AI-Powered Data Science Course & Internship in Chennai
Master data processing, machine learning and AI model deployment with a structured 130-day learning path covering Python, MySQL, NumPy, Pandas, Supervised/Unsupervised/Reinforcement Learning, Model Evaluation, Flask, FastAPI, Deep Learning and an ML Project at UNIQ Technologies.
Why Choose a Data Science with Python Course & Internship in Chennai?
Looking for a Data Science course in Chennai that teaches Python, SQL, NumPy, Pandas, Machine Learning, Deep Learning and practical model development? The Data Science with Python course and internship at UNIQ Technologies is designed for students, freshers and aspiring data professionals who want to build a strong foundation in Python-based Data Science and Machine Learning.
From Data Wrangling to Predictive Machine Learning
Data scientists clean raw datasets, train predictive machine learning models, and serve AI endpoints using Python, Pandas, Scikit-Learn and FastAPI.
Python Programming
Master Python fundamentals, OOP, data structures, file handling and data scripting.
MySQL Database
Execute SELECT queries, complex joins, subqueries, grouping and data extraction.
NumPy & Pandas
Perform fast numerical operations, DataFrame manipulations, data cleaning and transformation.
Machine Learning Fundamentals
Understand features, target variables, training vs testing splits, predictions and ML pipelines.
Supervised & Unsupervised Learning
Train classification, regression, clustering and semi-supervised algorithms.
Reinforcement Learning
Understand agents, environments, states, actions and reward-based learning loops.
Model Evaluation
Calculate accuracy, precision, recall, F1-score, MSE and confusion matrices for model tuning.
Flask Web Framework
Build Python web applications and serve lightweight Machine Learning models.
FastAPI Development
Build high-performance REST APIs to serve ML prediction endpoints for production apps.
Deep Learning Fundamentals
Understand neural networks, activation functions, hidden layers and deep learning workflows.
ML Capstone Project
Connect raw data, preprocessing, model training, evaluation and API deployment in a 20-day project.
Program Objective
Provide a practical pathway from data manipulation to machine learning algorithms and API deployment, preparing learners for modern Data Science and ML Engineering roles.
Who Can Join Data Science Training?
The program starts with core fundamentals and moves towards full stack development—designed for anyone eager to build a career in software development.
BE / B.Tech Graduates
Engineering graduates looking to build careers in Data Science and Machine Learning.
BCA & MCA Graduates
Computer application graduates ready for practical Python, ML algorithms and API development.
B.Sc & M.Sc Graduates
Science postgraduates interested in statistical modeling, data processing and AI.
Python Developers
Developers expanding into NumPy, Pandas, Scikit-Learn algorithms and FastAPI serving.
Data Analysts
Analysts wanting to upgrade skills from basic reporting into predictive modeling and Machine Learning.
Aspiring ML Engineers
Learners focused on building, evaluating and deploying Machine Learning models.
Final-Year Students & Freshers
Students seeking practical hands-on project experience before entering IT recruitment.
Career Switchers
Professionals looking to transition into high-growth Data Science and AI opportunities.
Data Science Roadmap
A structured learning path that moves from fundamentals and backend frameworks to AI, cloud concepts, professional tools and job preparation.
Learning Path
Course Modules
Python Programming
Python forms the foundation of this Data Science program. Learners develop strong scripting and object-oriented programming skills required for data analysis.
Key Topics
What you'll learn
Python Fundamentals
Variables, data types, operators, conditionals and loops
Data Structures
Lists, tuples, sets, dictionaries and string manipulations
Functions & OOP
Functions, modules, packages, classes, objects and inheritance
File & Exception Handling
Working with files, exception handling and modular scripts
Learning Focus
Mastering Python allows Data Scientists to clean data, write automation scripts, and build machine learning workflows.
A structured path. No rushed learning.
The learning program distributes time across programming, databases, backend development, AI, professional tools, and career preparation.
Overall Program
The duration is intentionally spread across the complete learning journey instead of compressing the syllabus into a short-term crash course.
Python Programming
ProgrammingMySQL Database
DatabaseNumPy & Pandas
Data ProcessingMachine Learning
ML CoreSupervised / Unsupervised / Semi-Supervised
ML AlgorithmsReinforcement Learning
Advanced AIModel Evaluation
OptimizationFlask
Web FrameworkFastAPI
API ServingDeep Learning
Neural NetworksMachine Learning Project
Capstone ProjectCore programming receives one of the largest portions of the learning timeline.
Approximate days across frontend, core programming, databases, and web foundations.
Overall program duration covering technical and career preparation.
What Skills Can You Develop During the Full Stack Internship?
Progress through the syllabus and understand how the major parts of a modern application connect—from the user interface to backend services and databases.
Data Ingestion & Wrangling
Extract business data with MySQL, clean and reshape DataFrames using Pandas.
Machine Learning Modeling
Train Supervised/Unsupervised models, tune hyper-parameters and evaluate metrics.
Deployment & API Serving
Expose ML model predictions via REST APIs using Flask and high-speed FastAPI.
Supporting skills for modern development
The core application stack is supplemented by exposure to modern development practices, cloud technologies, AI and software delivery tools.
Practical Data Science & ML Training for Freshers
Freshers often face a particular problem when applying for their first software job: employers expect practical understanding even when candidates have no professional experience. An internship-oriented training environment helps bridge this gap.
At UNIQ Technologies, the emphasis is on learning concepts through coding and practical implementation rather than depending exclusively on theory.
Students should be able to explain:
Essential topics for fresher technical interviews
How to clean and handle missing values in Pandas
Filtering, imputing null values, and transforming categorical data into numerical formats.
How to train Supervised vs Unsupervised ML models
Fitting Decision Trees, Random Forests, and K-Means clustering on processed feature datasets.
How to evaluate model performance with metrics
Analyzing Confusion Matrices, Precision, Recall, F1-Score, and Mean Squared Error.
How to serve ML predictions using FastAPI
Creating REST API endpoints that accept JSON inputs and return real-time model predictions.
How to integrate ML models into Flask web apps
Loading pickled model files inside Flask routes to power interactive web dashboards.
How SQL queries prepare raw data for modeling
Writing SELECT queries with joins, aggregate functions, and subqueries to extract datasets.
How Deep Learning neural layers work
Understanding input layers, hidden dense layers, activation functions, and output predictions.
How to build an end-to-end ML project pipeline
Connecting raw data extraction, preprocessing, model training, evaluation, and API deployment.
Full Stack Projects That Connect the Full Stack
Project-based learning is an important part of becoming comfortable with Full Stack development. A practical project helps students understand how individual technologies work together as one complete application.
Project Architecture
One application. Multiple technologies. One workflow.
Data Extraction & SQL
MySQL + Python
Extract raw datasets from MySQL databases using Python connectors.
Data Wrangling
NumPy + Pandas
Clean, reshape, scale features and handle missing values in DataFrames.
Model Training
Supervised / Unsupervised ML
Train classification/regression algorithms and tune model parameters.
Evaluation & Tuning
Metrics & Pickling
Calculate accuracy, F1-score, confusion matrix and pickle trained model files.
API Deployment
Flask + FastAPI
Expose prediction endpoints via FastAPI and deploy interactive web apps.
Data Science & ML Career Opportunities
Depending on skills, project knowledge, interview performance, and employer requirements, learners can explore various entry-level opportunities across the software industry.
Data Scientist
Junior Data Scientist
Machine Learning Engineer
Junior Machine Learning Engineer
Python Developer
ML Developer
AI/ML Associate
Data Analyst
Junior Data Analyst
AI Engineer
Machine Learning Intern
Frequently Asked Questions
Everything you need to know about the internship, learning path, technologies, practical training and career preparation.
A Data Science course teaches learners how to work with data using programming, databases, data processing, Machine Learning and related technologies.
UNIQ Technologies offers a Data Science with Python course and internship program in Chennai, covering Python, SQL, Machine Learning, Deep Learning and an ML project.
The complete course duration is 130 days.
Yes. Python is a major part of the program and is covered for 35 days.
Python is widely used for programming, data processing, Machine Learning and building data-driven applications.
Yes. MySQL is covered for 30 days.
SQL helps professionals retrieve, filter, join and manipulate data stored in relational databases.
Yes. NumPy is taught together with Pandas for 10 days.
Yes. Pandas is part of the 10-day NumPy & Pandas module.
Pandas is commonly used for working with tabular datasets, data cleaning, transformation, filtering and analysis.
Yes. Machine Learning is included in the syllabus.
The dedicated Machine Learning module is 5 days, followed by additional modules covering different learning approaches.
Yes. Supervised Learning is included as part of the Supervised / Unsupervised / Semi-Supervised Learning module.
Yes. Unsupervised Learning is included.
Yes. Semi-Supervised Learning is included in the syllabus.
The combined module is allocated 10 days.
Yes. Reinforcement Learning is included for 3 days.
Yes. Deep Learning is included for 5 days.
Yes. Model Evaluation is included for 2 days.
Model evaluation helps determine how well a Machine Learning model performs and supports comparison and improvement of models.
Yes. Flask is covered for 5 days.
Flask can help developers build web applications around Python-based functionality and Machine Learning models.
Yes. FastAPI is covered for 5 days.
FastAPI can be used to expose Python functionality and Machine Learning models through API endpoints.
The syllabus includes Flask and FastAPI, which provide exposure to creating applications and APIs around Python-based ML functionality.
Yes. The program includes a dedicated 20-day ML Project.
The ML Project is allocated 20 days.
Yes. The course can be considered by freshers who want to develop Python, SQL, data-processing and Machine Learning skills.
Yes. BCA students interested in programming, databases and Machine Learning can consider this program.
Yes. B.Tech and engineering students can consider Data Science training to develop programming and Machine Learning skills.
Yes. MCA graduates can consider the course as a pathway for developing Data Science and Machine Learning skills.
Yes. Beginners can start with Python and SQL fundamentals and progress through data processing and Machine Learning modules.
SQL is an important practical skill because data is frequently stored in relational databases.
Python is an important foundation, but Data Science generally requires additional skills such as data processing, statistics, Machine Learning and model evaluation.
Data Analytics generally focuses on analyzing data for insights and reporting, while Data Science can include predictive modelling, Machine Learning and broader computational approaches.
Yes. Machine Learning is an important component of many Data Science workflows.
Yes. Deep Learning is included for 5 days.
Yes. Reinforcement Learning is covered for 3 days.
The syllabus focuses specifically on Data Science, Machine Learning and Deep Learning. These are important areas within the broader AI ecosystem.
Generative AI is not specifically listed in the supplied Data Science syllabus.
Natural Language Processing is not specifically listed in the supplied syllabus.
Computer Vision is not specifically listed in the supplied syllabus.
TensorFlow is not specifically listed in the supplied syllabus.
PyTorch is not specifically listed in the supplied syllabus.
Power BI is not specifically listed in the supplied Data Science syllabus.
Tableau is not specifically listed in the supplied syllabus.
Excel is not specifically listed in the supplied Data Science syllabus.
Yes. Flask and FastAPI are included, providing exposure to Python-based web/API development.
Flask is a Python web framework that can be used to build web applications and services around Python functionality.
FastAPI is a Python framework commonly used to build APIs and web services.
Reinforcement Learning is a Machine Learning approach where an agent learns through interactions with an environment using concepts such as actions, states and rewards.
Deep Learning is a branch of Machine Learning that uses neural-network-based architectures to learn patterns from data.
Supervised learning trains models using labelled examples to make predictions or classifications.
Unsupervised learning works with data without predefined target labels to identify patterns, structures or groups.
Semi-supervised learning uses a combination of labelled and unlabelled data during the learning process.
Model evaluation is the process of measuring how effectively a trained Machine Learning model performs on relevant data.
No. Python is the foundation, but the program also covers MySQL, NumPy, Pandas, Machine Learning, learning approaches, model evaluation, Flask, FastAPI, Deep Learning and an ML project.
Python is the primary programming language specified in the course syllabus.
MySQL is included for 30 days.
The complete Data Science with Python course is 130 days.
Python is covered for 35 days.
MySQL is covered for 30 days.
NumPy & Pandas are covered for 10 days.
Deep Learning is covered for 5 days.
Reinforcement Learning is covered for 3 days.
Model Evaluation is covered for 2 days.
Flask is covered for 5 days, and FastAPI is covered for 5 days.
The program is presented in the context of Data Science course and internship-oriented training at UNIQ Technologies.
The syllabus provides exposure to Python, SQL, data processing, Machine Learning, Deep Learning, APIs and project work.
The syllabus combines Python, MySQL, NumPy, Pandas, Machine Learning, supervised/unsupervised/semi-supervised learning, Reinforcement Learning, model evaluation, Flask, FastAPI, Deep Learning and a 20-day ML project into a structured 130-day program.