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Data Science

 

Data science course in hyderabad

Campusguide.in is providing the Data science course in hyderabad. campusguide.in is a training institute and also software company. we are providing training and projects with realtime well experienced professionals,with the limited members. Professional trainer interact with the students and clarify the concepts. we are also providing 100% placement assistance or the job offer with in our company.

About The Course

campusguide.in is scheduling the Devops certification training in hyderabad. Become an expert in data analytics using the R programming language in this data science certification training course. You’ll master data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. With this data science course, you’ll get hands-on practice on R CloudLab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment.

What skills will you learn?

This data science training course will enable you to

Gain a foundational understanding of business analytics

Install R, R-studio, and workspace setup, and learn about the various R packages

Master R programming and understand how various statements are executed in R

Gain an in-depth understanding of data structure used in R and learn to import/export data in R

Define, understand and use the various apply functions and DPLYP functions

Understand and use the various graphics in R for data visualization

Gain a basic understanding of various statistical concepts

Understand and use hypothesis testing method to drive business decisions

Understand and use linear, non-linear regression models, and classification techniques for data analysis

Learn and use the various association rules and Apriori algorithm

Who should learn this course?

IT professionals looking for a career switch into data science and analytics

Software developers looking for a career switch into data science and analytics

Professionals working in data and business analytics

Graduates looking to build a career in analytics and data science

Anyone with a genuine interest in the data science field

Experienced professionals who would like to harness data science in their fields

Prerequisites: There are no prerequisites for this data science online training course. If you are new in the field of data science, this is the best course to start with.

Course duration : 45 days

Course details and related concepts teached by the professional :

Detail Data Science Course Content

Installing R and R Studio (MAC & Windows)

Types of variables

Using Variables

Logical Variables and Operators

The "While" Loop

Using the console

The "For" Loop

The "If" statement

What is a Vector?

Let's create some vectors

Using the [] brackets

Vectorized operations

The power of vectorized operations

Functions in R

Packages in R

Matrices

Building Your First Matrix

Naming Dimensions

Colnames() and Rownames()

Matrix Operations

Visualizing With Matplot()

Subsetting

Visualizing Subsets

Creating Your First Function

Importing data into R

Using the $ sign

Exploring your dataset

Basic operations with a Data Frame

Filtering a Data Frame

Introduction to qplot

Building Dataframes

Merging Data Frames

Loop Functions

lapply()

sapply()

apply()

tapply()

mapply()

Regular Expressions

Saving r objects

Shiny Apps

dplyr package

Homework Solution Law Of Large Numbers

Homework Solution Section 3: Financial Statement Analysis

Homework Solution Section 4: Basketball Free Throws

Homework Solution Section 5: World Trends

Homework Solution Section 6: Movie Domestic % Gross

Descriptive

Data collection

Data collection - Questionaire Designing

Data collection - Observation

Data collection - Case Study Method

Data Patterns

Qualitative Data Vs Quantitative Data

Deciles Statistics

Venn Diagram

Central limit theorem

Chebyshev's Theorem

Kurtosis

Normal Distribution

Laplace Distribution

Log Gamma Distribution

Rayleigh Distribution

Exponential distribution

Multinomial Distribution

Binomial Distribution

Beta Distribution

F distribution

Gamma Distribution

Negative Binomial Distribution

Geometric Mean

Chi-squared Distribution

Harmonic Mean

Outlier Function

Stem and Leaf Plot

Poisson Distribution

Cumulative Poisson Distribution

Homework

Inverse Gamma Distribution

Continuous Uniform Distribution

Hypergeometric Distribution

Power Calculator

Harmonic Number

Trimmed Mean

Reliability Coefficient

Linear regression

Logistic Regression

Quadratic Regression

Regression Intercept Confidence Interval

Residual sum of squares

Equation Sum of Square

Standard Error ( SE )

Root Mean Square

Ti 83 Exponential Regression

Shannon Wiener Diversity Index

Statistics Notation

Sample planning

Sampling methods

Simple random sampling

Cluster sampling

Stratified sampling

Required Sample Size

Hypothesis testing

Signal to Noise Ratio

Best Point Estimation

Type I & II Error

T-Distribution Table

Residual analysis Z table

F Test Table

Chi Squared table

Statistical Significance

Co-efficient of Variation Interval

Rcode for Inferential

Student T Test

One Proportion Z Test

Kolmogorov Smirnov Test

Mcnemar Test

Anova(1-way,2-way)

Manova

Chi-square test

Probability

Factorial

Odd and Even Permutation

Permutation with Replacement

Circular Permutation

Geometric Probability Distribution

Probability Density Function

Probability Bayes Theorem

Permutation

Combination

Black-Scholes model

Probability Additive Theorem

Combination with replacement

Probability Multiplecative Theorem

Estimation Process Capability (Cp) & Process Performance (Pp)

Statistics Formulas

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