Carte thermique des interactions de campagne

La visualisation des données met souvent en évidence des tendances qui ne sont pas immédiatement visibles. En marketing, comprendre comment différents facteurs interagissent et influencent le succès d’une campagne est essentiel. En créant une carte thermique des conversions selon le style de message et le moment de la journée, vous pouvez rapidement repérer les combinaisons les plus performantes et celles qui doivent être réévaluées. Cet outil visuel est précieux pour les équipes marketing qui cherchent à optimiser leurs stratégies pour un impact maximal.

Les données de l’exercice précédent ont été chargées pour vous sous la forme de marketing_pivot, seaborn est importé en tant que sns, et matplotlib.pyplot en tant que plt.

Cet exercice fait partie du cours

<cours>Conception expérimentale en Python</cours>

Exercice interactif pratique

Essayez cet exercice en complétant ce code d’exemple.

# Visualize interactions with a heatmap
sns.____(____, 
         ____, 
         ____,
         ____)

plt.show()

Modifier et exécuter le code

Cet exercice fait partie du cours

<cours>Conception expérimentale en Python</cours>

IntermédiaireNiveau de compétence

4.8+

Commencer le cours gratuitement

Building knowledge in experimental design allows you to test hypotheses with best-practice analytical tools and quantify the risk of your work. You’ll begin your journey by setting the foundations of what experimental design is and different experimental design setups such as blocking and stratification. You’ll then learn and apply visual and analytical tests for normality in experimental data.

Exercise 1: Setting up experiments Exercise 2: Non-random assignment of subjects Exercise 3: Random assignment of subjects Exercise 4: Experimental data setup Exercise 5: Blocking experimental data Exercise 6: Stratifying an experiment Exercise 7: Which was stratified?Exercise 8: Normal data Exercise 9: Visual normality in an agricultural experiment Exercise 10: Analytical normality in an agricultural experiment

You'll delve into sophisticated experimental design techniques, focusing on factorial designs, randomized block designs, and covariate adjustments. These methodologies are instrumental in enhancing the accuracy, efficiency, and interpretability of experimental results. Through a combination of theoretical insights and practical applications, you'll acquire the skills needed to design, implement, and analyze complex experiments in various fields of research.

Exercise 1: Plans factoriels : principes et applications Exercise 2: Comprendre l’efficacité d’une campagne marketing Exercise 3: Carte thermique des interactions de campagne

Exercice actuel

Exercise 4: Plans factoriels et plans en blocs randomisés Exercise 5: Plan en blocs randomisés : contrôler la variance Exercise 6: Mettre en œuvre un plan en blocs aléatoires Exercise 7: Visualiser la productivité par bloc et par incitation Exercise 8: ANOVA au sein des blocs d’employés Exercise 9: Ajustement par covariables dans la conception expérimentale Exercise 10: Importance des covariables Exercise 11: Ajustement par covariable avec la croissance des poussins

Master statistical tests like t-tests, ANOVA, and Chi-Square, and dive deep into post-hoc analyses and power analysis essentials. Learn to select the right test, interpret p-values and errors, and skillfully conduct power analysis to determine sample and effect sizes, all while leveraging Python's powerful libraries to bring your data insights to life.

Exercise 1: Choosing the right statistical test Exercise 2: Choosing the right test: petrochemicals Exercise 3: Choosing the right test: human resources Exercise 4: Choosing the right test: finance Exercise 5: Post-hoc analysis following ANOVA Exercise 6: Anxiety treatments ANOVA Exercise 7: Applying Tukey's HSD Exercise 8: Applying Bonferoni correction Exercise 9: P-values, alpha, and errors Exercise 10: Analyzing toy durability Exercise 11: Visualizing durability differences Exercise 12: Role of significance levels Exercise 13: Power analysis: sample and effect size Exercise 14: Effect size purpose Exercise 15: Estimating required sample size for energy study

Hop into the complexities of experimental data analysis. Learn to synthesize insights using pandas, address data issues like heteroscedasticity with scipy.stats, and apply nonparametric tests like Mann-Whitney U. Learn additional techniques for transforming, visualizing, and interpreting complex data, enhancing your ability to conduct robust analyses in various experimental settings.

Exercise 1: Synthesizing insights from complex experiments Exercise 2: Visualizing loan approval yield Exercise 3: Exploring customer satisfaction Exercise 4: Effectively communicating experimental data Exercise 5: Addressing complexities in experimental data Exercise 6: Check for heteroscedasticity in shelf life Exercise 7: Exploring and transforming shelf life data Exercise 8: Applying nonparametric tests in experimental analysis Exercise 9: Visualizing and testing preservation methods Exercise 10: Further analyzing food preservation techniques Exercise 11: Congratulations!