| Course Duration | 65 hours Video + Practice and Study Material |
| Tools | R Studio/ R |
Data Science with R + R Programming
R is a free programming language for data analysis, statistical modeling, machine learning and visualization. It is one of the most popular tool in data science world. Its popularity is getting better day by day. According to recruiting firm Burtch 2017 survey, R is the most preferred language for data science. In another popular KDnuggets Analytics software survey poll, R scored top rank with 49% vote. These survey polls answers the question about scope of R. If you really want to boost your career in analytics, R is the language you need to focus on.
Through this online training course you will get hands-on experience to manipulate and analyse data using machine learning algorithm with R. You will get trained on R Programming, advanced statistical techniques and machine learning.
Features!
- Faculty Support : Get your doubts cleared
- Live Project & Case Studies using real world datasets
- Industry ways of solving problems
- Simplify machine learning techniques via excel sheet demos
- Interview Preparation
- Money Back Guarantee
Who Should do this course?
This course is ideal for candidates who want to make a career in data science.
Any candidate pursuing graduation / post graduation or already graduate can apply for this course. No prior programming and statistics knowledge is required before enrolling for this course.
Testimonial
The course is well organized in terms of content and delivery. The instructor is well-versed with the ability to explain complex issues to the ordinary mind. I would highly recommend this course to anyone who wants to become a Data scientist with practical experience. You will have a hands-on real-world project to prepare you for your job.
I always aspired for working in data science. This aspiration turned into reality once I enrolled for Predictive modeling and data science course at listendata. The faculty at listendata focuses a lot on conceptual clarity making difficult topics very easy to understand. The best part of listendata training is emphasis on all practical aspects of predictive modelling using SAS and R. I am very thankful to Deepanshu for guiding me at every step which enabled me to crack toughest analytics interviews with flying colors. The faculty makes sure that each student queries are answered.
Curriculum - Data Science using R + R Programming
- Introduction to R
- Introduction to RStudio
- Data Structures in R
- Importing / Exporting Data in R
- Data Exploration
- Data Manipulation with dplyr package - Basics
- Data Manipulation with dplyr package - Intermediate
- Data Manipulation with dplyr package - Advanced
- Character and Numeric Functions in R
- Data & Time Functions in R
- Data Visualization in R
- Loops in R (Apply Family of Functions & For Loop)
- R Functions - Part I
- R Functions - Part II
- Introduction to Data Science
- Predictive Modeling in Financial Services Industry
- Hypothesis Testing with R
- Correlation Analysis with R
- Steps of Predictive Modeling
- Data Preparation in Predictive Modeling
- Variable Selection Methods in Predictive Modeling
- Segmentation - Introduction
- Segmentation - Cluster Analysis : Theory
- Segmentation - Cluster Analysis : Data Preparation
- Segmentation - Cluster Analysis : k-means and Hierarchical
- Segmentation - Cluster Analysis : Density Based Clustering
- Segmentation - Cluster Analysis : Cluster Performance
- Segmentation - Cluster Analysis : Insight Generation
- Principal Component Analysis (PCA) - Theory
- Running and Understanding PCA with R
- Linear Regression - Theory
- Linear Regression - Assumptions and Treatment
- Linear Regression - Important Metrics
- Linear Regression - Variable Selection Methods
- Linear Regression - Model Development
- Linear Regression - Model Validation
- Linear Regression - Model Performance
- Linear Regression - Model Implementation
- Logistic Regression - Theory
- Logistic Regression - Assumptions and Treatment
- Logistic Regression - Important Metrics
- Logistic Regression - Variable Selection Methods
- Logistic Regression - Model Development
- Logistic Regression - Model Validation
- Logistic Regression - Model Performance
- Logistic Regression - Model Implementation
- K-Nearest Neighbor - How it works
- K-Nearest Neighbor - Model Development
- K-Nearest Neighbor - Model Validation
- K-Nearest Neighbor - Model Performance
- K-Nearest Neighbor - Model Implementation
- Decision Tree - How it works
- Decision Tree - Model Development
- Decision Tree - Model Validation
- Decision Tree - Model Performance
- Decision Tree - Model Implementation
- Machine Learning - Basics
- Random Forest - How it works
- Decision Tree
- Random Forest - Model Development
- Random Forest - Model Validation
- Random Forest - How it works
- Gradient Boosting - How it works
- Gradient Boosting - Model Development
- Gradient Boosting - Model Validation
- Support Vector Machine - How it works
- Support Vector Machine - Model Development
- Support Vector Machine - Model Validation
- Ensemble Stacking / Blending
- Time Series Forecasting - Theory
- Time Series Analysis with R
- Special Cases - Handle rare event model
- Text Mining Basics & Applications
- Case Studies - Propensity to Buy
- Case Studies - Attrition / Churn Model (BFSI / Telecom)
- Case Studies - Customer Segmentation I
- Case Studies - Customer Segmentation II
- Case Studies - Sales Forecasting
- Case Studies - Text Mining
- Interview Tips - Common Interview Questions
- Mock Interview
Case Studies
Identifying customers most likely to purchase fixed deposit product. Create strategies to implement predictive model.
To identify the bad customers with high likelihood to default on credit card payment. Build scorecard for the same.
Segment retail bank customers based on historic activity patterns. It was used to improve contact strategies in the marketing department.
Develop a customer segmentation model to define marketing strategy for credit card holders
To identify the factors that push the total spend of credit card
To identify customers who are most likely to leave the telecom provider. Phone number portability makes it easy for customers to switch mobile phone services to providers. Prepare retention strategies to retain existing customers
Predict the annual restaurant sales of regional locations using demographic, real estate, and commercial data
Forecast next 3 months sales of a retail store
Forecast international air travels
Generate insights from human resource data
Live Project
Objective : Identify which customers are most likely to leave the bank.
I. First you need to create the analytics data mart using the following data sources.
- Demographic data
- Accounts data
- Leasing data
- Call center contacts
- Value segment scores
Student also needs to think of the plausibility of the created numbers in the data set.
II. You need to create target variable based on some business logic.
III. You need to do feature engineering for prediction
IV. Develop and Validate Model
V. Build Strategies - How stakeholders would utilize the model.
Live Project (R Programming)
Objective : Analyse point-of-sales data for a retail store by creating a customer datamart which will be used to segment customers based on how recently a customer has purchased (recency), how often they purchase (frequency), and how much the customer spends (monetary).
FAQs
I never studied statistics and programming during graduation. Can I still enroll for this course?
Yes, the course has been designed to keep in mind the needs of beginners. Only prerequisite is hard work and zeal for learning.
For how long are the videos and course material available to me?
You will get 24*7 life-time access to recorded classes and course material on our learning management system
What kind of career assistance does ListenData offer?
We have a solid network of analytics professionals in the field of analytics and data science. We are constantly in touch with various companies and job consultancy firms for hiring. We will keep you informed about current openings in analytics industry.
In addition, we provide assistance in the following areas.
- Interview Preparation using extensive practical interview questions
- Resume Building
- Mock Interviews
Can I download the recordings and course material?
Yes, you can download soft copy, code and datasets of each lessons of the course. To access recorded videos, you need to login to your account on Learning Management System (LMS)
Downloading of videos is not allowed as it may lead to distribution of videos illegally. Illegal downloading is against ListenData acceptable use policy and can result in disciplinary action.
Will I get a certificate in the end?
Yes. All our courses are certified. As part of the course, students get weekly assignments and live project. Once all your submissions are received and evaluated, the certificate shall be awarded.
How can I reach out to someone if I have doubts post class?
You can connect with instructor via email, discussion forum or scheduled phone call. Learning Management System (LMS) has a discussion forum where you can post your questions and instructor will get your doubts cleared.
What is the refund policy?
We offer a full refund up to 3 days post your enrollment if you would like to cancel, though with such a good deal we wonder who would!No question asked refund policy!
After payment, when will I get access to the course?
After payment you will get access to the course immediately.
What are the different modes of payment available for enrollment?
Indian users can pay through net banking, debit card or credit card. Non-Indian users can pay via credit card or PayPal.
How to download and install software?
We will provide you instruction guide to download and install software. In case you still find difficulty, you can send your queries via email and discussion forum.
System Requirement : Minimum 4GB RAM