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Our Courses
Data Analysis in Python with pandas & matplotlib in Spyder
Code and run your first Python script in minutes without installing anything! This course is designed for learners with no coding experience, providing a crash course in Python, which enables the learners to delve into core data analysis topics that can be transferred to other languages.
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Course by
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Self Paced
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11 hours
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English
Publication-Ready Tables in R
Learn how to create Publication-Ready Tables in R for descriptive statistics, contingency tables, correlation tables, model summary tables and survival probabilities tables
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Course by
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Self Paced
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4 hours
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English
Combining and Analyzing Complex Data
In this course you will learn how to use survey weights to estimate descriptive statistics, like means and totals, and more complicated quantities like model parameters for linear and logistic regressions. Software capabilities will be covered with R® receiving particular emphasis. The course will also cover the basics of record linkage and statistical matching—both of which are becoming more important as ways of combining data from different sources. Combining of datasets raises ethical issues which the course reviews.
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Course by
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Self Paced
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10 hours
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English
Statistics for Marketing
This course takes a deep dive into the statistical foundation upon which marketing analytics is built. The first part of this course will help you to thoroughly understand your dataset and what the data actually means. Then, it will go into sampling including how to ask specific questions about your data and how to conduct analysis to answer those questions.
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Course by
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Self Paced
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17 hours
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English
Calculating Descriptive Statistics in R
Welcome to this 2-hour long project-based course Calculating Descriptive Statistics in R. In this project, you will learn how to perform extensive descriptive statistics on both quantitative and qualitative variables in R. You will also learn how to calculate the frequency and percentage of categorical variables and check the distribution of quantitative variables.
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Course by
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Self Paced
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3 hours
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English
Data Analysis in R with RStudio & Tidyverse
Code and run your first R program in minutes without installing anything! This course is designed for learners with no prior coding experience, providing foundational knowledge of data analysis in R. The modules in this course cover descriptive statistics, importing and wrangling data, and using statistical tests to compare populations and describe relationships.
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Course by
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Self Paced
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10 hours
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English
Probability and Statistics: To p or not to p?
We live in an uncertain and complex world, yet we continually have to make decisions in the present with uncertain future outcomes. Indeed, we should be on the look-out for "black swans" - low-probability high-impact events. To study, or not to study? To invest, or not to invest? To marry, or not to marry? While uncertainty makes decision-making difficult, it does at least make life exciting! If the entire future was known in advance, there would never be an element of surprise.
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Course by
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Self Paced
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16 hours
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English
RStudio for Six Sigma - Basic Descriptive Statistics
Welcome to RStudio for Six Sigma - Basic Description Statistics. This is a project-based course which should take approximately 2 hours to finish. Before diving into the project, please take a look at the course objectives and structure. By the end of this project, you will learn to perform Basic Descriptive Analysis (Six Sigma) tasks hands-on using RStudio. Both R language and RStudio tools are Open Source and can be used for most Six Sigma analysis tasks without needing commercial software.
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Course by
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Self Paced
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2 hours
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English
Pivot Tables in Google Sheets
This is a self-paced lab that takes place in the Google Cloud console. Create pivot tables to quickly summarize large amounts of data and reference data using named ranges. Use functions and formulas to calculate descriptive statistics.
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Course by
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Self Paced
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1 hour
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English
The Power of Statistics
This is the fourth of seven courses in the Google Advanced Data Analytics Certificate. In this course, you’ll discover how data professionals use statistics to analyze data and gain important insights. You'll explore key concepts such as descriptive and inferential statistics, probability, sampling, confidence intervals, and hypothesis testing. You'll also learn how to use Python for statistical analysis and practice communicating your findings like a data professional.
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Course by
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Self Paced
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37 hours
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English
Descriptive and Inferential Statistics in R
In this 1-hour long project-based course, you will learn how to summarize descriptive statistics, calculate correlations and perform hypothesis testing in R Note: This course works best for learners who are based in the North America region.
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Course by
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Self Paced
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3 hours
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English
Introduction to Business Analysis Using Spreadsheets: Basics
In this 1-hour 30-mins long project-based course, you will learn the responsibilities of a Business Analyst such as Learn the basic concepts of data analysis and descriptive statistics. Learn how to manipulate, analyze, and visualize data in Google Sheets using functions, aggregation functions, and logical aggregation functions.
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Course by
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Self Paced
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3 hours
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English
Managing, Describing, and Analyzing Data
In this course, you will learn the basics of understanding the data you have and why correctly classifying data is the first step to making correct decisions. You will describe data both graphically and numerically using descriptive statistics and R software. You will learn four probability distributions commonly used in the analysis of data. You will analyze data sets using the appropriate probability distribution.
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Course by
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Self Paced
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18 hours
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English
Analyze Data in Azure ML Studio
Did you know that you can use Azure Machine Learning to help you analyze data? In this 1-hour project-based course, you will learn how to display descriptive statistics of a dataset, measure relationships between variables and visualize relationships between variables. To achieve this, we will use one example diabetes data.
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Course by
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Self Paced
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3 hours
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English
Using Descriptive Statistics to Analyze Data in R
By the end of this project, you will create a data quality report file (exported to Excel in CSV format) from a dataset loaded in R, a free, open-source program that you can download. You will learn how to use the following descriptive statistical metrics in order to describe a dataset and how to calculate them in basic R with no additional libraries.
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Course by
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Self Paced
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3 hours
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English
Data Understanding and Visualization
The "Data Understanding and Visualization" course provides students with essential statistical concepts to comprehend and analyze datasets effectively. Participants will learn about central tendency, variation, location, correlation, and other fundamental statistical measures. Additionally, the course introduces data visualization techniques using Pandas, Matplotlib, and Seaborn packages, enabling students to present data visually with appropriate plots for accurate and efficient communication. Learning Objectives: 1.
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Course by
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25 hours
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English
Data Literacy
This specialization is intended for professionals seeking to develop a skill set for interpreting statistical results. Through four courses and a capstone project, you will cover descriptive statistics, data visualization, measurement, regression modeling, probability and uncertainty which will prepare you to interpret and critically evaluate a quantitative analysis.
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Course by
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Self Paced
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English
Statistics for Data Science with Python
This Statistics for Data Science course is designed to introduce you to the basic principles of statistical methods and procedures used for data analysis. After completing this course you will have practical knowledge of crucial topics in statistics including - data gathering, summarizing data using descriptive statistics, displaying and visualizing data, examining relationships between variables, probability distributions, expected values, hypothesis testing, introduction to ANOVA (analysis of variance), regression and correlation analysis.
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Course by
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Self Paced
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14 hours
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English
Basic Statistics
Understanding statistics is essential to understand research in the social and behavioral sciences. In this course you will learn the basics of statistics; not just how to calculate them, but also how to evaluate them. This course will also prepare you for the next course in the specialization - the course Inferential Statistics. In the first part of the course we will discuss methods of descriptive statistics. You will learn what cases and variables are and how you can compute measures of central tendency (mean, median and mode) and dispersion (standard deviation and variance).
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Course by
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Self Paced
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27 hours
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English
Applied Machine Learning in Python
This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled.
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Course by
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31 hours
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English
Basic Data Descriptors, Statistical Distributions, and Application to Business Decisions
The ability to understand and apply Business Statistics is becoming increasingly important in the industry. A good understanding of Business Statistics is a requirement to make correct and relevant interpretations of data. Lack of knowledge could lead to erroneous decisions which could potentially have negative consequences for a firm. This course is designed to introduce you to Business Statistics. We begin with the notion of descriptive statistics, which is summarizing data using a few numbers.
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Course by
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Self Paced
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21 hours
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English