Data Science Online Course

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Data Science

All levels
English

Course Description

Mock test + HR skill Development + Placement Data science provides meaningful information based on large amounts of complex data or big data. Data science, or data-driven science, combines different fields of work in statistics and computation to interpret data for decision-making purposes. Data is drawn from different sectors, channels, and platforms including cell phones, social media, e-commerce sites, healthcare surveys, and Internet searches. The increase in the amount of data available opened the door to a new field of study based on big data—the massive data sets that contribute to the creation of better operational tools in all sectors.

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What you’ll learn

Flexible schedule - Our Flexibles Schedules allow candidates to start or finish their course when they want.
Interview Preparation - Most important step to land to a job is being prepared for the interview. Oytie provides the environment where one gets the platform to practice and improvise interview skills.
Resume Preparation - Resumes help employers make hiring decisions and help you get your first interview. That's why it matters how you structure your resume and what information you decide to include.
Live Project Training - Live Project training is important to learn ethics, discipline and working environment of a Company.
Practice Course Material - Learning materials are important because they can significantly increase student achievement by supporting student learning. For example, a worksheet may provide a student with important opportunities to practice a new skill gained in class.

Covering Topics

Python - Data Science

PANDA

NUMPY

SciPY

MATPLOTLIB

R-Programming

Curriculum

  Python - Data Science

•	Data Science-Overview
•	What is the Data Structure
•	Data Science Frameworks

  PANDA

•	Panda in Data Science
•	Panda Framework in DS
•	Panda Framework Architecture
•	Panda Installation and Setup
•	Panda Data Frames
•	Panda Series
•	Panda Statistics
•	Panda Functions
•	Panda Iteration
•	Panda Aggregate Functions
•	Python Pandas – Introduction
•	Python Pandas – Installation
•	Python Pandas – Series
•	Python Pandas – Iteration
•	Python Pandas – Sorting
•	Python Pandas SQL- GroupBy,OrderBy
•	Python Pandas – Merging/Joining
Python Pandas – Concatenation

  NUMPY

•	NUM PY in Data Science
•	NUMPY Overview
•	NUM PY –Components
•	NUM PY ND Array
•	NUM PY Data Types
•	NUM PY with Pandas
•	NUM PY Functions
•	NUM PY Statistics
•	NUM PY Library
•	NUM PY Advance Functions
•	Basic Functions of Numpy
•	Shape,size,dtype,itemsize,data,sum
•	Min,max,empty ,arange
•	Linspace,logspace
•	Reshape,random,exp,sqrt

  SciPY

•	Sci PY Introduction
•	Sci PY Installation and Setup
•	Sci PY Interpolation
•	Sci PY Input and Output
•	Sci PY Cluster
•	Sci PY Algebra
•	Sci PY Transformation
•	Sci PY – Constants
•	Sci PY – Integrate
•	Sci PY – CSGraph
Sci PY Advance

  MATPLOTLIB

•	Matplotlib Overview
•	Matplotlib – Installation
•	Types Of Plots
•	Working With Multiple Plots
•	Matplotlib – Figure Class and Axes Class
•	Matplotlib – Formatting Axes
•	Matplotlib – Bar Plot
•	Matplotlib – Pie Chart
•	Matplotlib – Scatter Plot and Contour Plot
•	Matplotlib – 3D Plot
•	Matplotlib – Working With Text

  R-Programming

•	Introduction of R-Programming
•	Environment Setup
•	Basic Syntax
•	Variables & Constants in R-Programming
•	Operators in R-Programming
•	Conditional Statements in R-Programming
•	loops in R-Programming
•	Functions and Strings in R-Programming
•	Vectors in R-Programming
•	Matrix in R-Programming
•	Data Frame in R-Programming
•	Pie Charts in R-Programming
•	3D Plot in R-Programming
•	Statistics in R-Programming
•	Mean, Median & Mode in R-Programming
•	Regression in R-Programming
•	Analysis of Covariance in R-Programming
•	Time Series Analysis in R-Programming
•	CSV Files in R-Programming
20.XML Files in R-Programming