Resume and project-description support for presenting skills, tools, projects and measurable contributions clearly
Build practical skills in Python, SQL, statistics, data analysis, visualization, machine learning and AI with Skillonit's structured Data Science Course for learners in Mandangad. The program is designed for beginners, students, graduates, freshers, working professionals and career switchers who want to learn through live explanation, guided practice and real-world projects. Learners in this subdistrict can join live online training, and classroom or hybrid options are shown only when verified Skillonit centre batches are active.
Start from the fundamentals, work with real datasets, build dashboards and machine learning models, complete portfolio-ready projects and receive career-preparation support throughout the learning journey. Learners in Mandangad can request current batch, fee and delivery-mode details from the admissions team.
Learners who want face-to-face teaching, fixed batches, and local classroom guidance.
Included in this mode
Learning flow
Data skills are useful across technology, finance, retail, manufacturing, healthcare, education, logistics, marketing and professional services. Learners in Mandangad can use this course to build a foundation for analytical careers, strengthen their current role or prepare for advanced study in Machine Learning and Artificial Intelligence. Skillonit's curriculum connects technical skills to a complete workflow: understand a problem, access and clean data, write Python and SQL, explore patterns, create visual reports, build introductory machine learning models and explain results clearly.
The course starts with the required foundations and does not assume that every learner is already a programmer or statistician. Sessions combine explanation, demonstration and hands-on work. The curriculum covers Python, SQL, data cleaning, statistics, exploratory analysis, visualization, business intelligence and introductory Machine Learning. Learners work on realistic problems and receive guidance on documentation, evaluation, presentation and improvement. Eligible learners receive resume, profile, portfolio and interview-preparation support. Skillonit provides assistance, not guaranteed employment.
Course level: beginner to intermediate with selected advanced concepts introduced practically
Recommended duration: approximately 3 months, generally 12-16 weeks depending on batch format
Live online training available across India
Classroom or hybrid delivery available only at verified Skillonit centres and announced batches
Active module
1Data Science, Analytics and the Modern Data Workflow: Data Science, Data Analytics, Machine Learning, Artificial Intelligence, Business Intelligence, analytical questions, data types, project lifecycles, data quality, ethics and communication.
2Computer, File, Spreadsheet and Development Environment Basics: files, folders, file extensions, cloud storage, command-line basics, spreadsheet essentials, Python setup, Jupyter Notebook, package installation and notebook organization.
3Python Programming Foundations: variables, values, data types, type conversion, operators, input and output, comments, conditional statements, loops, functions, parameters, return values, scope and error reading.
4Python Data Structures and Practical Problem Solving: strings, lists, tuples, dictionaries, sets, indexing, slicing, iteration, searching, sorting, aggregation, nested structures, comprehensions and basic file handling with text, CSV and JSON.
5NumPy for Numerical Computing: arrays, shapes, dimensions, selection, reshaping, vectorized operations, indexing, slicing, broadcasting, aggregation, statistical functions, random data and missing numerical values.
Selected batch
Start DateUpcoming
TimingsRegular live sessions
Duration12-16 weeks
ModeOnline
Lead Trainer
Data Specialist
6+
Years Experience
LAB
Practice Support
Skillonit's teaching approach combines clear explanation, guided practice, project review and career preparation. Trainers help learners understand not only how to run code but also how to define a problem, check data quality, choose an appropriate method and communicate findings responsibly. Verified trainer profiles should include professional photograph with consent, current role, relevant experience, core skills, selected project or industry exposure and a verified professional profile where available.
Kabir Sharma focuses on practical Data Science training with a learner-first approach. Sessions combine clear concept explanation, demonstrations, lab-based practice, and interview-oriented guidance so students understand how the skill is applied in real work.
Beginner-friendly explanations without oversimplifying important concepts
Live coding and step-by-step demonstrations
Practical examples connected to real analytical problems
Feedback on assignments, notebooks and projects
Guidance on debugging and improving code quality
Discussion of assumptions, limitations, bias and ethical use
Support in presenting projects and preparing for interviews
Eligible learners receive a Skillonit Data Science course-completion certificate after meeting the completion criteria defined for the batch. The certificate confirms participation and successful completion of Skillonit's internal learning requirements. It should not be described as a university degree, government licence or third-party professional certification unless a separate verified accreditation applies.
Minimum attendance as defined in the admission policy
Completion of required assignments or assessments
Submission of the capstone or final project
Participation in the final presentation or review
Adherence to academic integrity and project guidelines
Credential can complement a resume, LinkedIn profile and project portfolio
Certificate ID
SKL-DAT-2026
Certificate of Completion
This certifies that the student has completed Skillonit’s Data Science training with practical tasks, quizzes, and project assessment.
Presented to
Student Name
For successful completion of Data Science Course
Completion Date
18 Jun 2026
Authorized Signature
Verification-ready certificate preview for LMS completion.
Course level: beginner to intermediate with selected advanced concepts introduced practically
Recommended duration: approximately 3 months, generally 12-16 weeks depending on batch format
Live online training available across India
Classroom or hybrid delivery available only at verified Skillonit centres and announced batches
Prerequisites: basic computer usage and willingness to practice
Prior programming experience is not compulsory
Main technologies: Python, Jupyter Notebook, NumPy, Pandas, Matplotlib, Seaborn, SQL, Excel, Power BI or Tableau, scikit-learn, Git and GitHub
Introductory exposure to TensorFlow, Keras, big data and model deployment
Learning format: live explanations, demonstrations, guided coding, practice exercises, assignments, case studies, mini projects, capstone work, doubt-clearing and project review
Tools students practice
This course blends clear concepts, practical training, guided projects, quizzes, and career-focused assessment to help learners build confidence and move toward real opportunities.
Learners in Mandangad move through foundations, Python programming, data handling, SQL, statistics, exploratory analysis, visualization, dashboards, machine learning, portfolio building and career preparation. This local page is designed for learners who want clear access to the Data Science course from Mandangad while still following nationally relevant skills and project standards.
Skillonit provides career preparation and placement assistance to help eligible learners present their skills and approach suitable opportunities. Assistance is designed to improve readiness; it is not a promise of employment, a specific salary or a fixed interview timeline. Hiring decisions are made by employers and depend on skill, experience, education, portfolio, interview performance, location, role availability and other factors.
Structured learning from foundations to projects
Practical, guided learning through demonstrations, coding, exercises and project work
Beginner-friendly explanations with examples and incremental practice
Project and portfolio development that prioritizes quality over unfinished project count
Flexible live online access across India with classroom options only where verified
Responsible use of data and AI with discussion of privacy, bias, fairness, limitations and ethical communication
Continued learning roadmap for analytics, BI, Machine Learning, AI, Data Engineering or advanced project work
Admission enquiry
Share your details and our team will help you choose the right Data Science batch, learning mode, syllabus, fee plan, and career path.
College students can build practical skills alongside their degree. Fresh graduates can prepare for entry-level analytics, reporting, BI and Data Science opportunities that match their overall profile. Working professionals can strengthen data-driven decision-making or prepare for a transition into analytics, BI, Machine Learning or related roles. Non-IT learners from commerce, management, science, arts and other backgrounds can join, though additional programming practice may be required. Entrepreneurs and freelancers can learn customer data analysis, performance reporting, forecasting, dashboard development and introductory analytical projects.
Build strong foundation skills in Data Science
Apply concepts through guided practical projects
Use portfolio work to start client-ready practice
Prepare for internships, jobs, or higher learning
Structured beginner-to-project learning pathway
Practical classes with exercises and assignments
Python, SQL, statistics, visualization and Machine Learning in one program
Guided project and portfolio development
Live online access across Mandangad
Classroom information only where a real centre and active batch exist
Career-preparation support with transparent expectations
Responsible discussion of data quality, privacy, bias and limitations
Clear next-step guidance for Data Analytics, BI, Machine Learning, AI or Data Engineering
Internship Program
Skillonit’s Data Science Internship Program in Mandangad is a mentor-guided practical pathway available in 1-month, 3-month and 6-month formats. Participants learn how to solve data problems through Python, SQL, statistics, data cleaning, exploratory analysis, visualization, machine learning, experimentation and model communication. The program combines structured training, assignments, project reviews, documentation, portfolio support and a completion certificate subject to published requirements.
The internship is designed for students, graduates, analysts, developers, researchers, working professionals and career switchers. It turns course knowledge into structured practice through mentor-led onboarding, guided tasks, project milestones, review meetings, documentation and a final presentation. Participants are expected to build, test, explain and improve their work instead of only watching demonstrations.
Available durations
1 month, 3 months and 6 months
Delivery modes
Live online, classroom or hybrid—subject to the selected batch and location
Local availability
Local centre, mode, start date and seat availability are shared only from verified city data.
Learning model
Training + mentor-guided tasks + project reviews + portfolio evidence
Projects
Mini project, guided projects and capstone according to duration
Support
Onboarding, doubt clearing, task planning, review feedback and presentation guidance
About Program
Skillonit’s Data Science internship is designed as a bridge between learning and practical delivery. Interns begin with a baseline assessment and a clear task plan, then progress through demonstrations, guided exercises, independent work and review. The focus is on understanding why a solution works, how to communicate decisions and how to improve quality after feedback.
The work is aligned with Python, SQL and spreadsheet-based data work, feature engineering and predictive modelling and advanced machine learning and time-series work. Longer pathways include more complex requirements, collaboration, documentation, testing and presentation. Interns maintain a task log or project board so that progress can be reviewed objectively rather than judged only by the final output.
The internship does not guarantee employment, freelance income, client allocation or a stipend. It provides practical exposure, evidence of completed work and career-readiness support. Any employment or paid internship opportunity is published separately with its own eligibility, application deadline, compensation and selection process.
Duration Options
The one-month pathway is a focused four-week experience for learners who want an introduction to professional data science practice. It prioritizes orientation, essential tools, one clear workflow and a mentor-reviewed mini project. It is most suitable for beginners, students testing the domain, or course learners who need a short practical component.
Week 1 – Orientation and foundation: skill assessment, program rules, tool setup, task tracking, Python, SQL and spreadsheet-based data work and a short guided exercise.
Week 2 – Core practice: data cleaning and exploratory analysis, mentor demonstration, individual practice and a quality checklist.
Week 3 – Mini project build: apply statistics, visualization and business problem framing to a scoped project with milestone review and corrections.
Week 4 – Finalization: testing or quality review, documentation, presentation, mentor feedback and next-learning roadmap.
Expected 1-month evidence: One completed mini project, task log, project summary, mentor feedback record and final presentation. Example project: Sales and customer analysis.
The three-month pathway is the recommended option for learners who want meaningful portfolio evidence. It combines a foundation phase, an applied phase and a capstone phase. Interns work on at least two guided assignments and one larger project, with regular reviews that focus on quality, documentation and problem-solving.
Month 1 – Foundation and workflow: Python, SQL and spreadsheet-based data work, data cleaning and exploratory analysis, statistics, visualization and business problem framing; tool setup; guided exercises; communication and documentation standards.
Month 2 – Applied delivery: feature engineering and predictive modelling, model evaluation and experiment design, dashboards, storytelling and stakeholder communication; first project review; debugging, critique or analysis; iteration after feedback.
Month 3 – Capstone and portfolio: build Demand or sales forecasting or another approved project; complete testing, documentation, presentation and portfolio packaging.
Expected 3-month evidence: Two guided projects, one capstone, weekly progress records, review notes, final presentation and a portfolio-ready case study. Suggested project options include Customer churn prediction, Demand or sales forecasting and Recommendation system.
The six-month pathway is intended for learners seeking deeper, sustained practical experience. It includes specialization, team workflow, quality assurance and a production-style capstone. Interns gradually take greater ownership while remaining accountable to scope, security, ethics and mentor review.
Month 1 – Foundation: Python, SQL and spreadsheet-based data work and data cleaning and exploratory analysis, baseline tasks and work standards.
Month 2 – Core build: statistics, visualization and business problem framing plus the first guided project and review cycle.
Month 3 – Applied specialization: feature engineering and predictive modelling and model evaluation and experiment design with an intermediate project.
Month 4 – Integration: dashboards, storytelling and stakeholder communication and cross-functional workflow, documentation and quality checks.
Expected 6-month evidence: A structured portfolio containing three or more projects, an advanced capstone, documented iterations, mentor reviews, a presentation and a skills matrix. Suggested advanced projects include Recommendation system, Fraud or anomaly detection study and End-to-end data science capstone.
Skills and Tools
Working Environment
The exact stack may vary by batch and project. Typical tools include Python, Jupyter, pandas, NumPy, Matplotlib, Seaborn, SQL, Power BI or Tableau, scikit-learn, GitHub, Streamlit or FastAPI. Skillonit should publish only the tools that are actually supported in the selected batch and provide setup guidance, access requirements and alternatives where paid licences are involved.
Project Practice
Real client projects are included only when an approved project, permission and review process are available; otherwise learners work on realistic industry simulations or mentor-designed capstones.
Training Support
Workflow
Eligibility
Basic computer knowledge is required. Mathematics, statistics, Python or SQL familiarity is helpful but can be introduced in the longer pathways.
Assessment
Evaluation should be transparent and based on evidence rather than vague participation. Recommended criteria include attendance, timely task completion, understanding of the work, quality of implementation, response to feedback, documentation, ethics and final presentation. The certificate should state the program title, duration, completion date and credential ID only when those fields are actually maintained by Skillonit.
Certification and Career
The internship can support preparation for roles such as Data Science Intern, Data Analyst Intern, Machine Learning Intern, Business Intelligence Intern, Research Analytics Intern, Junior Data Product Intern. It may also help learners demonstrate practical work for further study, entry-level applications, freelance proposals, startup prototypes or internal role transitions. Outcomes depend on the learner’s starting level, effort, project quality and the requirements of each opportunity; no job, income, client or admission result is guaranteed.
Program Features
Search Focus
Apply Now
Apply for the Data Science Internship Program in Mandangad and choose the duration that matches your goals. Complete the application form with accurate information so the admissions and mentor team can recommend the appropriate pathway.
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Internship FAQs
Skillonit’s Data Science Internship Program in Mandangad is a mentor-guided practical pathway available in 1-month, 3-month and 6-month formats. Participants learn how to solve data problems through Python, SQL, statistics, data cleaning, exploratory analysis, visualization, machine learning, experimentation and model communication. The program combines structured training, assignments, project reviews, documentation, portfolio support and a completion certificate subject to published requirements.
Learners can choose 1 month, 3 months or 6 months. The one-month track is introductory, the three-month track is portfolio-focused, and the six-month track provides deeper project and production-style experience.
Yes, the foundation pathway is designed for learners who meet the basic device and participation requirements. Basic computer knowledge is required. Mathematics, statistics, Python or SQL familiarity is helpful but can be introduced in the longer pathways.
Projects may include Sales and customer analysis, Customer churn prediction, Demand or sales forecasting, Recommendation system. Final projects are selected according to duration, learner level, mentor capacity and available project briefs.
Yes. The program combines concept refreshers, guided exercises and tool setup before independent tasks. Training depth depends on the selected duration and baseline assessment.
The program includes mentor-guided projects. A real client or production project is offered only when a suitable approved opportunity is available; otherwise, the learner completes a realistic industry simulation or internal capstone.
A completion certificate may be issued after the learner meets the published attendance, task, project, evaluation and final-presentation requirements.
A stipend is not automatic. Stipend status must be displayed for each approved opening. A training internship may have a program fee, while a genuine employment internship must be published separately with its own terms.
Online, classroom and hybrid options may be available. The exact mode, schedule and mentor availability should be confirmed for the selected batch.
Yes, subject to eligibility, schedule and mentor capacity. Weekend or evening options may be offered when listed in the active batch information.
No. The program provides practical learning, portfolio evidence and career-readiness support, but employment, freelance income, client allocation and interview results depend on external selection processes.
Select Apply Now, choose the duration and mode, submit accurate education and skill details, and complete any baseline task requested by the internship team.
Classroom availability depends on the verified local centre, mentor capacity and active batch. Display the exact address and mode only when confirmed.
The approved fee should be loaded from the CMS for the selected duration and batch. Do not place a generic or outdated fee in the page copy.
Yes, when an online batch is active and the learner has the required device, internet access and schedule availability.
Popular searches covered
These are the common search topics this page answers through the course information, syllabus, FAQs, fees, batches, tools, projects, certification, and career-support sections.
Yes. Live online Data Science training is available to learners in Mandangad. Classroom or hybrid availability is shown only when a verified local centre and active batch exist.
Learners can attend live online classes from Mandangad. Classroom batches are displayed only when a verified Skillonit centre batch is active.
The recommended duration is approximately three months, usually 12-16 weeks depending on the batch format.
Yes. The course begins with foundations and is suitable for committed beginners. Prior programming experience is not compulsory.
Yes. Learners from commerce, management, science, arts and other backgrounds can join, though they may need additional coding and mathematics practice.
Yes. Weekend or evening batches may be available. Check the current batch schedule or request a callback.
The core learning includes Python, Jupyter Notebook, NumPy, Pandas, SQL, Matplotlib, Seaborn, Power BI or Tableau and scikit-learn, with selected advanced concepts.
Yes. The program includes practical exercises, guided projects and a capstone or final project according to the batch plan.
Eligible learners receive a Skillonit course-completion certificate after meeting the defined requirements.
Skillonit provides career and placement assistance to eligible learners. Assistance may be delivered online and is not a guarantee of employment.
Fees may vary by batch and included services. Use the Get Fees button to receive the current written details.
Yes. Submit the demo form and the admissions team will share the next suitable demo or counselling option.
A laptop or desktop computer, reliable internet connection and the ability to install or access the required software are recommended.
Recording access depends on the enrolled batch terms. Confirm the current access period before enrollment.
Book a demo or submit the inquiry form. The admissions team will share the current batch, delivery mode, fees, requirements and enrollment process.