Search result for Data science specialization Online Courses & Certifications
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Python for Genomic Data Science
by Mihaela Pertea, PhD , Steven Salzberg, PhD- 4.3
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Approx. 9 hours to complete
This is the third course in the Genomic Big Data Science Specialization from Johns Hopkins University. 1: Data Structures Part 1 (11:58) 2: Data Structures Part 2 (10:41)...
Data Science Math Skills
by Daniel Egger , Paul Bendich- 4.5
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Approx. 13 hours to complete
Data science courses contain math—no avoiding that! This course is designed to teach learners the basic math you will need in order to be successful in almost any data science math course and was created for learners who have basic math skills but may not have taken algebra or pre-calculus. Welcome to Data Science Math Skills...
Fundamentals of Scalable Data Science
by Romeo Kienzler- 4.3
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Approx. 20 hours to complete
This is the first course of a series of courses towards the IBM Advanced Data Science Specialization. We strongly believe that is is crucial for success to start learning a scalable data science platform since memory and CPU constraints are to most limiting factors when it comes to building advanced machine learning models....
AI Workflow: Data Analysis and Hypothesis Testing
by Mark J Grover , Ray Lopez, Ph.D.- 4.2
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Approx. 11 hours to complete
List several best practices concerning EDA and data visualization Describe strategies for dealing with missing data 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. Data Analysis Introduction to Data Visualizations...
Data Structures and Performance
by Christine Alvarado , Mia Minnes , Leo Porter- 4.8
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Approx. 42 hours to complete
Many of the data structures and algorithms that work with introductory toy examples break when applications process real, large data sets. You will explain how these data structures make programs more efficient and flexible. Welcome (Object Oriented Java Programming: Data Structures and Beyond Specialization) In the Real World: Data Abstraction When I struggled: Data structures...
Project Planning and Machine Learning
by David Sluiter- 4.7
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Approx. 17 hours to complete
This course can also be taken for academic credit as ECEA 5386, part of CU Boulder’s Master of Science in Electrical Engineering degree. * How basic file systems operate, and types of file systems used to store big data Big Data Analytics Segment 1 - Learning Outcomes, Definition of Big Data...
AI Workflow: Enterprise Model Deployment
by Mark J Grover , Ray Lopez, Ph.D.- 4.2
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Approx. 9 hours to complete
Build a data ingestion pipeline using Apache Spark and Apache Spark streaming 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. Introduction to Data at Scale Data at scale: Through the Eyes of Our Working Example...
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Algorithms: Design and Analysis, Part 1
by Tim Roughgarden- 0.0
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6 Weeks
This specialization is an introduction to algorithms for learners with at least a little programming experience. This specialization is an introduction to algorithms for learners with at least a little programming experience. The specialization is rigorous but emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details. Data structures (heaps, balanced search trees, hash tables, bloom filters)...
$149
Cloud Computing Foundations
by Noah Gift- 4.7
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Approx. 19 hours to complete
This course is ideal for beginners as well as intermediate students interested in applying Cloud computing to data science, machine learning and data engineering. Specialization Project Roadmap: Course 1 Copy Hugo Data into AWS Cloud9 S3 Bucket...
Statistics with R Capstone
by Merlise A Clyde , Colin Rundel , David Banks , Mine Çetinkaya-Rundel- 4.6
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Approx. 6 hours to complete
A large and complex dataset will be provided to learners and the analysis will require the application of a variety of methods and techniques introduced in the previous courses, including exploratory data analysis through data visualization and numerical summaries, statistical inference, and modeling as well as interpretations of these results in the context of the data and the research question....
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