Introduction to Python, Django and Big Data Analysis(In-House Internship)

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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