UC Davis - Learn SQL Basics for Data Science Specialization
offered by Coursera

Estd. 2012

Learn SQL Basics for Data Science Specialization
at
Coursera
Overview

Equip with essential SQL skills crucial for data science and the comprehensive introduction to the standard language for managing and manipulating relational databases

Duration	6 months
Start from	Start Now
Mode of learning	Online
Official Website	Go to Website
Credential	Certificate

Learn SQL Basics for Data Science Specialization
at
Coursera
Highlights

Earn a certificate after completion of the course
Financial aid facility available
Assignment for better practice

Learn SQL Basics for Data Science Specialization
at
Coursera
Course details

Who should do this course?

Aspiring Data Scientists

Data Analysts

Students and Academics

Business Analysts

What are the course deliverables?

Use SQL commands to filter, sort, & summarize data; manipulate strings, dates, & numerical data from different sources for analysis

Assess and create datasets to solve your business questions and problems using SQL

Use the collaborative Databricks workspace and create an end-to-end pipeline that reads data, transforms it, and saves the result

Develop a project proposal & select your data, perform statistical analysis & develop metrics, and present your findings & make recommendations

More about this course

This is a comprehensive introductory course designed to provide participants with a solid foundation in SQL, the standard language for managing and manipulating databases

This specialization focuses on the fundamental aspects of SQL that are essential for data science, enabling students to efficiently retrieve, manipulate, and analyze data stored in relational databases

This course is structured to build confidence and competence in using SQL, preparing participants for more advanced data science techniques and applications

Learn SQL Basics for Data Science Specialization
at
Coursera
Curriculum

SQL for Data Science

Identify a subset of data needed from a column or set of columns and write a SQL query to limit to those results

Use SQL commands to filter, sort, and summarize data

Create an analysis table from multiple queries using the UNION operator

Manipulate strings, dates, & numeric data using functions to integrate data from different sources into fields with the correct format for analysis

Data Wrangling, Analysis and AB Testing with SQL

Validate and clean a dataset

Assess and create datasets to answer your questions

Solve problems using SQL

Build a simple testing framework to touch on AB Testing

Distributed Computing with Spark SQL

Use the collaborative Databricks workspace to write scalable Spark SQL code that executes against a cluster of machines

Inspect the Spark UI to analyze query performance and identify bottlenecks

Create an end-to-end pipeline that reads data, transforms it, and saves the result

Build a medallion (bronze, silver, gold) lakehouse architecture with Delta Lake to ensure the reliability, scalability, and performance of your data

SQL for Data Science Capstone Project

Develop a project proposal and select your data

Perform descriptive statistics as part of your exploratory analysis

Develop metrics and perform advanced techniques in SQL

Present your findings and make recommendations

Learn SQL Basics for Data Science Specialization
at
Coursera
Faculty details

Don Noxon

Don is a Research Data Scientist at Intel Corporation, where he has worked in many roles in his 22+ years with the company. Currently he is working on high-value business problems where they attempt to predict the future using Machine Learning and Deep Learning techniques; oftentimes needing to develop their own algorithms to be successful.

Conor Murphy

Conor lives at the intersection of data science and data engineering, driving data-driven decisions through distributed machine learning and inferential statistics. Currently he is a data scientist and machine learning subject matter expert at Databricks where he both teaches and consults. He transitioned to the tech sector after spending four years leveraging data for more impactful humanitarian interventions in developing countries with a focus on business development.