Data Analytics with R Programming Certification Training For Beginners

Introduction

The Data Analytics with R Programming Certification Training is a comprehensive online course focusing mainly on different data analysis techniques. The first few modules of this course focus mainly on introduction, fundamental concepts, and tools, while the rest of the modules mainly focus on different strategies, programming, and advanced techniques that are important for professionals. The course ends with a project where the students are expected to implement all the skills and tools they have learned to sort the available data.

For each concept and topic within the course, a theory-based explanation along with examples and tools is added to help the students sort out data. For hands-on experience, the course also includes some projects, assignments, and exercises as well.

What Will You Gain From This Course?

Following the competition of this certification, the participants will be able to:

1. Gain Foundational knowledge of data analytics and data science.
2. Get to know more about the core concepts and principles of data analytics i.e. data manipulation, visualization, and analysis.
3. Learn about using data to make informed decisions.
4. Gain proficiency in R, a very important programming language used in the field of data analytics and statistics.
5. Learn about the essential R functions, libraries, and techniques for data analysis tasks.
6. Get familiar with different data manipulation and exploration techniques.
7. Learn to clean and prepare data for analysis by sorting out missing values, data wrangling, and data transformation skills.
8. Develop data visualization and become an expert in creating compelling and informative data visualizations with the help of tools like ggplot2.
9. Learn to explore and analyze data to understand its underlying patterns and trends.

Skills Acquired:

1. Data Analysis
2. R Programming
3. Data Cleaning
4. Data Visualization
5. Machine Learning

Who Can Benefit From This Course?

This course is designed for:

1. Individuals working in other fields seek hands-on courses to kick-start their careers in data analytics.
2. Data enthusiasts trying to learn about the latest tools and techniques in the data analysis field.
3. Junior professional in the field of data analytics trying to improve their skills with R programming.
4. Beginners in R programming familiar with the basics of data analysis but lack R proficiency.
5. Individuals trying to learn more about using R effectively for data manipulation, visualization, and modeling.
6. Business analysts who want to learn about data visualization so they can present their business pitch to the stakeholders better.
7. Researchers who want to utilize data analysis, data extraction, and data communication techniques for improving business decisions and research projects.
8. Marketing, Finance, and Technology majors are trying to boost their data literacy and leverage R programming to analyze trends in their respective fields better.

Course Content

12 Modules – Certificate of Completion

Introduction to Data Analytics

The first module of this course offers a walkthrough of data analytics. The module starts with a basic introduction to some fundamental data analytics terms such as business intelligence, business analytics, data, and information. The module then dives into the information hierarchy and different ways to improve and introduce it. Moreover, the module also covers business analytics, R, R language, its community, and its ecosystem.

The students will also get to know more about the use of 'R' in the industry, its influence, and more. Other important topics explained in this module include a comparison between R and other software in analytics, a demo of Installing R and other useful packages, ways to perform basic operations in R with the help of the command line, using IDE R Studio and Various GUI and utilizing ‘R help’ feature in R. Finally, the module ends with some simple tips to help the students collaborate with worldwide R community.

Introduction to R Programming

The second module is all about R Programming. The module starts with a basic introduction to R programming, its types, and its uses. The students will also get to know more about built-in functions in R like seq(), cbind (), rbind(), merge(), various subsetting methods, using functions like str(), class(), length(), nrow(), ncol() to summarize data and using functions like head(), tail() to inspecting data. Moreover, the students will also get to participate in class activities to summarize data and learn about the dplyr package to perform SQL join in R.

Data Manipulation in R

The third module of this course covers data manipulation. The students will get to know the different steps involved in data cleaning, the functions used in data inspection, and handling the problems faced during data cleaning. By the end of this module, there is also a section for explaining the uses of the functions like grepl(), grep(), sub(), coerce the data, and uses of the apply() functions.

Data Import Techniques in R

The fourth module of this training program covers important data techniques in R. The students will learn how to import data from spreadsheets and text files into R and how to import data from other statistical formats like sas7bdat and SPSS packages. Moreover, the students will also get to know about the installation used for database import, ways to connect to RDBMS from R using ODBC, the fundamentals of SQL queries in R, and web scraping.

Exploratory Data Analysis

The fifth module of this course is dedicated to exploratory data analysis. The module starts with the introduction of Exploratory Data Analysis (EDA) and then looks at the implementation of EDA on various datasets. This module also includes some other important topics like Boxplots, cor() in R, EDA functions like summarize(), llist (), multiple packages in R for data analysis, and fancy plots like the segment plot and HC plot in R.

Data Visualization in R

The sixth module of this training program explores different ways to visualize data in R. The students will learn about data visualization in general and then learn about some important graphical functions present in R and plot various graphs like tableplot, histogram, and Boxplot. This module also covers some other important topics like customizing graphical parameters to improvise plots, knowledge about GUIs like Deducer and R Commander, and the basics of spatial analysis.

Data Mining: Clustering Techniques

The seventh module of this course offers an in-depth understanding of data mining. The module starts with a basic introduction to data mining and then elaborates on machine learning. For a comprehensive understanding and some skill-based learning, the module also covers topics like Supervised and Unsupervised Machine Learning Algorithms as well as K-means Clustering.

Data Mining: Association Rule Mining & Collaborative Filtering

The eighth module of this course covers some advanced topics related to data mining. The module starts with a basic introduction to association rule mining but then gets into more details about User-Based Collaborative Filtering (UBCF) and Item-Based Collaborative Filtering (IBCF). Since both these topics are skill-based, the module also includes hands-on exercises related to recommender engines: User-Based Collaborative Filtering (UBCF) and Item Based Collaborative Filtering (IBCF).

Linear and Logistic Regression

The ninth module of this course focuses mainly on regression. The module starts with a basic introduction to regression, its roles in data analytics, and its uses. After covering the basics, the module then gets into detail about its types, i.e., linear regression and logistic regression. For a better understanding, the students also get an opportunity to learn more about the application of both regression types and their use.

Anova and Sentiment Analysis

The tenth module of this training program sheds light on the Analysis of Variance (Anova) Technique. The course starts with a basic introduction, theory, and utility. Moreover, there is a section with hands-on exercises where the students can practice Anova Technique. Finally, this module ends with explaining Sentiment Analysis, i.e., fetching, extracting, and mining live data from Twitter, along with some practice exercises as well.

Data Mining: Decision Trees and Random Forest

The eleventh module of this online learning program focuses on decision trees and random forests with data mining. The module starts with some important topics like Decision Tree, Entropy, and Gini Index. Moreover, the module also has a section exploring other data mining-related topics like Pruning, Information Gain, and Algorithms for creating Decision Trees. Finally, the module ends with some advanced topics like Bagging of Regression and Classification Trees, Random Forests, Working on Random Forests, and Features of Random Forests.

Project Work

The final module of this course is a hands-on project. The students will be required to use all the knowledge learned in the above modules to complete this project. For this project, the students are required to run data analytics on the topic of census data to predict insights on the income of the people based on factors like age, education, work class, and occupation using Decision Trees. Moreover, the students will also be analyzing the Sentiment of Twitter data, where the data to be analyzed is streamed live from Twitter, and sentiment analysis is performed on the same data. 

Description

The Data Analytics with R Programming Certification Training is a full course that can help anyone become an expert in data analysis. The course focuses on different data sorting, filtering, cleaning, and analyzing tools. This is a beginner-friendly course; however, people with a basic understanding of data analytics can also benefit from it. The course aims to help both professionals and aspirants learn the best data analysis techniques that can help them sort data and find valuable insight. Overall, there are twelve modules within this course that are arranged in a progressive order so the students can experience a zero-to-hero journey.

Meet the Instructor

Edureka

Edureka is one of the best online learning platforms for blockchain chain, data science, and tech aspirants. This is a professional learning site where experts from different professions bring years of experience and advanced knowledge to the students. Founded in 2011, this learning platform aims to offer equal learning opportunities for all, regardless of their skillset, education, or financial background. The platform bridges the gap between industry demand and their skill sets. With its vast library of courses across various domains like Big Data, Cloud Computing, Artificial Intelligence, Blockchain, and Digital Marketing, Edureka has gained a lot of popularity among beginners, aspirants, and professionals trying to expand their skills in related fields.