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SQL for Data Scientists

Get a hands-on experience in SQL with uCertify’s course SQL for Data Scientists, which is designed to be a learning resource for anyone who wants to become (or who already is) a data analyst or data scientist. It teaches the ability to pull data from databases to build their own datasets without having to rely on others in the organization to query the source system and transform it into flat files (or spreadsheets) for them.
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  • Price: $279.99
  • Delivery method: eLearning
  • DIR Discount: 20%

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Test Prep
57+ Pre Assessment Questions | 57+ Post Assessment Questions |
Features
33+ LiveLab | 33+ Video tutorials | 47+ Minutes
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  • Get assistance every step of the way from our Texas-based team, ensuring your training experience is hassle-free and aligned with your goals.
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  • Benefit from being taught by Professionally Certified Instructors with expertise in their fields and a strong commitment to making sure you learn and succeed.

Outline

Lessons 1:
Introduction

  • Who This Course Is For?
  • Why You Should Learn SQL if You Want to Be a Data Scientist?
  • Conventions

Lessons 2:
Data Sources

  • Data Sources
  • Tools for Connecting to Data Sources and Editing SQL
  • Relational Databases
  • Dimensional Data Warehouses
  • Asking Questions About the Data Source
  • Introduction to the Farmer’s Market Database
  • A Note on Machine Learning Dataset Terminology
  • Exercises

Lessons 3:
The SELECT Statement

  • The SELECT Statement
  • The Fundamental Syntax Structure of a SELECT Query
  • Selecting Columns and Limiting the Number of Rows Returned
  • The ORDER BY Clause: Sorting Results
  • Introduction to Simple Inline Calculations
  • More Inline Calculation Examples: Rounding
  • More Inline Calculation Examples: Concatenating Strings
  • Evaluating Query Output
  • SELECT Statement Summary
  • Exercises Using the Included Database

Lessons 4:
The WHERE Clause

  • The WHERE Clause
  • Filtering SELECT Statement Results
  • Filtering on Multiple Conditions
  • Multi-Column Conditional Filtering
  • More Ways to Filter
  • Filtering Using Subqueries
  • Exercises Using the Included Database

Lessons 5:
CASE Statements

  • CASE Statement Syntax
  • Creating Binary Flags Using CASE
  • Grouping or Binning Continuous Values Using CASE
  • Categorical Encoding Using CASE
  • CASE Statement Summary
  • Exercises Using the Included Database

Lessons 6:
SQL JOINs

  • Database Relationships and SQL JOINs
  • A Common Pitfall when Filtering Joined Data
  • JOINs with More than Two Tables
  • Exercises Using the Included Database

Lessons 7:
Aggregating Results for Analysis

  • GROUP BY Syntax
  • Displaying Group Summaries
  • Performing Calculations Inside Aggregate Functions
  • MIN and MAX
  • COUNT and COUNT DISTINCT
  • Average
  • Filtering with HAVING
  • CASE Statements Inside Aggregate Functions
  • Exercises Using the Included Database

Lessons 8:
Window Functions and Subqueries

  • ROW NUMBER
  • RANK and DENSE RANK
  • NTILE
  • Aggregate Window Functions
  • LAG and LEAD
  • Exercises Using the Included Database

Lessons 9:
Date and Time Functions

  • Setting datetime Field Values
  • EXTRACT and DATE_PART
  • DATE_ADD and DATE_SUB
  • DATEDIFF
  • TIMESTAMPDIFF
  • Date Functions in Aggregate Summaries and Window Functions
  • Exercises

Lessons 10:
Exploratory Data Analysis with SQL

  • Demonstrating Exploratory Data Analysis with SQL
  • Exploring the Products Table
  • Exploring Possible Column Values
  • Exploring Changes Over Time
  • Exploring Multiple Tables Simultaneously
  • Exploring Inventory vs. Sales
  • Exercises

Lessons 11:
Building SQL Datasets for Analytical Reporting

  • Thinking Through Analytical Dataset Requirements
  • Using Custom Analytical Datasets in SQL: CTEs and Views
  • Taking SQL Reporting Further
  • Exercises

Lessons 12:
More Advanced Query Structures

  • UNIONs
  • Self-Join to Determine To-Date Maximum
  • Counting New vs. Returning Customers by Week
  • Summary
  • Exercises

Lessons 13:
Creating Machine Learning Datasets Using SQL

  • Datasets for Time Series Models
  • Datasets for Binary Classification
  • Taking Things to the Next Level
  • Exercises

Lessons 14:
Analytical Dataset Development Examples

  • What Factors Correlate with Fresh Produce Sales?
  • How Do Sales Vary by Customer Zip Code, Market Distance, and Demographic Data?
  • How Does Product Price Distribution Affect Market Sales?

Lessons 15:
Storing and Modifying Data

  • Storing SQL Datasets as Tables and Views
  • Adding a Timestamp Column
  • Inserting Rows and Updating Values in Database Tables
  • Using SQL Inside Scripts
  • In Closing
  • Exercises