DATA SCIENCE
COURSE IN HYDERABAD
Become a Job-Ready Data Scientist
Build real-world analytics and ML projects, create a strong data science portfolio, prepare for technical interviews, and get career & placement assistance from AIDEAS Academy.
Get the complete syllabus + batch details + fee information
Our Alumini Work At
Why Learn Data Science in 2026?
Don't Learn Just Python. Learn How to Turn Data Into Decisions.
Knowing Python syntax is only the beginning.
Companies need analysts and data scientists who can clean data, build models, explain results and present insights with SQL, statistics, machine learning and Power BI.
Our Data Science program takes you from Python fundamentals to building and presenting production-style analytics and ML projects.
Data Wrangling + Statistical Modeling + Predictive Machine Learning + Executive Visual Storytelling.
Complete Data Science Stack
Python
Build a strong programming foundation for analytics.
SQL
Extract, join and analyze data from real databases.
Statistics
Learn inference, probability and hypothesis testing from scratch.
Machine Learning
Train classification, regression and ensemble models.
Deep Learning
Build CNN and sequence models for AI workflows.
Power BI
Turn analysis into executive-ready dashboards.
Pandas & NumPy
Clean, transform and explore datasets at scale.
Model Deployment
Package notebooks, models and dashboards for real use.
Want to See the Complete Roadmap?
Get the Data Science Syllabus
The Data Science Curriculum
- Introduction to SDLC and its importance in software development
- Phases of SDLC:
- Requirement gathering and analysis
- System design
- Development
- Testing
- Deployment
- Common SDLC models:
- Waterfall model
- Agile methodology
- Scrum framework overview
- Waterfall methodology overview
- Agile principles and values
- Scrum roles, ceremonies and artifacts
- Kanban basics
- Business Analyst and requirement analysis
- UI/UX Designer responsibilities
- Frontend Developer role
- Backend Developer role
- Full Stack Developer role
- QA / Software Tester role
- DevOps Engineer role
- Project Manager / Scrum Master role
- Product Owner role
- What is Data Science
- Why Data Science
- Impact of Data Science
- Future of Data Science
- Data Science Life Cycle
- Introduction to Pre-Core Python
- Introduction to Jupyter Notebook
- Overview of Data Science Real-Time IDEs
- Introduction to Google Co-laboratory Notebook
- Introduction to the UNIX Operating System
- Python introduction and environment setup
- Software required to learn Python
- Variables (identifiers)
- Data types in Python (Boolean, Complex, Integer)
- Python collections
- Python type casting
- Python control statements
- Python data structures: Lists and Arrays
- Python conditions and branching
- Python functions and methods
- Exceptions and file handling
- Python OOP concepts and advanced coding
- PDBC and database communication
- Practice questions and reviews
- Live application implementation
- NumPy
- Pandas
- Matplotlib
- Seaborn
- TensorFlow
- SciPy
- Pillow
- Data visualization concepts
- Basic plotting for data visualization
- Data manipulation for visualization
- One-dimensional data analysis
- Histograms
- Boxplots
- Violin plots
- Introduction to Power BI
- Data extraction process
- Data transformations
- Data modeling and DAX
- Data visualization with analytics
- Basics of statistics
- Types of statistics
- Population and sample
- Measures of central tendency
- Percentiles and dispersion
- Statistics implementation with Python
- Range, sample variance and standard deviation
- Correlation and causation
- Hypothesis testing
- Parametric and non-parametric tests
- What is probability
- Importance of probability in machine learning
- Basics of probability
- Random variables
- Probability distributions
- Maximum likelihood estimation
- Bayes theorem
- Information theory
- Cross entropy
- Information gain
- Scalars and vectors
- Vector addition and subtraction
- Scalar multiplication of vectors
- Dot product of vectors
- Cross product of vectors
- Scalars, vectors and matrices
- Types of matrices
- Transpose of a matrix
- Matrix addition and subtraction
- Eigenvalues and eigenvectors
- Introduction to data structures
- Big O notation and time complexity
- Arrays in Python and Big O analysis
- Static vs dynamic arrays
- Linked lists and doubly linked lists
- Hash tables and hash maps
- Collision handling and chaining
- Stack implementation
- Queue implementation
- Trees and binary trees
- Binary search trees
- Graph basics
- Linear search and binary search
- Sorting algorithms
- Recursion in Python
- Introduction to regression
- Simple linear regression
- Multiple linear regression
- Linear regression assumptions
- Regularization techniques
- Lasso regression
- Ridge regression
- HPolynomial regression
- Stepwise regression
- Elastic net regression
- R-squared and adjusted R-squared
- Introduction to classification
- Types of classifiers
- Linear classifiers
- Logistic regression
- Multinomial logistic regression
- Non-linear classifiers
- Decision trees
- CART algorithm
- ID3 algorithm
- Multi-class SVM
- Naive Bayes
- Neural networks for classification
- Perceptron algorithm
- Multilayer perceptron
- Backpropagation algorithm
- Activation functions
- Confusion matrix
- Feature selection techniques
- Hyperparameter tuning techniques
- Model selection techniques
- Bias-variance tradeoff
- Cross-validation
- Leave-one-out cross validation
- Ensemble methods
- Bagging algorithms
- Boosting algorithms
- XGBoost
- Gradient Boosting
- AdaBoost
- Stacking technique
- Blending technique
- Introduction to clustering
- K-means clustering
- Hierarchical clustering
- Dimensionality reduction concepts
- Principal component analysis (PCA)
- Singular value decomposition (SVD)
- t-SNE
- Linear discriminant analysis (LDA)
- Truncated SVD
- Descriptive statistics
- What is machine learning
- Types of machine learning
- Supervised learning
- Unsupervised learning
- Applications of machine learning
- Types of data: continuous and categorical
- Data exploration and visualization
- Inferential statistics
- Data distributions
- Correlation and covariance
- Introduction to deep learning
- Difference between machine learning and deep learning
- Biological neural networks
- Deep learning applications
- Artificial neural networks
- Convolutional neural networks
- Recurrent neural networks
- CNN and computer vision
- Image preprocessing with OpenCV
- CNN architecture
- Image classification case study
- Transfer learning case study
- Introduction to text processing
- Text preprocessing using NLTK and SpaCy
- Vectorization techniques
- Text classification project
- Recurrent neural networks
- Sequence tagging project
- LSTMs
- Autoencoders
Don't Just Watch Classes. Build Models.
Build Projects That You Can Actually Show in an Interview.
Your portfolio should demonstrate what you can build — not just what you studied.
Fraud Detection Engine
Build a classification pipeline to detect anomalous transactions with feature engineering and threshold tuning.
- Feature engineering for transaction risk signals
- Classification models with precision-recall tuning
- Threshold selection for business risk tolerance
- Model evaluation reports you can present in interviews
Demand Forecasting Studio
Build a time-series forecasting workflow for sales planning using model comparison and backtesting.
- Time-series cleanup and seasonality analysis
- Forecast model comparison with backtesting
- Business-ready demand planning visuals
- Explain forecast error in stakeholder language
Customer Segmentation Lab
Build a clustering-based segmentation system for marketing personalization with interpretable business cohorts.
- Exploratory analysis of customer behaviour features
- K-Means clustering with cluster quality checks
- Interpretable marketing cohorts with Seaborn visuals
- Persona summaries that business teams can use
Build Your Own Production-Style Data Science Portfolio
Take everything you learned and build a complete project from: Requirement → EDA → Modeling → Evaluation → Dashboard → Deployment
YOUR FINAL PORTFOLIO CAN INCLUDE:
What You Get Beyond Technical Training
We Don't Stop at the Last Class.
Learning to code is only one part of becoming interview-ready. Your career preparation includes:
01 — Resume Preparation
01 — Resume Preparation
Create a developer-focused resume highlighting your analytics skills and projects.
Why AIDEAS Academy?
Learn. Build. Prepare. Get Career Support.
Designed from the ground up for career switchers, fresh graduates, and ambitious analysts seeking practical, production-grade data skills.
Industry-Oriented Curriculum
Learn technologies and workflows used in modern analytics and ML teams.
Hands-On Learning
Apply concepts by building models and dashboards instead of only watching demonstrations.
Live + Recorded Learning
Attend live sessions and use recordings for revision.
Beginner-Friendly Approach
Start from Python fundamentals and progress toward advanced analytics and ML.
Real-World Projects
Build portfolio-oriented projects based on practical analytics scenarios.
Career Preparation
Resume guidance, mock interviews, technical preparation and placement assistance.
Expert Guidance
Learn with trainers and mentors focused on practical data science skills.
A Complete Data Science Stack
Work with industry-standard libraries, statistical methods, ML frameworks and visualization tools.
PYTHON & EDA
MACHINE LEARNING
DEEP LEARNING
STATISTICS
VISUALIZATION
SQL & TOOLS
What Can You Do After Learning Data Science?
Explore Multiple Analytics Career Paths
Your Data Science skills can prepare you for roles such as:
The actual role and compensation you qualify for will depend on your skills, projects, experience, interview performance and the employer.
Is This Course Right For You?
You Can Start Even If You Are Starting From Zero.
Suitable For
You Don't Need
We begin with absolute basics — command line, variables, conditions, and step-by-step logic.
You Do Need
Your Data Science Career Roadmap
Progress from Python and SQL to machine learning, dashboards, and verified interview readiness.
Zero to Job-Ready Data Scientist
Continuous 9-stage roadmap spanning Python, SQL, statistics, machine learning, Power BI, and career launch.
Learn Python
Core Python syntax, data structures, control flow, functions, and logic for data manipulation.
Learn SQL
Relational schema design, SELECT queries, JOINs, aggregations, window functions, and database extraction.
Master Statistics
Descriptive statistics, normal distributions, probability theory, hypothesis testing, and A/B testing logic.
Master Machine Learning
Supervised learning algorithms, regression, decision trees, random forests, XGBoost, and model evaluation.
Learn Deep Learning
Perceptrons, multi-layer neural networks, CNNs for computer vision, RNNs, and modern AI workflows.
Build Power BI Dashboards
Data ingestion, Power Query transformation, DAX calculations, and interactive executive reporting.
Build Data Science Projects
End-to-end case studies from raw datasets to predictive modeling, evaluation, and visualization.
Prepare for Interviews
Technical Python/SQL challenges, ML concept drills, portfolio code review, and mock interview simulations.
Start Applying for Data Roles
Active referral distribution across hiring partners in Hyderabad & PAN India, and continuous placement support.
Qualify for In-Demand Data Science Opportunities
Connect with our network of 150+ hiring partners across Hyderabad & PAN India, participate in technical mock interviews, and graduate with a verified live project portfolio.
AIDEAS Academy By The Numbers
See What Our Students Say
Real Students. Real Learning Experiences.
Arjun Varma
Data Science Masterclass • Batch 2024
“The machine learning and deep learning curriculum was phenomenal. Working on end-to-end predictive pipelines prepared me directly for interviews.”
“The combination of Python, SQL, and Power BI in this course is perfect for business analytics roles. I use the concepts I learned here every single day at my job.”
“Feature engineering and model tuning with Scikit-Learn and XGBoost were taught with real business intuition. The mock interviews helped me crack multiple rounds.”
“I had zero coding knowledge coming from commerce. They started with basic Python and statistics. The mentors made complex concepts easy to grasp.”
Arjun Varma
Data Science Masterclass • Batch 2024
“The machine learning and deep learning curriculum was phenomenal. Working on end-to-end predictive pipelines prepared me directly for interviews.”
“The combination of Python, SQL, and Power BI in this course is perfect for business analytics roles. I use the concepts I learned here every single day at my job.”
“Feature engineering and model tuning with Scikit-Learn and XGBoost were taught with real business intuition. The mock interviews helped me crack multiple rounds.”
“I had zero coding knowledge coming from commerce. They started with basic Python and statistics. The mentors made complex concepts easy to grasp.”
“Coming from a non-CS background, SQL and data wrangling with Pandas seemed daunting. The hands-on project labs gave me the confidence to switch into analytics.”
“The Power BI and DAX modeling module is top-notch. Interviewers were thoroughly impressed with my portfolio dashboards.”
Sai Krishna
Data Science Career Track • Batch 2024
“Placement support is 100% genuine. They reviewed my GitHub repositories, conducted 4 technical mock interviews, and referred me directly.”
“The project-based learning is what sets them apart. Building fraud detection and demand forecasting models gave me talking points in every interview.”
“Coming from a non-CS background, SQL and data wrangling with Pandas seemed daunting. The hands-on project labs gave me the confidence to switch into analytics.”
“The Power BI and DAX modeling module is top-notch. Interviewers were thoroughly impressed with my portfolio dashboards.”
Sai Krishna
Data Science Career Track • Batch 2024
“Placement support is 100% genuine. They reviewed my GitHub repositories, conducted 4 technical mock interviews, and referred me directly.”
“The project-based learning is what sets them apart. Building fraud detection and demand forecasting models gave me talking points in every interview.”
What Makes Our Data Science Program Different?
Most Courses Teach Technologies. We Focus on the Complete Journey.
Learn
Understand the concepts from the foundation.
Practice
Solve analytics problems and work through guided exercises.
Build
Create real models and dashboards using the technologies you learn.
Deploy
Learn how notebooks, models and dashboards are prepared and presented.
Prepare
Build your resume, GitHub profile and interview skills.
Apply
Get career guidance and placement assistance.
FREE DATA SCIENCE DEMO CLASS
Not Sure If Data Science Is Right for You?
Don't decide based on a brochure.
Attend a Free Demo Session.
In the demo, discover:
This Saturday & Sunday
Online Live & Hyderabad Classroom
Senior Data Science Practitioner
Frequently Asked Questions
Everything you need to know about the curriculum, eligibility, projects, and admissions.
No prior coding experience is required. We start from Python basics and gradually move to advanced ML and AI topics.
Basic school-level math is enough to begin. We teach the required statistics and probability concepts step by step.
Yes, the curriculum includes supervised and unsupervised learning, model evaluation, and deep learning foundations.
Yes, you will work on real-world projects like fraud detection, forecasting, and customer segmentation to build a strong portfolio.
Yes, we provide resume guidance, mock interviews, and job referral support to help you get placed.
Yes. The program is structured to build skills progressively, making it suitable for students and fresh graduates who are willing to practice consistently.
The program includes live training along with recorded sessions for revision.
Yes. Discuss the available schedules and learning format with the course counsellor.
Course fees and available offers can vary by batch and learning format. Submit the form to receive the current fee, batch and payment details.
Your Next Step Is Simple
Don't Spend Months Learning Random Tutorials.
Python → SQL → Stats → ML → Deep Learning → Power BI → Projects → Interview Preparation
Start Your Data Science Journey With AIDEAS Academy
Learn the skills. Build the projects. Prepare for the opportunities.