If you are planning to build a successful career in Data Science, understanding the Data Science Course fees in Hyderabad is crucial. Hyderabad has emerged as one of the leading hubs for technology and data professionals in India, offering a wide range of Data Science Course options to suit various learning goals and budgets. This guide by Online IT Guru will give you complete clarity on course fees, training features, placement assistance, and what to expect from a comprehensive Data Science learning journey.
Why Choose a Data Science Course in Hyderabad?
Hyderabad is home to top IT companies, startups, and global MNCs that actively hire data scientists and AI specialists. Opting for a Data Science Course in Hyderabad opens doors to:
- Practical exposure through real-time projects.
- Industry-recognized certification.
- Direct placement opportunities with leading companies.
- Access to experienced trainers and mentors.
- Affordable course fees compared to other metro cities.
Data Science Course Fees in Hyderabad: What Factors Affect the Cost?
The Data Science Course fees in Hyderabad can vary significantly depending on multiple factors:
- Training Mode: Live online, self-paced, or classroom training impact pricing.
- Course Duration: Intensive boot camps vs long-term weekend batches.
- Certifications Included: Additional cost for globally recognized certificates like IABAC, NASSCOM, or Microsoft.
- Project & Internship Options: Programs offering live projects and internships may have higher fees.
- Trainer Profile: Courses led by senior data scientists or PhDs typically cost more.
On average, the Data Science Course fees in Hyderabad at Online IT Guru start at ₹16,000 for self-paced learning and can go up to ₹65,000–₹1,10,000 for comprehensive instructor-led programs with certification and placement support.
Online IT Guru Data Science Course Fee Structure
At Online IT Guru, we offer flexible pricing plans to suit different learners:
Self-Paced Learning
- Fee: ₹16,280 (Discounted from ₹18,500)
- Features:
- 60+ hours of video content
- 2 real-world projects
- 18 downloadable resources
- Lifetime access
- 24x7 learner support
- Certificate on completion
Live Online Training
- Fee: ₹31,150 – ₹35,000 (varies by batch timing & schedule)
- Features:
- Interactive sessions with live trainers
- Access to LMS + recorded sessions
- Real-life case studies
- Certification guidance
- Placement support
Corporate / Customized Batches
- Fee: Based on team size and custom requirements
- Features:
- Tailored syllabus for project needs
- Flexible schedule
- Dedicated trainer
- Certification and placement assistance
What’s Included in the Fee?
When you enroll in a Data Science Course at Online IT Guru, the fee includes:
- Complete syllabus coverage (Python, statistics, ML, data visualization, etc.)
- Hands-on projects & assignments
- Access to the cloud lab environment (where applicable)
- Certification guidance
- Job assistance, including resume building & interview preparation
- Lifetime access to learning materials
Syllabus Overview of Our Data Science Course
The curriculum is designed to give you end-to-end expertise:
The field of data science combines statistical knowledge, programming skills, and domain expertise to uncover insights from data. Our course is carefully designed to guide students through every stage — from mastering core concepts to applying them on real-world problems. Let’s explore each module:
Module 1: Introduction to Data Science
This opening module lays the foundation for everything that follows. Here, learners gain:
- A clear understanding of what data science is.
- Insight into how data science fits into modern industries.
- Knowledge about the data science lifecycle, including stages like:
- Defining business problems.
- Collecting data.
- Cleaning and preparing data.
- Building models.
- Communicating results.
You’ll explore real-world applications:
Customer behavior prediction in retail.
Fraud detection in banking.
Disease diagnosis support in healthcare.
This module also introduces key roles — data scientist, data analyst, machine learning engineer, data engineer — and how they collaborate.
Module 2: Python Essentials
Python is the language of choice for data scientists, thanks to its simplicity, readability, and rich ecosystem of libraries. This module ensures that even beginners can build a solid programming foundation.
Topics include:
- Core Python concepts: variables, data types, loops, conditionals, functions.
- Data structures: lists, tuples, dictionaries, sets — essential for organizing data.
- Writing functions to make code reusable and modular.
- Error handling and debugging — critical for writing robust code.
You’ll also work with key libraries:
- NumPy: For numerical operations and handling multi-dimensional arrays.
- Pandas: For data manipulation, cleaning, and exploration.
Practical exercises might include:
- Reading and summarizing data files.
- Cleaning messy datasets.
- Writing scripts to automate data processing.
By the end of this module, you’ll be comfortable using Python for everyday data tasks — a skill highly valued in the industry.
Module 3: Data Import/Export
Data scientists work with data from many sources — files, databases, APIs, and the web. This module teaches you to access and manage data effectively.
Key skills:
- Importing data from:
- CSV, Excel, JSON files.
- SQL databases (e.g., MySQL, PostgreSQL).
- APIs (fetching data from web services).
- Exporting data:
- Saving cleaned and processed data for use in reports or models.
- Writing data back into databases or files for sharing or storage.
You’ll also cover:
- Handling large files without overwhelming your system.
- Data type conversions (e.g., dates, categories).
These skills are essential for building data pipelines — the backbone of any analytics or machine learning project.
Module 4: Data Analysis & Visualization
Once you have data, the next step is to explore and understand it. This module focuses on exploratory data analysis (EDA) and turning data into visual insights.
What you’ll learn:
- Summarizing data using descriptive statistics (mean, median, mode, standard deviation).
- Identifying patterns, trends, and anomalies in datasets.
- Visualizing data using:
- Matplotlib (basic plotting: line, bar, scatter plots).
- Seaborn (advanced statistical plots: heatmaps, pairplots, boxplots).
- Plotly or interactive dashboards (optional in some plans).
Why it matters:
- Visualizations make data more accessible to non-technical stakeholders.
- EDA helps shape hypotheses, refine models, and detect data issues early.
Real-world case studies (e.g., exploring sales data, customer demographics) help you apply these techniques to practical problems.
Module 5: Statistics & Mathematics
Data science is as much about mathematics as it is about code. This module builds your statistical thinking, helping you interpret data and validate models.
Topics include:
- Descriptive statistics: Measures of central tendency and spread.
- Probability: The foundation for uncertainty modeling and prediction.
- Common distributions: Normal, binomial, Poisson — understanding these helps model real-world phenomena.
- Inferential statistics: Drawing conclusions about a population from sample data.
- Hypothesis testing: Designing experiments and determining whether results are significant.
You’ll also learn basics of:
- Correlation analysis (understanding relationships between variables).
- Simple regression models (predicting one variable from another).
By mastering these concepts, you’ll be able to:
Test ideas rigorously.
Avoid false conclusions.
Communicate findings with confidence.
Module 6: Machine Learning Algorithms
Machine learning (ML) powers many modern technologies — recommendation engines, voice recognition, fraud detection. This module teaches you the core techniques.
Supervised learning:
- Regression models: Predicting continuous values (e.g., sales forecasting).
- Classification models: Assigning categories (e.g., spam detection).
Algorithms covered may include:
- Linear regression, logistic regression.
- Decision trees, random forests.
- Support vector machines (SVM).
Unsupervised learning:
- Clustering algorithms: Grouping similar data points (e.g., customer segmentation).
- Dimensionality reduction: Simplifying data for easier analysis and visualization (e.g., Principal Component Analysis - PCA).
Model evaluation:
- Accuracy, precision, recall, F1-score.
- ROC-AUC for classification.
- Cross-validation to assess model stability.
Tuning:
- Hyperparameter optimization (grid search, random search).
Hands-on exercises involve building models, tuning them, and applying them to real-world datasets.
Module 7: Big Data Basics (Optional in Some Plans)
For those enrolled in plans that include Big Data, this module introduces tools and frameworks for handling massive datasets that don’t fit into memory.
Topics include:
- Hadoop ecosystem: Understanding distributed storage (HDFS) and processing (MapReduce).
- Apache Spark: Using Spark for distributed data processing and machine learning.
- Working with large-scale databases: SQL and NoSQL (e.g., MongoDB, Cassandra).
Why Big Data matters:
- Modern data sources (IoT devices, social media, clickstreams) generate terabytes of data daily.
- Big data tools enable analysis of these massive datasets to derive insights that wouldn’t be visible otherwise.
Module 8: Capstone Project
This is where you bring it all together — applying the skills you’ve learned to a complete, end-to-end project.
What you’ll do:
- Define a realistic business problem (e.g., predicting churn, detecting fraud, forecasting demand).
- Collect and clean data from multiple sources.
- Conduct exploratory analysis and generate visual insights.
- Build and validate machine learning models.
- Present your findings through dashboards or reports.
This project simulates working in a real data science team:
Dealing with messy, imperfect data.
Making choices about models and features.
Explaining technical results to non-technical stakeholders.
Employers value capstone projects because they show that you can move beyond theory and apply data science in practice.
How These Modules Build Your Data Science Journey
From beginner to job-ready:
The curriculum starts with basic programming and data handling, progressing through statistical reasoning and machine learning, and ending with big data and applied projects.
Hands-on experience:
Each module includes practical exercises, mini-projects, or case studies. By the end of the course, you’ll have a portfolio of work to showcase to employers.
Alignment with industry roles:
The course prepares you for roles like:
- Data analyst (strong on EDA, visualization, SQL)
- Data scientist (strong on machine learning, statistics)
- ML engineer (strong on modeling, deployment, big data)
Job Assistance After Data Science Course
Your investment in the Data Science Course not only covers learning but also helps you:
- Get 100% placement support.
- Access to job opportunities through 200+ hiring partners globally.
- Participate in mock interviews and resume workshops.
Flexible Payment Options
To make the Data Science Course fees in Hyderabad accessible:
- EMI options are available for eligible learners.
- Fee installments can be arranged for longer-term courses.
- Special discounts for group enrollments, referrals, and early sign-ups.
Why Choose an Online IT Guru for Your Data Science Course?
- Affordable and flexible fee plans.
- Real-time industry projects.
- Certification preparation as per the latest industry requirements.
- Job-oriented training designed for freshers, professionals, and career switchers.
- 24x7 learner support.
Tips to Choose the Best Data Science Course Fees in Hyderabad
- Compare what is included beyond just price: live projects, mentorship, certification.
- Check placement success rate of the institute.
- Look for a transparent fee structure with no hidden costs.
- Prioritize courses with flexible payment and EMI options.
If you are looking for a Data Science Course in Hyderabad that offers great value, industry-ready training, and solid placement support, Online IT Guru is your destination. Our Data Science Course fees in Hyderabad are designed to be affordable while delivering maximum value through live projects, real-time mentoring, and lifetime access to learning resources.
FAQs About Data Science Course Fees in Hyderabad
What is the average Data Science Course fee in Hyderabad?
It ranges from ₹16,000 for self-paced courses to ₹1,10,000 for comprehensive, live-instructor programs with placement support.
Does the course fee include certification?
Yes, Online IT Guru course fees include certification preparation and guidance.
Can I pay the Data Science Course fee in installments?
Yes, installment options and no-cost EMI plans are available.
Are there discounts available on Data Science Course fees?
Yes, group, referral, and early-bird discounts are offered.
Does the fee cover placement assistance?
Yes, placement assistance is included at no extra cost.
Is there any additional fee for live projects?
No, live projects are part of the course fee.
What is the refund policy for the Data Science Course fee?
Refund policies are governed by our standard terms, with details shared during enrollment.
Does fee vary for online vs classroom Data Science Course?
Yes, classroom courses may have higher fees due to infrastructure and on-site facilities.
Can corporate clients get custom fee quotes?
Yes, custom fee quotes are provided for corporate or team training.
Are taxes included in the Data Science Course fee displayed?
The fee displayed is generally inclusive of applicable taxes. Confirm at the time of payment.