Data Analytics using R Programming
Master the fundamentals of data analysis and R programming with this comprehensive online course
Development,Data Science and AI ML,Data Analysis
Lectures -83
Resources -82
Duration -68.5 hours
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Course Description
Data Analysis using R Programming is a thorough course that gives students a clear understanding of the most recent and sophisticated features that are offered in a variety of formats. It provides a detailed explanation of how to use R programming to carry out various data analysis tasks. There are several resources in the course that provide step-by-step instructions on how to use a specific feature.
Data Analytics using R Programming Overview
The foundation of data analytics is the process of turning massive amounts of unstructured raw data gathered from many sources into a data product usable for enterprises.
Over the past ten years, the amount of data that one must manage has increased to unfathomable levels while the cost of data storage has steadily decreased. Terabytes of information regarding user interactions, business transactions, social media activity, and sensor data from autos and mobile phones are collected by private companies and academic organizations. Making sense of this deluge of data is the problem of our time.
Data analytics primarily entails gathering data from various sources, processing it so that analysts can use it, and then producing products that are beneficial to the organization's operations.
We will cover the most cutting-edge theories and practices of data analytics in this online course.
Who this course is for:
Data Analyst
Developers curious about Data Analytics
Those who are practicing Machine Learning, and Data Science
Goals
What will you learn in this course:
Learn fundamentals of data analysis
Understand the basics of R programming
Learn how to use R programming for data analysis
Develop robust data analysis skills
See how real-world data sets work
Prerequisites
What are the prerequisites for this course?
Basic knowledge of statistics
Basic programming knowledge is a plus
Curriculum
Check out the detailed breakdown of what’s inside the course
DATA ANALYTICS using R Programming
82 Lectures
- Introduction to Data Analytics and R Programming 20:05 20:05
- R Installation & Setting R Environment 50:16 50:16
- Variables, Operators & Data types 53:10 53:10
- Structures 47:08 47:08
- Vectors 01:04:04 01:04:04
- Vector Manipulation & Sub Setting 01:06:03 01:06:03
- Constants 41:38 41:38
- RStudio Installation & Lists Part 1 01:02:20 01:02:20
- Lists Part 2 47:44 47:44
- List Manipulation, Sub Setting & Merging 45:01 45:01
- List to Vector & Matrix Part 1 49:52 49:52
- Matrix Part 2 44:02 44:02
- Matrix Accessing 48:26 48:26
- Matrix Manipulation, rep fn & Data Frame 56:08 56:08
- Data Frame Accessing 54:01 54:01
- Column Bind & Row Bind 50:32 50:32
- Merging Data Frames Part 1 50:04 50:04
- Merging Data Frames Part 2 54:26 54:26
- Melting & Casting 52:55 52:55
- Arrays 43:50 43:50
- Factors 50:53 50:53
- Functions & Control Flow Statements 40:27 40:27
- Strings & String Manipulation with Base Package 53:22 53:22
- String Manipulation with Stringi Package Part 1 58:33 58:33
- String Manipulation with Stringi Package Part 2 & Date and Time Part 1 48:13 48:13
- Date and Time Part 2 53:19 53:19
- Data Extraction from CSV File 42:02 42:02
- Data Extraction from EXCEL File 50:40 50:40
- Data Extraction from CLIPBOARD, URL, XML & JSON Files 50:04 50:04
- Database management systems 50:22 50:22
- Structured Query Language 41:35 41:35
- Data Definition Language Commands 01:02:24 01:02:24
- Data Manipulation Language Commands 47:29 47:29
- Sub Queries & Constraints 16:07 16:07
- Aggregate Functions, Clauses & Views 07:21 07:21
- Data Extraction from Databases Part 1 52:31 52:31
- Data Extraction from Databases Part 2 & DPlyr Package Part 1 52:39 52:39
- DPlyr Package Part 2 51:36 51:36
- DPlyr Functions on Air Quality DataSet 57:01 57:01
- Plyr Package for Data Analysis 46:51 46:51
- Tidyr Package with Functions 50:48 50:48
- Factor Analysis 57:11 57:11
- Prob.Table & Cross Table 50:22 50:22
- Statistical Observations Part 1 51:48 51:48
- Statistical Observations Part 2 40:35 40:35
- Statistical Analysis on Credit Data set 01:00:29 01:00:29
- Data Visualization, Pie Charts, 3D Pie Charts & Bar Charts 59:20 59:20
- Box Plots 54:38 54:38
- Histograms & Line Graphs 45:26 45:26
- Scatter Plots & Scatter plot Matrices 01:03:47 01:03:47
- Low Level Plotting 56:01 56:01
- Bar Plot & Density Plot 46:31 46:31
- Combining Plots 35:37 35:37
- Analysis with Scatter Plot, Box Plot, Histograms, Pie Charts & Basic Plot 51:07 51:07
- MatPlot, ECDF & BoxPlot with IRIS Data set 01:02:55 01:02:55
- Additional Box Plot Style Parameters 01:01:41 01:01:41
- Set.Seed Function & Preparing Data for Plotting 01:09:42 01:09:42
- QPlot, ViolinPlot, Statistical Methods & Correlation Analysis 59:26 59:26
- ChiSquared Test, T Test, ANOVA, ANCOVA, Time Series Analysis & Survival Anal 54:42 54:42
- Data Exploration and Visualization 51:00 51:00
- Machine Learning, Types of ML with Algorithms 01:04:53 01:04:53
- How Machine Solve Real Time Problems 43:33 43:33
- K-Nearest Neighbor(KNN) Classification 01:07:45 01:07:45
- KNN Classification with Cancer Data set Part 1 01:03:15 01:03:15
- KNN Classification with Cancer Data set Part 2 43:12 43:12
- Navie Bayes Classification 43:53 43:53
- Navie Bayes Classification with SMS Spam Data set & Text Mining 58:43 58:43
- WordCloud & Document Term Matrix 56:39 56:39
- Train & Evaluate a Model using Navie Bayes 01:11:40 01:11:40
- MarkDown using Knitr Package 01:02:15 01:02:15
- Decision Trees 57:16 57:16
- Decision Trees with Credit Data set Part 1 47:03 47:03
- Decision Trees with Credit Data set Part 2 45:11 45:11
- Support Vector Machine, Neural Networks & Random Forest 46:50 46:50
- Regression & Linear Regression 44:04 44:04
- Multiple Regression 48:24 48:24
- Generalized Linear Regression, Non Linear Regression & Logistic Regression 35:37 35:37
- Clustering 29:04 29:04
- K-Means Clustering with SNS Data Analysis 01:06:18 01:06:18
- Association Rules (Market Basket Analysis) 39:33 39:33
- Market Basket Analysis using Association Rules with Groceries Data set 56:19 56:19
- Python Libraries for Data Science 22:32 22:32
Instructor Details
DATAhill Solutions Srinivas Reddy
Data ScientistMr. Srinivas Reddy is Founder & MD of DATAhill Solutions
He is Research Scholar (Ph.D) on Artificial Intelligence & Machine Learning
He Received Masters of Technology in Computer Science & Engineering from JNTU, MICROSOFT Certified Professional, IBM Certified Professional & Certified from IIT Kanpur & IIT Ropar.
Having 10+ Years of Experience in Software & Training.
His Experience includes Managing, Data Processing, Data Cleaning, Predicting and Analyzing of Large volume of Business Data.
Expertise in Data Science, Data Analytics, Machine Learning, Deep Learning, Artificial Intelligence, Python, R, Weka, Data Management & BI Technologies.
Having Patents and Publications in Various Fields such as Artificial Intelligence, Machine Learning and Data Science Technologies.
Professionally, He is Data Science Management Consultant with over 7+ years of Experience in Finance, Retail, Transport and other Industries.
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