Freshers who are uncertain where to begin receive a sequential roadmap. The course starts with computer and data foundations, develops tool confidence and repeatedly connects technical work to business questions.
No advanced programming background is required to begin. Learners receive a step-by-step foundation and gradually work toward more independent analysis. The exact duration, delivery mode, trainer, batch schedule and fees are displayed from the current approved course record so that every enrollment decision is based on accurate information.
Learn Excel, SQL, Power BI, Tableau and Python in one connected Data Analytics roadmap. Practice data cleaning, reporting, dashboard design and business-focused analysis. Complete assignments, case studies, mini projects and a guided capstone portfolio. Develop the communication skills needed to explain insights to technical and non-technical audiences. Access career and placement assistance according to the services included in the active batch.
Learners who want face-to-face teaching, fixed batches, and local classroom guidance.
Included in this mode
Learning flow
Organizations collect more operational, customer, financial and digital data than many teams can use effectively. The challenge is no longer only obtaining data; it is preparing it correctly, defining reliable metrics and turning it into understandable decisions. This creates a continuing need for people who can work between raw information and business action. For professionals, the same skills support better reporting, automation and decision-making. A repeatable Power Query process can reduce manual spreadsheet work; a well-designed SQL query can replace error-prone copying; a Power BI semantic model can create consistent KPIs; and a clear dashboard can help teams act faster.
Projects should require learners to make decisions. Copying a finished dashboard is not the same as analyzing data. A strong project asks the learner to inspect data quality, define useful metrics, justify chart choices, calculate KPIs, document assumptions and present recommendations. Feedback should evaluate both technical accuracy and clarity of communication. Transparency is equally important. Before enrolling, learners should be able to review the current syllabus, actual delivery mode, expected practice time, batch schedule, fees, trainer information, certificate conditions, recording policy and career-support services. Any claim about guaranteed employment, salary, interview rates, trainer experience, partnerships or external recognition should be supported by current evidence.
Learn Excel, SQL, Power BI, Tableau and Python in one connected Data Analytics roadmap.
Practice data cleaning, reporting, dashboard design and business-focused analysis.
Complete assignments, case studies, mini projects and a guided capstone portfolio.
Develop the communication skills needed to explain insights to technical and non-technical audiences.
Active module
1Business questions, dimensions and measures
2KPIs, metrics and targets
3Analytical workflow and documentation
4Ethics, privacy and responsible interpretation
5Guided practice: Translate a business scenario into a question tree, metric definition sheet and analysis plan.
6Module 2: Microsoft Excel Fundamentals for Analysts
7Workbook and worksheet structure
Selected batch
Start DateUpcoming
TimingsRegular live sessions
Duration16 weeks
ModeClassroom
Lead Trainer
AI/ML & Data Science Instructor
6+
Years Experience
LAB
Practice Support
Supports learners with AI, machine learning, data science concepts, applied projects, and analytical thinking.
Prathamesh Pathak focuses on practical Data Analytics 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.
Concept-first explanations
Hands-on practice and assignments
Project and portfolio guidance
Doubt-solving support
Career-oriented mentoring
Responsible tool usage and documentation
The certificate section should state the exact eligibility rules, issuing organization, issue date, credential or verification identifier where available, and the process for correcting a learner’s name. A sample image may be displayed only when it matches the certificate that will actually be issued. Learners should support the credential with practical evidence: Excel workbooks, SQL case studies, dashboards, notebooks, project documentation and a concise presentation. Employers commonly evaluate what a candidate can explain and demonstrate, not only the certificate title.
Course-completion certificate
Assignment and project-based validation
Useful for resume and portfolio building
Certificate details subject to current course policy
Certificate ID
SKL-DAT-2026
Certificate of Completion
This certifies that the student has completed Skillonit’s Data Analytics training with practical tasks, quizzes, and project assessment.
Presented to
Student Name
For successful completion of Data Analytics Course
Completion Date
18 Jun 2026
Authorized Signature
Verification-ready certificate preview for LMS completion.
Learn Excel, SQL, Power BI, Tableau and Python in one connected Data Analytics roadmap.
Practice data cleaning, reporting, dashboard design and business-focused analysis.
Complete assignments, case studies, mini projects and a guided capstone portfolio.
Develop the communication skills needed to explain insights to technical and non-technical audiences.
Access career and placement assistance according to the services included in the active batch.
Business questions, dimensions and measures
KPIs, metrics and targets
Analytical workflow and documentation
Ethics, privacy and responsible interpretation
Workbook and worksheet structure
Data types and formatting
Excel tables and structured references
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.
The capstone brings the full workflow together. Learners select or receive a realistic business case, clarify the questions, inspect and prepare the data, use appropriate tools, build a report or dashboard and present recommendations. Assessment should reward accuracy, reasoning, documentation and communication rather than visual appearance alone. Learner outcome: Demonstrate a connected Data Analyst workflow in a portfolio-ready capstone.
Freshers who are uncertain where to begin receive a sequential roadmap. The course starts with computer and data foundations, develops tool confidence and repeatedly connects technical work to business questions. BCom, BBA, MBA and commerce learners Commerce and management learners often understand finance, marketing or operations but need stronger technical analysis skills. Excel, SQL, Power BI and business metrics can help them combine domain knowledge with data-driven reporting. BCA, BSc, MCA and engineering learners A technology degree is not mandatory. Non-IT learners should be prepared to practise consistently, learn database concepts and become comfortable with structured problem solving. The program introduces the required foundations gradually.
Projects should require learners to make decisions. Copying a finished dashboard is not the same as analyzing data. A strong project asks the learner to inspect data quality, define useful metrics, justify chart choices, calculate KPIs, document assumptions and present recommendations. Feedback should evaluate both technical accuracy and clarity of communication.
Transparency is equally important. Before enrolling, learners should be able to review the current syllabus, actual delivery mode, expected practice time, batch schedule, fees, trainer information, certificate conditions, recording policy and career-support services. Any claim about guaranteed employment, salary, interview rates, trainer experience, partnerships or external recognition should be supported by current evidence.
Learn Excel, SQL, Power BI, Tableau and Python in one connected Data Analytics roadmap.
Practice data cleaning, reporting, dashboard design and business-focused analysis.
Complete assignments, case studies, mini projects and a guided capstone portfolio.
Develop the communication skills needed to explain insights to technical and non-technical audiences.
Access career and placement assistance according to the services included in the active batch.
Business questions, dimensions and measures
KPIs, metrics and targets
Analytical workflow and documentation
Admission enquiry
Share your details and our team will help you choose the right Data Analytics batch, learning mode, syllabus, fee plan, and career path.
Students and recent graduates College students and graduates can use the program to build practical evidence beyond academic marks. A portfolio containing Excel analysis, SQL queries, Power BI dashboards, a Python notebook and a capstone case study can help demonstrate learning in internships and entry-level interviews. Freshers beginning a data career Freshers who are uncertain where to begin receive a sequential roadmap. The course starts with computer and data foundations, develops tool confidence and repeatedly connects technical work to business questions. BCom, BBA, MBA and commerce learners Commerce and management learners often understand finance, marketing or operations but need stronger technical analysis skills. Excel, SQL, Power BI and business metrics can help them combine domain knowledge with data-driven reporting.
Build strong foundation skills in Data Analytics
Apply concepts through guided practical projects
Use portfolio work to start client-ready practice
Prepare for internships, jobs, or higher learning
Projects should require learners to make decisions. Copying a finished dashboard is not the same as analyzing data. A strong project asks the learner to inspect data quality, define useful metrics, justify chart choices, calculate KPIs, document assumptions and present recommendations. Feedback should evaluate both technical accuracy and clarity of communication.
Transparency is equally important. Before enrolling, learners should be able to review the current syllabus, actual delivery mode, expected practice time, batch schedule, fees, trainer information, certificate conditions, recording policy and career-support services. Any claim about guaranteed employment, salary, interview rates, trainer experience, partnerships or external recognition should be supported by current evidence.
Learn Excel, SQL, Power BI, Tableau and Python in one connected Data Analytics roadmap.
Practice data cleaning, reporting, dashboard design and business-focused analysis.
Complete assignments, case studies, mini projects and a guided capstone portfolio.
Develop the communication skills needed to explain insights to technical and non-technical audiences.
Access career and placement assistance according to the services included in the active batch.
Business questions, dimensions and measures
KPIs, metrics and targets
Analytical workflow and documentation
Learn Excel, SQL, Power BI, Tableau and Python in one connected Data Analytics roadmap.
Practice data cleaning, reporting, dashboard design and business-focused analysis.
Internship Program
Skillonit’s Data Analytics Internship Program in India is a mentor-guided practical pathway available in 1-month, 3-month and 6-month formats. Participants learn how to convert raw business data into reliable reports, dashboards, KPIs and decisions using Excel, SQL, Power BI, Tableau and Python. 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, commerce and management graduates, professionals, business users, analysts 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
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
Certificate
Issued after required attendance, submissions, evaluation and final presentation
About Program
Skillonit’s Data Analytics 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 Excel, data cleaning and structured reporting, Power BI or Tableau dashboard development and data modelling, DAX and advanced SQL. 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 analytics and business intelligence 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, Excel, data cleaning and structured reporting and a short guided exercise.
Week 2 – Core practice: SQL querying and database basics, mentor demonstration, individual practice and a quality checklist.
Week 3 – Mini project build: apply statistics, KPIs and analytical thinking 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: Excel MIS and performance report.
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: Excel, data cleaning and structured reporting, SQL querying and database basics, statistics, KPIs and analytical thinking; tool setup; guided exercises; communication and documentation standards.
Month 2 – Applied delivery: Power BI or Tableau dashboard development, business case analysis and data storytelling, Python-based analysis with pandas; first project review; debugging, critique or analysis; iteration after feedback.
Month 3 – Capstone and portfolio: build Power BI executive dashboard 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 SQL sales analysis, Power BI executive dashboard and Marketing campaign analysis.
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: Excel, data cleaning and structured reporting and SQL querying and database basics, baseline tasks and work standards.
Month 2 – Core build: statistics, KPIs and analytical thinking plus the first guided project and review cycle.
Month 3 – Applied specialization: Power BI or Tableau dashboard development and business case analysis and data storytelling with an intermediate project.
Month 4 – Integration: Python-based analysis with pandas 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 Marketing campaign analysis, Operations or inventory analytics and End-to-end BI capstone.
Skills and Tools
Working Environment
The exact stack may vary by batch and project. Typical tools include Microsoft Excel, Power Query, SQL, Power BI, DAX, Tableau, Python, pandas, GitHub, approved cloud spreadsheet or database. 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 skills are enough for the foundation path. Familiarity with spreadsheets is useful but not mandatory.
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 Analytics Intern, Business Intelligence Intern, MIS Reporting Intern, Power BI Intern, SQL Analytics Intern, Business Analyst 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 Analytics Internship Program 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.
Apply Now
Internship FAQs
Skillonit’s Data Analytics Internship Program in India is a mentor-guided practical pathway available in 1-month, 3-month and 6-month formats. Participants learn how to convert raw business data into reliable reports, dashboards, KPIs and decisions using Excel, SQL, Power BI, Tableau and Python. 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 skills are enough for the foundation path. Familiarity with spreadsheets is useful but not mandatory.
Projects may include Excel MIS and performance report, SQL sales analysis, Power BI executive dashboard, Marketing campaign analysis. 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.
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.
Course guide
A detailed, student-friendly guide covering the learning path, tools, projects, career preparation, certification, and course expectations.
Please contact Skillonit for current course details.
Yes. The program starts with foundations and progresses through Excel, Power Query, SQL, statistics, Python and BI tools. Beginners must practise consistently outside live sessions.
No. Prior programming is not compulsory. Python is introduced from the beginning for analytical tasks.
Basic arithmetic, percentages and logical thinking are useful. The statistics required for the course are taught step by step; advanced mathematics is not required for the core pathway.
The planned toolset includes Excel, Power Query, SQL, Power BI, DAX, Tableau, Python, Jupyter Notebook, pandas, NumPy, Git and GitHub. The active batch syllabus should confirm exact depth and versions.
Yes. The curriculum includes structured tables, formulas, lookups, conditional calculations, PivotTables, charts and reporting practices.
Yes. Learners progress from SELECT and filtering to joins, subqueries, common table expressions and window functions.
Yes. The course covers data preparation, modelling, relationships, DAX measures, interactive reports and publishing or sharing awareness.
Please contact Skillonit for current course details.
Not every role uses Python daily, but it is valuable for repeatable cleaning, exploration and automation. Excel, SQL and BI tools remain central to many analyst roles.
Please contact Skillonit for current course details.
Please contact Skillonit for current course details.
Please contact Skillonit for current course details.
A Data Analyst usually works directly with datasets, SQL, spreadsheets and BI tools. A Business Analyst often focuses on processes, requirements and stakeholders, although roles vary.
Yes. Non-IT learners can join and should allow extra time for databases, software setup and technical practice.
Yes. BCom, BBA, MBA and finance learners can combine domain knowledge with Excel, SQL and dashboard skills.
Learners may join after 12th subject to the current admission policy. They should build a strong foundation and continue formal education where appropriate for their career goal.
Please contact Skillonit for current course details.