The Data Science course runs for 8 weeks (2 months). You’ll attend live sessions + guided practice with a flexible routine:

• Live classes: 3–4 days/week (interactive + practical demos).
• Practice & assignments: ~45–90 minutes/day (hands-on coding + case studies).
• Weekly reviews: quiz + mini task to check progress.

Even if you’re a beginner, the plan is designed to help you build skills step-by-step without overload.

Yes. This program is beginner-friendly. You do not need prior Data Science experience.

• Basic requirement: ability to use a computer and willingness to practice.
• Helpful (not mandatory): basic math (percentages, graphs) and basic Excel understanding.
• We teach from scratch: Python basics, data handling, analysis, and project workflow.

We start with foundations and then move toward real-world datasets and industry tasks.

A laptop is highly recommended for Data Science because you will code, analyze datasets, and build projects.

• Minimum: i3 / Ryzen 3, 8GB RAM, 256GB SSD (or HDD), Windows 10/11.
• Recommended: i5 / Ryzen 5, 16GB RAM for smoother work with bigger datasets.
• Tools used: Python, Jupyter/Colab, Pandas, NumPy, Matplotlib, SQL tools.

If you don’t have a laptop initially, you can attend live sessions from mobile, but for assignments and projects you should use a laptop or lab system.

This course covers the complete Data Science workflow from data collection to insights and basic modeling:

• Python for Data Science: syntax, functions, data types, loops, file handling.
• Data Analysis: Pandas, NumPy, data cleaning, missing values, outliers.
• Visualization: Matplotlib/Seaborn basics, charts, storytelling with data.
• Statistics & Basics of ML: mean/median, probability concepts, evaluation basics.
• SQL for Analytics: select, joins, group by, window basics (as needed for jobs).
• Real-world workflow: problem statement → dataset → analysis → insights → presentation.

You’ll learn skills that directly match Data Analyst / Junior Data Scientist requirements.

You will complete 4 industry-style Data Science projects plus smaller assignments. Example project types include:

• Sales / Revenue Insights Dashboard: clean data, analyze trends, create charts & insights.
• Customer Segmentation: understand customer behavior and group patterns.
• Churn / Retention Analysis: identify reasons users leave and recommend actions.
• Basic Prediction Model: simple supervised learning + evaluation (beginner-friendly).

Each project is built in a portfolio-ready format: notebook + report + key insights.

Yes. After successful completion, you will receive a Data Science course completion certificate. The certificate is provided after you:

• Attend the required sessions (or complete the recorded learning if provided).
• Submit assignments and complete the capstone/projects.
• Clear the final evaluation (project review / final assessment).

The certificate helps strengthen your resume and LinkedIn profile for Data Science roles.

Yes, this is a live, interactive Data Science program. Doubt support is provided throughout:

• Live doubt clearing: Q&A during sessions + dedicated doubt time.
• Mentor support: guidance on assignments, project debugging, and career direction.
• Practice help: we explain not just “what” but also “why” (concept clarity).

You’ll never feel stuck—support is built into the learning journey.

Yes, you’ll get career support focused on Data Science/Analytics roles. This typically includes:

• Resume & LinkedIn optimization: highlight your Data Science projects properly.
• Interview preparation: Python/SQL questions, case study approach, project explanation.
• Portfolio guidance: how to present notebooks, reports, and insights clearly.
• Internship guidance: how to apply + what to prepare for entry-level opportunities.

(Final opportunities depend on your performance, consistency, and project quality.)

You’ll use industry-standard Data Science tools used by Data Analysts and Data Scientists:

• Python: core language for analysis and modeling.
• Jupyter Notebook / Google Colab: for practical coding sessions and projects.
• Pandas & NumPy: for data cleaning, processing, and analysis.
• Matplotlib/Seaborn: for charts and visual storytelling.
• SQL: for data extraction and analytics queries (job-ready).

Everything is taught step-by-step and you’ll get practice datasets to build confidence.

After completion, you can start applying for entry-level roles based on your project portfolio and skill level:

• Data Analyst (Fresher / Intern)
• Business Analyst (Entry-Level)
• Junior Data Scientist (Trainee)
• Analytics Intern
• Reporting / MIS Analyst

You’ll also learn how to explain your projects in interviews, which is a key requirement for Data Science hiring.