BI Foundations with SQL, ETL and Knowledge Warehousing Specialization

WHAT YOU WILL LEARN

• Write SQL queries to work with relational databases together with CREATE TABLE, SELECT, INSERT, UPDATE, DELETE, ORDER, JOIN, Features, and many others.

• Execute generally used Linux instructions; Automate Extract, Rework and Load (ETL) jobs and knowledge pipelines utilizing BASH scripts, Apache Airflow & Kafka

• Design Knowledge Warehouses utilizing star and snowflake schemas, loading and confirm knowledge in staging areas, construct cubes, rollups and materialized views/tables

• Analyze knowledge in warehouses utilizing interactive studies and dashboards utilizing BI instruments equivalent to Cognos Analytics

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SKILLS YOU WILL GAIN

• Shell Script
• Bash (Unix Shell)
• Linux
• Cloud Databases
• Python Programming
• Ipython
• Relational Database Administration System (RDBMS)
• SQL
• Extraction, Transformation And Loading (ETL)
• Apache Kafka
• Apache Airflow
• Knowledge Pipelines

About this Specialization

Professionals with SQL, ETL, Enterprise Knowledge Warehousing (EDW), Enterprise Intelligence (BI) and Knowledge Evaluation abilities are in nice demand. This Specialization is designed to offer profession related information and abilities for anybody desirous to pursue a job position in domains equivalent to Knowledge Engineering, Knowledge Administration, BI or Knowledge Analytics. This system consists of 4 on-line programs. Within the first course you study the fundamentals of SQL and how one can question relational databases with this highly effective language. Subsequent you study to make use of important Linux instructions and create fundamental shell scripts. You proceed your journey by studying to construct and automate ETL, ELT and knowledge pipelines utilizing BASH scripts, Apache Airflow and Apache Kafka. Within the last course you study Knowledge Lakes, Knowledge Marts in addition to work with Knowledge Warehouses. You additionally create interactive studies and dashboards to derive insights from knowledge in your warehouse. Notice that this specialization has a major emphasis on hands-on observe using actual instruments utilized by knowledge professionals. Each course has quite a few hands-on labs in addition to a course challenge. Whereas you’ll profit from some prior programming expertise, it isn’t completely essential for this course. The one pre-requisites for this specialization are fundamental laptop and knowledge literacy, and a ardour to self-learn on-line.

Utilized Studying Undertaking

Every course offers a lot of observe utilizing hands-on labs and tasks on cloud-based environments with actual instruments. Arms-on workouts embrace: working Linux instructions and pipes, in creating shell scripts, scheduling jobs utilizing cron, constructing ETL and knowledge pipelines, creating & monitoring Airflow DAGs, working with streaming knowledge utilizing Kafka, designing a knowledge warehouses with star and snowflake schemas, verifying knowledge high quality, loading staging and manufacturing warehouses, writing SQL queries and joins with PostgreSQL, MySQL & DB2 databases, growing cubes, rollups and materialized views/tables, creating interactive studies & dashboards, and analyzing warehouse knowledge utilizing BI instruments like Cognos Analytics.

There are 4 Courses in this Specialization

COURSE1

Hands-on Introduction to Linux Commands and Shell Scripting

4.7

stars

361 ratings

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72 reviews

This course provides a practical introduction to Linux and commonly used Linux / UNIX shell commands. It teaches you the basics of Bash shell scripting to automate a variety of tasks. 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 perform the labs.

You will learn how to interact with the Linux Terminal, execute commands, navigate directories, edit files, as well as install and update software. 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 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. 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.

COURSE2

Databases and SQL for Data Science with Python

4.6

stars

15.425 ratings

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1.973 reviews

Much of the world's data resides in databases. SQL (or Structured Query Language) is a powerful language which is used for communicating with and extracting data from databases. A working knowledge of databases and SQL is a must if you want to become a data scientist.

The purpose of this course is to introduce relational database concepts and help you learn and apply foundational knowledge of the SQL language. It is also intended to get you started with performing SQL access in a data science environment. The emphasis in this course is on hands-on and practical learning . As such, you will work with real databases, real data science tools, and real-world datasets. You will create a database instance in the cloud. Through a series of hands-on labs you will practice building and running SQL queries. You will also learn how to access databases from Jupyter notebooks using SQL and Python. No prior knowledge of databases, SQL, Python, or programming is required. Anyone can audit this course at no-charge. If you choose to take this course and earn the Coursera course certificate, you can also earn an IBM digital badge upon successful completion of the course. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.

COURSE3

ETL and Data Pipelines with Shell, Airflow and Kafka

4.5

stars

90 ratings

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27 reviews

After taking this course, you will be able to describe 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.

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 importing 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. Finally, you will complete a shareable final project that enables you to demonstrate the skills you acquired in each module.

COURSE4

Getting Started with Data Warehousing and BI Analytics

4.5

stars

41 ratings

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9 reviews

Data is one of an organization’s most valuable commodities. But how can organizations best use their data? And how does the organization determine which data is the most recent, accurate, and useful for business decision making at the highest level?

After taking this course, you will be able to describe different kinds of repositories including data marts, data lakes, and data reservoirs, and explain their functions and uses. A data warehouse is a large repository of data that has been cleaned to a consistent quality. Not all data repositories are used in the same way or require the same rigor when choosing what data to store. Data warehouses are designed to enable rapid business decision making through accurate and flexible reporting and data analysis. A data warehouse is one of the most fundamental business intelligence tools in use today, and one that successful Data Engineers must understand. You will also be able to describe how data warehouses serve a single source of data truth for organization’s current and historical data. Organizations create data value using analytics and business intelligence applications. Now that you have experienced the ELT process, gain hands-on analytics and business intelligence experience using IBM Cognos and its reporting, dashboard features including visualization capabilities. Finally, you will complete a shareable final project that enables you to demonstrate the skills you acquired in each module.