BI Foundations with SQL, ETL and Data Warehousing Specialization | Coursera

The job market for business intelligence (BI) analysts is expected to grow by23 percent from 2021 to 2031 (US Bureau of Labor Statistics). This IBM specialization gives you sought-after skills employers look for when recruiting for a BI analyst.

BI analysts gather, clean, and analyze key business data to find patterns and insights that aid business decision-making.

During this specialization, you’ll learn the basics of SQL, focusing on how to query relational databases using this popular and powerful language. You’ll use essential Linux commands to create basic shell scripts. Plus, you’ll learn how to build and automate ETL, ELT, and data pipelines using BASH scripts, Apache Airflow, and Apache Kafka.

You’ll discover why companies use data lakes and data marts, and work with adata warehouse. Plus, you’ll dive into the rewarding aspect of BI, creating interactive reports and dashboards so you can derive insights from data in your warehouse.

Additionally, you’ll gain valuable hands-on practice employing real-world tools used by data professionals. Each course has numerous hands-on labs and includes a project, which gives you plenty to talk about in interviews.

When you complete the full program, you’ll have a shareableportfolio of projects and a Specialization certificate for your resume and LinkedIn profile.

Get started on an in-demand business intelligence role. Enroll today.

Applied Learning Project

Using hands-on labs and course projects hosted in cloud-based environments, you’ll gain job-ready experience with the following real-world tools and tasks:

• Writing SQL queries and creating joins using PostgreSQL, MySQL, and DB2 databases

• Running Linux commands and pipes, creating shell scripts

• Scheduling jobs using cron, building ETL and data pipelines, creating & monitoring Airflow DAGs, working with streaming data using Kafka

• Designing data warehouses using star and snowflake schemas

• Verifying data quality, loading staging, and production warehouses

• Developing cubes, rollups, and materialized views/tables

• Creating interactive reports and dashboards

• Analyzing warehouse data using BI tools such as Cognos Analytics