Kursus
Analitik Data: Statistik dan Visualisasi
Gambarkan data dengan angka yang tepat, uji klaim di balik angka, dan buat grafik yang jujur, setiap pelajaran disertai kode yang bisa dijalankan.
- Pelajaran
- 16
- Waktu baca
- 4 jam 6 menit
- Level
- Pemula
- Bahasa pengantar
- Inggris
- Format
- Artikel dengan kode yang bisa dijalankan
Kursus
Yang akan Anda pelajari
Enam belas pelajaran artikel dalam empat bagian. Statistik deskriptif: jenis data, mean, median dan modus, sebaran, persentil dan outlier. Distribusi: bentuk dan kemencengan, distribusi normal, binomial, Poisson dan eksponensial, sampling dan teorema limit pusat. Hubungan dan inferensi: korelasi, asosiasi antar kategori, interval kepercayaan, dan uji hipotesis. Lalu visualisasi: memilih grafik, membuatnya dengan matplotlib dan seaborn, merancangnya dengan jujur, dan analisis dari pertanyaan hingga laporan. Setiap pelajaran membuat dataset kecilnya sendiri, sehingga kodenya berjalan apa adanya.
- Descriptive statistics
- Distributions
- Relationships and inference
- Visualisation
Sebelum mulai
Komputer yang bisa menjalankan Python. Setiap pelajaran menyebutkan apa yang menjadi dasarnya.
Silabus
Silabus
Setiap pelajaran menjelaskan satu gagasan, lalu menjalankannya dalam kode beserta output-nya. Pelajaran pertama terbuka untuk semua orang.
01 · Descriptive statistics
- 01Types of dataNumerical and categorical, discrete and continuous, nominal and ordinal: what each type allows you to compute and plot.Pratinjau gratis17 mnt
- 02Mean, median and modeThree answers to “what is typical?”: how each is computed, how outliers and skew pull them apart, and which one to report.14 mnt
- 03Measures of spreadRange, variance, standard deviation and the interquartile range: how far values sit from the centre and why n − 1 appears.15 mnt
- 04Percentiles, z-scores and outliersRank values with percentiles and z-scores, read a five-number summary and box plot, and decide what to do with an outlier.14 mnt
02 · Distributions
- 05Distributions, histograms and skewSee the shape of a variable with histograms and density plots: bins, modality, skewness, kurtosis and log scales.14 mnt
- 06The normal distributionThe bell curve, the 68–95–99.7 rule, standardising values, and checking normality with Q–Q plots and tests.15 mnt
- 07Binomial, Poisson and other distributionsCounts of successes, events per hour, waiting times and skewed amounts: the distributions behind everyday data, with SciPy.15 mnt
- 08Sampling and the central limit theoremPopulations and samples, sampling bias, the standard error, and why sample means turn normal whatever the data looks like.15 mnt
03 · Relationships and inference
- 09CorrelationCovariance, Pearson and Spearman correlation, correlation matrices, and the many ways correlation is not causation.17 mnt
- 10Association between categoriesContingency tables, row and column percentages, the chi-square test and Cramér’s V, and Simpson’s paradox.15 mnt
- 11Confidence intervalsReport a range, not just a number: intervals for a mean and a proportion, the bootstrap, and what “95% confident” really means.16 mnt
- 12Hypothesis testingNull and alternative, p-values, t-tests and their non-parametric cousins, errors of both kinds, effect sizes and multiple testing.16 mnt
04 · Visualisation
- 13Choosing a chartMatch the chart to the question: comparison, trend, distribution, relationship or composition, and when a table beats a chart.15 mnt
- 14Charts with matplotlib and seabornBar, line, scatter, histogram, box, violin and heatmap charts, small multiples with facets, and styling a figure for a report.17 mnt
- 15Designing honest chartsAxes that start where they should, colour that carries meaning, clutter removed, annotations that make the point, and charts that mislead.15 mnt
- 16From question to report, end to endOne dataset, one business question: describe, compare, test, chart and write a short report a manager can act on.16 mnt
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