Course HighlightsCOURSE
SQL for Data Science

SQL for Data Science

SQL for Data Science Highlights

Course enrollment

Starts on

15 June 2021

Enrollment closes on
31 December 2022

  Course Fee

Fee

US$99 - US$199

Course enrollment

Starts on

15 June 2021

Enrollment closes on
31 December 2022

Course Fee

Fee

US$99 - US$199

About SQL for Data Science course

Much of the world's data lives in databases. SQL (or Structured Query Language) is a powerful programming language that is used for communicating with and extracting various data types from databases. A working knowledge of databases and SQL is necessary to advance as a data scientist or a machine learning specialist. 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, 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.

What You Will Learn

  • Learn and apply foundational knowledge of the SQL language
  • How to create a database in the cloud
  • How to use string patterns and ranges to query data
  • How to sort and group data in result sets and by data type
  • How to analyze data using Python

Meet your instructors

Course Staff Image #1
Rav Ahuja

AI and Data Science Program Director

Course Certificate

Earn your certificate

Once you have completed this course, you will earn your certificate.

SQL for Data Science

FAQs

IBM Watson for AI has been designed by IBM to enable businesses to collect information from various sources and then analyze the data. It’s a very a popular tool because it helps businesses automate complex processes, and this in turn cuts costs and significantly improves productivity.

Data science is composed of a variety of elements including statistical analysis, programming tools, algorithms, and machine learning techniques. It includes the application of several methodologies, including statistics, scientific methods, artificial intelligence (AI) and data analysis.

1. Facebook: Facebook is now the world's most popular social networking platform. It has millions of users across the globe and is always undertaking large-scale quantitative research utilizing data science to learn more about social relationships. Face recognition and text analysis are two fundamental applications of deep learning, a cutting-edge data science technology that Facebook employs. Facebook also uses powerful neural networks to classify faces in photos. Plus, it uses their Deep Text engine that was created in-house to classify written words.

2. Amazon: Amazon has always sought to be a consumer platform that constantly improves client satisfaction. By employing data science methodologies, Amazon utilizes predictive shipping technologies to analyze massive quantities of data to predict what products people will buy. It tracks buying habits and stores products in nearby warehouses where possible. Amazon also monitors user behavior, order history, rival prices, availability of the product, and so on. Fraud detection is another issue that all e-commerce platforms face. As a result, Amazon has developed its own techniques and algorithms. Plus Amazon uses workflow data to increase warehouse product packing and packaging line productivity.

Once you’ve completed SQL for Data Science, you will be able to:

  • • Create a database in a cloud.
  • • Apply foundational knowledge of SQL language.
  • • Analyze data using Python.
  • • Sort and group data in result sets and by data type.
  • • Use string patterns and ranges to query data.

Data is a valuable asset, and data-driven business processes are helping to boost efficiency and innovation. As a result, the need for data scientists with extraordinary talents and strong skills is growing, and firms are willing to offer excellent remuneration packages to attract the best candidates. The following are some well-known data science service providers:

  • • Oracle
  • • Amazon
  • • JP Morgan Chase
  • • Teradata
  • • Accenture