Duration
5 days
Overview
In a 5 day course students will setup entire environment from ground 0 by deploying Cloudera on Google cloud and later use same environment for development purposes.
Intended Audience
The intended audience of the course is:
- Hadoop Developers
- Data Engineers
- Integration Engineers
- Hadoop Administrators
- Data Steward
Pre-requisite and Lab Details
The intended audience should have a background on:
- Software Development
- Unix and Hadoop
The lab setup for this course would be:
- Windows 7 or Higher
- 4 GB RAM
- Google Cloud Free Account
Course Outline
Fundamental Skills for Big Data
The Case for Apache Hadoop
- Why Hadoop?
- Fundamental Concepts
- Core Hadoop Components
Hadoop Installation
- Lab: Hadoop (CDH) Installation
The Hadoop Distributed File System (HDFS)
- HDFS Features
- Writing and Reading Files
- Web UIs for HDFS
- Using the Hadoop File Shell
- Lab: HDFS Hands-On
Python Algorithm Learning
Python Setup
- Command Line Basics
- Installing Python
- Running Python Code
Python Object and Data Structure Basics
- Introduction to Python Data Types
- Numbers
- Variable Assignments
- Introduction to Strings
- Indexing and Slicing with Strings
- Lists in Python
- Dictionaries in Python
- Tuples with Python
- Sets In Python
- Boolean in Python
- I/O with Basic Files in Python
- Lab: Python Basics Hands-On
Python Comparison Operator
- Python Statements
- If Elif and Else Statements
- For Loops
- While Loops
- Lab: Python Statements Hands-On
Methods and Functions
- Functions in Python
- Lambda Expression, Map and Filter Functions
- Lab: Python Functions Hands-On
Python for Data Analysis – NumPy
- Introduction to NumPy
- NumPy Arrays
- NumPy Array Indexing
- NumPy Operations
- Lab: NumPy Hands-On
Python for Data Analysis – Pandas
- Introduction to Pandas
- Series
- DataFrames
- Missing Data
- Groupby
- Merging Joining and Concatenating
- Lab: Pandas Hands-On
Linear Regression
- Linear Regression Theory
- Lab: Linear Regression with Python
Logistic Regression
- Logistic Regression Theory
- Lab: Logistic Regression with Python
K Nearest Neighbors
- K Nearest Neighbors Theory
- Lab: K Nearest Neighbors with Python
Decision Trees and Random Forests
- Introduction to Tree Methods
- Lab: Decision Trees and Random Forests with Python
Support Vector Machines
- SVM theory
- Lab: SVM with Python
K Means Clustering
- K Means Algorithm Theory
- Lab: K Means with Python
Principal Component Analysis
- Lab: PCA with Python
Hierarchical clustering
- Lab: Hierarchical clustering with Python
Time Series Analysis
- ARIMA
- SARIMA
- Holt-Winters
- Lab: Time Series Analysis with Python
Apriori Algorithm
- Association Rule
- Lab: Use case of Apriori
Programme scope, delivery arrangements and suitability are confirmed during enquiry.