Data Science is an interdisciplinary system about processes and methods to extract expertise or observations from data in several forms, sometimes structured or unstructured, which is a continuation of a number of the data analysis systems for example data mining, analysis, and predictive analytics, much like Knowledge Discovery in Databases. Data science implements tactics and hypotheses drawn from many fields in the broad areas of mathematics, chemo metrics, statistics, computer science and information science, including signal processing, machine learning, probability models, data mining, statistical learning, database, data engineering, learning and pattern recognition, predictive analytics, visualization, uncertainty modeling, data compression, computer programming, data warehousing, high performance computing and artificial intelligence.

COURSE SUMMARY:

• Deep understanding of the Roles of a Data Scientist
• How to use R, Hadoop and Machine Learning to analyze Big Data
• Understand the Life Cycle of Data Analysis
• Learn the techniques and tools for data transformation
• Understand Data Mining techniques and their implementation
• How to use machine learning algorithms in R to analyze data
• Understanding of data optimization and visualization techniques
• Understand the parallel processing features in R

PREREQUISITES:

• No Pre-requisites are required.

MODULE 1 : INTRODUCTION TO DATA ANALYTICS

• Origin of R
• Downloading & Installing R, R Studio
• Interface of R-
• R Components.

MODULE 2: DATA INPUTTING IN R

• Data Types, Data objects & Data structures
• Creating a vector & Vector operations
• Sub setting
• Writing Data
• Reading Tabular data files
• Reading CSV data files
• Initializing Data frame
• Selecting Data frame columns by position and name.
• Redirecting R output.

MODULE 3: DATA MANIPULATIONS IN R

• Appending data to a vector
• Combining Multiple Vectors
• Merging data frames
• Data Transformation
• Control structures
• Nested loops
• Splitting
• String and Dates
• Handling NA and missing values
• Matrices & Arrays
• Functions in R
• Logical operators
• Relational operators
• Generating Random Variables
• Accessing Variables
• Matrices multiplication
• Managing subset of data
• Data Aggregation
• Multiple Aggregations
• 4 Control Structures, Functions
• Looping on Command Line
• Debugging
• Simulations
• Plotting – Base Graphics
• Plotting – Lattice Graphics
• Plotting – Mathematical Annotations
• Plotting & Color

STATISTICS

MODULE 4: UNIVARIATE ANALYSIS

• Measure of central tendency
• Dispersions
• Distributions
• Tests

MODULE 5: MULTI VARIATE ANALYSIS

• Correlation
• Regression
• A brief Introduction of Machine learning
• Supervised learning
• Unsupervised learning
• Reinforcement learning

MODULE 6: REGRESSION MODELS

• linear regression models
• Non Linear regression models
• Logistic
• Regression models using Excel

MODULE 7: FACTOR ANALYSIS

• Introduction to PCA
• Association Rule mining
• Market basket analysis

MODULE 8: TREE MODELS

• Decision Tree
• Random forest
• Time series / forecasting.