Search result for Exploratory data analysis Online Courses & Certifications
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Data Analysis and Interpretation Capstone
by Jen Rose , Lisa Dierker- 4.7
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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. You will use real world data to complete a project with our industry and academic partners. Identify Your Data and Research Question Exploratory Data Analysis...
Data Science 2021 : Complete Data Science & Machine Learning
by Jitesh Khurkhuriya- 4.6
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25.5 hours on-demand video
Machine Learning A-Z, Data Science, Python for Machine Learning, Math for Machine Learning, Statistics for Data Science Data Science and Machine Learning are the hottest skills in demand but challenging to learn. Statistics and Statistical Analysis for Data Science Data Visualization is one of the key techniques of Data Science and Machine Learning and is used for Exploratory Data Analysis....
$14.99
AWS Certified Machine Learning Specialty: 3 PRACTICE EXAMS
by Abhishek Singh | 9x AWS- 0.0
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Each of the two practice exams spans the four domains of Data Engineering, Exploratory Data Analysis, Modeling, and Machine Learning Implementation and Operations. Just like the real exam, there are scenario based questions on Big Data services such as Glue, Athena, S3 and Kinesis Family....
$12.99
Designing, Running, and Analyzing Experiments
by Scott Klemmer , Jacob O. Wobbrock- 3.6
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Approx. 15 hours to complete
In this course, you’ll learn how to design user-centered experiments, how to run such experiments, and how to analyze data from these experiments in order to evaluate and validate user experiences. Exploring Data and a First Test of Proportions Validity in Design and Analysis Data Assumptions and Distributions Long-Format and Wide-Format Data Tables...
Machine Learning Rapid Prototyping with IBM Watson Studio
by Mark J Grover , Meredith Mante- 4.6
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Approx. 9 hours to complete
This automation will provide rapid-prototyping of models and allow the Data Scientist to focus their efforts on applying domain knowledge to fine-tune models. Learners will be provided with test data sets for two use cases. This course is intended for practicing Data Scientists. Automated Data Preparation and Model Selection Automated Data Preparation...
Forecasting Models for Marketing Decisions
by David Schweidel- 4.3
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Approx. 12 hours to complete
Companies still struggle to unlock customer data analytics insight Conducting Exploratory Analysis Quiz...
Exploratory Data Analysis | Coursera
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The course on Exploratory Data Analysis was highly enjoyable. Very nice course, plotting data to explore and understand various features and their relationship is the key in any research ....
Top Exploratory Data Analysis Courses - Learn Exploratory ...
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Exploratory data analysis (EDA) is an approach to data analysis used to investigate sets of data, summarize their characteristics, and figure out how to best work with data to get answers while providing a visual to help businesses, scientists, researchers, and analysts learn more from that data....
Exploratory Data Analysis (EDA) Online Course from Metis
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The Live Online Exploratory Data Analysis course format requires a full-time commitment Monday through Friday for two weeks....
8 Online Courses For Exploratory Data Analysis - Analytics ...
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Exploratory Data Analysis for Machine Learning: Udemy . This Udemy course is for beginners and teaches the basics of exploratory data analysis– multicollinearity, identifying the relationship between variables, transforming continuous data, central tendency vs dispersion, identifying outliers, and transforming categorical data, among others....
What is Exploratory Data Analysis? | IBM
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Exploratory data analysis (EDA) is used by data scientists to analyze and investigate data sets and summarize their main characteristics, often employing data visualization methods. It helps determine how best to manipulate data sources to get the answers you need, making it easier for data scientists to discover patterns, spot anomalies, test ....
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