Formation Data science

Module 1: Data Science Tools

Installation of R, R-Studio, Github, and other essential tools

Explanation of fundamental study design concepts

Familiarization with data, issues, and tools commonly used by data analysts

Establishment of a Github repository
 

Module 2: R Programming

Grasping key concepts of the programming language

Exploration of R loop functions and debugging tools

Configuration of statistical programming software

Collection of detailed information using the R profiler
 

Module 3: Data Acquisition and Cleaning

Understanding common data storage systems

Utilizing R for text and date manipulation

Application of basic data cleaning principles for ensuring data integrity

Retrieval of usable data from the web, APIs, and databases
 

Module 4: Analytical Data Exploration

Understanding analytical charts and basic plotting in R

Creation of graphical representations for high-dimensional data

Usage of advanced graphics systems such as the Lattice system

Application of cluster analysis techniques to identify patterns in data
 

Module 5: Reproducible Research

Organization of data analysis for reproducibility

Assessment of project reproducibility

Creation of reproducible data analysis using knitting

Publishing of reproducible web documents using Markdown
 

Module 6: Statistical Inference

Understanding the process of drawing conclusions from data about populations or scientific truths

Description of variability, distributions, limits, and confidence intervals

Utilization of p-values, confidence intervals, and permutation tests

Making informed decisions in data analysis
 

Module 7: Regression Models

Application of regression analysis, least squares, and inference

Understanding ANOVA and ANCOVA model cases

Examination of residual analysis and variability

Description of novel uses of regression models such as scatterplot smoothing
 

Module 8: Practical Machine Learning

Utilization of basics in building and applying prediction functions

Understanding concepts such as training and test sets, overfitting, and error rates

Description of machine learning methods like regression or classification trees

Explanation of the entire process of constructing prediction functions
 

Module 9: Data Product Development

Development of basic applications and interactive charts using GoogleVis

Utilization of Shiny to create annotated interactive maps

Construction of an R Markdown presentation that includes data visualization

Creation of a data product that effectively communicates a story to a mass audience
 

Module 10: Final Data Science Project

Creation of a useful data product for public consumption

Application of data exploratory analysis skills

Building an effective and accurate prediction model

Development of a presentation portfolio to showcase results