IBM
Data Engineering Foundations Specialization

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IBM

Data Engineering Foundations Specialization

Build the Foundation for a Data Engineering Career. Develop hands-on experience with Python, SQL, and Relational Databases and master the fundamentals of the Data Engineering ecosystem.

Abhishek Gagneja
Joseph Santarcangelo
Rav Ahuja

Instructors: Abhishek Gagneja +6 more

20,128 already enrolled

Included with Coursera Plus

Get in-depth knowledge of a subject
4.7

(1,396 reviews)

Beginner level

Recommended experience

2 months to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
4.7

(1,396 reviews)

Beginner level

Recommended experience

2 months to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Working knowledge of Data Engineering Ecosystem and Lifecycle. Viewpoints and tips from Data professionals on starting a career in this domain.

  • Python programming basics including data structures, logic, working with files, invoking APIs, using libraries such as Pandas and Numpy, doing ETL.

  • Relational Database fundamentals including Database Design, Creating Schemas, Tables, Constraints, and working with MySQL, PostgreSQL & IBM Db2.

  • SQL query language, SELECT, INSERT, UPDATE, DELETE statements, database functions, stored procs, working with multiple tables, JOINs, & transactions.

Skills you'll gain

  • Category: Database Design
  • Category: MySQL
  • Category: Data Structures
  • Category: Extract, Transform, Load
  • Category: Data Governance
  • Category: IBM DB2
  • Category: Data Store
  • Category: Database Management Systems
  • Category: Data Architecture
  • Category: Stored Procedure
  • Category: SQL
  • Category: Data Pipelines

Details to know

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Taught in English

Advance your subject-matter expertise

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  • Earn a career certificate from IBM

Specialization - 5 course series

What you'll learn

  • List basic skills required for an entry-level data engineering role.

  • Discuss various stages and concepts in the data engineering lifecycle.

  • Describe data engineering technologies such as Relational Databases, NoSQL Data Stores, and Big Data Engines.

  • Summarize concepts in data security, governance, and compliance.

Skills you'll gain

Category: Data Pipelines
Category: Extract, Transform, Load
Category: Data Warehousing
Category: Data Architecture
Category: Data Security
Category: Relational Databases
Category: Data Store
Category: Apache Spark
Category: NoSQL
Category: SQL
Category: Data Governance
Category: Big Data
Category: Databases
Category: Apache Hadoop
Category: Data Science
Category: Data Lakes

What you'll learn

  • Develop a foundational understanding of Python programming by learning basic syntax, data types, expressions, variables, and string operations.

  • Apply Python programming logic using data structures, conditions and branching, loops, functions, exception handling, objects, and classes.

  • Demonstrate proficiency in using Python libraries such as Pandas and Numpy and developing code using Jupyter Notebooks.

  • Access and extract web-based data by working with REST APIs using requests and performing web scraping with BeautifulSoup.

Skills you'll gain

Category: Pandas (Python Package)
Category: Data Structures
Category: Web Scraping
Category: NumPy
Category: Python Programming
Category: Object Oriented Programming (OOP)
Category: Application Programming Interface (API)
Category: Jupyter
Category: File Management
Category: Data Analysis
Category: Programming Principles
Category: Computer Programming
Category: Data Manipulation
Category: Data Import/Export
Category: Restful API

What you'll learn

  • Demonstrate your skills in Python for working with and manipulating data

  • Implement webscraping and use APIs to extract data with Python

  • Play the role of a Data Engineer working on a real project to extract, transform, and load data

  • Use Jupyter notebooks and IDEs to complete your project

Skills you'll gain

Category: Web Scraping
Category: Data Manipulation
Category: Extract, Transform, Load
Category: Python Programming
Category: Code Review
Category: Databases
Category: Application Programming Interface (API)
Category: Unit Testing
Category: Style Guides
Category: SQL
Category: Integrated Development Environments
Category: Restful API
Category: Data Processing
Category: Data Transformation

What you'll learn

  • Describe data, databases, relational databases, and cloud databases.

  • Describe information and data models, relational databases, and relational model concepts (including schemas and tables). 

  • Explain an Entity Relationship Diagram and design a relational database for a specific use case.

  • Develop a working knowledge of popular DBMSes including MySQL, PostgreSQL, and IBM DB2

Skills you'll gain

Category: Relational Databases
Category: SQL
Category: Database Design
Category: MySQL
Category: PostgreSQL
Category: Data Manipulation
Category: Database Architecture and Administration
Category: Database Management Systems
Category: Data Modeling
Category: Data Integrity
Category: Data Management
Category: IBM DB2
Category: Databases
Category: Command-Line Interface

What you'll learn

  • Analyze data within a database using SQL and Python.

  • Create a relational database and work with multiple tables using DDL commands.

  • Construct basic to intermediate level SQL queries using DML commands.

  • Compose more powerful queries with advanced SQL techniques like views, transactions, stored procedures, and joins.

Skills you'll gain

Category: SQL
Category: Transaction Processing
Category: Pandas (Python Package)
Category: Stored Procedure
Category: Data Manipulation
Category: Databases
Category: Query Languages
Category: Database Management
Category: Data Analysis
Category: Relational Databases
Category: Python Programming
Category: Jupyter
Category: Database Design

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Instructors

Abhishek Gagneja
Abhishek Gagneja
IBM
6 Courses224,248 learners
Joseph Santarcangelo
Joseph Santarcangelo
IBM
36 Courses2,090,353 learners

Offered by

IBM

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