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Next cohort Starts

7th Jan, 2025

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Program duration

11 months

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Learning Format

Live, Online, Interactive

Why Choose Our Data Science Course in Noida?

Noida is a hub for technology and innovation, offering numerous opportunities for aspiring data scientists. Our Data Science course in Noida is designed to equip you with the skills and knowledge required to excel in this high-demand field. From Python programming to machine learning, our course covers everything you need to succeed in the data-driven world.

Key Highlights of this Data Scientist Course at IISTP

This comprehensive program ensures you're job-ready and confident to tackle the challenges of the data science world.

What You’ll Learn in Our Data Science Course

In today’s data-driven world, mastering data science is not just a skill—it’s a necessity for professionals looking to stay ahead. Our comprehensive course is carefully designed to help you build a solid foundation in data science while preparing you to solve real-world problems. Here's a detailed breakdown of the modules you'll explore:

  • Python Programming
  • Data Wrangling and Visualization
  • Machine Learning Fundamentals
  • Deep Learning and AI
  • Big Data Analytics
  • Capstone Project

Why Choose Our Data Science Course in Noida?

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Access Multiple Tools

Learn to analyze data effectively using diverse advanced tools.

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Live Session

Interactive live sessions with expert guidance and real-time queries.

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Multiple Projects

Hands-on experience with multiple real-world data science projects.

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Comprehensive Curriculum

Extensive syllabus covering core and advanced Data Science topics.

Course Duration

Duration: 11 months (weekend and weekday batches available)

Fees: ₹60,000 (inclusive of all taxes)

Scholarships: Up to 20% for early enrollments

Small batches with only 8-10 students for better attention. Regular assessments to track your progress.

Agile Activity

Next cohort starts on 7th Jan

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Data Science Certification Advantage

IISTP offers a Data Science Certification Course in Noida and training in data analysis, machine learning, and big data tools. Designed for beginners and professionals, this course equips you with practical skills in Python, R, SQL, and data visualization. Gain hands-on experience with real-world projects and enhance your career prospects in the rapidly growing data science industry. Learn from expert instructors and become a certified data science professional in Noida.

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Simplilearn Certificate

Earn Your Data Scientist Certificate

  • Check Industry-recognized certificate by IISTP
  • Check Dedicated live sessions by industry experts
  • Check Lifetime access to self-paced learning content
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IBM Certificate

Get Ahead with IBM Advantage

  • Check Content and certificate by NASSCOM & ISO
  • Check Masterclasses by Industry experts
  • Check Interactive Learning Opportunities

Data Science Course Curriculum Overview

What You’ll Learn in Our Data Science Course

1. Python for Data Science Python Basics
2. Data Science Primer and Statistics Python for DS
3. Machine Learning Python for DS
4. Deep Learning Python for DS
5. Data Visualization and Story Telling Python for DS
6. Natural Language Processing Python for DS
7. SQL Python for DS
8. Excel Python for DS
9. Tableau Python for DS
10. Power BI Python for DS
  • Need for Programming
  • Advantages of Programming
  • Overview of Python
  • Organizations using Python
  • Python Applications in Various Domains
  • Python Installation
  • Variables
  • Operands and Expressions
  • Conditional Statements
  • Method of Accepting User Input and eval Function
  • Python - Files Input/Output Functions
  • Lists and Related Operations
  • Tuples and Related Operations
  • Strings and Related Operations
  • Sets and Related Operations
  • Dictionaries and Related Operations
  • User-Defined Functions
  • Concept of Return Statement
  • Concept of name = "main"
  • Function Parameters
  • Different Types of Arguments
  • Global Variables
  • Global Keyword
  • Variable Scope and Returning Values
  • Lambda Functions
  • Various Built-In Functions
  • Introduction to Object-Oriented Concepts
  • Built-In Class Attributes
  • Public, Protected and Private Attributes, and Methods
  • Class Variable and Instance Variable
  • Constructor and Destructor
  • Decorator in Python
  • Core Object-Oriented Principles
  • Inheritance and Its Types
  • Method Resolution Order
  • Overloading
  • Overriding
  • Getter and Setter Methods
  • Inheritance-In-Class Case Study
  • Standard Libraries
  • Packages and Import Statements
  • Topics: Working with Modules and Handling Exceptions
  • Reload Function
  • Important Modules in Python
  • Sys Module
  • Os Module
  • Math Module
  • Date-Time Module
  • Random Module
  • JSON Module
  • Regular Expression
  • Exception Handling
  • Basics of Data Analysis
  • NumPy - Arrays
  • Operations on Arrays
  • Indexing, Slicing, and Iterating
  • NumPy Array Attributes
  • Matrix Product
  • NumPy Functions
  • Functions
  • Array Manipulation
  • File Handling Using NumPy
  • Array Creation and Logic Functions
  • File Handling Using NumPy
  • Introduction to pandas
  • Data Structures in pandas
  • Series
  • Data Frames
  • Importing and Exporting Files in Python
  • Basic Functionalities of a Data Object
  • Merging of Data Objects
  • Concatenation of Data Objects
  • Types of Joins on Data Objects
  • Data Cleaning using pandas
  • Exploring Datasets
  • What is Data Science?
  • What does Data Science involve?
  • Era of Data Science
  • Business Intelligence vs Data Science
  • Life cycle of Data Science
  • Tools of Data Science
  • Application of Data Science
  • Introduction
  • Stages of Analytics
  • CRISP DM Data Life Cycle
  • Data Types
  • Introduction to EDA
  • First Business Moment Decision
  • Second Business Moment Decision
  • Third Business Moment Decision
  • Fourth Business Moment Decision
  • Correlation
  • What is Feature
  • Feature Engineering
  • Feature Engineering Process
  • Benefit
  • Feature Engineering Techniques
  • Basics Of Probability
  • Discrete Probability Distributions
  • Continuous Probability Distributions
  • Central Limit Theorem
  • Concepts Of Hypothesis Testing - I: Null And Alternate Hypothesis, Making A Decision, And Critical Value Method
  • Concepts Of Hypothesis Testing - II: P-Value Method And Types Of Errors
  • Industry Demonstration Of Hypothesis Testing: Two-Sample Mean And Proportion Test, A/B Testing
  • Simple Linear Regression
  • Simple Linear Regression In Python
  • Multiple Linear Regression
  • Multiple Linear Regression In Python
  • Industry Relevance Of Linear Regression
  • Univariate Logistic Regression
  • Multivariate Logistic Regression: Model Building And Evaluation
  • Logistic Regression: Industry Applications
  • Data mining classifier technique
  • Application of KNN classifier
  • Lazy learner classifier
  • Altering hyperparameter(k) for better accuracy
  • Black box
  • SVM hyperplane
  • Max margin hyperplane
  • Kernel tricks for non-linear spaces
  • Rule based classification method
  • Different nodes for developing decision trees
  • Discretization
  • Entropy
  • Greedy approach
  • Information gain
  • Challenges with standalone model
  • Reliability and performance of a standalone model
  • Homogeneous & Heterogeneous Ensemble Technique
  • Bagging & Boosting
  • Random forest
  • Stacking
  • Voting & Averaging technique
  • Difference between cross-sectional and time series data
  • Different components of time series data
  • Visualization techniques for time series data
  • Model-based approach
  • Data-driven based approach
  • Difference between Supervised and Unsupervised Learning
  • Prelims of clustering
  • Measuring distance between records and groups
  • Linkage functions
  • Dendrogram
  • Dimension reduction
  • Application of PCA
  • PCA & its working
  • SVD & its working
  • Point of Sale
  • Application of Association rules
  • Measure of association rules
  • Drawback of measure of association rules
  • Conditional probability
  • Lift ratio
  • Black box techniques
  • Intuition of neural networks
  • Perceptron algorithm
  • Calculation of new weights
  • Non-linear boundaries in MLP
  • Integration function
  • Activation function
  • Error surface
  • Gradient descent algorithm
  • Imagenet classification challenges
  • Convolution network applications
  • Challenges in classifying the images using MLP
  • Parameter explosion
  • Pooling layers
  • Fully connected layers
  • Alexnet case study
  • Modelling sequence data
  • Vanishing/Gradient descent explosion
  • What is a Deep Learning Platform?
  • H2O.ai
  • Dato GraphLab
  • What is a Deep Learning Library?
  • Theano
  • Deeplearning4j
  • Torch
  • Caffe
  • Bar Charts
  • Histograms
  • Pie Charts
  • Box Plots
  • Scatter Plots
  • Line Plots and Regression
  • Pair Plot
  • Word Clouds
  • Radar Charts
  • Waffle Charts
  • Text data generating sources
  • How to give structure to text data using bag of words
  • Terminology used in text data analysis
  • DTM & TDM
  • TFIDF & its usage
  • Word cloud and its interpretation
  • Introduction to Databases
  • How to create a Database instance on Cloud?
  • Provision a Cloud hosted Database instance.
  • What is SQL?
  • Thinking About Your Data
  • Relational vs. Transactional Models ER Diagram
  • CREATE Table Statement and DROP tables
  • UPDATE and DELETE Statements
  • Retrieving Data with a SELECT Statement
  • Creating Temporary Tables
  • Adding Comments to SQL
  • Basics of Filtering with SQL
  • Advanced Filtering: IN, OR, and NOT
  • Using Wildcards in SQL
  • Sorting with ORDER BY
  • Math Operations
  • Aggregate Functions
  • Grouping Data with SQL
  • Using Subqueries
  • Subquery Best Practices and Considerations
  • Joining Tables
  • Cartesian (Cross) Joins
  • Inner Joins
  • Aliases and Self Joins
  • Advanced Joins: Left, Right, and Full Outer Joins
  • Unions
  • Working with Text Strings
  • Working with Date and Time Strings
  • Date and Time Strings Examples
  • Case Statements
  • Views
  • Data Governance and Profiling
  • Using SQL for Data Science
  • How to access databases using Python?
  • Writing code using DB-API
  • Connecting to a database using DB API
  • Create Database Credentials
  • Connecting to a database instance
  • Creating tables, loading, inserting data and querying data
  • Analyzing data with Python
  • Input data & handling large spreadsheets
  • Tricks to get your work done faster
  • Automating data analysis (Excel VLOOKUP, IF Function, ROUND, and more)
  • Transforming messy data into shape
  • Cleaning, Processing, and Organizing large data
  • Spreadsheet design principles
  • Drop-down lists in Excel and adding data validation to cells
  • Creating Charts & Interactive reports with Excel Pivot Tables, PivotCharts, Slicers, and Timelines
  • Functions like: COUNTIFS, COUNT, SUMIFS, AVERAGE, and many more
  • Excel features: Sort, Filter, Search & Replace, Go to Special, etc.
  • Importing and Transforming data (with Power Query)
  • Customize the Microsoft Excel interface
  • Formatting correctly for professional reports
  • Commenting on cells
  • Automating data entry with Autofill and Flash-fill
  • Writing Excel formulas & referencing other workbooks/worksheets
  • Printing options
  • Charts beyond column and bar charts: Pareto chart, Histogram, Treemap, Sunburst, and more
  • Introduction to Data Visualization
  • Tableau Introduction and Tableau Architecture
  • Exploring Data using Tableau
  • Working with Data using Tableau, including Data Extraction and Blending
  • Various Charts in Tableau (Basics to Advanced)
  • Sorting: Quick Sort, Sort from Axis, Legends, Axis, Sort by Fields
  • Filtering: Dimension Filters, Measure Filters, Date Filters, Tableau Context Filters
  • Groups, Sets, and Combined Sets
  • Reference Lines, Bands, and Distribution
  • Parameters, Dynamic Parameters, and Actions
  • Forecasting: Exponential Smoothing Techniques
  • Clustering
  • Calculated Fields in Tableau, Quick Tables
  • Tableau Mapping Features
  • Tableau Dashboards, Dashboard Actions, and Stories
  • Introduction to Power BI – Need, Importance
  • Power BI – Advantages and Scalable Options
  • Power BI Data Source Library and DW Files
  • Business Analyst Tools, MS Cloud Tools
  • Power BI Installation
  • Power BI Desktop – Installation, Usage
  • Sample Reports and Visualization Controls
  • Understanding Desktop & Mobile Editions
  • Report Rendering Options and End User Access
  • Report Design with Database Tables
  • Report Visuals, Fields, and UI Options
  • Reports with Multiple Pages and Advantages
  • Pages with Multiple Visualizations. Data Access
  • “GET DATA” Options and Report Fields, Filters
  • Report View Options: Full, Fit Page, Width Scale
  • Report Design using Databases & Queries

Tools and Technologies You'll Master

Data Science Job Opportunities in Noida

Noida is home to numerous IT companies, startups, and multinational corporations, making it an ideal location to start your career in Data Science. According to recent reports:

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10,000+ job openings

Data Science and related fields in Noida.

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Top Companies

TCS, Accenture, IBM, and Infosys are actively hiring data scientists.

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Average starting salary:

₹6-8 LPA for freshers and ₹15+ LPA for experienced professionals.

What Makes Us the Best Data Science Institute in Noida?

Stock Price Prediction using Machine Learning

Predict future stock prices using historical data and machine learning algorithms

  • ✅ Use Python and machine learning libraries like Scikit-learn
  • ✅ Work with real-world stock data and clean it
  • ✅ Implement linear regression, decision trees, and time-series analysis
  • ✅ Train models to predict future stock movements

In Collaboration with

Finance Expert
Tools Used
Python Scikit-learn Pandas

Stock Price Prediction using Machine Learning

Predict future stock prices using historical data and machine learning algorithms

  • ✅ Use Python and machine learning libraries like Scikit-learn
  • ✅ Work with real-world stock data and clean it
  • ✅ Implement linear regression, decision trees, and time-series analysis
  • ✅ Train models to predict future stock movements

In Collaboration with

Finance Expert
Tools Used
Python Scikit-learn Pandas

Stock Price Prediction using Machine Learning

Predict future stock prices using historical data and machine learning algorithms

  • ✅ Use Python and machine learning libraries like Scikit-learn
  • ✅ Work with real-world stock data and clean it
  • ✅ Implement linear regression, decision trees, and time-series analysis
  • ✅ Train models to predict future stock movements

In Collaboration with

Finance Expert
Tools Used
Python Scikit-learn Pandas

Stock Price Prediction using Machine Learning

Predict future stock prices using historical data and machine learning algorithms

  • ✅ Use Python and machine learning libraries like Scikit-learn
  • ✅ Work with real-world stock data and clean it
  • ✅ Implement linear regression, decision trees, and time-series analysis
  • ✅ Train models to predict future stock movements

In Collaboration with

Finance Expert
Tools Used
Python Scikit-learn Pandas

Stock Price Prediction using Machine Learning

Predict future stock prices using historical data and machine learning algorithms

  • ✅ Use Python and machine learning libraries like Scikit-learn
  • ✅ Work with real-world stock data and clean it
  • ✅ Implement linear regression, decision trees, and time-series analysis
  • ✅ Train models to predict future stock movements

In Collaboration with

Finance Expert
Tools Used
Python Scikit-learn Pandas

Stock Price Prediction using Machine Learning

Predict future stock prices using historical data and machine learning algorithms

  • ✅ Use Python and machine learning libraries like Scikit-learn
  • ✅ Work with real-world stock data and clean it
  • ✅ Implement linear regression, decision trees, and time-series analysis
  • ✅ Train models to predict future stock movements

In Collaboration with

Finance Expert
Tools Used
Python Scikit-learn Pandas

Build a Career Plan with our Data Science Training in Noida

Fast-track your career with our comprehensive Data Science Course. Gain industry-relevant skills, hands-on experience, and expert guidance to excel in data analytics, machine learning, and AI. Unlock high-paying opportunities and achieve professional growth faster.

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150%

Maximum salary hike

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70%

Average salary hike

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2900+

Hiring partners

Our Alumni In Top Companies

Amazon Amity JustDial IBM Adobe Walmart Deloitte BlooHash Nasscomm AppInventiv

What Our Learners Have To Say

How to Select the Best Specialization for Your Data Science Course in Noida

Choosing the right specialization for your Data Science Course in Noida is crucial for shaping your career in this high-demand field. With numerous options and career paths available, it's important to make an informed decision that aligns with your interests and goals. Here are some key factors to consider:

1. Understand the Scope of Data Science Specializations

The field of data science offers a wide range of specializations such as:

  • Machine Learning and Artificial Intelligence: Ideal for those interested in building smart systems and predictive models.
  • Data Analytics: Focuses on interpreting and analyzing data to support business decisions.
  • Big Data Engineering: Involves working with large datasets and managing data pipelines.
  • Business Intelligence: Combines data analysis with business strategy for decision-making.

Research each specialization to understand the job roles, skills required, and industry demand.

2. Assess Your Career Goals

Are you aiming for a technical role, such as a Data Scientist or Machine Learning Engineer, or do you prefer analytical roles like Data Analyst or Business Intelligence Specialist? Your career aspirations should guide your choice of specialization during your Data Science Certification in Noida.

3. Evaluate Your Skills and Interests

Consider your existing skills and interests:

  • Do you enjoy coding and algorithms? Opt for Machine Learning or Big Data.
  • Are you good at visualizing data and drawing insights? Business Intelligence might be the right fit.
  • Do you have a knack for statistical analysis? Data Analytics could be your specialization.

4. Explore the Curriculum of the Data Science Institute in Noida

Different institutes offer varied curricula. Check if the Data Science Training in Noida you are considering provides:

  • Comprehensive coverage: Foundational and advanced topics.
  • Hands-on projects: Industry-relevant case studies.
  • Support: Access to mentors and placement assistance.

5. Stay Updated with Market Trends

The data science field evolves rapidly. Specializations like Deep Learning, Natural Language Processing (NLP), and Cloud-based Data Science are gaining traction. Ensure your chosen Data Science Certification in Noida includes emerging trends.

6. Seek Guidance

Talk to industry professionals, alumni of reputed Data Science Institutes in Noida, and career counselors. Their insights can help you make a well-informed decision.

7. Focus on Certification and Recognition

A recognized certification from a reputed Data Science Institute in Noida can significantly enhance your credibility and job prospects. Verify the accreditation and industry acceptance of the course.

The right specialization can open doors to exciting opportunities in data science. By considering your goals, skills, and market demand, you can make a choice that propels your career forward. If you're looking for expert guidance and industry-recognized Data Science Training in Noida, explore our courses today and take the first step toward a successful data science career!

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Career Services

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assistance Placement Assistance
job-portal Exclusive access to intellipat job portal
mock-interview Mock Interview Preparation
career-session One-on-One Career Mentoring Sessions
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resume-profile Resume & LinkedIn Profile Building

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Program Benefits

  • Masterclasses delivered by Purdue faculty and staff and IBM
  • Data Science and GenAI program completion certificate
  • Exposure to ChatGPT, GenAI, prompt engineering, and more
  • Alumni Association Membership from Purdue University
  • Simplilearn’s Career Assistance post program completion

F.A.Q

1. What is the eligibility for this course?

Graduates in any discipline with basic programming knowledge can apply. Even working professionals looking to switch careers can join.

3. Is this course suitable for beginners?

Absolutely! Our Data Science course in Noida starts with the basics and gradually moves to advanced topics, making it ideal for beginners and professionals alike.

5. Do you offer placement assistance?

Yes, we at IISTP provide 100% placement support, including resume building, mock interviews, and job referrals to top companies.

7. What is the mode of training?

We offer both online and classroom training. You can choose whichever suits your schedule best.

9. What tools and technologies will I learn in this course?

You’ll gain hands-on experience with tools like Python, R, SQL, Tableau, Power BI, and machine learning frameworks like TensorFlow and Scikit-learn.

11. What are the class timings?

We offer flexible batch timings, including weekday and weekend options, to cater to both students and working professionals.

13. Will I get a certificate after completing the course?

Yes, you will receive an industry-recognized certificate upon successfully completing the course.

15. What kind of projects will I work on?

You will work on real-world projects like predictive analytics, recommendation systems, sentiment analysis, and more, using actual datasets.

17. Is there a demo class available?

Yes, we offer a free demo class to help you understand the course structure and teaching methodology.

19. Are there any scholarships or discounts available?

Yes, we offer scholarships and early-bird discounts of up to 20% for eligible candidates.

2. What career roles can I expect after completing this course?

You can pursue roles such as Data Scientist, Data Analyst, Machine Learning Engineer, Business Analyst, or AI Specialist.

4. Do I need prior programming knowledge to join?

Basic programming knowledge is helpful but not mandatory. We provide foundational programming sessions as part of the course.

6. How can I enroll in the course?

You can enroll by filling out the online registration form or visiting our Noida center. For assistance, contact our support team.

8. What happens if I miss a class?

All sessions are recorded, and you’ll have access to the recordings, so you can revisit any missed classes.

10. Can I switch batches if needed?

Yes, batch switching is allowed based on availability and your convenience.

12. Is this course aligned with industry standards?

Absolutely! Our curriculum is updated regularly to align with the latest industry trends and demands.

14. What is the average salary of a Data Scientist in Noida?

The average starting salary for a Data Scientist in Noida is ₹6-8 LPA, with experienced professionals earning ₹15+ LPA.

16. Can I pay the fees in installments?

Yes, we offer EMI options to make it easier for you to afford the course.

18 How do I know if Data Science is the right career for me?

If you enjoy solving problems, working with data, and learning new technologies, Data Science could be a perfect fit. Our counselors can help you decide during a free consultation.

20. Will I receive career guidance during the course?

Yes, we provide career counseling, resume preparation, mock interviews, and placement assistance to ensure you are job-ready.