این آموزش تصویری علوم داده ها با R را آموزش می دهد. علوم داده ها شامل علوم کامپیوتر ، برنامه نویسی ، نظریه اطلاعات ، آمار ، هوش مصنوعی و … می باشد. در این دوره نحوه استفاده از R ، برنامه نویسی در Java ، C/C++/C# ، Python و Perl ، کار با فرمول ها ، تجزیه و تحلیل ترافیک و نحوه حل مشکلات را می آموزید.

این دوره آموزشی محصول موسسه Udemy است.

سرفصل های دوره:

  • علوم داده ای چیست؟
  • عناصر اساسی علوم داده ها
  • کار با عناصر علوم داده
  • نحوه ایجاد روابط
  • نحوه مدل سازی برنامه
  • موارد استفاده برای علوم داده
  • نحوه راه اندازی برنامه
  • تجزیه و تحلیل داده ها
  • مدیریت آمار و فناوری اطلاعات
  • کار با انواع داده ها
  • توزیع های آماری
  • کار با برنامه نویسی R
  • کار با R Studio
  • مبانی زبان آر
  • کار با ماتریس ها
  • نحوه دستکاری داده ها
  • کار با آمار در R
  • کار با متغیرها و بردارها
  • آموزش مهندسی داده
  • نحوه اکتساب داده ها
  • نحوه حذف داده ها
  • تجزیه و تحلیل نتایج
  • آموزش رگرسیون خطی
  • نحوه ساخت درخت ها
  • کار با کلاسترها
  • کار با دستورات تصمیم گیری
  • و…

عنوان دوره: Udemy Applied Data Science with R
سطح: متوسط
مدت زمان: 11 ساعت
نویسنده: V2 Maestros


توضیحات:

Udemy Applied Data Science with R

V2 Maestros
11 Hours
Intermediate Level

Learn how to execute an end-to-end data science project and deliver business results
"Data Science is the sexiest job of the 21st century - It has exciting work and incredible pay".
Learning Data Science though is not an easy task. The field traverses through Computer Science, Programming, Information Theory, Statistics and Artificial Intelligence. College/University courses in this field are expensive. Becoming a Data Scientist through self-study is challenging since it requires going through multiple books, websites, searches and exercises and you will still end up feeling "not complete" at the end of it. So how do you acquire full-stack Data Science skills that will get you a and give you the confidence to execute it?
Applied Data Science with R addresses the problem. This course provides extensive, end-to-end coverage of all activities performed in a Data Science project. If teaches application of the latest techniques in data acquisition, transformation and predictive analytics to solve real world business problems. The goal of this course is to teach practice rather than theory. Rather than deep dive into formulae and derivations, it focuses on using existing libraries and tools to produce solutions. It also keeps things simple and easy to understand.
Through this course, we strive to make you fully equipped to become a developer who can execute full fledged Data Science projects. By taking this course, you will
Appreciate what Data Science really is
Understand the Data Science Life Cycle
Learn to use R for executing Data Science Projects
Master the application of Analytics and Machine Learning techniques
Gain insight into how Data Science works through end-to-end use cases.
By becoming a student of V2 Maestros, you will also get maximum discounts on all of our other current and future courses (coupon codes inside the course material). You will also get prompt support of all your queries and questions. We continuously strive to improve our course material to reflect the latest trends and technologies
What are the requirements?
Programming Experience in at least one language like Java, C/C++/C#, Python, Perl
Experience in analyzing Data preferred
What am I going to get from this course?
Over 55 lectures and 11 hours of content!
Appreciate what Data Science really is
Understand the Data Science Life Cycle
Learn to use R for executing Data Science Projects
Master the application of Analytics and Machine Learning techniques
Gain insight into how Data Science works through end-to-end use cases.
What is the target audience?
IT Professionals aspiring to be Data Scientists
Students who want to learn about Data Science domain
Statisticians and Project Managers who want to expand their horizon into Data Science

Section 1: Introduction
Lecture 1
About this Course
08:12
Lecture 2
About V2 Maestros
01:39
Lecture 3
Resource Bundle
Article
Section 2: What is Data Science?
Lecture 4
Basic Elements of Data Science
11:51
Lecture 5
The Dataset
10:44
Lecture 6
Learning from relationships
12:55
Lecture 7
Modeling and Prediction
09:31
Lecture 8
Use Cases for Data Science
07:47
Section 3: Data Science Life Cycle
Lecture 9
Stage 1 - Setup
11:46
Lecture 10
Stage 2 - Data Engineering
11:57
Lecture 11
Stage 3 & 4 - Analysis and Production
19:16
Section 4: Statistics for Data Science
Lecture 12
Types of Data
07:29
Lecture 13
Summary Statistics
16:10
Lecture 14
Statistical Distributions
19:05
Lecture 15
Correlations
10:09
Section 5: R Programming
Lecture 16
Downloading and Installing R and R Studio
Article
Lecture 17
R Studio - Walkaround
06:40
Lecture 18
R Language Basics
12:04
Lecture 19
Vectors and Lists
08:51
Lecture 20
Data Frames and Matrices
14:41
Lecture 21
Data Manipulation and I/O Operations
10:30
Lecture 22
Programming and Packages
12:41
Lecture 23
Statistics in R
03:01
Lecture 24
Graphics in R
06:51
Lecture 25
R Code Examples - Variables and Vectors
16:18
Lecture 26
R Code Examples - Data Frames and Matrices
15:05
Lecture 27
R Code Examples - Programming Elements
17:18
Lecture 28
R Code Examples - Statistics and Base Plotting System
17:29
Lecture 29
R Code Examples - ggplot
17:22
Section 6: Data Engineering
Lecture 30
Data Acquisition
16:01
Lecture 31
Data Cleansing
10:50
Lecture 32
Data Transformations
11:09
Lecture 33
Text Pre-Processing TF-IDF
14:53
Lecture 34
R Examples for Data Engineering
11:14
Section 7: Machine Learning and Predictive Analysis
Lecture 35
Types of Analytics
12:08
Lecture 36
Types of Learning
17:16
Lecture 37
Analyzing Results and Errors
13:46
Lecture 38
Linear Regression
19:00
Lecture 39
R Use Case : Linear Regression
18:01
Lecture 40
Decision Trees
10:42
Lecture 41
R Use Case : Decision Trees
19:36
Lecture 42
Naive Bayes Classification
19:21
Lecture 43
R Use Case : Naive Bayes
19:12
Lecture 44
Random Forests
10:31
Lecture 45
R Use Case : Random Forests
18:47
Lecture 46
K-means Clustering
11:53
Lecture 47
R Use Case : K-Means clustering
16:24
Lecture 48
Association Rules Mining
11:31
Lecture 49
R Use Case : Association Rules Mining
13:11
Section 8: Advanced Topics
Lecture 50
Artificial Neural Networks and Support Vector Machines
04:35
Lecture 51
Bagging and Boosting
11:27
Lecture 52
Dimensionality Reduction
07:28
Lecture 53
R Use Case : Advanced Methods
17:18
Section 9: Conclusion
Lecture 54
Closing Remarks
03:35
Lecture 55
BONUS Lecture : Other courses you should check out
Article