Data Science

Contents [hide]

  • 1 Data Science Training Overview
    • 1.1 Objectives of the Course
    • 1.2 Pre-Requites  of the Course
    • 1.3 Course Duration
  • 2 Data Science Course Content
    • 2.1 Introduction to Data Science
    • 2.2 Data
    • 2.3 Big Data
    • 2.4 Data Science Deep Dive
    • 2.5 Intro to R Programming
    • 2.6 R Programming Concepts
    • 2.7 Data Manipulation in R
    • 2.8 Data Import Techniques in R
    • 2.9 Exploratory Data Analysis (EDA) using R
    • 2.10 Data Visualization in R
    • 2.11 HADOOP
      • 2.11.1 Big Data and Hadoop Introduction
      • 2.11.2 Understand Hadoop Cluster Architecture
      • 2.11.3 Map Reduce Concepts
      • 2.11.4 Advanced Map Reduce Concepts
    • 2.12 Hadoop 2.0 and YARN
    • 2.13 PIG
    • 2.14 HIVE
      • 2.14.1 Module-9
    • 2.15 HBASE
      • 2.15.1 Module-11
    • 2.16 SQOOP
    • 2.17 Flume and Oozie
    • 2.18 Projects
    • 2.19 Project in Healthcare Domain
    • 2.20 Project in Finance/Banking Domain
    • 2.21 Spark
      • 2.21.1 Apache Spark
      • 2.21.2 Introduction to Scala
      • 2.21.3 Spark Core Architecture
      • 2.21.4 Spark Internals
      • 2.21.5 Spark Streaming
    • 2.22 Statistics + Machine Learning
      • 2.22.1 Statistics
        • 2.22.1.1 What is Statistics?
    • 2.23 Machine Learning
      • 2.23.1 Machine Learning Introduction
    • 2.24 Python
      • 2.24.1 Getting Started with Python
      • 2.24.2 Sequences and File Operations
    • 2.25 Deep Dive – Functions Sorting Errors and Exception Handling
    • 2.26 Regular Expressionist’s Packages and Object – Oriented Programming in Python
    • 2.27 Debugging, Databases and Project Skeletons
    • 2.28 Machine Learning Using Python
    • 2.29 Supervised and Unsupervised learning
    • 2.30 Algorithm
    • 2.31 Application Example
    • 2.32 Scikit and Introduction to Hadoop
    • 2.33 Hadoop and Python
    • 2.34 Python Project Work
      • 2.34.1 share training and course content with friends and students: