Online Course – Johns Hopkins University Certified Professional Internship in Data Science

Launch your career in data science. A ten-course introduction to data science, developed and taught by leading professors.

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קורס אונליין - התמחות מקצועית מוסמכת במדע הנתונים של אוניברסיטת ג'ונס הופקינס

Professional Certificate

Beginners

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

  • Machine Learning
  • R Language (R Programming)
  • Regression Analysis

What you will learn in the course

Courses for which the course is suitable

  • Data Analyst
  • Data Scientist
  • Business Intelligence Analyst
  • Data Visualization Specialist
  • Data Engineer
  • Research Analyst
  • Quantitative Analyst
  • Statistician
  • Machine Learning Engineer
  • Data Product Manager

Expertise – 10-part course series

Course Description

  • Ask the right questions.
  • manipulate data sets
  • Build visualizations to convey results

What you’ll learn

  • The concepts and tools you’ll need throughout the data pipeline
  • Asking the right questions
  • Drawing conclusions
  • Publication of results

Final project

  • Applying the skills you have learned
  • Building a data product using real-world data

payoff

  • Students will have a portfolio that demonstrates their expertise in the material studied.

Details of the courses that make up the specialization

Data Scientist Toolkit

Course 1

  • 17 hours
  • 4.6 (33,920 ratings)
Course Details
What you will learn:
  • Installing R, R-Studio, Github, and other useful tools
  • Understanding the data, problems, and tools that analysts use
  • Explaining basic concepts in research design
  • Creating a repository on Github
Skills you will acquire
  • Category: Statistics
  • Category: Statistical Inference
  • Category: Statistical hypothesis testing
  • Category: R Programming

Course 2

  • 57 hours
  • 4.5 (22,241 ratings)
Course Details
What you will learn:
  • Understanding critical programming language concepts
  • Statistical programming software definition
  • Using loop functions and debugging tools in R
  • Gathering detailed information using R profiler
Skills you will acquire
  • Category: Random Forests
  • Category: Machine Learning (ML) Algorithms
  • Category: Machine Learning
  • Category: R Programming

Course 3

  • 19 hours
  • 4.5 (8,060 ratings)
Course Details
What you will learn:
  • Understanding common data storage systems
  • Applying data cleansing basics to make data “tidy”
  • Using R to process text and dates
  • Extracting actionable data from the web, APIs, and databases
Skills you will acquire
  • Category: Data Science
  • Category: Github
  • Category: R Programming
  • Category: Rstudio

Course 4

  • 54 hours
  • 4.7 (6,065 ratings)
Course Details
What you will learn:
  • Understanding analytical graphs and the basic graphics system in R
  • Use of advanced graphics systems such as the Lattice system
  • Create graphical displays of very high-dimensional data
  • Applying cluster analysis techniques to find patterns in data
Skills you will acquire
  • Category: Interactivity
  • Category: Plotly
  • Category: Webkit Application
  • Category: R Programming

Course 5

  • 7 hours
  • 4.6 (4,172 ratings)
Course Details
What you will learn:
  • Organizing data analysis to facilitate recovery
  • Writing a reproducible data analysis using knitr
  • Assessing the reproducibility of the analysis project
  • Publish retrievable web documents using Markdown
Skills you will acquire
  • Category: Data Science
  • Category: Machine Learning
  • Category: R Programming
  • Category: Natural Language Processing

Course 6

  • 54 hours
  • 4.2 (4,433 ratings)
Course Details
What you will learn:
  • Understanding the process of drawing conclusions about populations or scientific truths from data
  • Description of variance, distributions, limits, and confidence intervals
  • Using p-values, confidence intervals, and permutation tests
  • Making informed data analysis decisions
Skills you will acquire
  • Category: Knitr
  • Category: Data Analysis
  • Category: R Programming
  • Category: Markup language

Course 7

  • 53 hours
  • 4.4 (3,352 ratings)
Course Details
What you will learn:
  • Using regression analysis, least squares, and inference
  • Understanding ANOVA and ANCOVA Model Examples
  • Investigating analysis of residuals and variances
  • Describe new uses of regression models such as scatter plots
Skills you will acquire
  • Category: Data Analysis
  • Category: Debugging
  • Category: R Programming
  • Category: Rstudio

Course 8

  • 8 hours
  • 4.5 (3,246 ratings)
Course Details
What you will learn:
  • Using basic components in building and developing predictive functions
  • Understanding concepts such as training and testing sets, overfitting, and error rates
  • Description of machine learning methods such as regression or classification trees
  • Explaining the complete process of building predictive functions
Skills you will acquire
  • Category: Cluster Analysis
  • Category: Ggplot2
  • Category: R Programming
  • Category: Research Data Analysis

Course 9

  • 10 hours
  • 4.6 (2,255 ratings)
Course Details
What you will learn:
  • Developing basic applications and interactive graphics using GoogleVis
  • Using Leaflet to create interactive, annotated maps
  • Building an R Markdown presentation that includes data visualization
  • Building a data product that conveys a message to the general public
Skills you will acquire
  • Category: Data Manipulation
  • Category: Regular Expression (REGEX)
  • Category: R Programming
  • Category: Data Cleanup

Data Science Final Course

  • 5 hours
  • 4.5 (1,226 ratings)
Course Details
What you will learn:
  • Creating a useful data product for the public
  • Applying researcher data analysis skills
  • Building an efficient and accurate forecasting model
  • Produce a presentation to present your findings
Skills you will acquire
  • Category: Model Selection
  • Category: General Linear Model
  • Category: Linear Regression
  • Category: Regression Analysis

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