Learn Python - the most popular programming language and for Data Science and Software Development.

Apply Python programming logic Variables, Data Structures, Branching, Loops, Functions, Objects & Classes.

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

Access and web scrape data using APIs and Python libraries like Beautiful Soup.

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

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

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.

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 pipeline through 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 query data 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 among 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 operations in Cassandra.

Explain the impact of big data, including use cases, tools, and processing methods.

Describe Apache Hadoop architecture, ecosystem, practices, and user-related applications, including Hive, HDFS, HBase, Spark, and MapReduce.

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

Use Spark’s RDDs and data sets, optimize Spark SQL using Catalyst and Tungsten, and use Spark’s development and runtime environment options.

Describe ML, explain its role in data engineering, summarize generative AI, discuss Spark's uses, and analyze ML pipelines and model persistence.

Evaluate ML models, distinguish between regression, classification, and clustering models, and compare data engineering pipelines with ML pipelines.

Construct the data analysis processes using Spark SQL, and perform regression, classification, and clustering using SparkML.

Demonstrate connecting to Spark clusters, build ML pipelines, perform feature extraction and transformation, and model persistence.

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.