Data science Training Online In India,USA

SIGNEXIT SYSTEMS is a brand and providing quality Data science Training in Hyderabad through online and classroom to students in world wide.This data science course includes the concepts and required tools through out the entire data science pipeline,by asking appropriated queries to making interference and publishing results. After completing your data science training with our project,you will apply the learned skills by building a data product using real world data.

To take data science training you require some programming experience in any language and working knowledge of mathematics up to algebra helps to understand concepts easily.

Why should you take data science training?

You may any kind of professional you can’t escape from big data science.To manage large number of data, data scientists are needed who are the most trained professionals.Processing data gives power to companies to study,research and analyze to improve services.The Signexit Systems is one best data scientist training institute in Hyderabad has real time faculty with years of experience.A new report from McKinsey Global Institute (MGI) estimates that “big data analytics could increase annual GDP in retail and manufacturing in US by up to $325 billion by 2020. By 2018, US will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts who can handle big data”.This study tells itself the requirement for data science professionals.

FACULTY: Real-time experience 24×7 technical support

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

We also providing R programming.

Our Data science Training Online Services providing worldwide like Asia, Europe, America, Africa, Sweden,North Korea, South Korea, Canada,Netherland,Italy, Russia,Israel,New Zealand ,Norway,Singapore,Malasia,etc,,

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