Big Data Analysis

Big data analytics is the process of analyzing large and complex datasets to uncover meaningful patterns, trends and insights that support data driven decision making. By combining traditional statistical techniques with modern computing tools it enables organizations to extract value from rapidly growing and diverse data sources.

  • Understanding the fundamentals, characteristics, and applications of Big Data
  • Learning Hadoop ecosystem, HDFS, MapReduce, Spark, and NoSQL technologies for Big Data storage and processing.
  • Performing batch and real-time data processing, analytics, and data mining using Python or R.
  • Implementing data governance, quality, privacy, and security measures in Big Data environments.
  • Using data visualization tools and apply Big Data analytics in real-world case studies and applications.

Scripting language Python

1. Introduction to Python

Python is a high-level, general-purpose programming language known for its simple and English-like syntax. 

2. Basic Concepts of Python

2.1 Syntax and Structure

  • Easy-to-read and write syntax
  • Uses indentation instead of brackets

2.2 Data Types

  • Integer
  • Float
  • String
  • Boolean

2.3 Operators

  • Arithmetic Operators
  • Relational Operators
  • Logical Operators
  • Assignment Operators

2.4 Control Statements

  • Conditional Statements (if, if-else, elif)
  • Looping Statements (for, while)
  • Jump Statements (break, continue, pass)

3. Functions, Modules, and Packages

3.1 Functions

  • Definition and calling of functions
  • Parameters and return values
  • Advantages of using functions

3.2 Modules

  • Concept of modules
  • Importing modules in Python

3.3 Packages

  • Organizing modules into packages
  • Use of Python Standard Library

4. Python Data Structures

4.1 Lists

  • Ordered and mutable collection

4.2 Tuples

  • Ordered and immutable collection

4.3 Sets

  • Unordered collection of unique elements

4.4 Dictionaries

  • Collection of key-value pairs

4.5 Strings

  • Sequence of characters and string operations

5. Object-Oriented Programming in Python

5.1 Classes and Objects

  • Definition and creation of classes and objects

5.2 Inheritance

  • Reusability of code through parent-child relationship

5.3 Polymorphism

  • Same function behaving differently in different situations

6. Python Libraries

6.1 NumPy

  • Numerical computations and array handling

6.2 Pandas

  • Data analysis and data manipulation

6.3 Matplotlib and Seaborn

  • Data visualization and graphical representation

6.4 Scikit-learn

  • Machine learning algorithms and models