Compreensão da eficácia da campanha de marketing

Imagine que você é um profissional de marketing digital que está analisando os dados de uma campanha recente para entender qual estilo de mensagem e horário do dia geram as maiores conversões. Essa análise é fundamental para orientar futuras estratégias de marketing, garantindo que suas mensagens cheguem aos clientes em potencial quando eles tiverem maior probabilidade de se envolver. Neste exercício, você está trabalhando com um conjunto de dados que fornece os resultados de diferentes estilos de mensagens ('Casual' versus 'Formal') e horários do dia ('Morning' versus 'Evening') nas taxas de conversão, um cenário comum na análise de dados de marketing.

Os dados foram carregados para você como um DataFrame chamado marketing_data, e pandas é carregado como pd.

Este exercicio faz parte do curso

Projeto experimental em Python

Instruções do exercicio

Crie uma tabela dinâmica com 'Messaging_Style' como o índice e 'Time_of_Day' como as colunas, calculando a média de Conversions.
Imprima essa tabela dinâmica.

exercicio interativo prático

Tente este exercicio completando este código de exemplo.

# Create a pivot table for marketing campaign data
marketing_pivot = marketing_data.____(
  values='____', 
  index='____', 
  columns='____', 
  aggfunc='____')

# View the pivoted results
print(____)

Editar e Executar Código

Este exercicio faz parte do curso

Projeto experimental em Python

IntermediárioNível de habilidade

4.8+

Comece o curso gratuitamente

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: Projetos fatoriais: princípios e aplicações Exercise 2: Compreensão da eficácia da campanha de marketing

Exercicio Atual

Exercise 3: Mapa de calor das interações da campanha Exercise 4: Projetos fatoriais e projetos de blocos aleatórios Exercise 5: Projeto de blocos aleatórios: controle de variância Exercise 6: Implementação de um projeto de blocos aleatórios Exercise 7: Visualizando a produtividade dentro dos blocos por incentivo Exercise 8: ANOVA dentro de quarteirões de funcionários Exercise 9: Ajuste de covariáveis no projeto experimental Exercise 10: Importância das covariáveis Exercise 11: Ajuste de covariável com crescimento do pintinho

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!