Introduces students to the fundamental principles of data science that underlie the algorithms, processes, methods, and data-analytic thinking.

 

About The Course

This Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on ‘R’ capabilities.

Who should go for this course ?

The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:

• Developers aspiring to be a ‘Data Scientist’
• Analytics Managers who are leading a team of analysts
• SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
• Business Analysts wanting to understand Machine Learning (ML) Techniques
• Information Architects wanting to gain expertise in Predictive Analytics
• ‘R’ professionals who want to captivate and analyze Big Data
• Hadoop Professionals who want to learn R and ML techniques
• Analysts wanting to understand Data Science methodologies
• Statisticians looking to implement the statistics techniques on big data

Pre-requisites

There is no specific pre-requisite for the course but exposure to core Java and mathematical ability are helpful. XoomTrainings can offer you complementary self-paced courses covering essentials of Hadoop, R and Mahout to brush up the fundamentals required for the course.

Why Learn Data Science?

Data Science coaching certifies you with ‘in demand’ Big Data Technologies to assist you grab the top paying Data Science job title with Big Data skills and experience in R programming, Machine Learning and Hadoop framework.

Data Science Introduction and Toolbox :

• Getting Started with Github
  • Introduction to Git
  • Introduction to Github
  • Creating a Github Repository
  • Basic Git Commands
  • Basic Markdown
• Getting Started with R
  • Overview of R
  • R data types and Objects
  • Getting Data In and Out of R
  • Subsetting R Objects
  • Dates and Times
  • Control structures
  • Functions
  • Scoping rules of R
  • Coding Standards for R
  • Dates and times
  • Loop Functions
  • Vectorizing a Function
  • Debugging
  • Profiling R Code
  • Simulation

Data Extraction, Preparation and Manipulation ( R, MYSQL, HDFS, HIVE and SQOOP)

•  Data Extraction
  • Downloading Files
  • Reading Local Files
  • Reading Excel Files
  • Reading JSON
  • Reading XML
  • Reading From WEB
  • Reading From API
  • Reading From HDFS
  • Reading From MYSQL
  • SQOOP
  • Reading FROM HIVE
  • Saving and Transporting Object
  • Reading Complex Structure
• Data Preparation
  • Subsetting and Sorting
  • Summarizing Data
  • Creating New Variable
  • Regular Expression
  • Working With Dates
• Data Manipulation
  • Managing DataFrame with dplyr package
  • Reshaping Data
  • Merging Data
• Descriptive Statistics
  • Univariate Data and Bivariate Data
  • Categorical and Numerical Data
  • Frequency Histogram and Bar Charts
  • Summarizing Statistical Data
  • Box Plot, Scatter Plot, Bar Plot, Pie Chart
• Probability
  • Conditional Probability
  • Bayes Rule
  • Probability Distribution
  • Correlation vs Causation
  • Average
  • Variance
  • Outliers
• Statistical Distribution
  • Binomial Distribution
  • Central Limit Theorem
  • Normal Distribution
  • 68-95-99.7 % Rule
  • Relationship Between Binomial and Normal Distribution
• Hypothesis Testing
  • Hypothesis Testing
  • Case Studies

Inferential Statistics

• Testing of Hypothesis
• Level of Significance
• Comparison Between Sample Mean and Population Mean
• z- Test
• t- Test
• ANOVA (f- Test)
  • ANCOVA
  • MANOVA
  • MANCOVA
• Regression and Correlation
  • Regression
  • Correlation
• CHI-SQUARE

Principal Of Analytic Graph

•   Introduction to ggvis
  • Exploratory and Explainatory
  • Design Principle
  • Load ggvis and start to explore
  • Plotting System in R
  • ggvis – graphics grammar
• Lines and Syntax
  • Properties for Lines
  • Properties for Points
  • Display Model Fits
• Transformations
  • ggvis and dplyr
• HTMLWIDGET
  • Geo-Spatial Map
  • Time Series Chart
  • Network Node

Predictive Models and Machine Learning Algorithm – Supervised Regression

• Regression Analysis
  • Linear Regression
  • Non- Linear Regression
  • Polynomial Regression
  • Curvilinear Regression
• Multiple Linear Regression
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance
• Logistic Regression
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance
• Time Series Forecast
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

Predictive Models and Machine Learning Algorithm – Supervised Classification

• Naive Bayes
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance
• Support Vector Machine
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance
• Random Forest
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance
• K- Nearest Neighbors
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance
• Classification and Regression Tree (CART)
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

Predictive Models and Machine Learning Algorithm – Unsupervised

• K Mean Cluster
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance
• Apriori Algorithm
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance
• Case Study : Customer Analytic – Customer Lifetime Value
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

Text Mining, Natural Language Processing and Social Network Analysis

• Natural Language Processing
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance
• Social Network Analysis
  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance
• Capstone Project
  • Saving R Script
  • Scheduling R Script