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Starting from Sep 26th, 2020
10:00 am – 1:00 pm

70,000.00 29,900.00

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  • 90 Hours Online Classroom Sessions
  • 11 Module 04 Projects 5 MCQ Test
  • 6 Months Complete Access
  • Access on Mobile and laptop
  • Certificate of completion
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Data Science Using R

Data Science is an art of making data driven decisions. To make that data driven decision it uses scientific methods, processes, algorithms to extract knowledge and insights from data. It is used for examining, cleaning, manipulating, transforming and generating information from the data. Now a days in Business world data analytics plays a vital role to form decisions more scientifically and help to increase operational effieciency.

4.7/5

What we will learn

Description

Top skill in demand now a days is to process raw data into business insights. There is no special programming language dedicated to data science but looking at the exciting features of the R language you can make your mind. R has great features like fast and high computational capability, extremely compatible, cross platform support , distributed computing and vector arithmetic. In this course we will learn r programming, statistics and machine learning used for business analytics. We will learn data wrangling, data cleansing as well as data visualization using popular R packages like dplyr and ggplot2. In this course you will get to learn apply exploratory data analytics the essential part of data analytics.

By the end of this you will be able to extract, read and write data from csv files, data cleansing, data manipulation,  data visualization, run inferential statistics, understand the business problems, based on problems you will be able to select and apply machine learning models and deploy it.

Who can go for this

Data Science is for everyone who wants grow in their field. In data analytics people are from different streams like economics, computer science, chemical, electrical, statistics, mathematics, operations. Yes you will find many are from technical background but its not the prerequisite

Course Content

LESSONS LECTURES DURATION
Probability 1 Lecture20:00
Random Variables1 Lecture25:00
Probability Distribution1 Lecture21:00
Central Limit Theorem1 Lecture25:00
Sampling1 Lecture25:00
Confidence Intervals1 Lecture25:00
Hypothesis Testing1 Lecture25:00
Chi Square Test1 Lecture25:00
Anova Test1 Lecture25:00

Data Types

1 Lecture50:00
Basic statistics using data examples1 Lecture30:00
Central tendencies1 Lecture43:00
Correlation analysis1 Lecture34:00
Data Summarization1 Lecture40:00
Data Dictionary1 Lecture29:00
Outliers /Missing Values1 Lecture30:00
Basic Linear Algebra – dot product, matrix multiplication and transformations1 Lecture38:00

 

Introduction to R Programming

1 Lecture12:00

Data Types in R

1 Lecture15:00

Functions in R

1 Lecture10:00

Summarizing data by using various functions

1 Lecture35:00

Indulge into a class activity to summarize the data

1 Lecture35:00

Various subsetting methods

1 Lecture35:00

Data import technique in R

1 Lecture34:00

Import data from spreadsheets and text files into R

1 Lecture24:00

Install packages used for data import

1 Lecture20:00

Connect to RDBMS from R using ODBC and basic SQL queries in R

1 Lecture30:00

Perform basic web scrapping

1 Lecture8:00

Know the various steps involved in data cleaning

1 Lecture15:00

Functions used for data inspection

1 Lecture18:00

Tacking the problem faced during data cleaning

1 Lecture25:00

How and when to use functions like grep, grepl, sub, gsub,
regexpr, gregexpr, strsplit

1 Lecture28:00

How to coerce the data

1 Lecture21:00

What is data exploration

1 Lecture15:00

Data exploring using Summary(), mean(), var(), sd(), unique()

1 Lecture18:00

Using Hmisc package and using summarize, aggregate function

1 Lecture25:00

Learning correlation and cor() function and visualizing the same using corrgram

1 Lecture28:00

Visualizing data using plot and its different flavours

1 Lecture21:00

Boxplots

1 Lecture22:00

Dist function

1 Lecture26:00

Gain understanding on data visualization

1 Lecture15:00

Learn the various graphical functions present in R

1 Lecture18:00

Plot various graph like tableplot, histogram, boxplot etc.

1 Lecture25:00

Customize graphical parameters to improvise the plots.

1 Lecture28:00

Understand GUIs like Deducer and R commander

1 Lecture21:00

Introduction to spatial analysis

1 Lecture21:00

What is machine learning

1 Lecture15:00

Different stages of ML project

1 Lecture18:00

Supervised vs Unsupervised ML

1 Lecture25:00

Algorithms in Supervised and Unsupervised learning

1 Lecture28:00

Introduction to Sklearn

1 Lecture21:00

Data preprocessing

1 Lecture22:00

Scaling techniques

1 Lecture26:00

Training /testing / validation datasets

1 Lecture28:00

Feature Engineering

1 Lecture38:00

How to deal with Categorical Variables – Dummy variables

1 Lecture26:00

Categorical embedding

1 Lecture16:00

Detailed explanation of Linear Regression – Linear regression assumption

1 Lecture15:00

Cost function

1 Lecture18:00

Gradient Descent

1 Lecture25:00

Linear regression using sklearn

1 Lecture28:00

Model accuracy metrics – RMSE , MSE, MAE

1 Lecture21:00

R2 vs Adjusted R2

1 Lecture22:00

Detailed explanation of Logistics Regression

1 Lecture15:00

Cost function

1 Lecture18:00

Logistics equation

1 Lecture25:00

Model accuracy metrics – Accuracy, ROC, Confusion Matrix, AUC

1 Lecture28:00

What are decision trees?

1 Lecture15:00

CART algorithms

1 Lecture18:00

Shortcoming of decision trees

1 Lecture25:00

Bagging and Boosting

1 Lecture28:00

Random Forest

1 Lecture21:00

Gradient Boosting

1 Lecture22:00

Explanations using sklearn

1 Lecture26:00

XGBoost

1 Lecture28:00

k Means Clustering

1 Lecture15:00

DBSCAN Clustering

1 Lecture18:00

PCA

1 Lecture25:00

Support Vector Machines

1 Lecture28:00

Naive Bayes Classifier

1 Lecture21:00

Feature selection techniques

1 Lecture22:00

Overfit vs Underfit

1 Lecture15:00

Bias Variance tradeoff

1 Lecture18:00

Grid Search

1 Lecture25:00

Random Search

1 Lecture28:00

Feature Engg examples

1 Lecture21:00

Ridge / Lasso Regression

1 Lecture22:00

SkLearn Pipelines

1 Lecture26:00

SkLearn Imputers

1 Lecture28:00
TOTAL28 LECTURES84:20:00

Mentors

Vipin Kumar

Hi I am Vipin Kumar and I am super excited that you are reading this.

Professionally, I am a data science management consultant with over 8+ years of experience in Banking, Capital Market, CCT, Media and other industry. I was trained by best analytics mentor at dunnhumby and now a days I leverage Data Science to drive business strategy, revamp customer experience and revolutionize existing operational processes.  

From this course you will get to know how I combine my working knowledge, experience and qualification background in computer science to deliver training step by step.  

Student feedback

4.5 OUT OF 5
4.7/5

Vijay Kumar

5/5
1 year ago

Good institute for data science course.. Vipin is very much expert in his domain…

Astik Pandey

5/5
1 year ago

It is a very good institute for data science training, i enrolled for data science using r. way of teaching of Mr. vipin is awesome clear all the doubts. No doubt all staff are very supportive. Overall my experience was good.

Instructor

Thank you Astik…

Mannu Rajwar

5/5
1 year ago

I learned so much and glad that i have made the decision to sign up for this institute. I specially appreciate all the session notes, extra links provided by Vipin. Vipin is a very good trainer, staff is too polite & friendly, all trainers are experienced and very helpful. Thank you !!

Instructor

Thanks Mannu thank you very much

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FAQ's

Data Science is a combination of three main skill set 1. Domain knowledge 2nd is Statistics and 3rd is programming, one who wants to be data analyst by profession must have to learn these. Its not at all difficult to learn

Data Science includes different processes like data gathering, data wrangling, data preprocessing, statistics, data visualization, machine learning. The mandate steps are Data preprocessing -> Data Visualization -> Exploratory Data Analysis ->Machine Learning -> Predictive Analysis

Pool of working professionals with several years experience in same field with different domains like banking, healthcare, retail, ecommerce and many more. 

Along with the high quality training you will get a chance to work on real time projects as well, with a proven record of high placement support. 

We will cover SQL for data gathering, Python programming, Machine Learning, Data Visualization with tableau. 

Its  Live interactive training, Ask your quesries on the go, no need to wait for doubt clearing.

you will have access to all the recordings, you can go through the recording as many times as you want.

Ask your questions on the go or you can post your question in group on facebook, our dedicated team will answer every query arises.

Yes we will help learners even after the subscription expires.

No you cannot download the recording it will be in your user access on LMS, you can go through at any point of time.

During the training and after as well we will be on  the same slack channel, where trainer and admin team will share study material, data, project, assignment.

you can write your questions at info@palin.co.in we will address your questions there.

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