Describe Python Basics including Data Types, Expressions, Variables, and Data Structures.

Apply Python programming logic using Branching, Loops, Functions, Objects & Classes.

Demonstrate proficiency in using Python libraries such as Pandas, Numpy, and Beautiful Soup.

Access web data using APIs and web scraping from Python in Jupyter Notebooks.

Demonstrate your skills in Python for data engineering tasks 

Implement webscraping and use APIs to collect data in Python

Assume the role of a Data Engineer working on a real project

Extract, Transform and Load (ETL) data using Jupyter notebooks

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.

Implement different relational model constraints.

Analyze data within a database using SQL and Python.

Create a relational database on Cloud and work with tables.

Write SQL statements including SELECT, INSERT, UPDATE, and DELETE.

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

Describe the Linux architecture and common Linux distributions and update and install software on a Linux system.

Perform common informational, file, content, navigational, compression, and networking commands in Bash shell.

Develop shell scripts using Linux commands, environment variables, pipes, and filters.

Schedule cron jobs in Linux with crontab and explain the cron syntax. 

Create, query, and configure databases and access and build system objects such as tables.

Perform basic database management including backing up and restoring databases as well as managing user roles and permissions. 

Monitor and optimize important aspects of database performance. 

Troubleshoot database issues such as connectivity, login, and configuration and automate functions such as reports, notifications, and alerts. 

Describe and contrast Extract, Transform, Load (ETL) processes and Extract, Load, Transform (ELT) processes.

Explain batch vs concurrent modes of execution.

Implement an ETL pipelinethrough shell scripting.

Describe data pipeline components, processes, tools, and technologies.

Explore the architecture, features, and benefits of data warehouses, data marts, and data lakes and identify popular data warehouse system vendors.

Design and populate a data warehouse, and model and querydata using CUBE, ROLLUP, and materialized views.

Identify popular data analytics and business intelligence tools and vendors and create data visualizations using IBM Cognos Analytics.

Design and load data into a data warehouse, write aggregation queries, create materialized query tables, and create an analytics dashboard.

Differentiate between the four main categories of NoSQL repositories.

Describe the characteristics, features, benefits, limitations, and applications of the more popular Big Data processing tools.

Perform common tasks using MongoDB tasks including create, read, update, and delete (CRUD) operations.

Execute keyspace, table, and CRUD operationsin Cassandra.

Explainthe impact of Big Data including use cases, tools, and processing methods.

ExplainApache Hadoop architecture, ecosystem, and practices, and userelatedapplications including HDFS, HBase, Spark, and MapReduce.

Apply Spark programming basics, including parallel programming basics forDataFrames, data sets, and Spark SQL.

UseSpark’s RDDsanddata sets, optimizingSparkSQLusing Catalyst and Tungsten, anduseSpark’s development and runtime environment options.

Explain how streaming data and Spark Structured Streaming empower machine learning and AI tasks.

Define graph theory, describe Apache Spark GraphFrames, and identify data suitable for GraphFrames.

Describe how ETL processes work with Apache Spark and machine learning and extend that knowledge to Spark MLlib capabilities and related benefits.

Explain supervised learning, unsupervised learning, and clustering, and explain how to use the k-means clustering algorithm with Spark MLlib.

Demonstrate proficiency in skills required for an entry-level data engineering role.

Design and implement various concepts and components in the data engineering lifecycle such as data repositories.

Showcase working knowledge with relational databases, NoSQL data stores, big data engines, data warehouses, and data pipelines.

Apply skills in Linux shell scripting, SQL, and Python programming languages to Data Engineering problems.