Learn From Industry Experts
100% Career Assistance
Hands-on Projects
No Cost EMI from ₹4,999
Course Benefits
Step into the emerging world of Data Science with Data Science Masters Program and earn the prestigious certificate from Jain University. Become a professional within 6 months and learn advanced skills in Data Mining, Data Visualization, Data Analysis, Artificial Intelligence, and much more.
Key Highlights
30+ Industry-based projects
100+ practice assignment
1:1 mentorship
Six months Program
Preparation of technical and
HR interview rounds
Concise Batches
Availability of class recordings
Assured interactive sessions
Certification from Jain University
3+ Bonus courses
Sessions on profile building
and networking
Learn job specific skills
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About Data Science Certification Course
In the 21st century, data is the fuel of various industries. The problem in the industry lies in the ability to use that data. To make this raw data meaningful, industries require a data scientist.

However, there aren’t enough required individuals who possess the skill to understand the data. By learning this golden skill you can grab good opportunities in various industries.
Fingertips is already training students and working professionals for years. We at fingertips provide industry-oriented training to all learners. The training is provided by the best in business. Trainers at fingertips are industry experts with a minimum of 5-7 years of experience.
To learn data science, you should have an undergraduate degree in any field. Learners from non-technical backgrounds who are interested in learning about artificial intelligence and data science can easily learn this course. The course will train you with skills required for data science like statistics, mathematics, analyzing techniques, etc and it will open career opportunities for positions like Data Scientist, Data Analytics, Data Engineer, etc.
Yes, fingertips assure placement assistance to all of its learners. After completion of this course, our career support team prepares you for HR and technical interview rounds. In addition to that, Fingertips provide sessions on profile building and networking to the learners.
The learners will receive two data science professional certificate. Certificates will be received after the final evolution. One certificate from Jain University, second from Fingertips Data Intelligence Solution, and many other certificates from LinkedIn.
Talk to us
Tools Covered
Real World Datasets
Learning Data Science is not only a theoretical concept, to get used to industry workings, but Real-life exposures are also necessary. Capstone projects for all the learners that involve real-life data sets for Hands-on-learning. Learn from basics like cleaning a large amount of data, organizing it, and much more. Spend around 360+ hours on these projects and get familiar with Industry problems and complexities in advance with these Datasets.

Programs Fee
• 6 Months Program
• Certification from JAIN (Deemed-to-be University)
• Online from Fingertips
• 100% Job Asistance
74,999 Enroll Now 74,990 Enroll Now

• 6 Months Program
• Certification from JAIN (Deemed-to-be University)
• Offline from Fingertips
• 100% Job Asistance
79,990 Enroll Now 79,999 Enroll Now

Course Content

Statistics Essentials & Fundamental of Data Science Live

Statistics is the vital input that data science people require. The module covers the concept building on techniques of data collection, organization, analysis, interpretation, and representation. It begins with understanding of the statistical population to modeling and testing.

Introduction to Data & Data Types
Numerical parameters to represent data
Data Science v/s Data Analytics v/s Business Intelligence
Importance and Applications of Data-Science in today’s data-driven world
Role of Data Scienstist
Introduction to Databases and it's Types
Steps of Data Science & Machine Learning
Use cases of Data Science in different industries
Introduction to Statistics
Descriptive v/s Inferential Statistics
Variables and Types of Variables
Measure of Center and Measure of Spread
Measures of Central Tendency
Measures of Dispersion
Mean, Mode and Median
Range, Standard Deviation, Variance,Quartile,IQR
Covariance and Correlation between data
Create and learn about Histogram
Introduction to Inferential Statistics
Sample v/s Population
Explore Hypothesis Testing
Null and Alternative hypotheses
Type I error vs Type II error
Establishing a rejection region and a significance level
What is the p-value and why is it one of the most useful tools for statisticians
Learning about T-test
One Sample, two Sample T-test
In Depth Knowledge about Anova, One Way Anova and Two way Anova
Chi-square Analysis
Parametric and Non-parametric tests
Introduction to probability
Why probability
Simple Probabilty
Addition Rule, Union and Intersection
Bayes Theorem
Bernoulli's Theorem
Independent & Dependent Events
Conditional Probability
Probability Distributions
Uniform Distribution
Normal Distribution
What is Central Limit Theorem
Skewness & Kurtosis
Sampling and different sampling techniques
What is Outlier and it's importance

My-SQL Live

Structured Query language has two modules that cover the data bases, its types and components, basics of SQL, Relational Database Management Systems, its functions and applications on different datasets.

What is Database
Types of Databases
Database Components
Introduction to Structured Query Language
Different types of databases
What is RDBMS-Relational Database Management System
Data types and functions
Creating Databases and Tables
Hands-on Learning/Exercise
Designing Your Own Database
Implement Data Modeling and Different Queries
Types of SQL Operators
SQL Arithmetic operators,SQL Comparison operators,SQL Logical operators,Compound operators,SQL Unary Operator
Hands-on Learning/Exercise
Gain experience with Arithmetic operators
Implement Comparision, Logical, compound and Unary operator to Filter Data
Creating Databases and Tables
Explore Entities and Relationships
DDL & DML Statement
Select Statement, Aggregate Functions
Insert into, Where, Order By, Distinct, Group By, Like, In, Between Operators, Limit Aliases, and & or Clause
Update & Delete Query
SQL Joins-What are Joins, Inner Join, Left Join, Right Join and Full Join
Multiple Joins-Joining More than two tables
Hands-on Learning/Exercise
Create Schema in SQL
Insert Records in database
Using Select Query
Using Union, Union All Query
Using Order By, Having, Group By, Joins and Intersect
Introduction to subqueries in sql and applications
How to write Subqueries in SQL
Methods to create and view subqueries
Subqueries with INSERT statement
Subqueries with UPDATE statement
Subqueries with DELETE statement
Hands-on Learning/Exercise
Gain experience with Subqueries
Implement Different Subqueries with Industry Data
Introduction to SQL Views
How to create, modify, delete, drop, rename, alter and manage views
What is Stored Procedures and its benefits
Working with stored procedures
Learning user-defined functions
Hands-on Learning
Implement and Understand the benefits of SQL Views
Implement Stored Procedures
Understanding of more Sql Functions
Learning about Sorting
Grouping Data together
Developing skill to Filter
Explore More about Subqueries
Primary Key, Foreign Key constraints
Unique key, Null constraints
Knowledge Check
Hands-on Learning
Implemet Various Joining, Union, Grouping and Filtering Operation
Work With Industry Data
Implement Data Modeling and Different Queries

Data Science with Python Live

The module provides the understanding of python programming language, application of python in data science, importance in current world, developing python environment, operations, variables, libraries and modules. Focus will be on to develop the insight in Analytics with Python.

Introduction to Python programming language
How Python is used for Data Science applications
Industries working with Python
Applications of Python in different sectors
Features of Python and how is Python different from other programming languages
Components of Python
Python installation and set up
Python IDE working mechanism
Running some Python basic commands
Python variables, data types and keywords
Libraries and Modules in Python
How to use indentation like tabs and space
Built in data types in Python
Number, Strings, List, Tupple, Set and Dictionaries
Basic Operators and Functions
Conditional and Control Statements like if,else, break, continue, Loops in Python-For, While and more
Lambda expression
Hands-on Learning/Exercise
Introduction to Numpy
What are arrays and matrices, array indexing, array math, Inspecting a NumPy array, NumPy array manipulation
Basic Numpy operations
Using Arithmetic Operators with Numpy
Using Numpy with Conditional Expressions
Arithmetic Operators with Numpy 2D Arrays
Arithmetic Functions in Numpy
Logical Operators in Numpy
Hands-on Learning/Exercise
Introduction to Pandas
Understanding DataFrame
Series object in pandas
DataFrame in Pandas
Loading and handling data with Pandas
Missing Values
Knowledge Check
Data Preprocessing
Replace Values
Manipulating dataframes
Indexing a dataframe
Read data from various sources
Concatenate the dataframes
Merge using different join
Manage Duplicates
Knowledge Check
Introduction to Data Visualization
Introduction to Matplotlib
Using Matplotlib for Plotting Graphs

Machine Learning with PythonLive

The module provides the understanding of python programming language, application of python in data science, importance in current world, developing python environment, operations, variables, libraries and modules. Focus will be on to develop the insight in Analytics with Python.

Introduction to Machine Learning
Types of Machine Learning
Machine Learning Modeling Flow
What is Unsupervised Learning?
What is Reinforcement Learning?
Knowledge Check
Understanding Simple Linear Regression
What is Multiple Linear Regression?
Learning about Lasso Regression
Learning about Ridge Regression
Measuring Performance Metrics
Knowledge Check
Hands-on Learning
Implementing Simple Linear Regression
Implementing Multiple Linear Regression
Implementing Lasso Regression
Implementing Ridge Regression
Introduction to Supervised Learning
Supervised Learning- Real-life Scenario
Supervised Learning Flow
Types of Supervisied Algorithms
What is Logistics Regression?
Linear Regression Vs Logistic Regression
Understanding Logistic Regression
What is Decision Tree?
Decision Tree Formation
Overfitting of Decision Trees
Information Gain
Gini Index
Knowledge Check
Understanding K Nearest Neighbour
Learning about Support Vector Machine
Kernel Functions in Support Vector Machine
Math Behind Support Vector Machine
Non-linear Support Vector Machine
Learning about Naive Bayes
Math Behind the Bayes Theorem
Accuracy Metrics
Confusion Matrix
Cost Matrix
Knowledge Check
Hands-on Learning
Implementing Logistic Regression
Implementing Decision Tree
Implementing Knn
Implementing Support Vector Machine
Implementing Naive Bayes
Introduction to Unsupervised Learning
Unsupervised Learning- Real-life Scenario
Unsupervised Learning Flow
Types of Unsupervisied Algorithms
What is Clustering?
Learning about K-means Clustering
Optimal Number of Clusters
Understanding Hierarchical Clustering
Hierarchical Clustering Example
What is PCA Technique?
Accuracy Metrics
Knowledge Check
Hands-on Learning
Implementing K Means Clustering
Implementing Hierarchical Clustering
Implementing Pca Technique
Understand Ensemble Learning
Ensemble Learning - Real-life Scenario
Ensemble Learning Flow
Types of Ensemble Learning Algorithm
Understanding about Random Forest
Math Behind Random Forest
Bagging & Boosting
Learn about Ada Boost
Adaboost Algorithm
Gradient Boosting
Xg Boost
Model Selection
Common Splitting Strategies
Knowledge Check
Hands-on Learning
Implementing Random Forest
Implementing Ada Boost
Implementing Xg Boost
Factor Analysis
Principal Component Analysis (pca)
First Principal Component
Eigenvalues and Pca
Practice: Pca Transformation
Feature Encoding
Feature Scaling
Feature Selection
Outlier Treatment
Knowledge Check
Hands-on Learning
Implementing Pca
Implementing Feature Encoding, Scaling and Selection

Data Science with R Live

This section covers building understanding of R, R environment, types, operations, functions and syntax etc. The advanced section of the module deals with Data manipulation, Cleaning, data framework and its applications.

Setting up R Environment
Data Types with R
Basic Operators and Functions
Basic Syntax
Knowledge Check
Conditional statements
Built in Functions
Use Defined Functions
Knowledge Check
Data Loading
Data Selection
Chaining & Pipeline
Missing Value Handling
Replace Values
Manipulating data
Indexing a dataframe
Concatenate the dataframes
Knowledge Check
Introduction to visualization
Different types of graphs
EDA using R
ggplot2 library
Knowledge Check

AI and Deep Learning Live

It covers the introduction to advanced levels of AI, Deep learning and the concept of tensorflow. The module also covers Machine Learning, real life application of deep learning, advanced concept of neural networks and application of deep learning at different platforms.

Introduction of AI
What is deep learning ?
Machine learning vs deep learning
Real life applications of Deep learning
Human Brain vs Artificial Neural network
Introduction to TensorFlow
Introduction to Keras
Knowledge Check
Introduction to TensorFlow
Tensorflow Hello World
Linear Regression With Tensorflow
Logistic Regression With Tensorflow
Deep Neural Networks
Intoduction to Convolutional Neural Network
CNN Design and Architecture
Neural Style Transfer
Transfer Learning Method
Famous CNN architectures
What is transfer learning?
Knowledge Check
Recurrent Neural Network (RNN)
Architecture of RNN
Backpropagation In RNN
Applications of RNN
Problems With RNN and Why we need LSTM
Long short-term memory (LSTM)
Knowledge Check
Image processing
Natural language processing (nlp)
Genrative Adversarial Neural Networks
Computer Vision
Object Detection
Audio Analysis
Knowledge Check

Big Data with Apache Spark and Python Live

The module covers the introduction of Big data with Hadoop, application and function of Hadoop. It also covers applications like apache spark, spark data frame, ML with MLib, streaming using spark etc.

Introduction to Big Data
How Hadoop Solves the Big Data Problem?
What is Hadoop? Preview
Hadoop’s Key Characteristics
Hadoop Ecosystem and HDFS
Hadoop Core Components
Hadoop Cluster and its Architecture
Hadoop: Different Cluster Modes
Hadoop Terminal Commands
Introduction to Apache Spark
Why Apache Spark
Data-Parallel to Distributed Data-Parallel
Introduction to Pyspark
Setting Spark with Python
Knowledge Check
Introduction to Spark DataFrames
Spark DataFrame Basics
Spark DataFrame Basic Operations
Groupby and Aggregate Operations
Missing Data
Dates and Timestamps
Knowledge Check
Introduction to Machine Learning and ISLR
Machine Learning with Spark and Python with MLlib
Applying Learning Regression using Spark
Linear Regression
Logistic Regression
Decision Tree
Random Forest
K Means Clustering
Using Spark for NLP
Knowledge Check
Understanding Streaming
Streaming Tweets using Spark
Knowledge Check

Working with Tableau Live

This module covers the tableau desktop, tableau public, use interface and data preparation. It also teaches the connecting data source of excel, file and database, work on metadata, data joining, blending, fillers and creation of an informative dashboard with charts and graphs etc.

What is Business Intelligence
What is data visualization Introduction to Tableau
Why use Tableau
Applications of Tableau in industry
Real use cases from various business domains
Tableau desktop vs. Tableau Public
Installing Tableau
Tableau user Interface
Data Preparation
Dimension vs Measures
Discrete vs. Continuous
Application of Discrete and Continuous Fields
Knowledge Check
Connecting to Various Data Source
Connection to Text File
Connection to Excel File
Connection to Database
Knowledge Check
Data types in tableau
Extracts and metadata Management
Rename, Hide, Unhide and Sort Columns
Default Properties of fields
Dealing with NULL values
Knowledge Check
What are Joins?
Relationships vs Joins
Create a Join
Join types
Inner Join
Left Join
Right Join
Full Join
Union Join
Join Clauses
Null Values in Join Keys
Cross-Database Joins
Knowledge Check
What are Blends
Steps for Blending
Understand Primary and Secondary Data Sources
Work Across Blended Data Sources
Define Blend Relationships for Blending
Establish a Link
Multiple Links
Differences between Joins and Blending
Differences between Relationships and Blending
Blending Limitations
Knowledge Check
Marks Card
Constant Sets
Computed sets
Knowledge Check
Types of filters
Dimension Filter
Date Filter
Measure Filter
Visual Filter
Interactive Filter
Data source Filter
Context Filter
Knowledge Check
Creating Charts in Tableau
Bar Chart
Stacked Bar Chart
Line Chart
Scatter Plot
Dual-Axis Charts
Combined-Axis Chart
Funnel Chart
Cross Tabs
Highlight Tables
Knowledge Check
Box and Whisker’s Plot
Bullet Chart
Bar in Bar Chart
Gantt Chart
Waterfall Chart
Pareto Chart
Control Chart
Funnel Chart
Bump Chart
Step and Jump Lines
Word Cloud
Donut Chart
Knowledge Check
LOD expressions and including concept
Expressions Syntax
Aggregation and replication with LOD expressions
Nested LOD expression
Introduction to Dashboards
Building a Dashboard
Dashboard Layouts and Formatting
Interactive Dashboards with actions
Designing Dashboards for devices
Story Points
Knowledge Check
Sharing options in Tableau
Presenting your reports
Printing your reports
Exporting your reports

Data Visualization with Power BI Live

This section gives an overview of the function and application of Power BI. It also explains the benefits, building blocks, architecture of power BI, power BI desktop, query editor, data modeling, DAX, Exploratory data analysis and creating an innovative dashboard.

Introduction to Power BI
Why use Power BI?
Essential Benefits of Power BI
Components of Power BI
Architecture of Power BI
Building Blocks of Power BI
Knowledge Check
Overview of Power BI Desktop
Data Sources in Power BI Desktop
Connecting to a Data Sources
Query Editor
Query Ribbon
Knowledge Check
Clean and Transform your data with Query Editor
Combining Data – Merging and Appending
Cleaning Irregularly Formatted Data
Keeping & Removing Rows
Removing Empty Rows
Appending Queries
Working with Columns
Formatting Data & Handling Formatting Errors
Pivoting & Unpivoting Data
Splitting Columns
Knowledge Check
Modeling Data
Manage Data Relationship
Cardinality: Many-to-One & One-to-One
Cross Filter Direction & Many-to-Many
Knowledge Check
Introduction to M Query
M Query Syntax
Let Expression
Type conversion
Create a query with Query Editor
Simple Power Query M formula steps
Power Query M function
Knowledge Check
Introduction to DAX
Why is DAX important?
DAX Syntax
Data Types
Diving Into Operators
DAX Functions
Filter and evaluation context
Measures in DAX
Tables and Filtering
DAX Queries
Create simple measures
Compund measures
Knowledge Check
Power BI Charts
Tables and Matrices
Map Visualizations
Gauges and Single Number Cards
Modifying colors in charts and visuals
Shapes, text boxes, and images
What Are Custom Visuals?
Page layout and formatting
KPI Visuals
Get detailed
Power View and Power Map
Formatting and customizing visuals
Visualization interaction
Types of Filters
Automatic filters
Manual filters
Include and exclude filters
Drill-down filters
Cross-drill filters
Drillthrough filters
URL filters
Pass-through filters
Knowledge Check
Report Server Basics
Web Portal
Paginated Reports
Data Gateways
Scheduled Refresh
Knowledge Check
Introduction to Dashboards
Building a Dashboard
Dashboard Layouts and Formatting
Interactive Dashboards with actions
Designing Dashboards for devices
Story Points
Knowledge Check

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Self-Paced Courses

NoSql - Mongodb

The module covers the complete understanding of No SQL database and Mongodb Atlas. NoSQL will cover the different aspects of unstructured data storing with more flexibility. Whereas the section of Mongodb covers the ways to store and retrieve data through Mongodb and how it supports huge volumes of both data and traffic.

Types of Databases
Challenges of Rdbms,
Non Relational Database & Its Significance
Benefit Over Rdbms
Non Relational Database and Big Data
Types of Non Relational Database
Introduction to Mongodb
Mongodb Installation
Knowledge Check
Understanding Databases, Collections & Documents
Creating Databases & Collections
Understanding JSON Data
Understanding BSON Data
Json Vs Bson
In Depth knowledge about MongoDB Data Types
Understanding Crud Operations in Mongodb
Finding Elements
Deleting Elements
Updating Elements
Arrays/br> Knowledge Check
What Is Schema?
Document Structure
Concepts of Data Modeling
Why Use Data Model
Types of Data Models
Challenges for Data Modeling in Mongodb
Model Relationships
Model Tree Structures
Model Specific Application Contexts
Knowledge Check
Index Introduction,
Index Concepts
Index Types
Index Properties
Index Creation
Indexing Reference
Introduction to Aggregation
Approach to Aggregation
Types of Aggregation
Aggregation Pipeline
Performance Tuning.
Knowledge Check
Why Replication
How Replication Works in Mongodb
Automatic Failover
How to Setup Replication in Mongodb
Sharded Cluster
Query Router
Hashed Sharding
Ranged Sharding
Knowledge Check
Administration concepts in MongoDB
Monitoring issues related to Database
Monitoring at Server, Database, Collection level
Database Profiling
Memory Usage
No of connections
page fault
Backup and Recovery Methods for MongoDB
Export and Import of Data to and from MongoDB
Run time configuration of MongoDB
Knowledge Check
Understanding Role Based Access Control
Creating a User
Built-in Roles
Assigning Roles to Users & Databases
Updating & Extending Roles to Other Databases
Mongodb Integration With Java
Knowledge Check

Advance Excel

The advance excel makes you updated with next level of MS Excel you know with different function like VLOOKUP, RANDBETWEEN, WORKDAY AND WORKDAY INTL, RELATIVE, ABSOLUTE and MIXED REFERENCE, ,COUNTBLANK, CONVERT Function, RAND Function, HLOOKUP Function, CHOOSE Function, INDEX AND MATCH Functions and Data Entry Form In Excel.

Introduction of MS Excel
Applications of Excel
Industry requirements of Excel
Introduction of Excel Workbook and Worksheet
Working with the Excel environment
Quick Access Toolbar
Customize the Ribbon
Learning Check/Quiz/Assesment
Introduction of Formatting
Importance of Formatting
Alignment Formatting
Number Formatting
Practise Exercise
Types of sorting in Excel
Sort by Numbers,Text
Sort by Date and Time
Sort by cell colour
Sort by font colour
Sort by cell icon
Sort by Custom list
Sort by more than one Column
Practise Exercise
Introduction of Conditional formatting and uses of Highlight Cells Rules
Top/Bottom Rules
Data Bars
Color Scales
Data Icon
Clear Rules
Custom Formatting Rule
Practise Exercise
Introduction to Functions and Formulas
Information functions
Math functions
Statistical functions
Date and time functions
Text functions
Transpose function
Database functions
Engineering functions
Conditoinal Logical functions-if ,nested if
Conditoinal Logical functions-and, or, not with if
Conditoinal Logical functions -sumif, sumifs
3d sum and consolidated sum
Practise Exercise
Number, Date and Time Validation
Text and list validation
Formula based validation
Dropdown list
Input message
Error message
Circle Invalid data
Practise Exercise
What is the LOOKUP Function
How to use the LOOKUP Function in Excel
vlookup with exact match
HLookup with approx match
Hlookup with exact match
Practise Exercise
Index Functions
Match Functions
Index with Match
Practise Exercise
Various methods to protect sheets in MS Excel
Protecting cells
Protect Sheet
Protect Workbook
Protect Ranges
Why Pivot Tables for Data Analysis
How to Create Pivot Table
Navigating the Pivot Table Field List
"Selecting, Clearing, Moving & Copying Pivot Tables"
Exploring Pivot Table Analyze & Design Options
Refreshing & Updating Pivot Table
Practise Exercise
"Sorting Data with Pivot Tables"
Filtering Data with Pivot Table Label & Selection Filter
"Filtering Data with Pivot Table Value Filters"
"Grouping & Segmenting Data with Pivot Tables"
Filtering Data with Pivot Table Slicers & Timelines
"Breaking Out Pivot Table Report Filter Pages"
Practise Exercise
"Aggregating & Summarizing Data with Pivot Tables"
Defining Calculated Fields with Pivot Tables
Practise Exercise
Setting Expectation
Analyzing U.S. Voter Demographics
"Introduction to Excel Charts & Graphs"
Chart Types
Formatting charts
Column Chart
Line Chart
Pie & Donut Charts
Clustered Bar Chart
Stacked Area Chart
Area Chart
"Radar Chart"
Scatter Chart
"Combo Charts"
"Speedometer Chart"
Changing Pivot Chart Types
"Customizing Pivot Chart Layouts & Styles"
Moving Pivot Charts to New Excel Worksheets
Applying Slicers & Timelines to Multiple Pivot Charts
Short Cuts of Excel
What exactly is a dashboard?
Best Practices for Excel Dsahboard Design
Concrete Design Tips for Better Excel Report
Adding table and charts to dashboard
Dynamic contents to dashboard
Adding graphics
Excel KPI Dashboard
Excel Dashboard Live Project
Creating and using Macros
Undestating why and How behind Excel Macros
Recording Macros
Marcro Task Basics
Running a Macro with a Button
Editing Macros
"Saving an Excel File that contains Macros"
Practical Uses of Excel Macros
Introduction to VBA
Excel VBA Concepts
The Visual Basic Editor
Excel VBA Modules
"Creating Excel VBA Procedures"
"Adding Code to a VBA Procedure"
Understanding Excel VBA Variables
Building Logic with an IF Statement
"Understanding Loop"
"Do While Loop"
"Create Function in VBA"
Goal seek - calculate the Value needed to Achieve the Target
"Set up for Data Table."
How to Create a Data Table with Single Input

How to Create a Date Table to get Multiple Results
Double Input Data Table
Learn More of Two Input Data Table
"How to Add different Scenarios"
"How to Use use Scenario Manager"
"How to Create a Summary report with Various scenarios"

Text Analytics with NLP

The module deals with Text Analytics with NLP like R where you learn big data extraction, corpus analysis, analysis of multilingual text data, customized word cloud, Future trends in NLP and CL and many more.

What is Web Scraping?
Origin of Web Scraping
Web Crawling vs Web Scraping
Uses of Web Scraping
Components of a Web Scraper
Working of a Web Scraper
Why Python for Web Scraping?
Important Python Libraries for web scraping
Knowledge Check
Setting up Python Environment for Web Scraping
Python Modules for Web Scraping
Extract data using Beautiful Soup
Full Table Scraping
Row Scraping
Header Scraping
Knowledge Check
Introduction to NLP
Introduction to Text Mining
What is NLP?
Typical NLP Tasks
Natural Language Toolkit (NLTK) Environment
Knowledge Check
Understanding text data
Stop Words
Spell Correction
Normalizing Text
Extracting Features from Text
Bag-of-Words(BoW), TF-IDF
Similarity score - Cosine similarity
Naïve Bayes Classifier
Knowledge Check
Data Type Conversions

Knowledge Check
Introduction to Data Visualization
Trying different Basic Data Visualization
Advance Data Visualization
Taking insights from Data
Knowledge Check
Enroll now to learn this course
Career Services By Fingertips
About Fingertips

Learn and explore under the mentorship of Industry Experts. Hand-on practice on live projects and real case studies to experience the actual industry atmosphere.


Thorough facilitation for personal mentorship, profile building, interview preparation of HR, technical and placements, follow up after successful completion of course.


Support by experienced instructors and program coordinator to resolve the doubts and instinct problems.

Data Science Program Advantage

The Data Science masters program associated with Jain University is designed to provide all-inclusive expertise in the Data Science domain along with the provision of reputable certification.

Collaboration with Jain University

  Widely recognized and valued

  85th NIRF Ranking (2020)

  Officially recognized by the University
     Grants Commission of India

Advanced career learning

   Industry oriented curriculum

  Build the job-specific skills

  Learning with High-Quality content

Success Tales
Frequently Asked Questions
Fingertips Data Science Master program is a 24 weeks intensive certificate Program in collaboration with Jain (Deemed to be University). The Program provides you deep learning of emerging tools and techniques of Data Science like Data Mining, Data Visualization, Data Analysis with tools like MySQL, Tableau, Power BI, Python and R. Learners will be provided hands-on practice on real industry of AI, ML and Business Intelligence.
Fingertips provides both instructor-led online and offline training in it’s Data Science Master Program.
The Program is designed in such a way that any aspirant who wants to make a career in Data Science and willing to learn can enroll in the course. Therefore, there are no prerequisites for enrollment in this course.
The course covers techniques like Data Mining, Data Visualization, Data Analysis, Regression Modeling, Statistics with tools like MySQL, Tableau, Power BI, Python, R, Artificial Intelligence and Machine Learning, Neural Networks, Keras, No SQL, MongoDB, Spark, Hadoop, Flask, LOD Expressions,DAX Expression and many more.
Yes, After completion of the program, our career support team trains you for interviews of HR and Technical and also facilitates placements in corporates.
Yes, We also offer interview preparation for both HR and technical rounds in the Data Science Masters Program.
The Program is designed in such a way that it offers both instructor led and Self Paced learning allowing learners to be trained under guidance of experts also nurturing their skills at their end. In this course, 9 live instructor-led courses and 3 self paced courses are covered. Apart from it, 16000+ other courses are also included to upscale your possibilities more in Data Science.
The duration of the course is for 24 Weeks, although if any candidate wants to extend the course in special circumstances, he/she needs to make prior approval of management.
After completion of course, one can expect their career in positions like Data Scientist, Data Analytics, Data Engineer, Data Science Expert, AI and ML Expert, Business Analytics and Business Forecasting Experts etc.
We try to solve all doubts, queries during the regular classes. Despite this, if any trainee has any doubt he/ she can generate a ticket via email on our support desk where they can discuss their problems with faculties.
a. At the end of the course, the final evaluation will be done by JAIN (Deemed to be University) and FingerTips. After that you will be awarded a completion certificate from Jain University (UGC and NAAC ‘A’ University) b. You will be also awarded the certificates from Fingertips Data Intelligence Solution (Data Science Company) c. Many other certificates from LinkedIn will also facilitated to achieve meanwhile
Fingertips has been training students and professionals for years. All the students/ Professionals who enrolled in our courses have gained a substantial benefit in their career by the completion of the course. Along with our courses, we also provide Hands- on practice on various projects, certification and much more.