Data Science

लक्ष्य - Data Science Bootcamp - Innozant MIS & Data Analytics & Data Science

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Description

What is Bootcamp
Innozant 6 to 8 Months Job Ready Bootcamp is an intensive, short-term employment-based Skill Achiever training program ideal for undergraduate, graduates, working non-technical and technical professionals seeking to enter or transition into the field of Data Science in a short period of time.
Bootcamp Benefit

Employment Driven Education
• Guaranteed Job Assistance
• Resume building/Screening
• Personality Development
• Live Project
• Pre-Screening Test
• Interview Preparation
• Mock Interviews
• 1 Year Membership
• Verifiable Certificate
• 10+ Years of exp in Education Industry
• Experienced & Certified Instructors
• Student Portal/Learning App
• Live Class Videos
• Master Edited Videos for Lifetime
• Course Certificate
• 6 Months Internship Certificate
Eligibility to Appear in Bootcamp
The students have to ensure that they are eligible for Bootcamp before they fill in the
application form to apply for the Assessment test.
Academic Criteria
Undergraduate, Graduate (any stream) , Diploma BCA,
Btech, MCA student.

This course has 4 Modules as follows:
Module1:   Python Basics
Module 2: Data Analytical Tools Using Python
Module 3: Machine Learning With Python
Module 4: Artificial Intelligence & Deep Learning With Python
Module 5: Advanced Excel
Module 6: Power BI

For more information about our Bootcamp JOB Ready Program kindly connect to our Academic Counselor

Topics for this course

48 Lessons

Module1: Python Basics

Introduction to Python00:00:00
Features and Applications of Python00:00:00
Introduction to Anaconda/Jupyter00:00:00
Basics of Jupyter00:00:00
Data types & Variables in Python00:00:00
Types of Operators in Python00:00:00
Strings: String indexing/ Slicing, String methods, immutability00:00:00
Conditional Statements: If, If-else, If-elif, Nested if00:00:00
Loops: Iterators/Iterables, For loops, range function,while loops00:00:00
Pattern based problems : Number patterns, Alphabet patterns, Shapes patterns, Mixed patterns00:00:00
User Defined function : def keyword , creating a function, return keyword, Function inside a function, Recursion, *args , ** kwargs, Practice problems on Functions00:00:00
List in Python : List indexing and slicing, Mutable Lists, Finding min, max and sum for a given list, Iteration in Lists using for and while loops00:00:00
List methods: I — append, extend, pop, insert , List methods II — sort, reverse, clear, remove, List methods III — index, count , List comprehension00:00:00
Tuple in Python : Definition and usage, Tuple indexing and slicing, Immutable Tuple, Iteration in Tuple using for and while Loop, Tuple methods — index and count00:00:00
Set in Python: Use, Set Methods & Comprehension00:00:00
Dictionary in Python: Definition and usage , Iteration in dictionary using for and while loops, Dictionary methods & Comprehension, Practice problems on list, tuple, set and dictionary00:00:00
Inbuilt functions in Python: Enumerate, zip, mop, reduce, filter, lambda function, evalu00:00:00
Exception handing : Errors and Exception00:00:00
File Handling: Open, Close, Read, Write, Append, File operations00:00:00
Date Time Module in Python00:00:00

Module 2: Data Analytical Tools Using Python

Module 3: Machine Learning With Python

Module 4: Artificial Intelligence & Deep Learning With Python

Al (Deep Learning)

Introduction to Google Colab

TensorFlow

Understanding different Activation Functions

Understanding different Optimizers

Understanding different Loss Functions

ANN : Artificial Neural Network

CNN : Convolutional Neural Network

  • Course level: Expert