Introduction to Python, Django and Big Data Analysis

Learn Introduction to Python, Django and Big Data Analysis


Learning Outcomes

Nowadays, Data Science is the most demanding profession in the software industry. To be a Data Scientist one should have a vast knowledge of Big Data and Machine Learning. For both cases, python provides best packages and libraries. In this course, a student will not only learn the basic of python but also the data analysis on Big Data. The Django part of this course will also provide introductory knowledge to build a small web application which will help students to design initial level data visualization for Big Data

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Course Syllabus

  • Overview
  • Environment Setup
  • Basic Syntax
  • Variable Types
  • Basic Operators
  • Decision Making
  • Loops
  • Numbers
  • Strings
  • Basic Operators
  • Decision Making
  • Loops
  • Numbers
  • Strings
  • Arrays
  • Matrix
  • Lists
  • Tuples
  • Dictionary
  • Images
  •  Tables
  •  Forms
  •  HTML Entities & “Special Characters”
  •  HTML Project: Code a Basic Web Page + Tutorial
  • Date & Time
  • Functions
  • Modules
  • Files I/O
  • Exceptions
  • Classes/Objects
  • Reg Expressions
  • Database Access
  • Sending Email
  • Multithreading
  • JSON Processing
  • Logging
  • Unit testing
  • What is Data Science
  • Big Data and Machine Learning for Data Science
  • Hadoop and Spark for Big Data
  • Python for Data Science
  • Python Libraries for Data Science
    • Introduction
    • Environment
    • Ndarray Object
    • Data Types
    • Array Attributes
    • Array Creation Routines
    • Array from Existing Data
    • Array From Numerical Ranges
    • Indexing & Slicing
    • Advanced Indexing
    • Broadcasting
    • Iterating Over Array
    • Array Manipulation
    • Binary Operators
    • String Functions
    • Mathematical Functions
    • Arithmetic Operations
    • Statistical Functions
    • Sort, Search & Counting Functions
    • Byte Swapping

    Matplotlib

    • Introduction
    • Environment Setup
    • Introduction to Data Structures
    • Series
    • DataFrame
    • Panel
    • Basic Functionality
    • Descriptive Statistics
    • Function Application
    • Reindexing
    • Iteration
    • Sorting
    • Working with Text Data

     

  • Options & Customization
  • Indexing & Selecting Data
  • Statistical Functions
  • Window Functions
  • Aggregations
  • Missing Data
  • GroupBy
  • Merging/Joining
  • Concatenation
  • Date Functionality
  • Timedelta
  • Categorical Data
  • Visualization
  • IO Tools
  • Sparse Data
  • Caveats & Gotchas
  • Comparison with SQL
  • Introduction
  • Environment Setup
  • SparkContext
  • RDD
  • Broadcast & Accumulator
  • SparkConf
  • SparkFiles
  • StorageLevel
  • MLlib
  • Serializers
  • Basics
  • Overview
  • Environment
  • Creating a Project
  • Apps Life Cycle
  • Admin Interface
  • Creating Views
  • URL Mapping
  • Template System
  • Models
  • Page Redirection
  • Sending E-mails
    • Generic Views
    • Form Processing
    • File Uploading
    • Apache Setup
    • Cookies Handling
    • Sessions
    • Caching

     

     

     

     


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