Interested in this College?
Get updates on Eligibility, Admission, Placements Fees Structure

Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study, Virtual Classroom	Video and Text Based

Course Overview

Realizing the increase in data and demand due to digitalization in the Data Science space, the Data Science Course Training in Delhi focuses on enhancing the skills and abilities of students who are looking to become successful Data Science Professionals.

This Data Science Course Training in Delhi offers both virtual classroom sessions and self-paced training where students can choose their preferred style of learning. After the Data Science training course in Delhi is completed, students will gain expertise which is equal to a Data Science professional. Data Science Course Training in Delhi syllabus each and every concept of Data Science like Data Cleansing, Data Transformation, Data Collection, Data Mining, Data Extraction, Data Visualization, and so on. Also, it teaches several skills and techniques like Python programming, Machine Learning, Hypothesis Testing, Neural Networks, Regression Modelling, Microsoft Azure, R-programming, Statistical Analysis, and so on.

Excel R has the best teaching faculty that ensures a masterclass learning experience for students. All the trainers are Data Scientists who are extremely skilled with over 15 years of experience. Most of the faculty are from prestigious institutions like IITs, IIMs, BITS Pilani, and so on. Students will get to work on various project works, assignments, and live-projects which will strengthen their technical skills and gain in-field experience. This Data Science Course Training in Delhi classes also provides an option for students to access e-learning such that students can watch the recorded sessions to recap and revise the topics or missed sessions.

Given the fact that Excel R is the best institution for Data Science certification Course Training in Delhi, it is highly-advantageous for students who want to start their career in the field of Data Science.

The Highlights

Instructor-led training
Live classroom virtual sessions
Self-paced learning
Lifetime access
Dual certification by SGIT and Excel R
6 months duration
In association with Steinbeis University
Placement assistance

Programme Offerings

assignments
Peer support
Live Projects
E-learning
Webinars
Self-paced learning
instructor-led training
Post training support
Interview preparation sessions
Amount in INR Live virtual classroom 68999Project works

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	FutureSkillsExcelR SolutionsSteinbeis Global Institute, Tubingen

The fee details of the Data Science Course Training in Delhi is given below:

Students will have options to choose between self-paced learning and live virtual classrooms.
Self-paced learning is priced at Rs 68999 .
There is a facility of monthly EMI at a zero interest rate on all major credit cards.

The table for the fee details is as follows:

Fee structure	Amount in INR
Live virtual classroom	68999

Eligibility Criteria

Education

The educational qualification for pursuing Data Science Course Training in Delhi is that students should possess technical knowledge of Python, R-programming, and SAS tools and mathematical skills. These are the important skill sets for course takers in order to learn this course.

Certification Qualifying Details

Upon the successful completion of the course, students must take an online exam conducted by SteinbeisUniversity and get a minimum of 60% to get the certificate of completion. It is a dual certificate from Excel R and SteinbeisUniversity which is a sign of excellence in the field of Data Science. Students will enjoy the alumni status of SGIT, SteinbeisUniversity.

What you will learn

Natural Language ProcessingMachine learningKnowledge of PythonTableau knowledgeKnowledge of deep learningKnowledge of Apache Spark

There is so much that students will extract from the Data Science Course Training in Delhi such as -

Many applications, theories, and concepts of Data Science like Data Exploration, Feature Engineering, Building Prediction models, Data Visualization, Data Transformation, Data Integration, Data Mining, Data Cleansing, and so on.
Various additional skills and tools like Machine Learning, Neural Networks, Statistical Analysis, Hypothesis Testing, Deep Learning, Tableau, Predictive Modelling, Spark, Programming languages like R programming, Python, R Studio, Text Mining, Predictive Analytics, Natural Language Processing, Hadoop, Regression Modelling and so on to carry out different tasks.

Who it is for

EconomistStatisticianBusiness Analyst

The Data Science Course Training in Delhi is highly suggested for the following individuals such as -

Freshers with good math, analytical skills, problem-solving and logical reasoning who want to kick start their career in Data Science.
Qualified professionals in data warehousing, business intelligence, and reporting tools who want to polish their skills.
Data Scientists, Data Analysts, Research Analysts, Business Analyst Consultants, and so on who want to brush up on their expertise and get many perks like new opportunities, promotions, high salary packages, and so on.

Admission Details

The admission process to enroll in this course is direct and easy. It is advised that students should keep their payment details handy.

Students should follow certain steps to register for the Data Science certification Course Training in Delhi is as follows -

Step 1: Go to the course page on the official website of Excel R: https://www.excelr.com/data-science-course-training-in-delhi

Step 2: Choose your preferred course option between self-paced training and live virtual classroom.

Step 3: Enter coupon code and apply if you have one.

Step 4: Click on ‘Proceed’ and log in to your Excel R account or register if you haven’t yet.

Step 5: Fill in your credentials.

Step 6: Choose your preferred payment option.

Step 7: Fill in your payment details.

Step 8: Click on ‘Make a Payment’ and confirm the payment.

Step 9: After the transaction is completed, you can have access to the course.

The Syllabus

Recap of Demo
Introduction to Types of Analytics
Project life cycle
An introduction to our E-learning platform

Data Types
Measure Of central tendency
Measures of Dispersion
Graphical Techniques
Skewness & Kurtosis
Box Plot
R
R Studio
Descriptive Stats in R
Python (Installation and basic commands) and Libraries
Jupyter notebook
Set up Github
Descriptive Stats in Python
Pandas and Matplotlib / Seaborn

Random Variable
Probability
Probility Distribution
Normal Distribution
SND
Expected Value
Sampling Funnel
Sampling Variation
CLT
Confidence interval
Assignments Session-1 (1 hr)
Introduction to Hypothesis Testing
Hypothesis Testing with examples
- 2 proportion test
- 2 sample t test
Anova and Chisquare case studies

Visualization
Data Cleaning
Imputation Techniques
Scatter Plot
Correlation analysis
Transformations
Normalization and Standardization

Principles of Regression
Introduction to Simple Linear Regression
Multiple Linear Regression

Multiple Logistic Regression
Confusion matrix
- False Positive, False Negative
- True Positive, True Negative
- Sensitivity, Recall, Specificity, F1 score
Receiver operating characteristics curve (ROC curve)

R shiny
Streamlit

Supervised vs Unsupervised learning
Data Mining Process
Visualization of clustering algorithm using Dendrogram

Hierarchical Clustering / Agglomerative Clustering

Measure of distance
- Numeric - Euclidean, Manhattan, Mahalanobis
- Categorical - Binary Euclidean, Simple Matching Coefficient, Jaquard’s Coefficient
- Mixed - Gower’s General Dissimilarity Coefficient
Types of Linkages
- Single Linkage / Nearest Neighbour
- Complete Linkage / Farthest Neighbour
- Average Linkage
- Centroid Linkage

K-Means

Non-Hierarchial
Measurement metrics of clustering - Within Sum of Squares, Between Sum of Squares, Total Sum of Squares
Choosing the ideal K value using Scree plot / Elbow Curve

DBSCAN

A general intuition for DBSCAN
Different parameters in DBSCAN
Metrics used to evaluate the performance of the model
Pro's and Con's of DBSCAN

PCA and tSNE
Why dimension reduction
Advantages of PCA
Calculation of PCA weights
2D Visualization using Principal components
Basics of Matrix algebra

What is Market Basket / Affinity Analysis
Measure of association
Support
Confidence
Lift Ratio
Apriori Algorithm

User-based collaborative filtering
The measure of distance/similarity between users
Driver for recommendation
Computation reduction techniques
Search based methods / Item to item collaborative filtering
The vulnerability of recommender systems

Workflow from data to deployment
Data nuances
Mindsets of modelling

Elements of Classification Tree - Root node, Child Node, Leaf Node, etc.
Greedy algorithm
Measure of Entropy
Attribute selection using Information Gain
Implementation of a Decision tree using C5.0 and Sklearn libraries

Encoding Methods
- OHE
- Label Encoders
- Outlier detection-Isolation Fores
Predictive power Score

Recurcive Feature Elimination
PCA

Splitting data into train and test
Methods of cross-validation
Accuracy methods

Bagging
Boosting
Random Forest
XGBM
LGBM

Deciding the K value
Building a KNN model by splitting the data
Understanding the various generalization and regulation techniques to avoid overfitting and underfitting
Kernel tricks

Lasso Regression
Ridge Regression

Artificial Neural Network
Biological Neuron vs Artificial Neuron
ANN structure
Activation function
Network Topology
Classification Hyperplanes
Best fit “boundary”
Gradient Descent
Stochastic Gradient Descent Intro
Back Propagation
Introduction to concepts of CNN

Sources of data
Bag of words
Pre-processing, corpus Document-Term Matrix (DTM) and TDM
Word Clouds
Corpus level word clouds
- Sentiment Analysis
- Positive Word clouds
- Negative word clouds
- Unigram, Bigram, Trigram
Vector space Modelling
Word embedding
Document Similarity using Cosine similarity
Extract Tweets from Twitter
Extract user reviews of the products from Amazon, Snapdeal and TripAdvisor
Install Libraries from Shell
Extraction and text analytics in Python

Sentiment Extraction
Lexicons and Emotion Mining

Probability – Recap
Bayes Rule
Naive Bayes Classifier
Text Classification using Naive Bayes

Introduction to time series data
Steps of forecasting
Components of time series data
Scatter plot and Time Plot
Lag Plot
ACF - Auto-Correlation Function / Correlogram
Visualization principles
Naive forecast methods
Errors in forecast and its metrics
Model Based approaches
- Linear Model
- Exponential Model
- Quadratic Model
- Additive Seasonality
- Multiplicative Seasonality
Model-Based approaches
AR (Auto-Regressive) model for errors
Random walk
ARMA (Auto-Regressive Moving Average), Order p and q
ARIMA (Auto-Regressive Integrated Moving Average), Order p, d and q
Data-driven approach to forecasting
Smoothing techniques
- Moving Average
- Simple Exponential Smoothing
- Holts / Double Exponential Smoothing
- Winters / HoltWinters
De-seasoning and de-trending
Forecasting using Python and R

Concept with a business case

End to End project Description with deployment using R and Python

Basic Statistics

Data types Identification and probability
Expected values, Measures of central tendencies
Skewness and Kurtosis & Boxplot
Practice Mean, Median, Variance, Standard Deviation and Graphical representations in R
Creating Python Objects
Practice Mean, Median, Variance, Standard Deviation and Graphical representations in Python
Confidence intervals and distributions

Hypothesis Testing

Buyer ratio
Customer Order Form
Cutlets
Pantaloons
Lab TAT

Linear regression

Prediction of weight based on Calories consumed
Delivery Time period Vs Sorting time
Employee Churn rate Vs Salary
Salary Prediction

R shiny and Flask

Practice R shiny and Python Flask for Linear Regression assignments

Multiple Linear Regression

50 startups case study
Computer data Case study
Toyota Corolla

Logistic Regression

Term deposit case study
Elections results Case study

Multinomial Regression

Student Program Case study

Hierarchical Clustering

Crime data
Eastwest Airlines

K means Clustering

Insurance policy
Crime data

PCA

Dimension Reduction for Wine data

Network Analytics

Node Properties practice in R

Association Rulesv

Association Rules for Book store
Association Rules for Mobile store
Association Rules for Retail Transactions

Recommendation Engine

Recommend Jokes for subscribers

Text mining, Web Extraction

Extraction of tweets from twitter
Reviews from ecommerce websites

Text mining

Sentiment Analysis on extracted data

NLP

Emotion mining by extracting a speech or novel from web

Naive Bayes

Spam and Ham classifications

KNN Classifier

Types of Glass
Classification of Animals

Decision Tree and Random Forest

Fraud Check
Sales prediction of an Organization

XGB and GLM

Social Networks Ads

Lasso and Ridge Regression

Practice Lasso and Ridge with multiple Linear Assignments

ANN

Forest Fires case study

SVM

Classification of Alphabets

Survival analysis

Prediction of Patient survival probability

Forecasting model based

Airlines Forecasting
Forecasting of sales for a soft drinks case study

Forecasting

Forecasting of Bike shares
Forecasting of Solar power consumption

Industry: Aviation

Predicting the flight delays

Industry: Manufacturing

Predict impurity in ore

Industry: Oil and Gas

Predicting the oil price

Industry : Automotive

Electric Motor Temperature

Industry : Daily Analysis of a product

"Daily" Twitter Data Analysis for a Product

Industry : E commerce

Natural Language Processing

Resume Preparation
Interview Support

Introduction to Big Data
Challenges in Big Data and Workarounds
Introduction to Hadoop and its Components
Hadoop components and Hands-on
Understand the MapReduce (Distributed Computation Framework) and its Drawback
Introduction to Spark
Spark Components
Spark MLlib and Hands-on (one ML model in spark)

Introduction to R Programming

Introduction to R
Data Types in R

How To Install R & R Studio

Data Structures in R

Variable in R
R-Overview
- Vector
- Matrix
- Array
- List
- Data-Frame
Operators in R
- arithmetic
- Relational
- Logical
- Assignment
- Miscellaneous
Conditiional Statement
- Decision Making
  - IF Statement
  - IF-Else Statement
  - Nested IF-Else Statement
  - Switch Statement
- Loops
  - While Loop
  - Repeat Loop
  - For Loop
- Strings
- Functions
  - User-defined Function
  - Calling a Function
  - Calling a Function without an Argument
  - Calling a Function with an Argument

Programming Statistical

Box Plots
Bar Charts
Histogram
Pareto Chart
Pie Chart
Line Chart
Scatterplot

How to Import Dataset in R

Read CSV Files
Read Excel Files
Read SAS Files
Read STATA Files
Read SPSS Files
Read JSON Files
Read Text Files

R-Packages

DpLyr
Hmisc or mise
Ggplot2
Caret
Data Table

How to Integrate R and SQL

How to Get Data From SQL to R

Introduction

Python Introduction - Programing Cycle of Python
Python IDE and Jupyter notebook

Variables

Variables
Data type

Code Practice Platform

create , insert , update and delete operation , Handling erros

Operators

Operator -Arthmatic ,comparison , Assignment ,Logical , Bitwise opeartor
Decision making - Loops

Loops

While loop, for loop and nested loop
Number type conversion - int(), long(). Float ()
Mathametical functions , Random function , Trigonometric function

Sting

Strings- Escape char, String special Operator , String formatting Operator
Build in string methods - center(), count()decode(), encode()

List

Python List - Accessing values in list, Delete list elements , Indexing slicing & Matrices
Built in Function - cmp(), len(), min(), max(), list comprehension

Tuples

Tuples - Accessing values in Tuples, Delete Tuples elements , Indexing slicing & Matrices
Built in tuples functions - cmp(), len ()

Dictionary

Dictionary - Accessing values from dictionary, Deleting and updating elements in Dict.
Properties of Dist. , Built in Dist functions & Methods, Dict comprehension
Date & time -Time Tuple , calendor module and time module

Function

Function - Define function , Calling function
pass by refernece as value , Function arguments , Anonymous functions , return statements
Scope of variables - local & global , Decorators and recursion
Map reduce and filter

Modules

Import statemnts , Locating modules - current directory , Pythonpath
Dir() function , global and location functions and reload () functions , Sys module and subprocess module
Packages in Python

Files

Files in Python- Reading keyboard input , input function
Opening and closing files . Syntax and list of modes
Files object attribute- open , close . Reading and writing files , file Position.
Renaming and deleting files
Pickle and Json

Directories

mkdir methid, chdir () method , getcwd method , rm dir

Exception Handling

Exception handling - List of exceptions - Try and exception
Try- finally clause and user defined exceptions

OOP

OOP concepts , class , objects , Inheritance
Overriding methods like _init_, Overloading operators , Data hiding

Regular Expressions

match function , search function , matching vs searching
Regular exp modifiers and patterns

SQLite and My SQL

Data base connectivity
Methods- MySQL , oracle , how to install MYSQL , DB connection
create , insert , update and delete operation , Handling erros

Framework

Introduction to Django framwork , overview , environment
Apps life cycle , creating views
Application, Rest API

Introduction to What is DataBase
Difference between SQL and NOSQL DB
How to Install MYSQL and Workbench
Connecting to DB
Creating to DB
What are the Languages inside SQL How to Create Tables inside DB and Inserting the Records
Select statement and using Queries for seeing your data
Joining 2 tables
Where clause usage
Indexes and views
Different operations in SQL
How to Connect to your applications from MYSQL includes R and Python

What is Data Visualization?

Why Visualization came into Picture?
Importance of Visualizing Data
Poor Visualizations Vs. Perfect Visualizations
Principles of Visualizations
What is R?
How to integrate Tableau with R?
Tableau Prep
Tufte’s Graphical Integrity Rule
Tufte’s Principles for Analytical Design
Visual Rhetoric
Goal of Data Visualization

Tableau – Data Visualization Tool

Introduction to Tableau
What is Tableau? Different Products and their functioning
Architecture Of Tableau
Pivot Tables
Split Tables
Hiding
Rename and Aliases
Data Interpretation

Tableau User Interface

Understanding about Data Types and Visual Cues

Basic Chart types

Text Tables, Highlight Tables, Heat Map
Pie Chart, Tree Chart
Bar Charts, Circle Charts

Intermediate Chart

Time Series Charts
Time Series Hands-On
Dual Lines
Dual Combination

Advanced Charts

Bullet Chart
Scatter Plot
Introduction to Correlation Analysis
Introduction to Regression Analysis
Trendlines
Histograms
Bin Sizes in Tableau
Box Plot
Pareto Chart
Donut Chart, Word Cloud
Forecasting ( Predictive Analysis)

Maps in Tableau

Types of Maps in Tableau
Polygon Maps
Connecting with WMS Server
Custom Geo coding
Data Layers
Radial & Lasso Selection

Adding Background Image

How to get Background Image and highlight the data on it
Creating Data Extracts
Filters and their working at different levels
Usage of Filters on at Extract and Data Source level
Worksheet level filters
Context, Dimension Measures Filter

Data Connectivity in-depth understanding

Joins
Unions
Data Blending
Cross Database Joins
Sets
Groups
Parameters

Creating Calculated Fields

Logical Functions
Case-If Function
ZN Function
Else-If Function
Ad-Hoc Calculations
Quick Table Calculations
Level of Detail (LoD)
Fixed LoD
Include LoD
Exclude LoD

Responsive Tool Tips

Dashboards
Actions at Sheet level and Dashboard level
Story

Connecting Tableau with Tableau Server

Publishing our Workbooks in Tableau Server
Publishing dataset on to Tableau Server
Setting Permissions on Tableau Server

Connecting Tableau with R

What is R?
How to integrate Tableau with R?
Tableau Prep

Parameter & Hyper parameter

Train, Test & Validation Set
Vanishing & Exploding Gradient
Dropout
Regularization
Optimization algorithm
Learning Rate
Tuning
Softmax

CNN

CNN
Deep Convolution Model
Detection Algorithm
Face Recognition

RNN

RNN
LSTM
Bi Directional LSTM

Introduction to ChatGPT and AI

What is ChatGPT?
The history of ChatGPT
Applications of ChatGPT
ChatGPT vs other chatbot platforms
Industries using ChatGPT
The benefits and limitations of ChatGPT
Future developments in ChatGPT technology
Ethical considerations related to ChatGPT and AI

Types of AI and Chatgpt architecture

Narrow AI
Strong AI
Superintelligence
Chatgpt architecture

ChatGPT Functionalities and Applications

How does ChatGPT work?
ChatGPT Functionalities
Drafting emails and professional communication
Automating content creation
Resume and Cover letter creation
Research and information gathering
Brainstorming ideas and creative problem solving
Best Practices for Using ChatGPT

ChatGPT Prompt Engineering

What is Prompt Engineering?
Types of Prompts
Crafting Effective Prompts
Using ChatGPT to generate prompt

Steinbeis University, Berlin Frequently Asked Questions (FAQ's)

1: Does this course offer any prerequisites?

There isn’t any prerequisite included in this course. But, it is recommended that students must have good command over mathematics, critical thinking, analytical skills, communication skills, Python, R, and SAS tools.

2: How long is this course?

The duration of this Data Science Course Training in Delhi is 6 months such that students will get ample time to learn concepts.

3: What is the minimum score required to pass the online test after the course completion?

Students must get a minimum of 60% to pass the exam. However, it varies from course to course.

4: What is the specialty of the certificate?

The certificate is a dual certificate provided by Steinbeis University and Excel R. It is a true symbol of excellence in Data Science Space. This certificate has a global recognition that fetches several new opportunities, placements, and so on. Also, students will enjoy the alumni of SGIT.

5: Is this a free course?

No, students must pay the Data Science Course Training in Delhi fee in order to pursue this course.

6: What are the different course types?

There are two-course options. Live virtual classroom and self-paced learning. Students should choose from these options.

7: What if I miss any lectures?

This course offers an e-learning feature where students can access the missed lectures or recap and revise the previous concepts.

8: Can I pay the course fee through EMI?

Students can avail of monthly EMI on all major credit cards at a zero interest rate.

9: Will, I get any placement guidance?

Yes, students will get job assistance after the completion of the course.

10: Do I need any additional skills to study this course?

Students must have a good grip on mathematics, problem-solving skills, analytical skills, Python, R, SAS tools, communication skills, and critical thinking skills.