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R for Data Science

Batch
Batch01

Duation
12 lessons(36 hours)

Reg Student: ৳ 7000
Professional: ৳ 9000
Course Introduction
Course Highlight

LevelBegining

Duration36 houres

last Date Of Reg.28 Feb, 2018

Start09 Mar, 2018

End14 Apr, 2018

Lesson12 lessons

No. Student25 availabilities
About the course
Life of a Data Scientist
Data scientists are big data wranglers. They take an enormous mass of messy data points (unstructured and structured) and use their formidable skills in math, statistics, and programming to clean, massage and organize them. Then they apply all their analytic powers – industry knowledge, contextual understanding, skepticism of existing assumptions – to uncover hidden solutions to business challenges.
Data Scientist Responsibilities
“A data scientist is someone who is better at statistics than any software engineer and better at software engineering than any statistician.”
On any given day, a data scientist may be required to:
 Conduct undirected research and frame openended industry questions
 Extract huge volumes of data from multiple internal and external sources
 Employ sophisticated analytics programs, machine learning and statistical methods to prepare data for use in predictive and prescriptive modeling
 Thoroughly clean and prune data to discard irrelevant information
 Explore and examine data from a variety of angles to determine hidden weaknesses, trends and/or opportunities
 Devise datadriven solutions to the most pressing challenges
 Invent new algorithms to solve problems and build new tools to automate work
 Communicate predictions and findings to management and IT departments through effective data visualizations and reports
 Recommend costeffective changes to existing procedures and strategies
Every company will have a different take on job tasks. Some treat their data scientists as glorified data analysts or combine their duties with data engineers; others need toplevel analytics experts skilled in intense machine learning and data visualizations.
As data scientists achieve new levels of experience or change jobs, their responsibilities invariably change. For example, a person working alone in a midsize company may spend a good portion of the day in data cleaning and data munging. A highlevel employee in a business that offers databased services may be asked to structure big data projects or create new products.
Source: Orange Tree Global
For detailed Course Contents and Registration: https://goo.gl/fQAX5r
Participants
Any Data Science and Big Data Enthusiasts.
What you will learn
Session 01: Introduction to the Data Science and R
1.1 What is Data Science?
1.2 Benefits and Uses of Data Science
1.3 What does Data Science involveData Science Process?
1.4 Era of Data Science 1.5 Business Intelligence vs Data Science
1.6 Lifecycle of Data Science
1.7 Tools of Data Science
1.8 Why learn R for Data Science?
1.9Installation and SetUp of R and R Studio?
1.10 Overview of R Screen: R Console, R Script, R EnvironmentWorkspace, History, R Documentation, Graphical Output
1.11 How to install Packages in R?
1.12 Basic Computations in R
Session 02: Introduction to R Basics: Data Input, Management and Manipulations in R
2.1 Introduction to R Basics
2.2 Arithmetic in R
2.3 Variables
2.4 R Basic Data Types
2.5 Vector Basics
2.6 Vector Operations
2.7 Vector Indexing and Slicing
2.8 Getting Help with R and RStudio
2.9 Comparison Operators
2.10 R Basics Training Exercise
2.11 Reading Data from Files
2.12 Reading Datasets from Different Data Format
2.13 Creating R Objects: List and Data Frame
2.14 Working with Different Data Format Types
2.15 Conversion between Different Data Format Types
2.16 Data Input, Management and Manipulations – Exercises
Session 03: Arrays, Matrices, Data Frames in R
3.1 R Arrays
3.2 Array indexing, subsections of an array
3.3 R Matrices
3.4 Creating a Matrix
3.5 Matrix Arithmetic
3.6 Matrix Operations
3.7 Matrix Selection and Indexing
3.8 Factor and Categorical Matrices
3.9 Matrix Training Exercise
3.10 R Data Frames
3.11 Data Frame Basics
3.12 Data Frame Indexing and Selection
3.13 Data Frame Operations
3.14 Data Frame Training Exercise
Session 04: R Programming Basics & R Lists
4.1 Introduction to Programming Basics
4.2 Logical Operators
4.3 if, else, and else if Statements
4.4 Conditional Statements Training Exercise
4.5 Conditional Statements Training Exercise  Solutions
4.6 While Loops
4.7 For Loops
4.8 Functions
4.9 Functions Training Exercise
4.10 R Lists
4.12 Constructing and modifying lists
4.13 List – Use and Examples
4.14 List  Exercises and Solutions
Session 05: Basic Statistics and Test of Hypothesis in R
5.1 Basic Statistics in R
1 Find Appropriate Statistics for Data
2 Summarize Your Data
3 Quantitative Techniques of Summarization
4 Summarizing Qualitative Data
5 Basic Statistics in R – Part 1
6 Basic Statistics in R – Part 2
7 Basic Statistics Training Exercise
5.2 Test of Hypothesis in R
1 Introduction of Statistical Tests
2 How to Construct a Hypothesis
3 Test of Mean: the Ztest and the ttest
4 ChiSquare Tests
5 Analysis of Variance (ANOVA)
6 Hypothesis Training Exercises
Session 06: Data Visualization in R
6.1 Data Visualization in R – Visualization Basics
1. Introduction to Graphical Methods of Presenting Data
2. Basic Graphical methods – Histogram, Scatter Plot, line plot, boxplot
3. Bar plot – Different Types and Applications
4. Pie Chart in R
5. Adding Color to Different Plots
6. Data Visualization Basics Training Exercises
7. Data Visualization Basics Training Exercises  Solutions
6.2 Advanced Data Visualization using ggplot2
1. Overview of ggplot2
2. Histogram
3. Scatterplot
4. Barplot
5. Boxplot
6. TwoVariable Plotting
7. Coordinates and Faceting
8. Themes
9. ggplot2 Exercises
10. ggplot2 Exercise Solutions
Session 07: Predictive Modeling using Machine Learning with R
7.1 Introduction to Machine Learning
7.2 Linear Regression Models
1. Introduction
2. Simple Linear Regression
3. Multiple Linear Regression
4. Select the Best Model
5. Understanding the Model Components
6. Prediction using Model
7. MLLinear Regression Training ExercisesSolutions
7.3 Logistic Regression Models
1. Introduction
2. Simple Logistic Regression
3. Multiple Logistic Regression
4. Select the Best Model
5. Understanding the Model Components
6. Adjusted Odds Ratio VS Unadjusted Odds Ratio
7. Prediction using Model
8. Logistic Regression Training Exercises
9. Logistic Regression Training Exercises Solutions
Session 08: Machine Learning with R Principal Component Analysis (PCA) & Factor Analysis
8.1 Principal Component Analysis (PCA)
1. Introduction
2. PCA – Part 1
3. PCA – Part 2
4. PCA Training Exercises
5. PCA Training Exercises Solutions
8.2 Factor Analysis
1. Introduction
2. Factor Analysis – Part 1
3. Factor Analysis – Part 2
4. Factor Analysis Training Exercises
5. Factor Analysis Training Exercises Solutions Session
09: Machine Learning with R Cluster, Kmeans Clustering, Hierarchical Clustering
9.1 Introduction to Cluster Analysis
9.2 Introduction to KMeans Clustering
9.3 K Means Clustering with R
9.4 Introduction to K Means Clustering Project
9.5 K Means Clustering Project  Solutions
9.6 Introduction to Hierarchical Clustering
9.7 Hierarchical Clustering with R
9.8 Hierarchical Clustering Training Exercises
Session 10: Machine Learning with R  K Nearest Neighbors, Decision Trees and Random Forests
10.1 Introduction to K Nearest Neighbors (KNN)
10.2 K Nearest Neighbors with R
10.3 K Nearest Neighbors Project Solutions
10.4 Introduction to Tree Methods
10.5 Decision Trees and Random Forests with R
10.6 Tree Methods Project Solutions
Session 11: Machine Learning with R  Support Vector Machines (SVM), Artificial Neural Network (ANN), Social Network Analysis (SNA)
11.1 Introduction to Support Vector Machines
11.2 Support Vector Machines with R
11.3 Support Vector Machines Project  Solutions
11.4 Introduction to ANN
11.5 Artificial Neural Network (ANN) – Part 1
11.6 Artificial Neural Network (ANN) – Part 2
11.7 Convolution Neural Network (CNN)
Session 12: Overview of the Course, Evaluation of the Course, Certificate Giving Ceremony
Facilities
i) Continuous help from the facilitators about basic knowledge of statistics and computing. ii) A certificate will be given after successful completion of the coursework. iii) R & R Studio software for the course and further research for individuals. iv) Resourceful materials on each topic. v) AC classroom with a computer for each participant.