Data Science Online Training

Introduction

Data Science Training

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, chemometrics, 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.

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 WHO WE ARE:

IQ Online Training provides the Excellent IT/Software Online Training Courses internationally with certified Trainers. Learn Data Science Online Training in Hyderabad. Attend Data Science Online course to be the best Data scientist every company wants and also we offer guidance in Data science certification training preparation. Check  Data Science Tutorial for Beginners and projects. Happy to say that IQ Online also offers Data Science Training Course globally like USA, UK, Canada, UAE, Switzerland, and many other Countries. Become Certified Advanced Data Science Professional. Learn Essential Statistics for Data Science, SAS, Data Science with R, Text Analysis using R, Predictive Analytics. Companies that Hire Data Scientist are Microsoft, Twitter, UBER, Apple, IBM, Linkedin, Intel, Instagram, Facebook, and Amazon. Download Data Science Course PDF Material/Course Content.

COURSE SUMMARY:

Course NameData Science / R predictive Analytics Online Training
ContentsBasics of Data Science, manipulations, statistics, machine learning etc.
Duration70 Hours with Flexible timings
DeliveryInstructor Led-Live Online Training
EligibilityAny Graduate
Ideal ForFreshers, aspirants seeking to learn the Data Science
Live Online TrainingLive Interactive Data Science Training by Certified & Industry Expert Trainers by Providing On-Demand Server and Lab access.
AvailabilityRegular/Weekend Batches. 24×7 teaching assistance and support.

Course Objectives

  • 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 data optimization and visualization techniques
  • Understand the parallel processing features in R

PREREQUISITES:

  • No Pre-requisites are required for Data Science Online Course.

Data Science Career Growth

Course Curriculum

MODULE 1: INTRODUCTION TO DATA ANALYTICS

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

MODULE 2: DATA INPUTTING IN R

  • Data Types, Data objects & Data structures
  • Creating a vector & Vector operations
  • Subsetting
  • 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

  • A measure of central tendency
  • Dispersions
  • Distributions
  • Tests

MODULE 5: MULTIVARIATE ANALYSIS

  • Correlation
  • Regression
  • A brief Introduction to 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.
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