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Diploma in Data Analytics with Python
This diploma course covers the importance and uses of data analytics in making business driven decisions in industries.
Description
The data analytics with python course begins by explaining the importance of data analysis and how data science can be used in making value based decisions to a business. You will learn about how python as a programming language is well suited for data analytics. You will learn about the basics of python programming and the simple python installation process. You will also be introduced to probability and how probability is used in solving analytical based problems.
The diploma course explains sampling techniques and the concept of random sampling in data analytics. You will get to know more about the various types of sampling distributions and how to solve probabilistic tasks using sample distributions. You will also learn about the process of statistical hypothesis testing and how it is used in statistical inference and predictive analytics.
The diploma course then explains the design, conduction of experiments involving randomized complete block designs and the concept of analysis of variance. You will learn about the simple linear and multiple regression model. You will learn about the maximum likelihood principle and how it is used in binomial distributions. You will also learn about the chi-square test, its applications, including a test for the goodness of fit and the test for independence.
Modules
Introduction to Data Analytics and Python Fundamentals
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Introduction to Data Analytics and Python Fundamentals - Learning Outcomes
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Introduction to Data Analytics
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Fundamentals of Python
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Central Tendency and Dispersion
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Introduction to Data Analytics and Python Fundamentals - Lesson Summary
Introduction to probability
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Introduction to probability - Learning Outcomes
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Introduction to Probability
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Probability Distribution
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Introduction to Probability - Lesson Summary
Sampling and Sampling Distribution
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Sampling and Sampling Distribution - Learning Outcomes
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Python Demo for Distributions
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Sampling and Sampling Distribution
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Distribution of Sample Means, Population and Variance
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Confidence Interval Estimation
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Sampling and Sampling Distribution - Lesson Summary
Hypothesis Testing
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Hypothesis Testing - Learning Outcomes
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Hypothesis Testing
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Errors in Hypothesis Testing
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Hypothesis Testing - Lesson Summary
Two Sample Testing
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Two Sample Testing - Learning Outcomes
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Two Sample Testing
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ANOVA
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Post Hoc Analysis - Tukey's Test
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Two Sample Testing - Lesson Summary
Diploma in Data Analytics with Python - First Assessment
Linear Regression
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Linear Regression - Learning Outcomes
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Randomized Block Design
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Two Way ANOVA
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Linear Regression
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Linear Regression - Lesson Summary
Regression Model Analysis
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Regression Model Analysis - Learning Outcomes
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Estimation and Prediction of Regression Model Residual Analysis
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Multiple Regression Model
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Categorical Variable Regression
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Regression Model Analysis - Lesson Summary
Maximum Estimations and Regression
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Maximum Estimations and Regression - Learning Outcomes
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Maximum Likelihood Estimations
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Logisitic Regression
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Relationship between Linear and Logistic Regression Model
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Maximum Estimations and Regression - Lesson Summary
Regression Analysis
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Regression Analysis - Learning Outcomes
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Confusion matrix and Receiver Operating characteristics
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Performance of Logistics Model
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Regression Analysis Model Building
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Regression Analysis - Lesson Summary
Chi Square Test
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Cluster Analysis
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Chi Square Test - Learning Outcomes
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Chi Square Test of Independence
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Chi Square Goodness of Fit Test
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Chi Square Test - Lesson Summary
Diploma in Data Analytics with Python - Second Assessment
Course assessment
Learning Outcomes
Upon successfull completion of this course, you will be able to:
- Explain why data analytics is important in today's business environment
- Explain the relationship between statistics, analytics and data science
- Explain solutions to problems using the laws of probability, including the laws of addition, multiplication and conditional probability
- Discuss Binomial, Poisson, Hyper geometric distributions in discrete distributions
- Explain the inferences on the differences between two population means
- Explain the application of quality engineering techniques and the systemic reduction of process variability
- Describe engineering experiments involving several factors using the factorial design approach
- Describe how ANOVA is used to analyze data from experiments
- Describe the test of independence using the investment example
Certification
"Todos os cursos da Alison são gratuitos para estudar. Para completar com sucesso um curso, você deve marcar 80% ou mais em cada avaliação do curso. Após a conclusão bem-sucedida de um curso",Você pode optar por tornar a sua conquista formal comprando um Diploma ou Certificado da Alison.
oficial. Ter um documento da Alison oficial é uma ótima forma de comemorar e compartilhar o seu sucesso. É:
- Ideal para incluir nos CVs,inscrições de trabalho e portfolios
- Uma forma de mostrar a sua habilidade de aprender e alcançar altos resultados
