Data Analytics

Data science is the dominion of analysis that deals with extensive volumes of data using modern tools and methodology to distinguish unseen patterns, procure meaningful information, and make business decisions, it uses composite machine learning algorithms to fabricate predictive models. Tech Booster is the best institute for learning Data Science.

This course comes under 100% Job Guaranteed package and hence anyone pursuing the course will be placed successfully in any of our tied up companies after completion of the training program.

Course Curriculum:

• Introduction to Python
•  Getting Started with Python
•  Keywords and Identifiers
•  Statements and Comments
•  Python Variables
•  Python Data Types
•  Python Type Conversion
•  Python I/O and Import
•  Python Operators
•  Python Namespace
• Python Flow Control
•  Python Conditional Statements
•  Python Loops
•  Python Break and Continue
•  Python Pass
•  Python goto
• Python Functions
•  Introduction to Functions
•  Structure of a Function
•  Creating a Simple function in Python
•  Create a Function with Argruments in Python
•  Create a Function with default Arguments in Python
•  Create a Function that returns a value in Python
•  Create a Function that Returns Multiple Values in Python
•  Create a Function that takes a Variable Number of Arguments in Python (Arbitrary Arguments)
•  Recursive Functions
•  Anonymous Functions / Lambda Functions
•  Global, Local and Non-local
•  Python Global Keyword
•  Python Modules
•  Python Package
• Python Files
•  Python Directory
•  Python Exception
•  Python Exception Handling
•  Python User-defined Exception
• Python Object and class
•  Python OOP
•  Python Class
•  Python Inheritance
•  Multiple Inheritance
•  Operator Overloading
• Python Date and Time
•  Python datetime Module
•  Python datetime.strftime()
•  Python datetime.strptime()
•  Current date & time
•  Get current time
•  Timestamp to datetime
•  Python time Module
•  Python time.sleep()
• Numpy
•  What is NumPy
•  Why is NumPy Important
•  . How to get started with NumPy
•  NumPy Ndarray
•  Dimensions of an Array
•  Size of each array element
•  Data type of each array item
•  Shape and size of an array
•  Reshaping the array objects
•  Slicing in the Array
•  Finding the maximum, minimum, and sum of the array elements
•  Array Axis
•  Square root and standard deviation
•  NumPy Data Types
•  Data Type Objects (dtype)
•  Numpy Array Creation
•  Numpy.zeros
•  NumPy.ones
•  NumPy - Array Creation From Existing Data
•  Numpy.asarray