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LinkedIn Learning

Advanced SQL for Data Science: Time Series

via LinkedIn Learning

Overview

Learn how to model time series data and apply advanced analysis techniques using SQL.

Syllabus

Introduction
  • Learn time series data analysis with SQL
  • What you should know
1. Introduction to Time Series Data
  • Characteristics of time series data
  • Examples of time series data
  • Writing time series data
  • Querying time series data
2. Installing Database and Tools
  • Installing PostgreSQL
  • Creating schema and tables
  • Timing a query
  • Evaluating query performance with EXPLAIN
3. Querying Time Series Data
  • Time window queries and aggregates
  • Sliding windows
  • Tumbling windows
  • Joining two time series
  • Denormalizing time series data
4. Modeling Time Series Data
  • Example data set 1: Temperature by time and location
  • Indexing data set 1: Time index only
  • Indexing data set 1: Time and location index
  • Creating a partitioned table
  • Querying a partitioned table
  • Example data set 2: CPU utilization and application type
  • Indexing data set 2: Time and type Indexing
5. Commonly Used Functions for Time Series
  • Lead
  • Lag
  • Rank
  • Percent rank
6. Time Series Analysis
  • Common Table Expressions and recursion
  • Calculating aggregates over windows
  • Previous day comparison
  • Moving averages
  • Weighted moving averages
  • Forecasting with linear regression
  • Exponential moving average
Conclusion
  • Next steps

Taught by

Dan Sullivan

Reviews

4.6 rating at LinkedIn Learning based on 314 ratings

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