Big Data Analysis Using Python
Abdur Rahman (Joy)

Abdur Rahman (Joy)

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Big Data Analysis Using Python (1st Batch)

Offline: TK 18000

Start Date : 2020-02-28  

Total Class : 20   Total Hours: 60

Location : D F Tower (Level-11A) Skill Jobs Digital Lab Skill Jobs Digital Lab, House # 11 (Level-11A), Road # 14, Dhanmondi, Shobhanbag, Dhaka-1209

Friday : 3:00 PM - 6:00 PM

Saturday : 6:00 PM - 9:00 PM

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Big data analytics applications enable big data analysts, data scientists, predictive modelers, statisticians and other analytics professionals to analyze growing volumes of structured transaction data, plus other forms of data that are often left untapped by conventional BI and analytics programs.

This encompasses a mix of semi- structured and unstructured data -- for example, internet clickstream data, web server logs, social media content, text from customer emails and survey responses, mobile phone records, and machine data captured by sensors connected to the internet of things (IoT).
The importance of big data analytics Driven by specialized analytics systems and software, as well as high-powered computing systems, big data analytics offers various business benefits, including:

  • New revenue opportunities
  • More effective marketing
  • Better customer service
  • Improved operational efficiency
  • Competitive advantages over rivals

Course Content

Introduction
The Python Environment

  • Starting Python
  • Using the interpreter
  • Running a Python script
  • Python scripts on Unix/Windows
  • Editors and IDEs

Getting Started

  • Using variables
  • Built-in functions
  • Strings
  • Numbers
  • Converting among types
  • Writing to the screen
  • Command-line parameters

Flow Control

  • About flow control
  • White space
  • Conditional expressions
  • Relational and Boolean operators
  • While loops
  • Alternate loop exits

Lists and Tuples

  • About sequences
  • Lists and list methods
  • Tuples
  • Indexing and slicing
  • Iterating through a sequence
  • Sequence functions, keywords, and operators
  • List comprehensions
  • Nested sequences

Dictionaries and Sets

  • About dictionaries
  • Creating dictionaries
  • Iterating through a dictionary
  • About sets
  • Creating sets
  • Working with sets

Working with Files

  • File overview
  • The with statement
  • Opening a text file
  • Reading a text file
  • Writing to a text file

Functions

  • About sequences
  • Function parameters
  • Global variables
  • Global scope
  • Returning values
  • Sorting data

Errors and Exception Handling

  • Syntax errors
  • Exceptions
  • Using try/catch/else/finally
  • Handling multiple exceptions
  • Ignoring exceptions

Using Modules

  • The import statement
  • Module search path
  • Zipped libraries
  • Creating Modules
  • Function and Module aliases

Classes

  • About OOPs programming
  • Defining classes
  • Constructors
  • Instance methods and data
  • Class/static methods and data
  • Inheritance
  • Lambda Expression
  • Course Introduction for Big Data analysis

Environment Set-Up

  • Jupyter Notebook Installation

Python for Data Analysis – NumPy

  • Numpy Arrays
  • Numpy Array Indexing
  • Numpy Operations

Python for Data Analysis – Pandas

  • Series
  • Missing Data
  • Group by
  • Merging Joining and Concatenating
  • Operations
  • Data Input and Output

Python for Data Visualization – Matplotlib
Data Visualization with Matplotlib
Python for Data Visualization – Seaborn

  • Distribution Plots
  • Categorical Plots
  • Matrix Plots
  • Regression Plots
  • Grid
  • Style and Color