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Data Scientist Online Training in Hyderabad,india

RS Trainings
RS Trainings

Data scientist Online Training 


RS Trainings: is a brand and providing quality online and offline trainings for students in world wide. Rs Trainings providing Best DataScience online training in Hyderabad.

HIGHLIGHTS IN OUR TRAINING SERVICE:

Here All faculties of us have experienced the joy of training and trained Resources is available throughout the world. Training leads to better understanding, new knowledge, skills and expertise. Our real time experienced trainers fulfill your dreams and create professionally driven environment. we will develop the Acquaintance with production, development and testing environments. Our trainers assist in Resume preparation, enhance interview skills, sample live projects, Assessments, clarifying doubts, providing materials, explaining bugs and critical issues, development activities, encourage innovative thoughts etc;

RStrainings is one best online and class room training center in Hyderabad,We are also providing training in India, usa,Uk,Canada,Japan,Malasia,Singapore, Australia etc..

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
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  • 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
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  •       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
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  • 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
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  • 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
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  • CAPSTONE PROJECT



  • Saving R Script
  • Scheduling R Script
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