Search result for Applied data science specialization Online Courses & Certifications
Get Course Alerts by Email
Data Analysis and Interpretation Capstone
by Jen Rose , Lisa Dierker- 4.7
Approx. 8 hours to complete
The Capstone project will allow you to continue to apply and refine the data analytic techniques learned from the previous courses in the Specialization to address an important issue in society. org, is committed to bringing cutting-edge practices in data science and crowdsourcing to some of the world's biggest social challenges and the organizations taking them on....
Importing Data in the Tidyverse
by Carrie Wright, PhD , Shannon Ellis, PhD , Stephanie Hicks, PhD , Roger D. Peng, PhD- 4.6
Approx. 15 hours to complete
Getting data into your statistical analysis system can be one of the most challenging parts of any data science project. Data must be imported and harmonized into a coherent format before any insights can be obtained. You will learn how to get data into R from commonly used formats and harmonizing different kinds of datasets from different sources....
Big Data - Capstone Project
by Ilkay Altintas , Amarnath Gupta- 4.4
Approx. 21 hours to complete
Welcome to the Capstone Project for Big Data! In this culminating project, you will build a big data ecosystem using tools and methods form the earlier courses in this specialization. During the five week Capstone Project, you will walk through the typical big data science steps for acquiring, exploring, preparing, analyzing, and reporting....
Become a Python Data Analyst
by Packt Publishing- 4
4.5 hours on-demand video
Take your data analytics and predictive modeling skills to the next level using the popular tools and libraries in Pytho world of Data Science and Analytics. known in the community as “Python’s Data Science Stack”. examples how to use the most popular tools for doing Data Science and training services in Data Science topics and has been a consultant for...
$15.99
Python for Data Science
by Ilkay Altintas , Leo Porter- 0.0
10 Weeks
In the information age, data is all around us. You will learn these tools all within the context of solving compelling data science problems. By learning these skills, you'll also become a member of a world-wide community which seeks to build data science tools, explore public datasets, and discuss evidence-based findings. Basic process of data science...
$350
Reinforcement Learning for Trading Strategies
by Jack Farmer , Ram Seshadri- 3.7
Approx. 12 hours to complete
You will learn how RL has been integrated with neural networks and review LSTMs and how they can be applied to time series data. Idiosyncrasies and challenges of data driven learning in electronic trading Applying LSTM to Time Series Data...
AI Workflow: AI in Production
by Mark J Grover , Ray Lopez, Ph.D.- 4.5
Approx. 17 hours to complete
This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses....
Related searches
Applied Text Mining in Python
by V. G. Vinod Vydiswaran- 4.2
Approx. 29 hours to complete
This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python....
RStudio For Beginner Data Scientists
by Bradley Pearson- 3.7
3 hours on-demand video
Code along with me as this course introduces you to RStudio and provides a foundation to become a Data Scientist. This course aims to cover the fundamental aspects of RStudio, whilst also providing some practical examples of how to use RStudio in a data science use case....
$9.99
Principles, Statistical and Computational Tools for Reproducible Data Science
by Curtis Huttenhower , John Quackenbush , Lorenzo Trippa , Christine Choirat- 0.0
8 Weeks
Learn skills and tools that support data science and reproducible research, to ensure you can trust your own research results, reproduce them yourself, and communicate them to others. These concepts are intended to translate to fields throughout the data sciences: physical and life sciences, applied mathematics and statistics, and computing. Key elements for ensuring data provenance and reproducible experimental design...
$99