Data Science Training

The Data Science training allows you to implement the methods and tools intended to interpret the data.

Pedagogical objectives of the Data Science Training:

• Use R to clean/analyze and visualize data
• Navigate the entire data science pipeline from data acquisition to publication
• Use GitHub to manage data science projects
• Perform regression/least squares analyzes and inferences using regression models

Data Science Training Course:

• Module 1 of the Data Science training: The tools of the data scientist
  • Set up R, R-Studio, Github and other useful tools
  • Explain the essential concepts of study design
  • Understand data, issues and tools used by data analysts
  • Create a Github repository
• Module 2 of the Data Science training: Programming in R
  • The essential concepts of the programming language
  • R loop functions and debugging tools
  • Configure statistical programming software
  • Gather detailed information using the R profiler
• Module 3 of the Data Science training: Obtaining and sorting data
  • Understand common data storage systems
  • Use R for text and date manipulation
  • Apply basic principles of data cleansing to make data/clean.
  • Obtain usable data from the web, APIs and databases
• Module 4 of the Data Science training: Analytical exploration of data
  • Understand analytical graphs and the basic plotting system in R
  • Realize very high-dimensional graphical representations of data
  • Use advanced graphics systems such as the Lattice system
  • Apply cluster analysis techniques to locate patterns in data
• Module 5 of the Data Science course: Reproducible research
  • Organize data analysis to make it more reproducible
  • Determine the reproducibility of the analysis project
  • Write a reproducible data analysis using knitting
  • Publish reproducible web documents using the Markdown feature
• Module 6 of the Data Science training: Statistical inference
  • Understand the process of drawing conclusions about populations or scientific truths from data
  • Describe variability, distributions, limits and confidence intervals
  • Use p-values, confidence intervals and permutation tests
  • Make informed data analysis decisions
• Data Science Training Module 7: Regression Models
  • Use regression analysis, least squares, and inference
  • Understand model cases of ANOVA and ANCOVA
  • Review residuals and variability analysis
  • Describe new uses for regression models such as scatterplot smoothing
• Module 8 of the Data Science training: Practical mechanical learning
  • Use the basics of constructing and applying prediction functions
  • Understand concepts such as training and testing sets, over-equipment, and error rates
  • Describe machine learning methods such as regression or classification trees
  • Explain the full process of constructing prediction functions
• Module 9 of the Data Science course: Development of data products
  • Develop basic applications and interactive graphics using GoogleVis
  • Use the flyer to create annotated interactive maps
  • Build an R Markdown presentation that includes a data visualization
  • Create a data product that tells a story to a mass audience
• Data Science Training Module 10: Final Data Science Project
  • Create a useful data product for the public
  • Apply your exploratory data analysis skills
  • Build an efficient and accurate prediction model
  • Create a presentation folder to showcase your results