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Data Science Training:

  • COURSE INTRODUCTION: Data Science course enables you to understand the practical foundations, helps you to effectively execute and take up Big Data and other analytics projects. The program covers topics from Big Data to Data Analytics Life Cycle. Understanding these topics helps in addressing business challenges that leverage Big Data.
  • PRE-REQUISITES: The course is designed for anyone who wishes to understand the concepts of Data Science from a Data Scientist’s perspective. Professionals who can benefit from this course –
  • Managers from the any field as Analytics is the best tool for managers these days
  • Business Analysts and Data Analysts who wish to upscale their Data Analytics skills.
  • Database professionals who aspire to venture into the field of Big Data by acquiring analytics skills.
  • Fresh graduates who wish to make a career in the fields of Big Data or Data Science
  • DURATION: 6 Days (48 HRS)
  • CERTIFICATON: At the end of the course you will get the certificate for completing the training.

 

Course Content:

DATA SCIENCE OVERVIEW

  • What is Data Science?
  • Skill-set required
  • Job Opportunities

DESCRIPTIVE & INFERENTIAL STATISTICS

  • Continuous vs. Categorical variables
  • Mean, Median, Mode, Standard Deviation, Quartile, IQR
  • Hypothesis testing, z-test, t-test

DATA ANALYTICS USING R PROGRAMMING – FUNDAMENTALS

Installation of R Studio

  • Overview of R Studio components
  • Data Structures
  • Vector
  • List
  • Matrices
  • Data Frame
  • Factor
  • Slicing and Sub-setting
  • Vector
  • List
  • Matrix
  • Data Frame

Functions in R

  • In-built functions
  • User-defined functions

Loops in R

  • while
  • for
  • break
  • next

Data Import in R

DATA ANALYTICS USING R PROGRAMMING – ADVANCED

Apply family of functions

  • lapply
  • sapply
  • tapply

Data Manipulation using dplyr

Data Visualization using ggplot2

MACHINE LEARNING USING R – PART 1

What is Machine Learning?

Supervised vs. Unsupervised Learning

Exploratory Data Analysis

  • Univariate analysis
  • Boxplot
  • Bivariate analysis
  • Scatterplot
  • Correlation
  • Outliers
  • Remove duplication
  • Missing value imputation

Underfitting vs. Overfitting

Linear Regression

  • Simple
  • Multiple
  • Assumptions of Linear Regression
  • Evaluating Accuracy of model: k-Fold Cross validation

Logistic Regression

  • Confusion Matrix
  • ROC Curve

Time Series Forecasting

  • Moving Average
  • Exponential smoothing
  • Holt Winter’s
  • ARIMA

MACHINE LEARNING USING R – PART 2

  • Naïve Bayes
  • Support Vector Machine
  • K-Nearest Neighbour
  • Decision tree
  • Random Forest
  • K-Means clustering

BIG DATA USING HADOOP & SPARK

  • Introduction to Big Data
  • Overview of Hadoop & its Ecosystem
  • Introduction to NoSQL
  • Overview of Apache Spark