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Description
KnowledgeHut’s in-depth workshop on Data Science with R will help you master R and use its inbuilt functions and libraries for creating applications and programs for data science. R is a much preferred program because of its robustness, flexibility and ease of coding. Its various techniques such as clustering, time-series analyses and classification techniques, nonlinear/linear modelling and classical statistical tests make it apt for use in the field of statistical computation and data science.
This intensive program covers a wide spectrum of Data Science teaching concepts like exploratory data analysis, statistics fundamentals, hypothesis testing, regression & classification modeling techniques and machine learning algorithms.
You will be able to build applications and work as data scientists in top companies in various sectors including pharmaceuticals, cyber security, government offices and retail. You will create R programs that will help discover and interpret relationships in complex information and solve real world problems. You will also learn to create R visualizations that will help analyse and handle large data sets. Enrol now and get trained for the hottest career of the decade.
What You Will Learn
1. Tools & Technologies
Get acquainted with various analysis and visualization tools such as Ggplot and plotly
2. Statistics for Data Science
Understand the behavior of data; build significant models to understand Statistics Fundamentals
3. R for Data Science
Learn about the various R libraries like Dplyr, Data.table used to manipulate data
4. Exploratory Data Analysis
Use R libraries and work on data manipulation, data preparation and data explorations
5. Data Visualization using R
Use of R graphics libraries like Ggvis, Plotly etc.
6. Advanced Statistics & Predictive Modeling
ANOVA, Linear Regression using OLS, Logistic Regression using MLE, KNN, Decision Trees
Prerequisites
While there are no prerequisites, elementary programming knowledge will benefit those who attend this course.
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Who should Attend?
- Those Interested in the field of data science
- Those looking for a more robust, structured R learning program
- Those wanting to use R for effective analysis of large datasets
- Software or Data Engineers interested in quantitative analysis with R
KnowledgeHut Experience
Instructor-led Live Classroom
Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.
Curriculum Designed by Experts
Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.
Learn through Doing
Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.
Mentored by Industry Leaders
Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.
Advance from the Basics
Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.
Code Reviews by Professionals
Get reviews and feedback on your final projects from professional developers.
Curriculum
Learning Objectives:
Get an overview of the world of data science. Get acquainted with various analysis and visualization tools used in data science.
Topics Covered:
- What is Data Science?
- Analytics Landscape
- Life Cycle of a Data Science Project
- Data Science Tools & Technologies
Hands-on: No hands-on
Learning Objectives:
In this module, you will get an introduction to R, and understand why it is so popular among Data Scientists. Starting with the installation of R and its components, you will load and learn about frequently used libraries. This module touches upon data structures in R, loops and control statements in R and teaches you how to write custom functions, nested functions and functions with arguments.You will learn all about loop functions available in R which are efficient and can be written with a single command.
Going further, you will explore string manipulations and regular expressions and see how functions can be extremely useful for text or unstructured data manipulations. This module also teaches how to import data from various sources in R and also how to write files from R and connect to various databases from R. You will get an overview of visualization in R with base and ggplot libraries, and grasp the Grammar of Graphics in a very structured easy-to-understand manner. The module ends with a hands-on session on a real-life case study.
Topics Covered:
- Intro to R Programming
- Installing and Loading Libraries
- Data Structures in R
- Control & Loop Statements in R
- Functions in R
- Loop Functions in R
- String Manipulation & Regular Expression in R
- Working with Data in R
- Data Visualization in R
- Case Study
Hands-on:
- Know how to install R, R Studio and other libraries
- Write R Code to understand and implement R Data Structures
- Write R Code to implement loop and control structures in R
- Write R Code to read and write data from/to R
- Read data not only from CSV files but also using direct connection to various databases
- Write R Code to implement ggplot for data visualization
- Complex Real-Life Data Manipulation, Preparation & Exploratory Data Analysis case study
Learning Objectives:
This module explores basics like mean (expected value), median and mode. You will understand the distribution of data in terms of variance, standard deviation and interquartile range and get basic summaries about data and its measures, together with simple graphics analysis.
Through daily life examples, you will understand the basics of probability, marginal probability and its importance with respect to data science. Learn Baye’s theorem and conditional probability, and alternate and null hypothesis including Type1 error, Type2 error, power of the test, and p-value.
Topics Covered:
- Measures of Central Tendency
- Measures of Dispersion
- Descriptive Statistics
- Probability Basics
- Marginal Probability
- Bayes Theorem
- Probability Distributions
- Hypothesis Testing
Hands-on:
Formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario.
Learning Objectives:
This module analyses Variance and its practical use, covering strong concepts, model building, evaluating model parameters, measuring performance metrics on Test and Validation set. You will use Linear Regression with Ordinary Least Square Estimate to predict a continuous variable. Further you will learn to enhance model performance by means of various steps like feature engineering & regularization.
Along the way, you will learn about Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis, including methods to find the optimum number of components/factors using scree plot, one-eigenvalue criterion. You will be able to cement the concepts learnt through real life case studies with Linear Regression and PCA & FA.
Topics Covered:
- ANOVA
- Linear Regression (OLS)
- Case Study: Linear Regression
- Principal Component Analysis
- Factor Analysis
- Case Study: PCA/FA
Hands-on:
- With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
- Reduce Data Dimensionality for a House Attribute Dataset for more insights & better modeling
Learning Objectives:
In this module you will explore Binomial Logistic Regression for Binomial Classification Problems, including evaluation of model parameters, model performance using various metrics like sensitivity, specificity, precision, recall, ROC Curve, AUC, KS-Statistics, and Kappa Value. You will work with a real-life case study with Binomial Logistic Regression.
Next, you will learn about KNN Algorithm for Classification Problem, including techniques that are used to find the optimum value for K. You will see a real-life case study with KNN Decision Trees, to help you understand regression & classification problems. At the end of this module you will have working knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID, among others.
Topics Covered:
- Logistic Regression
- Case Study: Logistic Regression
- K-Nearest Neighbor Algorithm
- Case Study: K-Nearest Neighbor Algorithm
- Decision Tree
- Case Study: Decision Tree
Hands-on:
- With various customer attributes describing customer characteristics, build a classification model to predict which customer is likely to default a credit card payment next month. This can help the bank be proactive in collecting dues.
- Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
- Wine comes in various types. With the ingredient composition known, we can build a model to predict the the Wine Quality using Decision Tree (Regression Trees).
Learning Objectives:
In this module, you will understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data; also work with the Exponential Smoothing Model and know when to use the same. You will know how to use Holt's model when your data has Constant Data, Trend Data and Seasonal Data and learn how to select the right smoothing constants for each set of circumstances.Finally, you will use Autoregressive Integrated Moving Average Model for building a Time Series Model and carry out a real-life case study with ARIMA.
Topics Covered:
- Understand Time Series Data
- Visualizing TIme Series Components
- Exponential Smoothing
- Holt's Model
- Holt-Winter's Model
- ARIMA
- Case Study: Time Series Modeling on Stock Price
Hands-on:
- Write R code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data
- Write R code to Use Holt's model when your data has Constant Data, Trend Data and Seasonal Data.
- How to select the right smoothing constants.
- Write R code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
- Dataset including features such as symbol, date, close, adj_close, volume of a stock. This data will exhibit characteristics of a time series data. We will use ARIMA to predict the stock prices.
Learning Objectives:
You will work on an industry mentor guided group project to handle a real-life project, the same way you would execute a data science project in any business problem.
Topics Covered:
- Industry relevant capstone project under experienced industry-expert mentor
Hands-on:
Project to be selected by candidates.
Projects
Predict House Price using Linear Regression
With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
Predict credit card defaulter using Logistic Regression
This project involves building a classification model.
Predict chronic kidney disease using KNN
Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
Predict quality of Wine using Decision Tree
Wine comes in various styles. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).
reviews on our popular courses
It was an excellent workshop and a great learning experience!
Trainer was really good. Session was very well-organized
The trainer (Marco Mulder) brought a great deal of industry-wide experience which helped to resolve the audience's day-to-day challenges.
The class was collaborative and vibrant. I was able to learn the principles of Scrum and gain the skills and knowledge needed to apply it effectively in my organization. In a short span of days following that class I've been able to implement Scrum across different departments and we've evolved our mindset and acquired the practical skills and tools to leverage the framework at all levels. I highly recommend this!
FAQs
The Course
On completing R and knowing the fundamentals of Data science, you can aim for a rewarding career in data science. Since the evolution of big data, data science and data analysis have become the most sought after career paths because of the huge demand for data science professionals. Not only high profile technology companies such as Google and Facebook but companies across sectors are hiring data scientists who can generate business and solve complex data related problems. This is the perfect course for you to step into the world of data science and make a career in what has been rated as the best job in America by Glassdoor.com.
- Get advanced knowledge of data science and how to use it in real life business
- Understand the statistics and probability of Data science
- Get an understanding of data collection, data mining and machine learning
- Learn tools like R
By the end of this course, you would have gained knowledge on the use of data science techniques and build applications on data statistics. This will help you land jobs as Data Scientists.
Tools and Technologies used for this course are
- R Programming
- MS Excel
There are no restrictions but participants would benefit if they have elementary programming knowledge and familiarity with statistics.
Yes, KnowledgeHut offers this training online.
On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.
Your instructors are R experts who have years of industry experience.
Finance Related
Any registration cancelled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of written request for refund. Kindly go through our Refund Policy for more details: http://www.knowledgehut.com/refund
KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our http://www.knowledgehut.com/refund.
The Remote Experience
In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.
Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor