BI Foundations with SQL, ETL and Data Warehousing

Overview

Syllabus

Courses

• 1 review

  20 hours 26 minutes

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  Working knowledge of SQL (or Structured Query Language) is a must for data professionals like Data Scientists, Data Analysts and Data Engineers. Much of the world's data resides in databases. SQL is a powerful language used for communicating with and extracting data from databases. In this course you will learn SQL inside out- from the very basics of Select statements to advanced concepts like JOINs. You will: -write foundational SQL statements like: SELECT, INSERT, UPDATE, and DELETE -filter result sets, use WHERE, COUNT, DISTINCT, and LIMIT clauses -differentiate between DML & DDL -CREATE, ALTER, DROP and load tables -use string patterns and ranges; ORDER and GROUP result sets, and built-in database functions -build sub-queries and query data from multiple tables -access databases as a data scientist using Jupyter notebooks with SQL and Python -work with advanced concepts like Stored Procedures, Views, ACID Transactions, Inner & Outer JOINs through hands-on labs and projects You will practice building SQL queries, work with real databases on the Cloud, and use real data science tools. In the final project you’ll analyze multiple real-world datasets to demonstrate your skills.
• 0 reviews

  17 hours 12 minutes

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  Delve into the two different approaches to converting raw data into analytics-ready data. One approach is the Extract, Transform, Load (ETL) process. The other contrasting approach is the Extract, Load, and Transform (ELT) process. ETL processes apply to data warehouses and data marts. ELT processes apply to data lakes, where the data is transformed on demand by the requesting/calling application. In this course, you will learn about the different tools and techniques that are used with ETL and Data pipelines. Both ETL and ELT extract data from source systems, move the data through the data pipeline, and store the data in destination systems. During this course, you will experience how ELT and ETL processing differ and identify use cases for both. You will identify methods and tools used for extracting the data, merging extracted data either logically or physically, and for loading data into data repositories. You will also define transformations to apply to source data to make the data credible, contextual, and accessible to data users. You will be able to outline some of the multiple methods for loading data into the destination system, verifying data quality, monitoring load failures, and the use of recovery mechanisms in case of failure. By the end of this course, you will also know how to use Apache Airflow to build data pipelines as well be knowledgeable about the advantages of using this approach. You will also learn how to use Apache Kafka to build streaming pipelines as well as the core components of Kafka which include: brokers, topics, partitions, replications, producers, and consumers. Finally, you will complete a shareable final project that enables you to demonstrate the skills you acquired in each module.
• 1 review

  14 hours 39 minutes

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  This course provides a practical understanding of common Linux / UNIX shell commands. In this beginner friendly course, you will learn about the Linux basics, Shell commands, and Bash shell scripting. You will begin this course with an introduction to Linux and explore the Linux architecture. You will interact with the Linux Terminal, execute commands, navigate directories, edit files, as well as install and update software. Next, you’ll become familiar with commonly used Linux commands. You will work with general purpose commands like id, date, uname, ps, top, echo, man; directory management commands such as pwd, cd, mkdir, rmdir, find, df; file management commands like cat, wget, more, head, tail, cp, mv, touch, tar, zip, unzip; access control command chmod; text processing commands - wc, grep, tr; as well as networking commands - hostname, ping, ifconfig and curl. You will then move on to learning the basics of shell scripting to automate a variety of tasks. You’ll create simple to more advanced shell scripts that involve Metacharacters, Quoting, Variables, Command substitution, I/O Redirection, Pipes & Filters, and Command line arguments. You will also schedule cron jobs using crontab. The course includes both video-based lectures as well as hands-on labs to practice and apply what you learn. You will have no-charge access to a virtual Linux server that you can access through your web browser, so you don't need to download and install anything to complete the labs. You’ll end this course with a final project as well as a final exam. In the final project you will demonstrate your knowledge of course concepts by performing your own Extract, Transform, and Load (ETL) process and create a scheduled backup script. This course is ideal for data engineers, data scientists, software developers, and cloud practitioners who want to get familiar with frequently used commands on Linux, MacOS and other Unix-like operating systems as well as get started with creating shell scripts.
• 0 reviews

  11 hours 17 minutes

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  Business Intelligence (BI) Analyst is one of the top 3 fastest growing roles, according to Statista in its ‘Which Jobs Have a Future’ update. IBM Cognos Analytics and Google Looker Studio are powerful BI tools used for data visualization, analytics, and reporting. This short course helps you to build IBM Cognos Analytics and Google Looker Studio skills that can open up opportunities in business analytics, data science, and BI across industries. The course introduces you to the features and capabilities of IBM Cognos Analytics and Google Looker Studio. You’ll learn the basics of visualizing data without writing code, plus how use both to create interactive dashboards. You’ll also gain practical experience through hands-on labs, and you’ll complete a final project in which you’ll create data visualizations and an interactive dashboard that you can share with prospective employers to highlight your skills. If you’re looking to get started as a data analyst, BI analyst or data warehouse specialist, this course provides the ideal introduction to two high profile tools used in these roles. Enroll in this self-paced course today, and develop valuable BI Dashboard skills you can talk about in interviews.
• 1 review

  15 hours 49 minutes

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  Whether you’re an aspiring data engineer, data architect, business analyst, or data scientist, strong data warehousing skills are a must. With the hands-on experience and competencies, you gain on this course, your resume will catch the eye of employers and power up your career opportunities. A data warehouse centralizes and organizes data from disparate sources into a single repository, making it easier for data professionals to access, clean, and analyze integrated data efficiently. This course teaches you how to design, deploy, load, manage, and query data warehouses, data marts, and data lakes. You’ll dive into designing, modeling, and implementing data warehouses, and explore data warehousing architectures like star and snowflake schemas. You’ll master techniques for populating data warehouses through ETL and ELT processes, and hone your skills in verifying and querying data, and utilizing concepts like cubes, rollups, and materialized views/tables. Additionally, you’ll gain valuable practical experience working on hands-on labs, where you’ll apply your knowledge to real data warehousing tasks. You’ll work with repositories like PostgreSQL and IBM Db2, and complete a project that you can refer to in interviews.

Taught by

Hima Vasudevan, IBM Skills Network Team, Jeff Grossman, Lavanya Thiruvali Sunderarajan, Ramesh Sannareddy, Rav Ahuja, Sabrina Spillner, Sam Prokopchuk, Shubhra Das and Yan Luo

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• united states