Data Science Bootcamp

Foundations

Start your journey in this prerequisite beginner's course by going over the fundamentals of data science and exposing you to the breadth of skills and tools in the industry professional's arsenal. In these first units, you will be introduced to the scientific programming environment, as well as the key concepts of both programming and statistical analysis.

Getting Started

Local Setup and Development Environment

Python Programming & Computer Science

Types, Flow Control, Data Structures, Functions, OOP and Time Complexity

SciPy Stack

NumPy, pandas and matplotlib

Mathematics

Statistics, Probability, Calculus and Linear Algebra

Data Analysis

Students will tackle a wide variety of topics under the umbrella of exploratory data analysis. Getting, cleaning, analyzing and visualizing raw data is the main job responsibility of industry data scientists. Here you will learn how to discover patterns and trends that influence your future modeling decisions.

Getting and Cleaning Data

Static Files, SQL, Web Scraping, APIs and Messy Data

Statistical Inference

Event Space, Probability, Distributions and Hypothesis Testing

Summarizing and Visualizing Data

Descriptive Statistics, Univariate and Multivariate Exploratory Data Analysis

Live Industry Based Projects

100% Placement Assistance

CV building

Confidence Building

Online & offline

Weekly & Weekend Classes

Interview Session

Mock Interview Session

Course Completion Certificate

Recorded Class

Group Discussion

Communication Skills

Interview Session

6 Monthe internship

Access to Class & Training Until Placement

Python Basics

• Need for Programming
• Advantages of Programming
• Overview of Python
• Organizations using Python
• Python Applications in Various Domains
• Python Installation
• Variables
• Operands and Expressions
• Conditional Statements
• Loops
• User-Defined Functions
• Concept of Return Statement
• Concept of name =” main ”
• Function Parameters
• Different Types of Arguments
• Global Variables
• Global Keyword
• Command Line Arguments

Working with Modules and Handling Exceptions

• Standard Libraries
• Packages and Import Statements
• Reload Function
• Important Modules in Python
• Sys Module
• Os Module
• Math Module
• Working with Modules and
• Handling Exceptions
• Date-Time Module
• Random Module

Functions and Object Oriented Programming

• User-Defined Functions
• Concept of Return Statement
• Concept of name =” main ”
• Function Parameters
• Different Types of Arguments
• Global Variables
• Global Keyword
• Variable Scope and Returning Values
• Lambda Functions
• Various Built-In Functions
• Introduction to Object-Oriented Concepts
• Built-In Class Attributes
• Public, Protected and Private Attributes,and Methods
• Class Variable and Instance Variable
• Constructor and Destructor
• Decorator in Python
• Core Object-Oriented Principles
• Inheritance and Its Types
• Method Resolution Order
• Overloading
• Overriding
• Getter and Setter Methods
• Inheritance-In-Class Case Study

Data Manipulation using pandas

• Introduction to pandas
• Data structures in pandas
• Series
• Data Frames
• Importing and Exporting Files in Python

• Basic Functionalities of a Data Object
• Merging of Data Objects
• Concatenation of Data Objects
• JSON Module
• Regular Expression
• Exception Handling

Data Structure and File Operations

• Method of Accepting User Input and eval Function
• Python - Files Input/Output Functions
• Lists and Related Operations
• Tuples and Related Operations
• Strings and Related Operations
• Sets and Related Operations
• Dictionaries and Related Operations

Introduction to NumPy

• Basics of Data Analysis
• NumPy - Arrays
• Operations on Arrays
• Indexing Slicing and Iterating
• NumPy ArrayAttributes
• Introduction to NumPy
• Matrix Product
• NumPy Functions
• Functions
• Array Manipulation
• File Handling Using NumPy
• Array Creation and Logic Functions
• File Handling Using Numpy
• Types of Joins on Data Objects
• Data Cleaning using pandas
• Exploring Datasets