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iHUB-IIT-R

Data Science Course in Vadodara

92,536 Ratings

This Data Science course in Vadodara by iHUB, IIT Roorkee (An Innovation Hub of IIT Roorkee) and Intellipaat helps you master Data Scientist skills like Machine Learning, Tableau, , Statistics, etc. Learn from eminent IIT Faculty and industry experts and get certified by iHUB, IIT Roorkee.

Ranked #1 Data Science Course by India TV

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Data Science Course Key Highlights

50+ Live sessions across 7 months
218 Hrs Self-paced Videos
200 Hrs Project & Exercises
Learn from IIT Roorkee Faculty and Industry Practitioners
1:1 with Industry Mentors and 24*7 Support
Resume Preparation and LinkedIn Profile Review
Campus Immersion at IIT Roorkee
No-cost EMI Option
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Data Science Course in Vadodara Overview

What will our experts teach you in this Data Science training?

In this course, our experts will teach you the following concepts:

  • Deploying Machine Learning models on Clouds ( MLOps).
  • Data analysis, project life cycle, and Data Science in the real world.
  • Python with Data Science.
  • Data Science projects, analytics, and recommender systems.
  • Natural Language Processing and its applications.
  • Git, Storytelling.
  • Techniques of evaluation, experimentation, and project deployment.
  • Data visualization with Tableau.
  • Machine Learning algorithms.
  • Microsoft Excel for data analysis and data transformation.
  • Data Science at scale with PySpark, AI with TensorFlow.
  • Analysis segmentation using clustering and the technique of prediction.

Our best Data Science training is helpful for the following professionals and aspirants:

  • Big Data professionals
  • Business Analysts
  • Business Intelligence professionals
  • UG and PG graduates who wish to pursue a career in this domain

There are no eligibility criteria needed to enroll in this Data Science training.

  • The average salary of a Data Scientist in Vadodara is ₹300,000 per annum – PayScale
  • The openings for professionals with Data Science skills will reach nearly 700,000 by 2022 – Forbes
  • There are just over 100 job listings for Data Science professionals in Gujarat – LinkedIn

Here are the roles and responsibilities of Data Scientists, Data Analysts, and Business Analysts in their respective teams:

  • Data Scientists perform statistical analysis and make decisions based on the given data.
  • The role of Data Analysts in a firm is to analyze the business needs and perform the entire life cycle of data analysis. 
  • Business Analysts come up with detailed business analysis, outlining the business problems and their solutions.
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We are happy to help you 24/7

With data collection, ‘the sooner, the better’ is the best answer. - The CEO of Yahoo
Everything is going to be connected with data and mediated by software - The CEO of Microsoft
The world is now awash in data, and we can see consumers more cleanly - The Co-founder of PayPal

Career Transition

55% Average Salary Hike

$1,20,000 The Highest Salary

12000+ Career Transitions

500+ Hiring Partners

Career Transition Handbook

*Past record is no guarantee of future job prospects

Meet the Data Science Mentors

Who can apply for the Data Scientist Training?

  • Information Architects and Statisticians
  • Developers looking to master Machine Learning and Predictive Analytics
  • Big Data, Business Analysis, Business Intelligence, and Software Engineering Professionals
  • Aspirants who are looking to work as Machine Learning Experts, Data Scientists, etc.
  • Anyone who wants to learn machine learning, artificial intelligence, data visualization, data analytics, data structures, and algorithms (DSA).
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Data Scientist

Design and implement scalable codes alongside effectively developing high-quality applications.

Analytics and Insights Analyst

Develop solutions for fixing quality issues in the data upon investigating the reported errors in the data.

AI & ML Engineer

Use Lambda functions and API Gateway to integrate machine learning models into web apps and deploy models in SageMaker.

Data Engineer & Data Analyst

Perform data cleansing, and data transformation, analyze the outcomes, and present the insights in reports and dashboards.

Junior Data Scientist

Analyze the operating behavior using advanced statistical techniques and tools. Also, create algorithms with prescriptive and descriptive methods.

Applied Scientist

Derive intelligence for business products through designing and developing machine learning models.

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Skills Covered

Python

Data Science

Data Analysis

AI

GIT

MLOps

Data Wrangling

SQL

Story Telling

Machine Learning

Prediction algorithms

NLP

PySpark

Model

Data visualization

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Tools Covered

pyspark python jupyter Scipy numpy pandas matplotlib tensorflow SQL tableau excel git SparkSQL
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Data Science Course Fees in Vadodara

Online Classroom Preferred

  • Live Classes from iHUB IIT Roorkee Faculty & Industry Experts
  • Certification from iHUB IIT Roorkee
  • Career Services (Mock Interviews, Resume Preparation)
  • Placement Assistance upon clearing PRT
  • Dedicated Learning Manager
28 Apr

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

$1,229 10% OFF Expires in

Corporate Training

  • Customized Learning
  • Enterprise Grade Learning Management System (LMS)
  • 24x7 Support
  • Enterprise Grade Reporting

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Data Science Course Curriculum

Live Course Self Paced Industry Expert Academic Faculty

Module 1 – Preparatory Session - Linux and Python

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Python 

  • Introduction to Python and IDEs – The basics of the Python programming language, how you can use various IDEs for python development like Jupyter, Pycharm, etc. 
  • Python Basics – Variables, Data Types, Loops, Conditional Statements, functions, decorators, lambda functions, file handling, exception handling ,etc.
  • Object Oriented Programming – Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.
  • Hands-on Sessions And Assignments for Practice – The culmination of all the above concepts with real-world problem statements for better understanding. 

Linux

  • Introduction to Linux  – Establishing the fundamental knowledge of how Linux works and how you can begin with Linux OS. 
  • Linux Basics – File Handling, data extraction, etc.
  • Hands-on Sessions And Assignments for Practice – Strategically curated problem statements for you to start with Linux. 
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Module 2 – Data Wrangling with SQL

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SQL Basics – 

  • Fundamentals of Structured Query Language
  • SQL Tables, Joins, Variables 

Advanced SQL –  

  • SQL Functions, Subqueries, Rules, Views
  • Nested Queries, string functions, pattern matching
  • Mathematical functions, Date-time functions, etc. 

Deep Dive into User Defined Functions

  • Types of UDFs, Inline table value, multi-statement table. 
  • Stored procedures, rank function, SQL ROLLUP, etc.

SQL Optimization and Performance

  • Record grouping, searching, sorting, etc. 
  • Clustered indexes, common table expressions.

Hands-on exercise: 

Writing comparison data between the past year and the present year with respect to top products, ignoring the redundant/junk data, identifying the meaningful data,  and identifying the demand in the future(using complex subqueries, functions, pattern matching concepts).

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Extract Transform Load

  • Interacting with APIs

Data Handling with NumPy

  • NumPy Arrays, CRUD Operations, etc.
  • Linear Algebra – Matrix multiplication, CRUD operations, Inverse, Transpose, Rank, Determinant of a matrix, Scalars, Vectors, Matrices.

Data Manipulation Using Pandas

  • Loading the data, data frames, series, CRUD operations, splitting the data, etc.

Data Preprocessing

  • Exploratory Data Analysis, Feature engineering, Feature scaling, Normalization, standardization, etc.
  • Null Value Imputations, Outliers Analysis and Handling, VIF, Bias-variance trade-off, cross-validation techniques, train-test split, etc.

Data Visualization

  • Bar charts, scatter plots, count plots, line plots, pie charts, donut charts, etc. with Python matplotlib.
  • Regression plots, categorical plots, area plots, etc, with Python seaborn.
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Descriptive Statistics – 

  • Measure of central tendency, the measure of spread, five points summary, etc. 

Probability 

  • Probability Distributions, Bayes’ theorem, central limit theorem. 

Inferential Statistics –  

  • Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.
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Introduction to Machine Learning 

  • Supervised, Unsupervised Learning.
  • Introduction to scikit-learn, Keras, etc.

Regression 

  • Introduction classification problems, Identification of a regression problem, dependent and independent variables.
  • How to train the model in a regression problem.
  • How to evaluate the model for a regression problem.
  • How to optimize the efficiency of the regression model.

Classification 

  • Introduction to classification problems, Identification of a classification problem, and dependent and independent variables.
  • How to train the model in a classification problem.
  • How to evaluate the model for a classification problem.
  • How to optimize the efficiency of the classification model.

Clustering 

  • Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables.
  • How to train the model in a clustering problem.
  • How to evaluate the model for a clustering problem.
  • How to optimize the efficiency of the clustering model.
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  • Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc.
  • Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc.
  • Decision Tree – Creating decision tree models on classification problems in a tree like format with optimal solutions.
  • Random Forest – Creating random forest models for classification problems in a supervised learning approach.
  • Support Vector Machine – SVM or support vector machines for regression and classification problems.
  • Gradient Descent – Gradient descent algorithm that is an iterative optimization approach to finding the local minimum and maximum of a given function.
  • K-Nearest Neighbors – A simple algorithm that can be used for classification problems.
  • Time Series Forecasting – Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting.
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  • K-means – The k-means algorithm that can be used for clustering problems in an unsupervised learning approach.
  • Dimensionality reduction – Handling multi dimensional data and standardizing the features for easier computation.
  • Linear Discriminant Analysis –  LDA or linear discriminant analysis to reduce or optimize the dimensions in the multidimensional data.
  • Principal Component Analysis – PCA follows the same approach in handling the multidimensional data.

Performance Metrics

  • Classification reports – To evaluate the model on various metrics like recall, precision, f-support, etc.
  • Confusion matrix – To evaluate the true positive/negative, and false positive/negative outcomes in the model.
  • r2, adjusted r2, mean squared error, etc.
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Artificial Intelligence Basics 

  • Introduction to keras API and TensorFlow

Neural Networks

  • Neural networks
  • Multi-layered Neural Networks
  • Artificial Neural Networks 

Deep Learning 

  • Introduction to Deep Learning (by Academic Faculty)
  • Deep neural networks
  • Convolutional Neural Networks 
  • Recurrent Neural Networks
  • GPU in deep learning
  • Autoencoders, restricted boltzmann machine 
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The Data Science capstone project focuses on establishing a strong hold of analyzing a problem and coming up with solutions based on insights from the data analysis perspective. The capstone project will help you master the following verticals: 

  • Extracting, loading and transforming data into usable format to gather insights. 
  • Data manipulation and handling to pre-process the data.
  • Feature engineering and scaling the data for various problem statements. 
  • Model selection and model building on various classification, regression problems using supervised/unsupervised machine learning algorithms.
  • Assessment and monitoring of the model created using the machine learning models.
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  • Recommendation Engine – The case study will guide you through various processes and techniques in machine learning to build a recommendation engine that can be used for movie recommendations, restaurant recommendations, book recommendations, etc.
  • Rating Predictions – This text classification and sentiment analysis case study will guide you towards working with text data and building efficient machine learning models that can predict ratings, sentiments, etc.
  • Census – Using predictive modeling techniques on the census data, you will be able to create actionable insights for a given population and create machine learning models that will predict or classify various features like total population, user income, etc.
  • Housing – This real estate case study will guide you towards real world problems, where a culmination of multiple features will guide you towards creating a predictive model to predict housing prices.
  • Object Detection – A much more advanced yet simple case study that will guide you toward making a machine learning model that can detect objects in real-time.
  • Stock Market Analysis – Using historical stock market data, you will learn about how feature engineering and feature selection can provide you with some really helpful and actionable insights for specific stocks.
  • Banking Problem – A classification problem that predicts consumer behavior based on various features using machine learning models.
  • AI Chatbot – Using the NLTK python library, you will be able to apply machine learning algorithms and create an AI chatbot.
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  • LSTM – What is LSTM?, How does LSTM work, Applications of LSTM, etc.
  • Transformers – What are transformers, how does a transformer work in deep learning, applications of transformers, types of transformers, encoder-decoded, self-attention, etc.
  • BERT – Language Models, What is BERT, How does BERT work, how is BERT different from LSTM, applications of BERT, etc.
  • GPT – What are generative pre-trained models (GPT), how does a GPT work?, real life examples of GPT, etc.
  • LLM – NLP and Language models, what are LLMs, how does a LLM work, applications of LLM, etc.
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Elective

Power BI Basics

  • Introduction to PowerBI, Use cases and BI Tools , Data Warehousing, Power BI components, Power BI Desktop, workflows and reports , Data Extraction with Power BI.
  • SaaS Connectors, Working with Azure SQL database, Python and R with Power BI
  • Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data ,M Query and Hierarchies in Power BI.

DAX 

  • Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features

Data Visualization with Analytics  

  • Slicers, filters, Drill Down Reports
  • Power BI Query, Q & A and Data Insights
  • Power BI Settings, Administration and Direct Connectivity
  • Embedded Power BI API and Power BI Mobile
  • Power BI Advance and Power BI Premium

Hands-on Exercise:

Creating a dashboard to depict actionable insights in sales data.

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Introduction to MLOps 

  • MLOps lifecycle
  • MLOps pipeline 
  • MLOps Components, Processes, etc

Deploying Machine Learning Models 

  • Introduction to Azure Machine Learning 
  • Deploying Machine Learning Models using Azure
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Version Control 

  • What is version control, types, SVN.

GIT

  • Git Lifecycle, Common Git commands, Working with branches in Git
  • Github collaboration (pull request), Github Authentication (ssh and Http)
  • Merging branches, Resolving merge conflicts, Git workflow
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  • Job Search Strategy
  • Resume Building
  • Linkedin Profile Creation
  • Interview Preparation Sessions by Industry Experts
  • Mock Interviews
  • Placement opportunities with 400+ hiring partners upon clearing the Placement Readiness Test.
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Excel Fundamentals 

  • Reading the Data, Referencing in formulae , Name Range, Logical Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering 
  • Working with Charts in Excel, Pivot Table, Dashboards, Data And File Security 
  • VBA Macros, Ranges and Worksheet in VBA 
  • IF conditions, loops, Debugging, etc.

Excel For Data Analytics 

  • Handling Text Data, Splitting, combining, data imputation on text data, Working with Dates in Excel, Data Conversion, Handling Missing Values, Data Cleaning, Working with Tables in Excel, etc.  

Data Visualization with Excel

  • Charts, Pie charts, Scatter and bubble charts
  • Bar charts, Column charts, Line charts, Maps
  • Multiples: A set of charts with the same axes, Matrices, Cards, Tiles

Excel Power Tools 

  • Power Pivot, Power Query and Power View

Classification Problems using Excel

  • Binary Classification Problems, Confusion Matrix, AUC and ROC curve 
  • Multiple Classification Problems  

Information Measure in Excel

  • Probability, Entropy, Dependence 
  • Mutual Information 

Regression Problems Using Excel

  • Standardization, Normalization, Probability Distributions 
  • Inferential Statistics, Hypothesis Testing, ANOVA, Covariance, Correlation
  • Linear Regression, Logistic Regression, Error in regression, Information Gain using Regression

Hands-on Exercise:

Classification problem using excel on sales data, and statistical tests on various samples from the population.

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Data Science Course Projects

Career Services

Career Services
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Career Oriented Sessions

Throughout the course

Over 10+ live interactive sessions with an industry expert to gain knowledge and experience on how to build skills that are expected by hiring managers. These will be guided sessions that will help you stay on track with your up upskilling.

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Resume & LinkedIn Profile Building

After 70% of course completion

Get assistance in creating a world-class resume & LinkedIn profile from our career services team and learn how to grab the attention of the hiring manager at the profile shortlisting stage

interview

Mock Interview Preparation

After 80% of the course completion.

Students will go through a number of mock interviews conducted by technical experts who will then offer tips and constructive feedback for reference and improvement.

expert

1 on 1 Career Mentoring Sessions

After 90% of the course completion

Attend one-on-one sessions with career mentors on how to develop the required skills and attitude to secure a dream job based on a learner’s educational background, past experience, and future career aspirations.

guaranteed

Placement Assistance

Upon movement to the Placement Pool

Placement opportunities are provided once the learner is moved to the placement pool upon clearing Placement Readiness Test (PRT)

job_portal

Exclusive access to Intellipaat Job portal

After 80% of the course completion

Exclusive access to our dedicated job portal and apply for jobs. More than 400 hiring partners’ including top start-ups and product companies hiring our learners. Mentored support on job search and relevant jobs for your career growth.

Data Science Certification

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What should I do to unlock Intellipaat’s certificate?

You can unlock Intellipaat’s certificate by following these three simple steps:

  1. Complete the Data Science course in Vadodara successfully
  2. Work on the industry-based projects included in the course
  3. Pass the certification exam conducted by Intellipaat

The Data Science certification you receive from Intellipaat is valid for your entire lifetime, and it is recognized by most of the leading organizations across the world.

When you complete the Data Science course in Vadodara and clear the Data Science certification exam conducted by Intellipaat, you will receive Intellipaat’s Data Science certificate on the Learning Management System (LMS). You can download the certificate or share it through email or LinkedIn.

Yes., This Data Science online certification issued by Intellipaat & iHUB, IIT Roorkee is well-recognized in the industry.

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Data Science Training in Vadodara FAQs

Can Intellipaat provide the Data Science course near me in Vadodara?

For Intellipaat’s courses, geographical boundaries do not apply. It does not matter in which locality of Vadodara you are in, be it Alkapuri, Sama Savl, Vasna Bhayali, Karelibagh, Fatehgunj, Sayajiganj, Manjalpur, Maneja, Akota, Atladara, or anywhere. You can access our online Data Science courses in Vadodara sitting at your home or office.

Intellipaat is the leading provider of Data Science courses in Vadodara. The courses like Artificial Intelligence, Machine Learning, R Certification, Data Science with Python, Python Certification Training, Business Analytics Course and others help you to become job-ready by focussing on practical implementations on real-time live projects.

To learn Data Science for free, you need to take a look at the blogs and videos published by Intellipaat. Read the top blogs on its Interview Questions and Answers, Tutorial and everything to know about Data Scientist.

Yes. At Intellipaat, we offer a Data Science Master’s training course designed by professionals from top organizations around the globe. They will teach you and help you gain proficiency in all the basic and advanced level modules in this domain, including Big Data Analytics with Spark, Python, SQL, Deep Learning methods, R statistical computation, real-time analytics, parsing machine-generated data, etc. Moreover, you will have exclusive access to the IBM Watson Cloud Lab for Chatbots. The training involves 10 courses, 53 real-world industry projects, and 1 CAPSTONE project that will give you hands-on experience.

The courses mentioned below will be covered in this training:

Online instructor-led courses:

Course 1: Data Science with R

Course 2: Python for Data Science

Course 3: Machine Learning

Course 4: Artificial Intelligence and Deep Learning with TensorFlow

Course 5: Big Data Hadoop & Spark

Course 6: Tableau Desktop 10

Course 7: Data Science with SAS

Self-paced courses:

Course 8: Advanced Excel

Course 9: MongoDB

Course 10: MS SQL

Intellipaat is one of the most affordable e-learning providers today. This online instructor-led Data Science course in Vadodara costs ₹85,044.

No Cost EMI options are available. For more a detailed information, contact our team @ IN: +91-7022374614

Intellipaat offers query resolution, and you can raise a ticket with the dedicated support team at any time. You can avail yourself of email support for all your queries. We can also arrange one-on-one sessions with our support team If your query does not get resolved through email. However, 1:1 session support is given for 6 months from the start date of your course.

Intellipaat provides placement assistance to all learners who have completed the training and moved to the placement pool after clearing the PRT (Placement Readiness Test). More than 500+ top MNCs and startups hire Intellipaat learners. Our alumni work with Google, Microsoft, Amazon, Sony, Ericsson, TCS, Mu Sigma, etc.

Apparently, no. Our job assistance is aimed at helping you land your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final hiring decision will always be based on your performance in the interview and the requirements of the recruiter.

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