Introduction to Fundamental Skills for Data Work: Data Processing

Develop expertise in transforming, cleaning, and manipulating data to make it analytics-ready and valuable for business insights. Learn essential data engineering skills including ETL/ELT processes, quality management, and batch and real-time processing methodologies. These courses help data professionals turn raw data into clean, structured, and analysis-ready datasets while ensuring integrity throughout the transformation process.

Transform raw data into meaningful formats.
Clean, integrate, and prepare data for analysis.
Handle complex data sets and enhance data usability.

Syllabus

Courses under this program:
Course 1: Data Engineering Foundations
-Learn the key facets of data engineering, from its place in the data science realm, to the specific tasks and skills every data engineer should possess.

Course 2: Python for Data Science and Machine Learning Essential Training Part 1
-Learn Python programming skills for data science and machine learning. Discover how to clean, transform, analyze, and visualize data, as you build a practical, real-world project.

Course 3: Python for Data Science and Machine Learning Essential Training Part 2
-In the second half of this two-part course, explore the essentials of using Python for data science and machine learning.

Course 4: Data Cleaning in Python Essential Training
-Improve the overall analytic workflow of your organization by boosting your data cleaning skills in Python.

Course 5: Apache Spark Essential Training: Big Data Engineering
-This course focuses on building full-fledged solutions that combine Apache Spark with other Big Data tools to create end-to-end data pipelines.

Course 6: ETL in Python and SQL
-Gain the knowledge you need to build data pipelines in a data-driven world.

Course 7: Data Quality: Core Concepts
-Explore data quality fundamentals, data stakeholders and tooling, common data quality issues, and the data lifecycle.