Data Science and Its Applications industry-aligned credential is awarded to earners who successfully complete Inferential Statistics - Hypothesis Testing; Inferential Statistics - Regression, ANOVA, and Forecasting; Power BI and Real-time Dashboards; Intro to R; Applications of R; Machine Learning and Predictive Modeling in R; Intro to Python; Data Analysis in Python; Machine Learning in Python. Rolling admission, self-paced.

 

Complete all 9 individual credentials to earn the entire Data Science and Its Applications credential or just choose the credentials that fit with your needs. 

Each credential is listed below, individually: 

Intro to R: Demonstrates understanding of R concepts including basic R functions, variable types, and ability to work with different types of variables including numerical, character, and date/time variables, ability to create and use functions and packages that are not part of the base installation of R.

Applications of R: Demonstrates an earner's understanding of R concepts including Flow Control, looping, the dplyr package and related Tidyverse commands, data management, data visualization, introductory statistics concepts, introductory analytical methods in R, random number generators and their applications, as well as how to perform basic model diagnostics.

Machine Learning and Predictive Modeling in R: Demonstrates an earner's understanding of machine learning and predictive modeling concepts and how to perform related analyses in R, including Linear and Logistic Regression, PCA and Nearest Neighbor Algorithms, Clustering Methods, Decision Trees and CART Modeling, Naïve Bayes Classification, Natural Language Processing, Market Basket Analysis, and Neural Networks and Support-Vector Classifiers.

Intro to Python: Learners will start with the basics of introductory commands and different types of Python variables, learn how to work with lists, learn how to use and write functions, gain experience with different types of loops and conditional programming applications, and will also learn about the architectural structure of modules, packages, and libraries, and how to use them.

Machine Learning in Python: Gain knowledge about the strengths and weaknesses of different machine learning algorithms; become able to train machine learning models to make predictions in Python, and learn how to employ appropriate machine learning model based on data form and desired applications.

Data Analysis in Python: Build on knowledge obtained from the Intro to Python credential. The course covers many applications within Python that are relevant to data science. At the end of the credentialing program, participants will have an intermediate mastery of Python programming skills. Users will gain experience using Python to read in datasets for analysis, analyze their data using an array of statistical and data science tools, and create visual representations of their data.

Inferential Statistics: Regression, ANOVA & Forecasting: Demonstrates understanding of inferential statistics concepts including ANOVA and diagnostic measures, regression and diagnostic measures, time-series, forecasting, and variance testing (QA/QC testing).

Inferential Statistics: Hypothesis Testing: Demonstrates understanding of inferential statistics concepts including hypothesis testing, t-tests, binomial and discrete distributions, tests involving categorical variables, descriptive statistics and data visualization as exploratory measures, sampling methods, Central Limit Theorem, normal and standard normal distributions, tests involving continuous variables and their underlying distributions, and testing differences in means and building confidence intervals about the mean.

Power BI and Real Time Dashboards: Demonstrates an understanding of real-time analytical dashboard concepts and successfully employed dashboard software, such as Power BI, to build real-time, interactive dashboards. The earner of this credential also demonstrates knowledge pertaining to dashboard graphics and reports, as well as advanced dashboard applications.

Please choose the credentials you would like to enroll in under the sub-sessions on the registration page. You will only pay for the credentials you choose to enroll in.