Link : Introduction to Time Series Analysis and Forecasting in R
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Work with time series and all sorts of time related data in R - Forecasting, Time Series Analysis, Predictive Analytics
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What you'll learn
- use R to perform calculations with time and date based data
- create models for time series data
- use models for forecasting
- identify which models are suitable for a given dataset
- visualize time series data
- transform standard data into time series format
Understand the Now – Predict the Future!
Time series analysis and forecasting is one of the key fields in statistical programming. It allows you to
- see patterns in time series data
- model this data
- finally make forecasts based on those models
Link : Time Series Analysis in Python 2020
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Time Series Analysis in Python: Theory, Modeling: AR to SARIMAX, Vector Models, GARCH, Auto ARIMA, Forecasting
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Link : [Intermediate] Spatial Data Analysis with R, QGIS & More
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Become an Open source GIS Guru and Tackle Spatial Data Analysis Using R, QGIS, GRASS & GOOGLE EARTH
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Link : R Data Pre-Processing & Data Management - Shape your Data!
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Learn how to prepare your data for great analytics in R.
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Link : Advanced Tableau - Level of Detail Expressions / LOD
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Take Your Tableau Data Visualization and Data Analytics Skills to the Next Level and Design Custom Solutions with Ease
What you'll learn
- Understanding LOD expressions and using them confidently
- Performing calculations in Tableau that are at a different level of detail than the view
- Analyzing and solving complex analytical challenges
- Understanding the different levels of details of multivariate datasets
- Cohort analysis
- Market basket analysis
- User retention analysis
- Binning aggregates by dimensions
- Proportional brushing
- Relative comparison of values/ categories
- Nesting LOD expressions
Have you ever had analytical questions that are easy to ask, but surprisingly hard to answer with regular analytical tools? Do you often find yourself asking questions involving different data layers? Like comparing a single category to a whole table; or applying filters on particular fields; or tracking the behaviour of custom cohorts over time - just to mention a few classic examples.
Do you want to know how to compare data aggregated at different levels of granularity?
Do you often bump into the error message: 'Cannot mix aggregate and non-aggregate values'?
Do Tableau terms FIXED, INCLUDE or EXCLUDE confuse you? Are you struggling choosing the right one for particular tasks?

