: Command Line Essentials

Part 10 (Elective): Git and Github

Module 01: Git and GitHub

Become a Data Engineer Prerequisites

Intermediate Python programming knowledge, of the sort gained through the Programming for Data Science Nanodegree program, other introductory programming courses or programs, or additional real-world software development experience. Including:

• Strings, numbers, and variables; statements, operators, and expressions
• Lists, tuples, and dictionaries; Conditions, loops
• Procedures, objects, modules, and libraries
• Troubleshooting and debugging; Research & documentation
• Problem solving; Algorithms and data structures

This content is also available in the Introduction to Python Programming course.

Intermediate SQL knowledge and linear algebra mastery, addressed in the Programming for Data Science Nanodegree program, including:

• Joins, Aggregations, and Subqueries
• Table definition and manipulation (Create, Update, Insert, Alter)

This content is also available in the SQL for Data Analysis course.

What software and versions will I need in this program?

There are no software and version requirements to complete this Nanodegree program. All coursework and projects can be done via Student Workspaces in the Udacity online classroom.

Pictures

Become a Data Engineer Introduction Video

Installation guide

In order to view the lessons of the course in an organized and regular manner, run the index.html file and run the videos through this file.

English subtitle

Quality: 720p

Download Links

Downloadly

Download Part 1 – 1 GB

Download Part 2 – 1 GB

Download Part 3 – 1 GB

Download Part 4 – 560 MB

Rapidgator

Download Part 1 – 1 GB

Download Part 2 – 1 GB

Download Part 3 – 1 GB

Download Part 4 – 560 MB

File password (s): www.downloadly.ir

Size

3.56 GB