Data Science

Best Data Science Institute

The Data Science is the combination of scientific knowledge and the statistical data. The knowledge can be acquired anywhere but the best training can be achieved only through School of data science where the training will be given by currently working professionals. Data Science Training in School of data science is  provided by experts to introduce you about the fundamentals of Data Science and also to make the learning individual as an efficient Data Scientist.

Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured similar to data mining. Data science is a “concept to unify statistics, data analysis and their related methods” in order to “understand and analyze actual phenomena” with data. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the subdomains of machine learning, classification, cluster analysis, data mining, databases, and visualization.

Who is a Data Scientist? “A Data Scientist is someone who is better at Statistics than any Software Engineer and better at Software Engineering than any Statistician.”

Data scientists use their data and analytical ability to find and interpret rich data sources; manage large amounts of data despite hardware, software, and bandwidth constraints; merge data sources; ensure consistency of datasets; create visualizations to aid in understanding data; build mathematical models using the data; and present and communicate the data insights/findings. The candidates who have undergone Data Science Course in Bangalore are often expected to produce answers in days rather than months, work by exploratory analysis and rapid iteration, and to produce and present results with dashboards (displays of current values) rather than papers/reports, as statisticians normally do.

Intended Audience for Data Science Institute :

This course is intended for:

  • Security auditors and analysts
  • Security professionals with little or no working knowledge of Data Science
  • Systems engineers who want advanced level knowledge of and hands-on management skills on Data Science

Profiles of a Data Scientist:

  • Conduct undirected research and frame open-ended industry questions
  • Extract huge volumes of data from multiple internal and external sources
  • Employ sophisticated analytics programs, machine learning and statistical methods to prepare data for use in predictive and prescriptive modeling
  • Thoroughly clean and prune data to discard irrelevant information
  • Devise data-driven solutions to the most pressing challenges
  • Invent new algorithms to solve problems and build new tools to automate work
  • Communicate predictions and findings to management and IT departments through effective data visualizations and reports
  • Recommend cost-effective procedures to existing procedures and strategies

Data Science Training Course syllabus

    • What is R?
    • Why R?
    • Installing R
    • R environment
    • How to get help in R
    • R Studio Overview

    • Variables in R
    • Scalars
    • Vectors
    • Matrices
    • List
    • Data frames
    • Cbind,Rbind, attach and detach functions in R
    • Factors
    • Getting a subset of Data
    • Missing values
    • Converting between vector types

    • Reading Tabular Data files
    • Reading CSV files
    • Importing data from excel
    • Loading and storing data with clipboard
    • Accessing database
    • Saving in R data
    • Loading R data objects
    • Writing data to file
    • Writing text and output from analyses to file

    • Selecting rows/observations
    • Rounding Number
    • Creating string from variable
    • Search and Replace a string or Number
    • Selecting columns/fields
    • Merging data
    • Relabeling the column names
    • Data sorting
    • Data aggregation
    • Finding and removing duplicate records

    • Apply Function Family
    • Commonly used Mathematical Functions
    • Commonly used Summary Functions
    • Commonly used String Functions
    • User defined functions
    • local and global variable
    • Working with dates

    • While loop
    • If loop
    • For loop
    • Arithmetic operations

    • Box plot
    • Histogram
    • Pie graph
    • Line chart
    • Scatterplot
    • Developing graphs
    • Cover all the current trending packages for Graphs

    • Sentiment analysis with Machine learning
    • C 5.0
    • Support vector Machines
    • K Means
    • Random Forest
    • Naïve Bayes algorithm

    • Correlation
    • Linear Regression
    • Non Linear Regression
    • Predictive time series forecasting
    • K means clustering
    • P value
    • Find outlier
    • Neural Network
    • Error Measure

    • Overture of R Shiny
    • What is Hadoop
    • Integration of Hadoop in R
    • Data Mining using R
    • Clinical research preface in R
    • API in R (Twitter and Facebook)
    • Word Cloud in R