Coursera - Data Science Specialization by Johns Hopkins University is your one-stop resource for anything and everything related to data science, including the analysis of different types of data. In ten practical and interesting lessons, you will cover all the stages of the data scientist’s work, from data collection and preparation to reporting.
In the first classes, you solve the configurations of core tools such as R, R-Studio, and GitHub and acquire the fundamental statistical concepts to build upon. From here, you will learn about programming with R, focusing on the R functionalities in data processing and analysis.
Recognizing that theory has a lot of underlying importance to data science, the “Coursera - Data Science Specialization by Johns Hopkins University” provides you with functional skills to help you source data, format it to be suitable for analysis, and begin exploratory analysis to look for patterns about the data. Great projects will help you learn such topics as regression analysis, machine learning, and creating engaging data products.
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Coursera - Data Science Specialization by Johns Hopkins University Table of Contents:
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The Data Scientist’s Toolbox
- Setting up R, R-Studio, GitHub, and other tools
- Understanding data, problems, and analytical tools
- Explaining essential study design concepts
- Creating a GitHub repository
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R Programming
- Understanding critical programming language concepts
- Configuring statistical programming software
- Making use of R loop functions and debugging tools
- Collecting detailed information using R profiler
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Getting and Cleaning Data
- Understanding common data storage systems
- Applying data cleaning basics to make data "tidy"
- Using R for text and date manipulation
- Obtaining usable data from the web, APIs, and databases
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Exploratory Data Analysis
- Understanding analytic graphics and the base plotting system in R
- Using advanced graphing systems such as the Lattice system
- Making graphical displays of very high-dimensional data
- Applying cluster analysis techniques to locate patterns in data
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Reproducible Research
- Organizing data analysis for reproducibility
- Writing up a reproducible data analysis using knitr
- Determining the reproducibility of analysis projects
- Publishing reproducible web documents using Markdown
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Statistical Inference
- Understanding the process of drawing conclusions from data
- Describing variability, distributions, limits, and confidence intervals
- Using p-values, confidence intervals, and permutation tests
- Making informed data analysis decisions
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Regression Models
- Using regression analysis, least squares, and inference
- Understanding ANOVA and ANCOVA model cases
- Investigating analysis of residuals and variability
- Describing novel uses of regression models such as scatterplot smoothing
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Practical Machine Learning
- Using the basic components of building and applying prediction functions
- Understanding concepts such as training and test sets, overfitting, and error rates
- Describing machine learning methods such as regression or classification trees
- Explaining the complete process of building prediction functions
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Developing Data Products
- Developing basic applications and interactive graphics using GoogleVis
- Using Leaflet to create interactive annotated maps
- Building an R Markdown presentation that includes data visualization
- Creating a data product that tells a story to a mass audience
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Data Science Capstone
- Creating a useful data product for the public
- Applying exploratory data analysis skills
- Building an efficient and accurate prediction model
- Producing a presentation deck to showcase the findings
Who is this course for?
- Aspiring Data Scientists
- Data analysts and anyone who aspires to be a master in data analysis techniques
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