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---
title: "Data Analytics with Statistics"
subtitle: "Course Overview"
---
Welcome to our course Data Analytics with Statistics! 👋
:::{.callout-note}
Note that this schedule will be updated as the seminar progresses.
:::
| Nr. | Topic | Literature | Slides | Links | Code | Activity |
| --- | --- | --- | --- | --- | --- | --- |
| 1 | **Introduction** | | | | | |
| 2 | Data driven decision making | | [📑](https://docs.google.com/presentation/d/1AfzqhjEAfJ5X4Q8YC9GK14jEBfdDXEIRmXonriwYHNY/export/pdf) | | | Lecture |
| 4 | Data Science Lifecycle Overview | | [📑](https://docs.google.com/presentation/d/1LR0LmvKFycE3MIjfDEp6L_bGdJtCOe60rFycWIYayBI/export/pdf) | | | Lecture |
| 5 | Use case identification | | [📑](https://docs.google.com/presentation/d/1RT7IhYgLgNJ8RiT28sMr8Mfyk5y_e-rfYg3jRsPKGEA/export/pdf) | | | Self-study |
| 6 | Frame the Problem | | [📑](https://docs.google.com/presentation/d/12WRK3EgiHOHz2vsJnYxIxNvDQmjTMax1iTSqsjVr7JY/export/pdf) | | | Self-study |
| 7 | Identify Variables | | [📑](https://docs.google.com/presentation/d/1VqXuDX_3VFNOGMw3y3uQmvFr2m7b6BlW3if_icQSstM/export/pdf) | | | Self-study |
| 8 | Define Metrics | | [📑](https://docs.google.com/presentation/d/1vlVqqU4Frt8y_GaCs4qVx7EVPyNravGhpgkV6tGeYmY/export/pdf) | | | Self-study |
| 9 | **Data** | | | | | |
| 10 | First Data Analysis | [📚](https://openintro-ims.netlify.app/data-hello.html#case-study-stents-strokes) | [📑](https://docs.google.com/presentation/d/1YXoCWZv37c3u_cMBc3o2_IG_atDlGdLhCwin_Rciwck/export/pdf) | | [💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ae/1_netflix.ipynb) | Lecture |
| 11 | Data basics | [📚](https://openintro-ims.netlify.app/data-hello.html#data-basics) | [📑](https://docs.google.com/presentation/d/1IlR_HTTNE865xiT3GlXD5_RNZDMMtlbNKRftLXPPmsw/export/pdf) | [☑️](https://forms.gle/EJT7mcYgPi8drKgR9) | | Lecture |
| 12 | How to obtain data | | [📑](https://docs.google.com/presentation/d/1zldRDAOqmjoJmY_D4-ZkWfhwaklt-UfAPKH4G5kmFVY/export/pdf) | | | *Optional* |
| 13 | Data wrangling: Pandas lab | [📚](https://pandas.pydata.org/docs/) | [💻](https://kirenz.github.io/lab-pandas-intro) | | | Application Exercise |
| 14 | Data analysis: Survey lab | | [💻](https://kirenz.github.io/lab-survey) | | | *Optional* |
| 15 | **Study Design** | | | | | |
| 16 | Population and sample | [📚](https://openintro-ims.netlify.app/data-design.html#data-design) | [📑](https://docs.google.com/presentation/d/1QPdxp5sFumf9zee-WypsuzSkY7n_hvkhFsdJkRF3-pw/export/pdf) | [☑️](https://forms.gle/qPYg55ncRyUGCqXH8) | | Self-study |
| 17 | Sampling methods | [📚](https://openintro-ims.netlify.app/data-design.html#sampling-principles-strategies) | [📑](https://docs.google.com/presentation/d/1Rby9NR9F8pu1xHka0Xt1vvbH-oDRfrFC-bwb49IbhA8/export/pdf) | [☑️](https://forms.gle/SnQsTPKF5CRQ1Wa49) | | Self-study |
| 18 | Experiments | [📚](https://openintro-ims.netlify.app/data-design.html#experiments) | [📑](https://docs.google.com/presentation/d/1FTNBLYBCId2qsL1sMJf3qgBtaFH3hpbUm8lPck5BaTQ/export/pdf) | [☑️](https://forms.gle/6Tu92Ez83XANW8Un6) | | Self-study |
| 19 | Observations | [📚](https://openintro-ims.netlify.app/data-design.html#observational-studies) | [📑](https://docs.google.com/presentation/d/1Oz5T_nuO1UFcjbliFmsEQWxG1dKJHDPPNWDeTvukxbE/export/pdf) | [☑️](https://forms.gle/V36KmsTjeH2finms9) | | Self-study |
| 20 | **EDA with categorical data** | | | | | |
| 21 | Loans data | [📚](https://openintro-ims.netlify.app/explore-categorical.html#explore-categorical) | [📑](https://docs.google.com/presentation/d/1o0TVpTvndPA1J23CdTtEThYSekjudkGXuW9HiEA65yc/export/pdf) | | [💻](https://colab.research.google.com/github/kirenz/lab-altair/blob/main/ae/04-1-data-overview.ipynb) | Self-study |
| 22 | Contingency tables | [📚](https://openintro-ims.netlify.app/explore-categorical.html#contingency-tables-and-bar-plots) | [📑](https://docs.google.com/presentation/d/1xIfx5wWjSi2ciGDo3OcYmjOYWzQZ5jWC-UwqioKKO6k/export/pdf) | | [💻](https://colab.research.google.com/github/kirenz/lab-altair/blob/main/ae/04-1-contingency-table.ipynb) | Lecture |
| 23 | Contingency tables with proportions | [📚](https://openintro-ims.netlify.app/explore-categorical.html#row-and-column-proportions) | [📑](https://docs.google.com/presentation/d/1fY1jRrnluZg9MDei9hcdtCRmRXlUqCg6lonQVzkItPQ/export/pdf) | | [💻](https://colab.research.google.com/github/kirenz/lab-altair/blob/main/ae/04-3-row-column-proportions.ipynb) | Self-study |
| 24 | Simple bar chart | [📚](https://openintro-ims.netlify.app/explore-categorical.html#contingency-tables-and-bar-plots) | [📑](https://docs.google.com/presentation/d/1RpYvnIxE5QCK3-urCa76CHUvDqOKlFb7TdAn6XgYjew/export/pdf) | | [💻](https://colab.research.google.com/github/kirenz/lab-altair/blob/main/ae/04-1-bar-chart-altair.ipynb) | Lecture |
| 25 | Stacked bar plot | [📚](https://openintro-ims.netlify.app/explore-categorical.html#bar-plots-with-two-variables) | [📑](https://docs.google.com/presentation/d/1TU5pqJqfgzcBRitDtSxjdRPhwl2TM9Rt3u3FHTPT020/export/pdf) | | [💻](https://colab.research.google.com/github/kirenz/lab-altair/blob/main/ae/04-2-stacked-bar-chart-altair.ipynb) | Lecture |
| 26 | Standardized bar plot | [📚](https://openintro-ims.netlify.app/explore-categorical.html#bar-plots-with-two-variables) | [📑](https://docs.google.com/presentation/d/1BV7XwkZLfTMe5acsDomL3pcSsNhatpswRa11XuzNuLQ/export/pdf) | | [💻](https://colab.research.google.com/github/kirenz/lab-altair/blob/main/ae/04-2-standardized-bar-chart-altair.ipynb) | Lecture |
| 27 | Pie chart | [📚](https://openintro-ims.netlify.app/explore-categorical.html#pie-charts) | [📑](https://docs.google.com/presentation/d/1Ks3jugleTrpLHwI0CDcPl2P7Uvj5z-iR35iTKJeNGxY/export/pdf) | | [💻](https://colab.research.google.com/github/kirenz/lab-altair/blob/main/ae/04-4-pie-charts-altair.ipynb) | Self-study |
| 28 | **EDA with numerical data** | | | | | |
| 29 | Scatterplot | [📚](https://openintro-ims.netlify.app/explore-numerical.html#scatterplots) | [📑](https://docs.google.com/presentation/d/1K13NYtk-fXgIZCszUWkSN1FWqLPf8Ey8U7l9YZSQDtU/export/pdf) | | [💻](https://colab.research.google.com/github/kirenz/lab-altair/blob/main/ae/05-1-scatterplot-paired-data-altair.ipynb) | Self-study |
| 30 | Dot plot mean median and mode | [📚](https://openintro-ims.netlify.app/explore-numerical.html#dotplots) | [📑](https://docs.google.com/presentation/d/1x0gxZ063LQOHHvvTdwP_086HPfl3Ul2PuzGDZFLXzI0/export/pdf) | | [💻](https://colab.research.google.com/github/kirenz/lab-altair/blob/main/ae/05-2-dot-plots-mean-altair.ipynb) | Lecture |
| 31 | Histogram | [📚](https://openintro-ims.netlify.app/explore-numerical.html#histograms) | [📑](https://docs.google.com/presentation/d/1tpbG5V28sIVdLuE_oIWzz59tRMRNL8baXu2xHzE4Hb4/export/pdf) | | [💻](https://colab.research.google.com/github/kirenz/lab-altair/blob/main/ae/05-3-histograms-altair.ipynb) | Lecture |
| 32 | Box Plot | [📚](https://openintro-ims.netlify.app/explore-numerical.html#boxplots) | [📑](https://docs.google.com/presentation/d/1VqT9rAbE_1zfhLrWNiQRiOjiJ0-pSjN1T0DM_BMAl_A/export/pdf) | | [💻](https://colab.research.google.com/github/kirenz/lab-altair/blob/main/ae/05-5-box-plot-altair.ipynb) | Lecture |
| 33 | Comparing numerical data across groups | [📚](https://openintro-ims.netlify.app/explore-categorical.html#comparing-numerical-data-across-groups) | [📑](https://docs.google.com/presentation/d/1yWzxDmaSVwygOQoAuRdYLq_32lIe_utHlaOWW5f63jk/export/pdf) | | [💻](https://colab.research.google.com/github/kirenz/lab-altair/blob/main/ae/04-6-comparisons-across-groups-altair.ipynb) | Lecture |
| 34 | Variance and standard deviation | [📚](https://openintro-ims.netlify.app/explore-numerical.html#histograms) | [📑](https://docs.google.com/presentation/d/1bsu5VJyS1LMFM3N6oh66dQ-Qf3DWiqRMPvasIbRqpLU/export/pdf) | | | Lecture |
| 35 | Kernel density plot | [📚](https://openintro-ims.netlify.app/explore-numerical.html#histograms) | | | [💻](https://colab.research.google.com/github/kirenz/lab-altair/blob/main/ae/05-3-histograms-kernel-density-altair.ipynb) | Self-study |
| 36 | Robust statistics and transformations | [📚](https://openintro-ims.netlify.app/explore-numerical.html#robust-statistics) | [📑](https://docs.google.com/presentation/d/16Oy2-hXJgTd4IUjYSj6ZN0gBimS_Oe5OSpe7B6I91qg/export/pdf) | | | Self-study |
| 37 | **Models** | | | | | |
| 38 | Statistical Learning, Machine Learning | | [📑](https://docs.google.com/presentation/d/1P3MkbJseVwLggUP_lGoNoZ3mk4_nOOBFZ6GmVzvBCzM/export/pdf) | | | Lecture |
| 39 | Types of Models | | [📑](https://docs.google.com/presentation/d/1ZycVKQLPSHGUv3Mga1CE3BHTL0W_gfszs6SkEh7fzeU/export/pdf) | | | Lecture |
| 40 | **Linear Regression models** | | | | | |
| 41 | Correlation | [📚](https://openintro-ims.netlify.app/model-slr.html#describing-linear-relationships-with-correlation) | [📑](https://docs.google.com/presentation/d/1JyvtQBRiUhjncLki3ozy4SvvgwRqS7Gmj_vUpFokzzo/export/pdf) | [☑️](https://forms.gle/5ntV6z8yHk8g4qgZ8) | [💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/07-2-correlation.ipynb) | Lecture |
| 42 | Sales and ads | | [📑](https://docs.google.com/presentation/d/1q9o_PycjwItr-5PxotZFFzkwdqvo8fVskv_Y0H1CbxE/export/pdf) | | [💻](https://colab.research.google.com/github/kirenz/lab-models/blob/main/ae/1_intro_sales.ipynb) | Lecture |
| 43 | Mean squared error | | [📑](https://docs.google.com/presentation/d/1DnX0RnGCguOE2elQJFrcu2efZp6hjre7fsTrAfBJQTA/export/pdf) | | | Application exercise |
| 44 | Fitting a line and residuals | [📚](https://openintro-ims.netlify.app/model-slr.html#fit-line-res-cor) | [📑](https://docs.google.com/presentation/d/18xWbx5rkptYfOaqzpfIc7RD-LC9Y-Wgs_7tz9Z2a2hg/export/pdf) | [☑️](https://forms.gle/JFMXzjByDRGZtbDx8) | [💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/07-1-fitting.ipynb) | Self-study |
| 45 | Least squares regression | [📚](https://openintro-ims.netlify.app/model-slr.html#least-squares-regression) | [📑](https://docs.google.com/presentation/d/1jreIuC0fOiVPHt6CUNYb-Kb4uKogCeftBZP5Zum6jSQ/export/pdf) | | [💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/07-3-least-squares.ipynb) | Self-study |
| 46 | R squared | [📚](https://openintro-ims.netlify.app/model-slr.html#r-squared) | [📑](https://docs.google.com/presentation/d/1ZIijR7F877M9peDH2rhb3IO7dP6JGDd1ypz_C2pAyGI/export/pdf) | | [💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/07-4-strength-fit.ipynb) | Lecture |
| 47 | Categorical predictors with two levels | [📚](https://openintro-ims.netlify.app/model-slr.html#categorical-predictor-two-levels) | [📑](https://docs.google.com/presentation/d/1Ca1pslYtl_bsPgY00JMhseV4klnU81AWw6s5Y8a15PA/export/pdf) | | [💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/07-5-categorical.ipynb) | Lecture |
| 48 | Outliers | [📚](https://openintro-ims.netlify.app/model-slr.html#outliers-in-regression) | [📑](https://docs.google.com/presentation/d/1UxbXpM0_BtZu9nAUczUopOOGT39hzn9Ucz0jOTYmM_Y/export/pdf) | | | Self-study |
| 49 | Multiple predictors regression 1 | [📚](https://openintro-ims.netlify.app/model-mlr.html#model-mlr) | [📑](https://docs.google.com/presentation/d/1ijhtWW58Kx1-ltulPSc1dKotJrXS7ZTGi6RFCKOGStA/export/pdf) | [☑️](https://forms.gle/wHPHMvbTDczNaQD97) | [💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/08a-1-multiple.ipynb) | Lecture |
| 50 | Multiple predictors regression 2 | | | | [💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/08a-2-multiple.ipynb) | Lecture |
| 51 | Multiple predictors regression 3 | | | | [💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/08a-3-multiple.ipynb) | Lecture |
| 52 | **Linear Regression with Data Splitting** | | | | | |
| 53 | Regression example happier | | [📑](https://docs.google.com/presentation/d/1ZrzKUPZqp7GlCAh4uFpXnvUW6kQTvUgD2G-YZ7B24aM/export/pdf) | | [💻](https://colab.research.google.com/github/kirenz/lab-models/blob/main/mr/happier-c.ipynb) | Self-study |
| 54 | Main model challenges | | [📑](https://docs.google.com/presentation/d/1WPUVfUe4rZu1bt61IwjneHqldhH9nC6U0F_u7Mj_ZL4/export/pdf) | | | Self-study |
| 55 | Data splitting | | [📑](https://docs.google.com/presentation/d/16DAGzUTUzI_prncqDeZx2HhpNnM_xTOL_Na0xTaDeVk/export/pdf) | | [💻](https://colab.research.google.com/github/kirenz/lab-models/blob/main/mr/happier-splitting-c.ipynb) | Self-study |
| 56 | Sales prediction | | [📑](https://docs.google.com/presentation/d/1CQUpb2fwezmni-GUeTsbxkE9ZekKK69eAzqKEdUBAm8/export/pdf) | | [💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/08c-1-sales.ipynb) | Self-study |
| 57 | Sales prediction with data splitting | | | | [💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/08c-2-sales.ipynb) | Lecture |
| 58 | **Advanced Linear Regression models** | | | | | |
| 59 | Regression splines | [📚](https://www.statlearning.com/) | | | [💻](https://colab.research.google.com/github/kirenz/notebooks/blob/main/ims/copy/99-1-splines.ipynb) | *Optional* |
| 60 | Generalized additive models | [📚](https://www.statlearning.com/) | | | [💻](https://colab.research.google.com/github/kirenz/notebooks/blob/main/ims/copy/99-1-gam.ipynb) | *Optional* |
| 61 | Adjusted R squared | [📚](https://openintro-ims.netlify.app/model-mlr.html#adjusted-r-squared) | [📑](https://docs.google.com/presentation/d/17l_8tVrVxyZRBZylkHa4J4ACCgp43gIqVgBQ5tuB9KM/export/pdf) | | | *Optional* |
| 62 | Regression diagnostics | | | | [💻](https://colab.research.google.com/github/kirenz/notebooks/blob/main/ims/copy/99-1-diagnostics.ipynb) | *Optional* |
| 63 | **Model Selection Methods** | | | | | |
| 64 | Model selection methods | [📚](http://www.feat.engineering/selection.html) | [📑](https://docs.google.com/presentation/d/1nosMLlMYxQ6gMw9betZ9AL6z8sHGqM7s9jL-Mf0VnXo/export/pdf) | | | *Optional* |
| 65 | Implicit model selection | [📚](https://www.statlearning.com/) | | | [💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/08d-1-implicit.ipynb) | *Optional* |
| 66 | Lasso regression | [📚](https://www.statlearning.com/) | | | [💻](https://colab.research.google.com/github/kirenz/notebooks/blob/main/ims/copy/99-1-lasso-c.ipynb) | *Optional* |
| 67 | Filter model selection | [📚](http://www.feat.engineering/selection.html) | | | [💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/08d-2-filter.ipynb) | *Optional* |
| 68 | Wrapper model selection | [📚](http://www.feat.engineering/selection.html) | | | [💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/08d-3-wrapper.ipynb) | *Optional* |
| 69 | **Classification models** | | | | | |
| 70 | Classification | | [📑](https://docs.google.com/presentation/d/1Uait12xIaTM9rdyxSEnzIbR8opENnoOOPefuPocze3s/export/pdf) | | | Lecture |
| 71 | Precision recall and F1 score | [📚](https://mlu-explain.github.io/precision-recall/) | [📑](https://docs.google.com/presentation/d/1X3_Ju8egEA8GwH_r76rxHHoC7tvHt8tMWXyT9WKfUko/export/pdf) | | | Lecture |
| 72 | ROC Curve and AUC | [📚](https://mlu-explain.github.io/roc-auc/) | [📑](https://docs.google.com/presentation/d/1wapuNTe4wVBUZVmH_X6-OxZlWkQL_YkhHsqZttQUWZk/export/pdf) | | | Lecture |
| 73 | Probability of an event | [📚](https://openintro-ims.netlify.app/model-logistic.html#modelingTheProbabilityOfAnEvent) | [📑](https://docs.google.com/presentation/d/1VeNM1TK6XXquHEmVNNUmzXOvdgqDFGVilN310Z6JKeg/export/pdf) | | [💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/09a-logistic-c.ipynb) | Self-study |
| 74 | Logistic regression in Python | | | | [💻](https://colab.research.google.com/github/kirenz/notebooks/blob/main/ims/copy/99-1-logistic-online.ipynb) | Lecture |
| 75 | **Statistical Inference** | | | | | |
| 76 | introduction | [📚](https://openintro-ims.netlify.app/foundations-mathematical.html) | [📑](https://docs.google.com/presentation/d/16CnOWEpi1E7ThpWlEJ4CaSNFmqRfFM322lUXXwFmKeQ/export/pdf) | | [💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/13a-mathematical-c.ipynb) | *Optional* |
| 77 | Cases and confidence | [📚](https://openintro-ims.netlify.app/foundations-mathematical.html#caseopp) | [📑](https://docs.google.com/presentation/d/1q-m2yp2zFZW3plabPVkDbUQxypoiUujbk4rmA4K_kHw/export/pdf) | | | *Optional* |
| 78 | Decision errors | [📚](https://openintro-ims.netlify.app/decerr.html) | [📑](https://docs.google.com/presentation/d/1ei-Tt6pqpPl_5oiMDMA9Coavf3ksCauxsJx2oK4U6L0/export/pdf) | | | *Optional* |
| 79 | Single proportion | [📚](https://openintro-ims.netlify.app/inference-one-prop.html#one-prop-norm) | [📑](https://docs.google.com/presentation/d/120AeFtpSKdjaDM9JFoM4d0_hDvgtWrl5DQTncSHISIU/export/pdf) | | | *Optional* |
| 80 | Single proportion hypothesis testing | [📚](https://openintro-ims.netlify.app/inference-one-prop.html#hypothesis-test-for-a-proportion) | [📑](https://docs.google.com/presentation/d/1703wnVRF9MEDzDuhIT6P94UJoPwvnXuoK6XLIkXz9mg/export/pdf) | | | *Optional* |
| 81 | Two proportions | [📚](https://openintro-ims.netlify.app/inference-two-props.html#math-2prop) | [📑](https://docs.google.com/presentation/d/1wxIcHV_tEoXnKcQg21s1-Hte85VQ3VN-qGkwzmGdNWc/export/pdf) | | | *Optional* |
| 82 | Two proportions hypothesis test | [📚](https://openintro-ims.netlify.app/inference-two-props.html#hypothesis-test-for-the-difference-between-two-proportions) | [📑](https://docs.google.com/presentation/d/1RAr_RnYry-a1FRsNIwVzB-vkycAsRD6etitSoV4N1a0/export/pdf) | | | *Optional* |
| 83 | Two proportions mammogram | [📚](https://openintro-ims.netlify.app/inference-two-props.html#hypothesis-test-for-the-difference-between-two-proportions) | [📑](https://docs.google.com/presentation/d/1ER-IJvtNJPHOY4bQGurpqAl83rexJwQ0Db2ujo8eLAE/export/pdf) | | | *Optional* |
| 84 | Two way tables | [📚](https://openintro-ims.netlify.app/inference-tables.html#mathchisq) | [📑](https://docs.google.com/presentation/d/19svJFkfUojZ4uqkh9ErsPXSV1gILew9bRIs8gi8rcZs/export/pdf) | | | *Optional* |
| 85 | Two means | [📚](https://openintro-ims.netlify.app/inference-two-means.html#mathematical-model-for-estimating-the-difference-in-means) | [📑](https://docs.google.com/presentation/d/12prlVzGJC9OndtHSYCExnrZVzUBzXQ3Kh0tSWE7pCNs/export/pdf) | | | *Optional* |
| 86 | Inference regression | [📚](https://openintro-ims.netlify.app/inf-model-slr.html#mathematical-model-interval-for-the-slope) | [📑](https://docs.google.com/presentation/d/18aJ5UJYxk3cphMOU_piKMdeJHUpy3vwkZmJ7CFlmJsY/export/pdf) | | | *Optional* |
| 87 | **Probability and Bayes** | | | | | |
| 88 | Probability and Bayes | [📺](https://www.youtube.com/watch?v=HZGCoVF3YvM) | [📑](https://docs.google.com/presentation/d/1dJ1b2iaWS9-tUO0AKLpjDJbohNtG4in1CCyQimPhXP0/export/pdf) | | | Self-study |
| 92 | **Optional content** | | | | | |
| 93 | Data storytelling | | | | | *Optional* |
| 94 | Generative AI | | [💻](https://llms-ws2324.github.io/website/) | | | *Optional* |