Knowing Python syntax is not enough to become confident in data science. You need to understand the data, select the right method, evaluate the model, explain its limits and turn the result into something another person can use.
The NeoNex MindX Data Science course guides you through that complete journey through live mentorship, practical assignments, end-to-end projects, deployment foundations and career preparation.
Tell us your education, current role, coding comfort and career goal. A course advisor will help you compare Data Science with Data Analytics and AI/ML Engineering before you enrol.
| Program Duration | 6–7 months |
|---|---|
| Training Mode | Live online, interactive sessions |
| Coding Requirement | Beginners can start with the included Python foundations |
| Core Learning | Python, SQL, statistics, probability, linear algebra, machine learning and deployment |
| Learning Access | LMS, recordings, notes and resources |
| Practical Learning | Assignments, projects and portfolio outputs |
| Internship | 3–6 month component with certification |
| Career Support | Resume, profile, interview and placement assistance |
| Batch Options | Weekday morning, weekday evening and weekend |
A data science course teaches you how to use programming, mathematics, statistics and machine learning to explore data, build predictive models and communicate evidence-based findings.
A strong program covers the complete workflow, from collecting and cleaning data to model evaluation, deployment and portfolio presentation.
This online Data Science course in India covers Python, NumPy, Pandas, SQL, exploratory data analysis, statistics, probability, linear algebra, feature engineering, supervised and unsupervised learning, deep learning foundations and model deployment.
The goal is not to memorise algorithms. It is to learn how to frame a problem, test an approach, evaluate the result and defend your decisions.
Data science becomes confusing when programming, mathematics and machine learning are learned as separate subjects. This program connects them through one practical workflow.
Build a structured foundation for entry-level data science and predictive analytics roles.
Progress from reporting and dashboards into feature engineering, machine learning and model evaluation.
Add data modelling, machine learning and deployment skills to an existing technical background.
Follow one guided path instead of attempting programming, mathematics and machine learning separately.
Use data science methods to investigate patterns and create predictive solutions within your field.
Build the statistical and machine-learning foundation needed before moving into advanced AI engineering.
Build Python foundations and write reusable code for files, tables and analytical tasks.
Use SQL, Pandas, statistics and visualisation to identify data-quality issues and meaningful patterns.
Connect probability, linear algebra and statistical reasoning to model behaviour.
Engineer features, prevent leakage and build reproducible preprocessing workflows.
Train baselines, select metrics, validate results and improve the workflow.
Move a trained model into a simple application or API foundation and document how it should be used.
Organise projects, practise explanations and strengthen your professional profile.
The curriculum begins with programming and data preparation, builds the mathematical foundations required for modelling, progresses through machine learning and ends with deployment, capstone work and career preparation.
Build Python, NumPy, Pandas, visualisation and database skills for reliable data preparation.
Modules 1–6
Understand statistics, probability, linear algebra and feature engineering before advanced modelling.
Modules 7–10
Build supervised and unsupervised models, improve their reliability and communicate outputs to business users.
Modules 11–15
Move models toward practical use and combine the full workflow into portfolio-ready projects.
Modules 16–17
What you will build: A Python coding assessment and data-handling assignment.
What you will build: A reusable Python utility package for a data project.
What you will build: A numerical analysis notebook.
What you will build: A cleaned analytical dataset and data-quality log.
What you will build: An exploratory data analysis report with a visual narrative.
What you will build: A model-ready dataset created through SQL queries.
What you will build: A statistical validation notebook.
What you will build: A probability-based modelling exercise.
What you will build: A PCA or similarity-based analysis.
What you will build: A reusable preprocessing pipeline.
What you will build: A regression and classification model comparison.
What you will build: A customer or behavioural segmentation project.
What you will build: An optimised model with an evaluation and limitation report.
What you will build: A model-results dashboard.
What you will build: An introductory neural-network project.
What you will build: A deployed prediction application.
What you will build: An end-to-end data science portfolio with deployed work.
Every tool is taught inside a defined module and end-to-end workflow rather than as an isolated software feature.
Project themes can be adapted to learner level and available datasets. The focus remains on clear reasoning, reproducibility and honest evaluation.
Prepare behavioural data, engineer features, compare classification models and explain precision, recall and business trade-offs.
Build a regression workflow, evaluate errors and communicate the limits of the prediction.
Use clustering and dimensionality reduction to identify meaningful groups and explain how they could be used.
Handle class imbalance, compare metrics and document responsible interpretation of model output.
Explore similarity, ranking or collaborative-filtering concepts and evaluate useful recommendation behaviour.
Create an end-to-end project including data preparation, modelling, evaluation, a simple application or API and project documentation.
Build Python, statistics, probability and linear algebra understanding before advanced modelling.
Learn problem framing, data preparation, model building, evaluation, reporting and deployment as one connected process.
Ask questions about code, assumptions, metrics and project decisions instead of learning through trial and error alone.
Create projects that show your reasoning, not just a final accuracy score.
Practise explaining models, trade-offs, errors and business relevance to technical and non-technical interviewers.
Attend a schedule that works for you and revisit recorded sessions through the LMS.
Learn technical concepts, projects and professional readiness with trainers covering data, AI, development, QA, DevOps and communication skills. Trainer allocation may vary by module and batch.
Developer & QA
LinkedIn Profile →DevOps
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Data Analytics, Business Analytics, Data Science & AI
LinkedIn Profile →
Data Analytics, Business Analytics, Data Science & AI
LinkedIn Profile →Data Analytics, Business Analytics, Data Science & AI
LinkedIn Profile →Data Analytics & Data Engineering
LinkedIn Profile →Developer & QA
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Communication Skills, Soft Skills & Profile Building
LinkedIn Profile →The program includes a 3–6 month internship component with certification, subject to current program requirements and successful completion of the relevant learning and project milestones.
Important: NeoNex MindX provides placement assistance, not a guaranteed job. Hiring outcomes depend on education, prior experience, technical depth, portfolio quality, communication, interview performance, employer requirements and market conditions.
Depending on your prior background, portfolio and employer requirements, the program supports preparation for roles such as:
Role eligibility varies by employer, education, experience, technical depth and project quality.
| Comparison Point | Data Science | Data Analytics | AI/ML Engineering |
|---|---|---|---|
| Primary Focus | Predictive modelling and experimentation | Reporting, dashboards and insights | Production AI systems and applications |
| Programming Depth | Intermediate | Beginner to intermediate | Intermediate to advanced |
| Mathematics Depth | Statistics, probability and linear algebra are important | Lower mathematical emphasis | Deeper model and optimisation focus |
| Typical Outputs | Models, experiments, notebooks and deployed predictions | Dashboards, reports and business recommendations | AI APIs, LLM applications and monitored systems |
| Choose This When | You want to understand data deeply and build predictive models | You want a more direct reporting and dashboard path | You want advanced deployment and AI-product development |
Monday to Friday
7:00 AM–9:30 AMMonday to Friday
8:00 PM–10:30 PMSaturday and Sunday
9:00 AM–1:30 PMBatch availability and start dates may vary. Confirm the current schedule during counselling.
Check the Next Available BatchData science combines programming, statistics, mathematics and domain knowledge to understand complex data, test hypotheses and build predictive models. The work usually includes data cleaning, exploratory analysis, feature engineering, model evaluation and communication.
Yes, provided you are ready to practise consistently. The program begins with Python and data foundations before statistics, probability, machine learning and deployment. Learners with no coding background should expect to spend additional time on exercises.
No advanced mathematics is required at the start. The course teaches the statistics, probability and linear algebra concepts needed to understand data science methods. Comfort with basic arithmetic and a willingness to practise are helpful.
Python is the primary programming language because it supports data preparation, visualisation, machine learning and deployment through a broad ecosystem of libraries. SQL is also taught for extracting and preparing data from databases.
The program covers regression, classification, clustering, feature engineering, preprocessing, cross-validation, hyperparameter tuning, model evaluation, dimensionality reduction and selected deep-learning foundations.
Yes. Project work can include churn prediction, price prediction, segmentation, risk classification, recommendation foundations and an end-to-end deployed capstone. Every project is expected to include documentation and evaluation.
Yes. The curriculum includes deep-learning foundations, neural networks and an introductory implementation using TensorFlow or PyTorch. Learners seeking deeper NLP, computer vision, LLM and MLOps coverage should consider the AI/ML Engineering program.
Data Science focuses on extracting insights and building predictive models from data. AI/ML Engineering goes further into deep learning, NLP, computer vision, LLM applications, APIs, deployment, monitoring and the production model lifecycle.
The program includes a 3–6 month internship component with certification, subject to current program requirements and successful completion of the relevant learning and project milestones.
Support can include resume and LinkedIn preparation, GitHub and portfolio review, mock interviews, project-explanation practice, technical assessments and relevant opening or referral support where applicable. A job is not guaranteed.
Yes. Learners receive LMS access with recorded sessions, notes and additional resources. This allows you to revise difficult concepts and catch up on missed live classes.
Call 084318 03839 or submit the course enquiry form. A NeoNex MindX advisor will share the current fee, EMI options, batch schedule, start date and full syllabus.
Get the detailed syllabus, compare the course with Data Analytics and AI/ML Engineering, and speak with an advisor before choosing your learning path.