Advanced SQL for Data Science: Time Series
5.0 /5- Offered byLinkedin Learning
Advanced SQL for Data Science: Time Series at Linkedin Learning Overview
Duration | 2 hours |
Total fee | ₹1,400 |
Mode of learning | Online |
Difficulty level | Advanced |
Credential | Certificate |
Advanced SQL for Data Science: Time Series at Linkedin Learning Highlights
- 1 exercise file
- 5 quizzes
- Access on tablet and phone
Advanced SQL for Data Science: Time Series at Linkedin Learning Course details
- This course teaches learner how to standardize and model time series data, Instructor Dan Sullivan discusses windowing and the difference between sliding and tumbling window calculations
- Then learn how SQL constructs such as OVER and PARTITION BY help to simplify analysis, and how denormalization can be used to augment data while avoiding joins
Advanced SQL for Data Science: Time Series at Linkedin Learning Curriculum
Introduction
Learn time series data analysis with SQL
What you should know
Introduction to Time Series Data
Characteristics of time series data
Examples of time series data
Writing time series data
Querying time series data
Installing Database and Tools
Installing PostgreSQL
Creating schema and tables
Timing a query
Evaluating query performance with EXPLAIN
Querying Time Series Data
Time window queries and aggregates
Sliding windows
Tumbling windows
Joining two time series
Denormalizing time series data
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
Commonly Used Functions for Time Series
Lead
Lag
Rank
Percent rank
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
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