Overview Of Data Science

Are you prepared to begin your way to turning into a Data Scientist! This extensive course will be your manual for figuring out how to utilize the intensity of Python to dissect information, make delightful perceptions, and utilize ground-breaking AI calculations!

Information Scientist has been positioned as the main activity on Glassdoor and the normal compensation of an information researcher is over $120,000 in the United States as per Indeed! Information Science is a remunerating vocation that permits you to explain a portion of the world's most intriguing issues!

Since 2010, KR Network Cloud has Provided individual and Corporate customers with a predominant learning experience under the world Class Experts. As a Trusted Education Institute we convey more than 250+ IT Training Programs From such driving innovation firms as CISCO, MICROSOFT, RED HAT, STAR CERTIFICATION, COMPTIA. We Leverage these Vendor,s Professional specialized Training substance and Combine it with our Award-winning course teachers to Produce a reliably predominant degree of Training.

Data Science Course Objective

This course is intended for the two novices with some programming experience or experienced designers hoping to take the leap toward Data Science! This extensive course is equivalent to other Data Science bootcamps that typically cost a great many dollars, however now you can become familiar with such data at a small amount of the expense! With more than 100 HD video addresses and a definite code scratch pad for each talk this is one of the most extensive courses for information science and AI .

Course Content Of Data Science

Course 1:- Introduction to Python Language

Module 1:- An Introduction

History of Python
Version of Python
Features of Python
Python Variables & Data Types
Type Casting
Types of Operators

Module 2:- Basics of Scripting

Input and Output
The if-else Statements
Python Looping Concept
Python Sequence Types

List Type
Tuple Type
Difference between List and Tuple
Dictionary Type
String Type
List comprehensions

Module 3:- Function and Module

Defining a function
Calling a function
Lambda Function
Creating a Module
Importing a Module
Install Module using pip command
Packages in python
If __name__ == ”main” statement

Module 4:- File Handling

Open file using open function
Open file using with statement
Read and Write data from txt file
working with other type of file csv, excel
copy and move files
zipping and unzipping
working with Directories
OS Module
Shutil Module

Module 5:- Exception Handling

what is Exception
Handling Exception using try and except block
Finally end else block
Raising our Own Exception
User Defined Exception

Module 6:- Regular Expression

re module
functions of re module
character classes
predefine character classes
quantifiers

Module 7:- Advanced python

Database Connection
Working with dates and time
Oops
Web Scraping

Course 2:- Mathematics and Statistics

Module 1:- Calculus

Differential Calculus
Integral Calculus

Module 2:- Probability

What is Probability?
Random Variables
Calculating Probability
Binomial Distribution (With implementation in R)
Continuous Random variable
Central Limit Theorem

Module 3:- Discrete Mathematic

Sets, Relations, & Functions
Mathematical Logic
Group Theory
Counting & Probability

Module 4:- Linear Algebra

Why Linear Algebra for Data Science
Problems in Linear Algebra
Matrix
Eigenvalues and Eigenvectors

Module 5:- Statistics

Basic Statistics
Descriptive Statistics
Inferential Statistics
Predictive Modeling
Population and sample
mean, median, mode
Probability and Distribution
Central Tendency
Coefficient of variation
Normal Distribution
Central Limit Theorem
Hypothesis
t- test of mean
z-test of proportions

Course 3:- Data Analysis and Visualization

Module 1:- Pandas

Introduction to Data Structures

Series
DataFrame

Series

Create Series
Accessing Data from Series

DataFrame

Create DataFrame
Create a DataFrame from List of Dicts
Column Selection, Addition, Deletion
Row Selection, Addition, and Deletion
Read CSV and Excel file using pandas
Load data from html and json
Load data from string

Basic Functionality
Re-Indexing
Iteration
Sorting
Options and Customization
Indexing and Selecting Data
Statistical Functions
Aggregations
Missing Data
GroupBy
Merging/Joining
Concatenation
Date Functionality
Categorical Data

Module 2:- Numpy

Numpy Array

Array Manipulation
Indexing and Slicing
Advanced Indexing
Broadcasting
Mathematical Functions
Arithmetic Operator
Statistical Function
Sort, Search, Count
Copies and Views
Matrix

Module 3:- MatplotLib

Variables and Data types
Identifiers in C
Variables and Data Types
Constants

Module 4:- Seaborn

HeatMap
JointPlot
Pairplot
Distplot
Countplot
Barplot
Boxplot
Realplot

Module 5:- Data Analysis

Data PreProcessing
Data Imputation
Data Cleaning
Data Transformation
Feature Engineering
Handling Missing value
Handling Categorical data
Handling Imbalanced dataset
Handing Errors in data
Exploratory data analysis

Module 6:- Project on Data Analysis

Course 4:- Tableau

What is Tableau?
Tableau vs Power BI
Tableau Architecture
How to Download & Install Tableau Public & Desktop
Connections to Multiple Data Sources & Replacing Data Source
Filter data in Tableau: Types & Examples
Sort Data, Create Group, Hierarchy & Sets
Charts & Graphs

Course 5:- Machine Learning

Module 1:- Introduction

What is Machine Learning?M
Intro about ML, DS,AI
Types Of Machine Learning(Supervised, Unsupervised , Reinforcement )

Module 2:- Regression

Simple Linear Regression
Multiple Linear Regression
Polynomial Regression
Support Vector Regression(SVR)
Decision Tree Regression
Random Forest Regression

Module 3:- Classification

Logistic Regression
K-Nearest Neighbors (K-NN)
Support Vector Machine(SVM)
Decision Tree Classification
Random Forest Classification

Module 4:- Clustering

K-Means Clustering
Hierarchical Clustering

Module 5:-Reinforcement

Upper Confidence Bound(UCB)

Course 6:- BigData

Why Learn Data Science?

Use Python for Data Science and Machine Learning.
Use Spark for Big Data Analysis.
Actualize Machine Learning Algorithms.
Figure out how to utilize NumPy for Numerical Data.
Figure out how to utilize Pandas for Data Analysis.
Figure out how to utilize Matplotlib for Python Plotting.
Figure out how to utilize Seaborn for measurable plots.
Utilize Plotly for intelligent unique representations.
Use SciKit-Learn for Machine Learning Tasks.
K-Means Clustering.
Strategic Regression.
Direct Regression.
Irregular Forest and Decision Trees Normal Language
Processing and Spam Filters
Neural Networks.
Backing Vector Machines.

The Top Reason why to choose KR Network Cloud

KR Network Cloud is the Star Certified Authorized Training Partner
We have a world-class experienced & Certified Trainer for Data Science Training
All lab facilities are available. labs are facilitated with computer
We provide training as well as Data Science Certification
KR Network Cloud will provide you the Notes, Videos, and Data Science Training books
We provide corporate as well as industrial training in Delhi
Demo session, Workshop, Exhibition, Back-Up Classes, Practice session... ETC
Provide Exam Preparations to the Student
Our trainer will also help to crack your interview.
Job assistance facility for our student is also available
Provides online as well as classroom training.
Provides More Opportunity for future