Monday, 31 July 2017

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data Science Isa Process For A R Programming Language And Big Data 


Analytices
R Programming Language - The King Of Statistical Computing Languages for
Analyzing And Visualizing Big Data Takes 1 Th Place In "the 2017 Top Ten

Programming Languages". In 2014, R Programming Was At The 9th Position

And The Drastic Move This Year Reflects The Significance Of R As A Powerful

Statistical Tool In Data Science.

R Programming Languages among The Top 10 Programming Languages to

Learn For 2015

A Survey Conducted By Rexer Analytics Stated That 70% Of Respondents Use R

Programming At Least Occasionally When Compared To 47% Respondents In

2012. Rexer Analytics Survey Revealed The Most Popular Statistical Analysis

Tool.

R Programming Language -most Popular Statistical Analysis Tool. Png

A Dice Tech Salary Survey Report Released On January 2014 Found That R

Programmers Are Among The Highest Paid Professionals With An Average

Average Salary For R Programmers

Organizations In Every Industry Have Started Realizing The Fact That - The

Secret To Success Is To Be Able To Collect, Store And Analyze Data At A Faster

Pace Than The Competitors. The Consequence Of This Problem Is That It Can Be

Used To Generate Data From A Computer Program.

Click Here To Get The 2016 Data Scientist

This Paper Presents The Results Of A Statistical Analysis Of The Statistical

Analysis Of The Data. Of Data More Quickly And Powerfully When Compared To

Other Statistical Computing Tools.

Data Science


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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

    •          GETTING STARTED WITH R





  • Control structures
  • Functions
  • Scoping rules of R
  • Coding Standards for R
  • Dates and times

    •           GETTING STARTED WITH R





  • 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

    • DATA EXTRACTION
  • 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

    NAÏVE 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


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