Udemy - Introduction to Data Science


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Torrent Hash : 9A61B6F44BAFB385704CAE37110040678DD2D061
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Torrent Size : 2.39 GB


Udemy - Introduction to Data Science
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Torrent File Content (34 files)


Udemy - Introduction to Data Science
    02 Modeling and Machine Learning
          002 Validating Models.mp4 -
112.59 MB

          015 Evaluating Models.mp4 -
141.37 MB

          TestTrainExample.Rproj -
257 bytes

          009 Decision Trees and Random Forest background.mp4 -
55.96 MB

          TestTrainSplit.Rmd -
2.45 KB

          007 Logistic Regression background.mp4 -
61.62 MB

          006 Linear Regression practice.mp4 -
142.75 MB

          005 Linear Regression background.mp4 -
159 MB

          013 Gradient Boosting.mp4 -
70.71 MB

          008 Logistic Regression practice.mp4 -
117.33 MB

          001 Mapping Business to Machine Learning Tasks.mp4 -
45.09 MB

          004 Naive Bayes practice.mp4 -
98.19 MB

          salaryData.rds -
12.23 KB

          011 Generalized Additive Models.mp4 -
58.18 MB

          010 Random Forest practice.mp4 -
76.62 MB

          Examples.R -
3.21 KB

          012 Support Vector Machines.mp4 -
143.36 MB

          003 Naive Bayes background.mp4 -
77.77 MB

          014 Regularization for Linear and Logistic Regression.mp4 -
67.29 MB

    04 Moving On
          001 Recommended Books.mp4 -
43.17 MB

          003 Next Steps.mp4 -
15.62 MB

          002 Further Topics.mp4 -
108.92 MB

    01 Course Overview
          homedata_lesson1_clean.rds -
1.38 KB

          Sec1_Lesson1_Walkthru.Rmd -
1.58 KB

          003 Starting with R and data.mp4 -
93.44 MB

          homedata_lesson1_dirty.rds -
1.42 KB

          002 Walk-through of a data science project.mp4 -
46.88 MB

          001 Course Introduction.mp4 -
27.75 MB

    03 Data
          002 Visualizing Data.mp4 -
109.44 MB

          004 The Shape of Data.mp4 -
141.71 MB

          005 Dealing with Categorical Variables.mp4 -
138.54 MB

          003 Missing Values.mp4 -
99.65 MB

          006 Useful Data Transformations.mp4 -
109.89 MB

          001 Loading Data in R.mp4 -
86.57 MB



Description



Miscellaneous
Use the R Programming Language to execute data science projects and become a data scientist. Implement business solutions, using machine learning and predictive analytics.

The R language provides a way to tackle day-to-day data science tasks, and this course will teach you how to apply the R programming language and useful statistical techniques to everyday business situations.

With this course, you'll be able to use the visualizations, statistical models, and data manipulation tools that modern data scientists rely upon daily to recognize trends and suggest courses of action.

Understand Data Science to Be a More Effective Data Analyst

?Use R and RStudio

?Master Modeling and Machine Learning

?Load, Visualize, and Interpret Data

Use R to Analyze Data and Come Up with Valuable Business Solutions

This course is designed for those who are analytically minded and are familiar with basic statistics and programming or scripting. Some familiarity with R is strongly recommended; otherwise, you can learn R as you go.

You'll learn applied predictive modeling methods, as well as how to explore and visualize data, how to use and understand common machine learning algorithms in R, and how to relate machine learning methods to business problems.

All of these skills will combine to give you the ability to explore data, ask the right questions, execute predictive models, and communicate your informed recommendations and solutions to company leaders.
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