This is the fifth of seven courses in the Google Advanced Data Analytics Certificate. Data professionals use regression analysis to discover the relationships between different variables in a dataset and identify key factors that affect business performance. In this course, you’ll practice modeling variable relationships. You'll learn about different methods of data modeling and how to use them to approach business problems. You’ll also explore methods such as linear regression, analysis of variance (ANOVA), and logistic regression.
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Regression Analysis: Simplify Complex Data Relationships
Dieser Kurs ist Teil von Google Advanced Data Analytics (berufsbezogenes Zertifikat)
Dozent: Google Career Certificates
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Was Sie lernen werden
Investigate relationships in datasets
Identify regression model assumptions
Perform linear and logistic regression using Python
Practice model evaluation and interpretation
Kompetenzen, die Sie erwerben
- Kategorie: regression modeling
- Kategorie: Python Programming
- Kategorie: Statistical Analysis
- Kategorie: Effective Communication
- Kategorie: Predictive Modelling
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In diesem Kurs gibt es 6 Module
You’ll begin by exploring the main steps for building regression models, from identifying your assumptions to interpreting your results. Next, you’ll explore the two main types of regression: linear and logistic. You’ll learn how data professionals use linear and logistic regression to approach different kinds of business problems.
Das ist alles enthalten
8 Videos3 Lektüren4 Quizzes1 Plug-in
You’ll explore how to use models to describe complex data relationships. You’ll focus on relationships of correlation. Then, you’ll build a simple linear regression model in Python and interpret your results.
Das ist alles enthalten
9 Videos8 Lektüren5 Quizzes5 Unbewertete Labore
After simple regression, you’ll move on to a more complex regression model: multiple linear regression. You’ll consider how multiple regression builds on simple linear regression at every step of the modeling process. You’ll also get a preview of some key topics in machine learning: selection, overfitting, and the bias-variance tradeoff.
Das ist alles enthalten
10 Videos4 Lektüren5 Quizzes3 Unbewertete Labore1 Plug-in
You’ll build on your prior knowledge of hypothesis testing to explore two more statistical tests: Chi-squared and analysis of variance (ANOVA). You’ll learn how data professionals use these tests to analyze different types of data. Finally, you’ll conduct two kinds of Chi-squared tests, as well as one-way and two-way ANOVA tests.
Das ist alles enthalten
9 Videos3 Lektüren4 Quizzes3 Unbewertete Labore
You’ll investigate binomial logistic regression, a type of regression analysis that classifies data into two categories. You’ll learn how to build a binomial logistic regression model and how data professionals use this type of model to gain insights from their data.
Das ist alles enthalten
8 Videos4 Lektüren5 Quizzes3 Unbewertete Labore
You’ll complete an end-of-course project by building a regression model to analyze a workplace scenario dataset.
Das ist alles enthalten
5 Videos10 Lektüren4 Quizzes6 Unbewertete Labore
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Association of International Certified Professional Accountants
University of Colorado Boulder
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Häufig gestellte Fragen
Organizations of all types and sizes have business processes that generate massive volumes of data. Every moment, all sorts of information gets created by computers, the internet, phones, texts, streaming video, photographs, sensors, and much more. In the global digital landscape, data is increasingly imprecise, chaotic, and unstructured. As the speed and variety of data increases exponentially, organizations are struggling to keep pace.
Data science and advanced data analytics are part of a field of study that uses raw data to create new ways of modeling and understanding the unknown. To gain insights, businesses rely on data professionals to acquire, organize, and interpret data, which helps inform internal projects and processes. Data scientists and advanced data analysts rely on a combination of critical skills, including statistics, scientific methods, data analysis, and artificial intelligence.
A data professional is a term used to describe any individual who works with data and/or has data skills. At a minimum, a data professional is capable of exploring, cleaning, selecting, analyzing, and visualizing data. They may also be comfortable with writing code and have some familiarity with the techniques used by statisticians and machine learning engineers, including building models, developing algorithmic thinking, and building machine learning models.
Data professionals are responsible for collecting, analyzing, and interpreting large amounts of data within a variety of different organizations. The role of a data professional is defined differently across companies. Generally speaking, data professionals possess technical and strategic capabilities that require more advanced analytical skills such as data manipulation, experimental design, predictive modeling, and machine learning. They perform a variety of tasks related to gathering, structuring, interpreting, monitoring, and reporting data in accessible formats, enabling stakeholders to understand and use data effectively. Ultimately, the work of data professionals helps organizations make informed, ethical decisions.
Large volumes of data — and the technology needed to manage and analyze it — are becoming increasingly accessible. Because of this, there has been a surge in career opportunities for people who can tell stories using data, such as senior data analysts and data scientists. These professionals collect, analyze, and interpret large amounts of data within a variety of different organizations. Their responsibilities require advanced analytical skills such as data manipulation, experimental design, predictive modeling, and machine learning.