Search result for Data science course in bangalore Online Courses & Certifications
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Foundations of Data Science: K-Means Clustering in Python
by Dr Matthew Yee-King , Dr Betty Fyn-Sydney , Dr Jamie A Ward , Dr Larisa Soldatova- 4.6
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Approx. 29 hours to complete
This MOOC, designed by an academic team from Goldsmiths, University of London, will quickly introduce you to the core concepts of Data Science to prepare you for intermediate and advanced Data Science courses. Week 1: Foundations of Data Science: K-Means Clustering in Python 7a Storing 2D Coordinates in a Single Data Structure...
Statistics for Genomic Data Science
by Jeff Leek, PhD- 4.2
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Approx. 9 hours to complete
An introduction to the statistics behind the most popular genomic data science projects. This is the sixth course in the Genomic Big Data Science Specialization from Johns Hopkins University. Welcome to Statistics for Genomic Data Science The Three Tables in Genomics (2:10) The Three Tables in Genomics (in R) (3:46) Statistics for Genomic Data Science Wrap-Up (1:53)...
Distributed Computing with Spark SQL
by Brooke Wenig , Conor Murphy- 4.5
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Approx. 14 hours to complete
This course is all about big data. Students will gain an understanding of the fundamentals of data analysis using SQL on Spark, setting the foundation for how to combine data with advanced analytics at scale and in production environments. The final module covers data lakes, data warehouses, and lakehouses. Continuing with Spark and Data Science...
Data Science Ethics
by H.V. Jagadish- 4.8
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Approx. 15 hours to complete
What are the ethical considerations regarding the privacy and control of consumer information and big data, especially in the aftermath of recent large-scale data breaches? Data Science Ethics - Course Preview Data Science Needs Ethics Course Syllabus Data Ownership Data Ownership Data Ownership Finale Data Validity Errors in Data Processing Errors in Model Design...
Leadership Through Marketing
by Greg Carpenter , Florian Zettelmeyer , Sanjay Khosla- 4.6
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Approx. 7 hours to complete
In this course, students will learn how to identify new opportunities to create value for empowered consumers, develop strategies that yield an advantage over rivals, and develop the data science skills to lead more effectively, allocate resources, and to confront this very challenging environment with confidence. Why You Need a Working Knowledge of Data Science...
Teaching Impacts of Technology: Workplace of the Future
by Beth Simon- 0.0
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Approx. 13 hours to complete
In this course you’ll focus on how the Internet has enabled new careers and changed expectations in traditional work settings, creating a new vision for the workplace of the future. Impacts (Advancing your career in the fast moving technical world): digital technology changing jobs, online classes, machines replacing jobs, data science and artificial intelligence...
Regression Models
by Brian Caffo, PhD , Roger D. Peng, PhD , Jeff Leek, PhD- 4.4
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Approx. 54 hours to complete
Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Introductory Data Example Book: Regression Models for Data Science in R Data Science Specialization Community Site Practical R Exercises in swirl Part 1...
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Introduction to Statistics
by Guenther Walther- 4.5
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Approx. 15 hours to complete
Stanford's "Introduction to Statistics" teaches you statistical thinking concepts that are essential for learning from data and communicating insights. [EXTRA] Industry Insights: Challenges to Using Data Science in Medicine [EXTRA] Industry Insights: Hiring Data Science Talent Analysis of Categorical Data Analysis of Categorical Data [EXTRA] Industry Insights: Starting Your Career in Data Science...
Wrangling Data in the Tidyverse
by Carrie Wright, PhD , Shannon Ellis, PhD , Stephanie Hicks, PhD , Roger D. Peng, PhD- 4.8
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Approx. 14 hours to complete
This course covers many of the critical details about handling tidy and non-tidy data in R such as converting from wide to long formats, manipulating tables with the dplyr package, understanding different R data types, processing text data with regular expressions, and conducting basic exploratory data analyses. Wrangling Data in the Tidyverse Course Project...
Analyze Datasets and Train ML Models using AutoML
by Antje Barth , Shelbee Eigenbrode , Sireesha Muppala , Chris Fregly- 4.6
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Approx. 14 hours to complete
In the first course of the Practical Data Science Specialization, you will learn foundational concepts for exploratory data analysis (EDA), automated machine learning (AutoML), and text classification algorithms. Practical data science is geared towards handling massive datasets that do not fit in your local hardware and could originate from multiple sources....
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