...
Full Bio
Today's Technology-Data Science
338 days ago
How to build effective machine learning models?
338 days ago
Why Robotic Process Automation Is Good For Your Business?
338 days ago
IoT-Advantages, Disadvantages, and Future
339 days ago
Look Artificial Intelligence from a career perspective
339 days ago
Every Programmer should strive for reading these 5 books
581826 views
Why you should not become a Programmer or not learn Programming Language?
242814 views
See the Salaries if you are willing to get a Job in Programming Languages without a degree?
152868 views
Have a look of some Top Programming Languages used in PubG
151197 views
Highest Paid Programming Languages With Highest Market Demand
138105 views
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