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Data Science courses which can be done simultaneously with some other courses
- Importing data into R from different file formats
- Web scraping
- How to tidy data using the tidyverse to better facilitate analysis
- String processing with regular expressions (regex)
- Wrangling data using dplyr
- Basic R syntax
- Foundational R programming concepts such as data types, vectors arithmetic, and indexing
- How to perform operations in R including sorting, data wrangling using dplyr, and making plots
- Fundamental R programming skills
- Statistical concepts such as probability, inference, and modeling and how to apply them in practice
- Gain experience with the tidyverse, including data visualization with ggplot2 and data wrangling with dplyr
- Become familiar with essential tools for practicing data scientists such as Unix/Linux, git and GitHub, and RStudio
- Implement machine learning algorithms
- In-depth knowledge of fundamental data science concepts through motivating real-world case studies
- How to load and clean real-world data
- How to make reliable statistical inferences from noisy data
- How to use machine learning to learn models for data
- How to visualize complex data
- How to use Apache Spark to analyze data that does not fit within the memory of a single computer
- Master the foundations of data science, statistics, and machine learning
- Analyze big data and make data-driven predictions through probabilistic modeling and statistical inference; identify and deploy appropriate modeling and methodologies in order to extract meaningful information for decision making
- Develop and build machine learning algorithms to extract meaningful information from seemingly unstructured data; learn popular unsupervised learning methods, including clustering methodologies and supervised methods such as deep neural networks
- Finishing this MicroMasters program will prepare you for job titles such as Data Scientist, Data Analyst, Business Intelligence Analyst, Systems Analyst, Data Engineer