Overview
Wissenhive's Data Analytics with R training course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It covers concepts of Data Manipulation, Exploratory Data Analysis, etc before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc.
Data Analytics course at our institute will prepare you to handle complex, multifaceted projects and Data Analytics certification improves your career opportunities. Our academy also offers Data Analytics online coaching at affordable cost to the individuals who are not able to attend the classroom training. We have industry experts with great experience as trainers. Register now and prove your expertise to your potential employers.
What you will learn
Here’s what you will learn!
- Learn to explore and visualize data and polish your skills in techniques such as Predictive Analytics, Association Rule Mining and much more
- Derive meaning from custom created charts that are used to represent complex data, manipulate this data and create statistical models for predictive analysis
- Learn to use R, not just as a statistical tool but to create your own functions, objects and packages
Benefits
"R" wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists.
The average salary for a Senior Data Scientist skilled in R is $123k (Payscale salary data)
Course Content
| R tools and their uses in Business Analytics | 14:15 | Play |
| Objectives | 14:15 | Play |
| Analytics | 14:15 | Play |
| Where is analytics applied? | 14:15 | Play |
| Responsibilities of a data scientist | 14:15 | Play |
| Problem definition | 14:15 | Play |
| Summarizing data | 14:15 | Play |
| Data collection | 14:15 | Play |
| Difference between R and other analytical languages | 14:17 | Play |
| Different data types in R | 14:17 | Play |
| Built in functions of R: seq(), cbind (), rbind(), merge() | 14:17 | Play |
| Subsetting methods | 14:17 | Play |
| Use of functions like str(), class(), length(), nrow(), ncol(),head(), tail() | 14:17 | Play |
| Steps involved in data cleaning | 14:18 | Play |
| Problems and solutions for Data cleaning | 14:18 | Play |
| Data inspection | 14:18 | Play |
| Use of functions grepl(), grep(), sub() | 14:18 | Play |
| Use of apply() function | 14:18 | Play |
| Coerce the data | 14:18 | Play |
| How R handles data in a variety of formats | 14:20 | Play |
| Importing data from csv files, spreadsheets and text files | 14:20 | Play |
| Import data from other statistical formats like sas7bdat and sps | 14:20 | Play |
| Packages installation used for database import | 14:20 | Play |
| Connect to RDBMS from R using ODBC and basic SQL queries in R | 14:20 | Play |
| Basics of Web Scraping | 14:20 | Play |
| Understanding the Exploratory Data Analysis(EDA) | 14:21 | Play |
| Implementation of EDA on various datasets | 14:21 | Play |
| Boxplots | 14:21 | Play |
| Understanding the cor() in R | 14:21 | Play |
| EDA functions like summarize() | 14:21 | Play |
| llist() | 14:21 | Play |
| Multiple packages in R for data analysis | 14:21 | Play |
| Segment plot HC plot in R | 14:21 | Play |
| Understanding on Data Visualization | 14:23 | Play |
| Graphical functions present in R | 14:23 | Play |
| Plot various graphs like tableplot | 14:23 | Play |
| Histogram | 14:23 | Play |
| Box Plot | 14:23 | Play |
| Customizing Graphical Parameters to improvise the plots | 14:23 | Play |
| Understanding GUIs like Deducer and R Commander | 14:23 | Play |
| Introduction to Spatial Analysis | 14:23 | Play |
| Introduction to Data Mining | 14:25 | Play |
| Understanding Machine Learning | 14:25 | Play |
| Supervised and Unsupervised Machine Learning Algorithms | 14:25 | Play |
| K-means Clustering | 14:25 | Play |
| Association Rule Mining | 14:26 | Play |
| Sentiment Analysis | 14:26 | Play |
| Linear Regression | 14:27 | Play |
| Logistic Regression | 14:27 | Play |
| Anova | 14:27 | Play |
| Predictive Analysis | 14:27 | Play |
| Decision Trees | 14:29 | Play |
| Algorithm for creating Decision Trees | 14:29 | Play |
| Greedy Approach: Entropy and Information Gain | 14:29 | Play |
| Creating a Perfect Decision Tree | 14:29 | Play |
| Classification Rules for Decision Trees | 14:29 | Play |
| Concepts of Random Forest | 14:29 | Play |
| Working of Random Forest | 14:29 | Play |
| Features of Random Forest | 14:29 | Play |
Course Details
The open source programming language R has increased in popularity in recent years, and is now universally accepted by statisticians and data miners as the number one language for data science. R uses cutting-edge technology to manipulate data and create statistical models and charts that can be used for predictive modelling. It gives quick results and because it is open source, it is supported by a worldwide community of over two million users and developers. KnowledgeHut’s Data Analytics training will take you through the basics of this powerful language R. From the ground up, you will learn how to develop data for analysis and apply statistical measures to create data visualisations. By exploring the characteristics of data sets, you can analyse and achieve optimum results based on past data.
Is this course right for you?
Statisticians, Data Analysts, Business Analysts and professionals keen to learn more about the science and practice of data analysis using R, will benefit from this data analytics certification course.
What do you need to be familiar with?
- Basic knowledge of a programming language such as Python or Java
- A background in Mathematics will be beneficial
Course Info.
Training Options
Selfpaced Training
299
- Lifetime access to high-quality self-paced eLearning content curated by industry experts
- 3 simulation test papers for self-assessment
- Lab access to practice live during sessions
- 24x7 learner assistance and support
Live Virtual Classes
399
- Online Classroom Flexi-Pass
- Lifetime access
- Practice lab and projects with integrated Azure labs
- Access to Microsoft official content aligned to examination
One on One Training
799
- Customized learning delivery model (self-paced and/or instructor-led)
- Flexible pricing options
- Enterprise grade learning management system (LMS)
- Enterprise dashboards for individuals and teams
- 24x7 learner assistance and support
Exam & Certification
No Exam Required.
you will be required to complete a project which will be assesd by our certified instructors. on succesful completion of the project you will be awarded a training certificate.
FAQs
You will never miss a lecture! You can choose either of the two options:
- View the recorded session of the class available in your LMS.
- You can attend the missed session, in any other live batch.
Towards the end of the course, all participants will be required to work on a project to get hands on familiarity with the concepts learnt. You will perform predictive analyses on the provided data using R programming language. This project, which can also be a live industry project, will be reviewed by our instructors and industry experts. On successful completion, you will be awarded a certificate.
Classes are held on weekdays and weekends. You can check available schedules and choose the batch timings which are convenient for you.
Reach Us
Useful Links
Company
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B-115, Sector-2, Noida, Uttar Pradesh - 201301
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+91-1204229536
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info@wissenhive.com
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