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Instructor
Jiang Zhou, Instructor - Oracle SQL and Best Practices for Data Science

Jiang Zhou

Jiang Zhou, Ph.D., has two decades of experience building predictive analytics solutions across industries including telecommunication, banking, insurance and smart city. These solutions have resulted in over $200 million savings for clients. He is a Member of Industrial Advisory Board at University of Massachusetts Boston and the Chief Data Scientist at 慧信(Smart Credit). Previously, he was the chief statistician at Lightbridge, Inc., a vice president at Citizens Bank and a consulting member of technical staff at Oracle Inc. Dr. Zhou has been involved in 3 head to head competitions to build the best predictive models, i.e., a customer credit risk model for a top 3 cell phone company, a bank card fraud detection model for a top 15 bank, and a direct sales model for a marketing company. Dr. Zhou's models have won all 3 competitions. In addition to his technical skills, he is, as one of his clients put it, "a great trainer, and a good presenter of theoretical data mining concepts so that they can be understood by most". He is the author of the blog www.deep-data-mining.com that is regarded as one of the best on data analytics and data mining. Dr. Zhou is the primary author of an award-wining scientific paper, "Using genetic learning artificial neural networks for spatial decision making in GIS" (Nov., 1996, PE & RS). His recent article , Use Big Data and Artificial Intelligence to Rationally Evaluate the Characteristic Town, is published on influential "Economic Information Daily" (in Chinese) .

Instructor: Jiang Zhou

Oracle SQL skills and best practices for data science/data analytics tasks.

  • Learn Oracle SQL skills and best practices to perform typical data science/data analytics tasks.
  • Manage your data and scripts better and be stress free when performing complex data work.
  • Instructor has a Ph.D. with two decades of experience building predictive analytics solutions across industries including telecommunication, banking, insurance and smart city.

Course Description

In this course, Dr. Jay Zhou, an industrial practitioner and and competition winner, will share his Oracle SQL skills and best practices to perform typical data science/data analytics tasks. Hopefully, after taking the course you will become a better data scientist/data analyst. In your future project, you will be more efficient, make less mistakes, manage your data and scripts better and be stress free when performing complex data work.

What am I going to get from this course?

Learn practical Oracle SQL skills and best practices for typical data science/data analytics tasks. Become more productive, making less mistakes, better managing the data and scripts, and stress free when performing complex data work.

Prerequisites and Target Audience

What will students need to know or do before starting this course?

Students should have basic knowledge about SQL before taking the course.

Who should take this course? Who should not?

The course is designed for those who analyze data stored in relational databases, particularly Oracle. Those include data scientists, data analysts and statisticians. It is not suitable for people who are not comfortable with coding.  

Curriculum

Module 1: Introduction

Lecture 1 About the Instructor

Dr. Jay Zhou has been involved in 3 head to head competitions to build the best models for clients and he won them all. His work has been used by top telecommunication companies and banks in America and Canada. His favorite tool is SQL. He is the author of the blog https://www.deep-data-mining.com, a Feedspot Top 30 Big Data Blogs Winner.

Lecture 2 Content of the Course

All slides used in the course are downloadable as a PDF file. The course will cover the following topics. *Why SQL for Data Science? *How to perform common tasks using SQL including: **Data Validation and Understanding **Data Cleansing and Preparation **Feature Variable Calculation. These tasks typically take 80% or more time when performing a data science project. The course will NOT cover predictive model building.

Lecture 3 About the SQL Scripts and Testing Data Used in the Course

There are 2 SQL script files used for this course. Script File 1. sql_for_ds_data_prep.sql. This file should be run first. -- This script file will create 3 tables and populate them with data. -- These tables are card_txn, sales and score. -- If these tables exist, they will be dropped first. -- There are 110, 9 and 817 records in table card_txn, sales and score, respectively. Script File 2. sql_for_ds.sql. This file contains the SQL queries presented in course. We may load them into SQL Clients such as SQL Developer and run them.

Module 2: Why SQL for Data Science

Lecture 4 Why SQL for Data Science? - Part 1
Lecture 5 Why SQL for Data Science? - Part 2
Lecture 6 Why SQL for Data Science? - Part 3
Lecture 7 Why SQL for Data Science? - Part 4

Module 3: Typical Tasks for Data Science Project

Lecture 8 Typical Tasks for Data Science Project - Part 1
Lecture 9 Typical Tasks for Data Science Project - Part 2

Module 4: Data Validation and Understanding

Lecture 10 SQL for Data Validation and Understanding - Part 1
Lecture 11 SQL for Data Validation and Understanding - Part 2
Lecture 12 SQL for Data Validation and Understanding - Part 3
Lecture 13 SQL for Data Validation and Understanding - Part 3b
Lecture 14 SQL for Data Validation and Understanding -Part 4
Lecture 15 SQL for Data Validation and Understanding -Part 5
Lecture 16 SQL for Data Validation and Understanding - Part 6
Lecture 17 SQL for Data Validation and Understanding - Part 7
Lecture 18 SQL for Data Validation and Understanding - Part 8
Lecture 19 SQL for Data Validation and Understanding - Part 9
Lecture 20 SQL for Data Validation and Understanding Part 10
Lecture 21 SQL for Data Validation and Understanding Part 11
Lecture 22 SQL for Data Validation and Understanding - Part 12
Lecture 23 SQL for Data Validation and Understanding - Part 13
Lecture 24 SQL for Data Validation and Understanding - Part 14

Module 5: SQL for Data Cleansing and Preparation

Lecture 25 SQL for Data Cleansing and Preparation - Part 1
Lecture 26 SQL for Data Cleansing and Preparation - Part 2
Lecture 27 SQL for Data Cleansing and Preparation - Part 3
Lecture 28 SQL for Data Cleansing and Preparation - Part 4
Lecture 29 SQL for Data Cleansing and Preparation - Part 5
Lecture 30 SQL for Data Cleansing and Preparation - Part 6
Lecture 31 SQL for Data Cleansing and Preparation - Part 7
Lecture 32 SQL for Data Cleansing and Preparation - Part 8
Lecture 33 SQL for Data Cleansing and Preparation - Part 9
Lecture 34 SQL for Data Cleansing and Preparation - Part 10
Lecture 35 SQL for Data Cleansing and Preparation - Part 11
Lecture 36 SQL for Data Cleansing and Preparation - Part 12
Lecture 37 SQL for Data Cleansing and Preparation - Part 13
Lecture 38 SQL for Data Cleansing and Preparation - Part 14
Lecture 39 SQL for Data Cleansing and Preparation - Part 15

Module 6: Feature Variable Calculation

Lecture 40 Feature Variable Calculation - Part 1
Lecture 41 Feature Variable Calculation - Part 2
Lecture 42 Feature Variable Calculation - Part 3
Lecture 43 Feature Variable Calculation - Part 4
Lecture 44 Feature Variable Calculation - Part 5
Lecture 45 Feature Variable Calculation - Part 6
Lecture 46 Feature Variable Calculation - Part 7
Lecture 47 Feature Variable Calculation - Part 8
Lecture 48 Feature Variable Calculation - Part 9

Module 7: Summaries and Highlights

Lecture 49 Summaries and Highlights

Module 8: All Slides Used in the Course

Lecture 50 All Slides for Used in the Course

The PDF file contains all the slides used in the course.

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