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Tableau'da İstatistiksel Teknikler

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Kursa Ücretsiz Başlayın

Exploratory data analysis, or EDA, is a fundamental step when doing data research. Getting the first insights of your data is easy in Tableau: you’ll be creating and interpreting tables, bar plots, histograms, and box plots in no time!

Exercise 1: Kursa hoş geldin!Exercise 2: Histogramları yorumlama Exercise 3: Tableau'da EDA: tablolar ve çubuk grafikler Exercise 4: Superstore verisi: tablo Exercise 5: Superstore verisi: çubuk grafik Exercise 6: Tableau'da EDA: histogramlar Exercise 7: Superstore verisi: histogram promosu Exercise 8: Superstore verisi: histogram sınıf (bin) boyutu Exercise 9: Kutu grafikleri ve dağılım özellikleri

Geçerli Egzersiz

Exercise 10: Hangi görselleştirmeyi seçmelisin?Exercise 11: Tableau'da EDA: kutu grafikleri Exercise 12: Superstore verisi: kutu grafiği Exercise 13: Superstore verisi: kutu grafikleri karşılaştır

In this more conceptual chapter, you’ll dive deeper into the use of different measures of center and spread, and how they should be used in Tableau. You’ll learn about the use of the summary card, the difference between sample and population, and how variance, standard deviation, and confidence intervals can be calculated and visualized.

Exercise 1: Measures of spread Exercise 2: Statistics: true of false Exercise 3: Tableau: summary cards and spread Exercise 4: Superstore data: summary card Exercise 5: Superstore data: standard deviation Exercise 6: Bonus question: sample vs population Exercise 7: Superstore data: variance Exercise 8: Standard error and confidence intervals Exercise 9: A bunch of statistics and estimates Exercise 10: Tableau: adding lines and distribution bands Exercise 11: Superstore data: high workload Exercise 12: Superstore data: confidence intervals Exercise 13: Superstore data: mean or median?

It's time to look at two variables at a time. Describing the relationship between two variables, or regression, is a great way to spot trends in your data. You'll learn how to find the best trend line, describe the trend model, and predict future observations, using dinosaur data!

Exercise 1: The relationship of two variables Exercise 2: Guess the correlation Exercise 3: Tableau: trend lines Exercise 4: Dinosaurs: what to predict?Exercise 5: Dinosaurs: how to disaggregate?Exercise 6: Dinosaurs: adding a trend line Exercise 7: Assessing a trend line Exercise 8: Model predictions: rigorous or randomness?Exercise 9: Tableau: describing trend models Exercise 10: Dinosaurs: model summary Exercise 11: Dinosaurs: model by clade Exercise 12: Dinosaurs: confidence intervals

In this last chapter, you’ll explore two more advanced statistical techniques: forecasting and clustering. Forecasting helps you detect recurring patterns in your time-series data, and can predict how these patterns will change in the future. With clustering, you’re able to detect patterns in unlabeled data, allowing you to slice and dice your dataset to reveal hidden insights.

Exercise 1: Forecasting Exercise 2: Forecasting: true or false?Exercise 3: Tableau: forecasting Exercise 4: T-shirt sales: first forecast Exercise 5: T-shirt sales: aggregating Exercise 6: T-shirt sales: forecast adjustments Exercise 7: Clustering Exercise 8: To cluster or not to cluster?Exercise 9: Tableau: clustering Exercise 10: World indicators: clustering countries Exercise 11: World indicators: describing clusters Exercise 12: World indicators: mapping the clusters Exercise 13: World indicators: cluster results Exercise 14: Congratulations!