Archived
February 1, 2023
Course Rating
★★★★★ 4.5/5
MTA Certifications:
Python: MTA 98-381
Database SQL: MTA 98-364
Course Description
For data analysis, the R programming language is commonly used by data miners and statisticians. Many statisticians who wish to create statistical models to solve complex problems favour it. A solid R programming tutorial, such as this Data Science with R programme, will walk you through the fundamentals of R programming. You’ll learn linear regression, logistic regression, decision trees, and other Data Science algorithms. Completing a R language course will help you build your resume, as many employers are looking for Data Science specialists that know R.
Why should one choose this course?
In India, a Data Scientist earn approximately Rs. 6.5 L. PA on an average. It can go up to Rs. 20 L. PA for experts who gain a lot of experience and skill.
In current data world, Data Scientists are in high demand. Data Science’s findings are already making an impact in our daily lives. Take the Internet for example; there are more websites than one can fathom – a single Google search yields 1 billion results, which is a staggering statistic. When you use Google to search for something, a data scientist is sifting through the 1 billion websites in the background to get you the information you need.
Companies have a lot of information. That won’t help if corporations don’t engage data scientists to examine the data and provide actionable insights.
After learning all the concepts provided in this programme you will be able to think like Data Scientist, solve complex real-world problems and provide best solutions to company.
What is the scope of this course?
Businesses and enterprises collect data on a daily basis for transactions and online interactions. Many businesses have the same problem: analysing and categorising the data they collect and store. In a case like this, a data scientist becomes the saviour. Companies can make significant progress if data is handled properly and efficiently, resulting in increased production.
Data science is a vast professional path that is always evolving, promising a plethora of options in the future. Job responsibilities in data science are projected to become more specialised, leading to specialties in the subject. People who are interested in this field can take advantage of their opportunities and pursue what best suits them by using these standards and specialities.
Data science is a vast professional path that is always evolving, promising a plethora of options in the future. Job responsibilities in data science are projected to become more specialised, leading to specialties in the subject. People who are interested in this field can take advantage of their opportunities and pursue what best suits them by using these standards and specialities.
Course Structure
Introduction of statistics
Types of Statistics
Types of variables
Levels of Measurements
Construction of frequency polygon
Constructing frequency table
Plotting histogram
Concept of population and sample
Measures of central tendency
Measures of dispersion
Measures of position
Probability concepts
Discrete probability distribution
Continuous probability distribution
Central limit theorem
Sampling methods
Estimation and confidence interval
p-values
Hypothesis testing
Types of Error
Analysis of Variance
Introduction to R
Installation R and R Studio Basic R Operators in R Data types in R Data Structures in R Data Frame
Lists
Read and write file
Useful Functions in R
Control Structure
Packages
Graphs in R
Regression
Classification techniques
Time Series Forecasting
Clustering
Terminologies – Records, Fields, Tables, Introduction to database
Concept of ER Modelling
Relational Algebra: The fundamental operations of relational algebra are as follows −
Select
Project
Union
Set different
Cartesian product
Rename
Introduction to SQL, SQL Syntax, SQL data Types, SQL Operators, Table creation in SQL- Create, Insert, Drop, Delete, and Update, Table access & Manipulation, Select with Where Clause (In between, logical, operators, wild cards, order, group by), Concepts of Join – Inner, Outer
SVM
KNN
Adaboost
Ridge and Lasso regression
Principle Component Analysis (PCA)
Gradient Boosting
Introduction, Natural language processing, Natural language processing, Text clustering, Topic modelling, Document summarization, Sentiment analysis, Text visualization
Introduction to deep learning, Neural Networks Basics, Shallow neural networks, Deep Neural Networks
1.Understanding the project topic
2.Research about the project
3.Constructing the project using R programming
4.Step by step building of project
5.Mentoring sessions for the project
6.Final product
Course Duration
40Hrs Technical + 15Hrs Soft skills
Entry Level
Approx. average Rs. 3.58 LPA
Mid-Level
Approx. average Rs. 8 LPA
Expert Level
Approx. average Rs. 1 Cr. PA
Approx. average Rs. 3.58 LPA
Mid-Level
Approx. average Rs. 8 LPA
Expert Level
Approx. average Rs. 1 Cr. PA
Accreditation Partner(s)
