Data Science Using

31,000+ Participants | 12,000+ Brands | 2200+ Trainings | 55+ Countries

[Since 2009]

(Video-Based Self Paced Course)

Ajay OhriData Scientist

Ajay Ohri is a Data Scientist and Blogger in an open source data science. Since 2007, he has published his blog DecisionStats.com.

Salient Features

Course Advisors

Programmers and Statisticians

Course Highlights

30+ Hours of Video-Based Learning

Lifetime Access toUpdated Content and

Videos

Industry andAcademia Faculty

15 Days of Project Work

Active Q/A Forum Class Labs/Home Assignment (10 hours/Week Learning Time)

Industry’s Data Analytics

Advisors

Top Data Analytics Tools Covered

Who is this Course for

Shweta GuptaVice President, Tech.

Shweta Gupta has 19+ years of Technology Leadership experience. She holds a patent and number of publications in ACM, IEEE and IBM journals like Redbook and developerWorks.

Manas Garg heads the Analytics for Marketing at Paypal. He takes Data Driven Decisions for Marketing Success.

Vishal is a Technology Influencer and CEO of Right Relevance. (A platform used by millions for content & influencer discovery)

Manas GargArchitect

Vishal MishraCEO & Co-Founder

Course Instructors

Course Curriculum

This will be an introduction session with a brief explaination about Data Analytics ecosystem, scope of thisfield and introducton to R platform.

Shantanu Garg is the Sr. Marketing Analyst at MakeMyTrip. He handles data science and web analytics projects. He has worked as the Analytics Specialist in Transorg and Research Associate for Nielsen. He is skilled in Probability, Statistics, Data Mining, PostgreSQL, R, Pentaho, Machine Learning, Adobe Analytics, Hive and Google Analytics.

INTRODUCTION TO DATA ANALYTICS

Introductory Session

Briefing about Analytics domainHow insides from data can help business solve day-to-day problems and find solutionVarious platforms which can help you in the journey of becoming Data ScientistIntroduction to R as a platform

The 'R for Data Analytics' course is thoughtfully designed to allow learners with some programming background to make a transition into the analytics industry with correct skillsets using R language. It is designed in a way that the student starts with the introduction to R programming, and in a very hands-on learning method using R Studio, will learn the nuts and bolts of R to perform the role of data analyst. The student will progress to applied statistics and machine learning concepts & applications. Post completion of the program, learners will be prepared to device solutions for real-time problems in the industry.

Nitika Malhotra is a Data Scientist at Zomato and handles data science and machine learning projects. She has worked as the Analytics Specialist at Transorg, Research Associate at IIT-Delhi and Research Intern at MOSPI (Ministry of Planning and Programme Implementation). She holds expertise in Probability, Statistics, Data Structures, PostgreSQL, R, SPSS, Pentaho, SAS, Machine Learning, and Hive.

NITIKA MALHOTRA

SHANTANU GARG

This session will be an introduction to Basics of coding on R Studio platform.

INTRODUCTION TO R PROGRAMMING

R Nuts and Bolts

Understanding different windows of R StudioBasics of R Programming and some important rules for coding in RInstalling predefined packagesEntering inputs and R objects (Vector, Matrix, Dataframes and Factors)R DatatypesUsing dplyr PackageText Manipulations using StringsReading data (csv file) in R

In-depth understanding about data manipulation using di�erent packages and functions & conditional loopings in R.

DATA MANIPULATIONS AND LOOPING IN R

In Detail Hands on for Learning Data Manipulations

Subsetting datasetDate and Time in RLoops: while & forConditionals: if-elseFunctions: Defining functions, Anonymous functionsApply family of functionsSampling in R

Exploratory Analysis will help you know more about the features of datasets, statistically. For understanding real-time data in the industry, this is the first step.

EXPLORATORY ANALYSIS IN R

Descriptive Statistical Analysis

Central TendenciesMeasurements of DispersionTest of NormalityNull Value TreatmentOutlier TreatmentCorrelation AnalysisReshaping DataMerging Data

Creating basic as well as interactive visualisation in R.

VISUALISATION

R Studio Visualisations Interactive Dashboard

Categorical Data: Barplot,Pie ChartNumeric: Boxplot, Histogram, Scatter Plot, Line ChartUsing different libraries to make graph presentable (ggplot2, Rcolorbrewer)

Using shiny to create interactive Graphical Dashboards

Inferential Analysis is very useful in knowing underline information of data. It is generally used in the industry for A/B or Test/Control group comparisons.

INFERENTIAL ANALYSIS IN R

Parametric Statistical Tests

Basic theory of Inferential StatisticsHypothesis tests using Z TestT-statistics TestTwo sampled Z Test and T TestANOVAPost-hoc Test

Non-Parametric Statistical Test

Wilcoxen TestMann-Whitney U Test

K.S. TestRunn Test

Chi-Square Test

This section begins with loading and bringing data from di�erent data sources in R.

DATA LOADING AND FILE FORMATS

Descriptive Statistical Analysis

Data loading and file formatsLoading JSON filesXML and HTML Web ScrapingInteracting with HTML and Web APIsInteracting with databasesText Mining/Text Analytics in R

Introduction to machine learining and its further bifurcations. Learning most of the industry-wise used machine learning techniques.

MACHINE LEARNING

Case Study- Linear Regresssion

What is Machine LearningMachine Learning real-world ExamplesAssumptions for Linear Regression

Linear Regression Assumptions checks in RBuilding Linear Regression Model in RStepwise method

Exploring DataDividing data into Test and TrainModel Building and RPredicting on Test Data using Model

Supervised Learning Techniques

Unsupervised Learning Techniques

Logistic Regression

Understanding Logistic RegressionClassification Model Building using Logistic ModelConfusion Matrix

Random Forest

Decision TreeRandom Forest

Unsupervised Learning

ClusteringK-meansHierarchical ClusteringTime Series Analysis

SVM and Naive Bayes

SVMNaïve Bayes

Tools

The Capstone project is the culminating assignment that will allow you to have an integrated experience of the program. The approach to this project is to think, define, design, code, test and tune your solution, in such a way that you apply all aspects of the data analytics process.

The real world is filled with text data and is usually messy hence cleaning and handling text is an important step towards making smarter Machine Learning algorithms. You will be working on one such usual messy dataset which hides a lot of information under the hood which is awaiting to be discovered.

Duration

Batch Options

Rs. 15,900+GST

Enroll and Start Anytime

Self Paced, Learn at Your Own Speed

Capstone Project (3 Weeks)

Fee

Our Participants Work at

Course Details

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+91-84680-02880

www.digitalvidya.com

Interested? Contact Us!

info@digitalvidya.com

Attend a Free Orientation Session: http://www.digitalvidya.com/data-analytics-course

- Naresh Mehta AVP – Data Science & Analytics ,

-Ajay Ohri Data Scientist,

“ ”Good to see Digital Vidya becoming increasingly more involved in covering data science vertical, look forward to collaborate with DV to help shape this industry.

“ ”Yes, I like the huge investment Digital Vidya is doing to create the next generation of talent. Initial feedback suggests Digital Vidya produces high-quality Data Analysts.

Industry Experts Speak

-Madhu Vadlamani Lead Analytics,

“ ”I can see a good course structure and well-designed syllabus for those who are passionate enough to enter into the analytics world. The platform helps people grow professionally and in very less time.

rthis Speak

What Makes us Proud?

-Vani Ananthamurthy(Business Operations Senior Analyst, Accenture)

“ ”I was looking for customized content and I found the same in Digital Vidya. Content is structured and well planned. Classes were very interactive and trainer’s presentation skills were very good. People who are new to the subject can also understand clearly. Thank you so much!

-Nanddeep Nasnodkar (Sr. Software Developer - Remote Software Solutions)

“ ”This course gets you started from very basics, makes you think and solve the assignments, and suddenly you find yourself doing Data Science all by yourself!