Data Science Specialization

What you will learn from the Data Science Specialization Course

  • Set up R, R-Studio, Github and other useful tools
  • Understand the data, problems, and tools that data analysts use
  • Explain essential study design concepts
  • Create a Github repository
  • Learn about version control and why it’s so important to data scientists
  • How to use Git and GitHub to manage version control in data science projects
  • Learn to use R Markdown
  • How to program in R and how to use R for effective data analysis
  • Understand critical programming language concepts
  • Configure statistical programming software
  • Make use of R loop functions and debugging tools
  • Collect detailed information using R profiler
  • Programming with R
  • Loop functions and the debugging tools in R
  • How to simulate data in R and most useful function in R
  • Understand common data storage systems
  • Apply data cleaning basics to make data “tidy”
  • Use R for text and date manipulation
  • Obtain usable data from the web, APIs, and databases
  • Finding data and reading different file types
  • Organizing, merging, and managing the data from the web or from databases like MySQL
  • Text and date manipulation in R
  • Understand analytic graphics and the base plotting system in R
  • Use advanced graphing systems such as the Lattice system
  • Make graphical displays of very high dimensional data
  • Apply cluster analysis techniques to locate patterns in data
  • Basics of analytic graphics and the base plotting system in R
  • Advanced graphing systems such as the Lattice system and the ggplot2 system
  • Cluster analysis techniques
  • Organize data analysis to help make it more reproducible
  • Write up a reproducible data analysis using knitr
  • Determine the reproducibility of analysis project
  • Publish reproducible web documents using Markdown
  • Core tools for developing reproducible documents
  • Basic checklist for ensuring that data analysis is reproducible
  • 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
  • Fundamentals of probability, random variables, expectations and more
  • Use regression analysis, least squares and inference
  • Understand ANOVA and ANCOVA model cases
  • Investigate analysis of residuals and variability
  • Describe novel uses of regression models such as scatterplot smoothing
  • Least Squares and Linear Regression & Multivariable Regression
  • Generalized linear models, including binary outcomes and Poisson regression
  • Use the basic components of building and applying prediction functions
  • Understand concepts such as training and tests sets, overfitting, and error rates
  • Describe machine learning methods such as regression or classification trees
  • Explain the complete process of building prediction functions
  • Regularized Regression and Combining Predictors
  • Develop basic applications and interactive graphics using GoogleVis
  • Use Leaflet to create interactive annotated maps
  • Build an R Markdown presentation that includes a data visualization
  • Create a data product that tells a story to a mass audience
  • How to develop basic applications and interactive graphics in shiny
  • How to compose interactive HTML graphics with GoogleVis
  • How to prepare data visualizations with Plotly
  • How to create R Markdown files and embed R code in an Rmd
  • How to create R packages
  • Create a useful data product for the public
  • Apply your exploratory data analysis skills
  • Build an efficient and accurate prediction model
  • Produce a presentation deck to showcase your findings

Data Science Specialization includes 10 Courses they are

  1. The Data Scientist’s Toolbox
  2. R Programming
  3. Getting and Cleaning Data
  4. Exploratory Data Analysis
  5. Reproducible Research
  6. Statistical Inference
  7. Regression Models
  8. Practical Machine Learning
  9. Developing Data Products
  10. Data Science Capstone

Course Instructors Jeff Leek, Roger D. Peng, Brian Caffo Offered by Johns Hopkins University Through Coursera

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