DATA SCIENCE & APPLIED AI
EDUCORA CAREER PROGRAMData Science & Generative AI
Learn Python, SQL, statistics and machine learning, with practical model evaluation and introductory generative AI.
3 months training + 3 months internship
BUILD PRACTICAL SKILLS
Explore data. Evaluate models. Explain results.
This practical, job-oriented Data Science course is designed for students and recent graduates who want to turn data into insights and build entry-level predictive applications. The course starts with Python and statistics, moves through data preparation, exploratory analysis and supervised learning, and ends with model evaluation, basic deployment, and applied generative AI concepts.
split → train / validate / test
model → compare with baseline
report → metrics + limitations
Illustrative learning project · program-specific practical work
A CLEAR LEARNING PATH
More than a list of topics
Develop practical skills, demonstrate your work and apply your learning during a company internship.
Learn the foundations
Follow a structured technical curriculum with progressive practical tasks.
Build and demonstrate
Develop projects, document your work and present your learning.
Work with AI responsibly
Use AI assistance while checking accuracy, privacy and limitations.
Apply your skills
Complete an actual three-month company internship after training.
FIND YOUR STARTING POINT
Who can join this program?
Review the audience and prerequisites to see how the program fits your background.
Final-year students and fresh graduates from BCA, B.Sc. (IT/CS/Mathematics/Statistics), B.Tech/BE, MCA or related disciplines.
B.Com, BBA, MBA and other graduates with a strong interest in data and a readiness to learn Python and statistics.
Aspiring Data Science Interns, Junior Data Analysts, ML Trainees and analytics professionals.
Early-career professionals looking to transition into practical data and machine-learning workflows.
Prerequisites and equipment
- Basic computer and spreadsheet knowledge, file handling, and internet use.
- Comfort with school-level algebra and percentages; willingness to learn probability and statistics.
- No professional Python experience required, although introductory programming is helpful.
- Laptop with at least 8 GB RAM (16 GB recommended), internet, and ability to run Python notebooks.
- Willingness to complete regular practice, troubleshoot code, and explain findings in English.
FROM LEARNING TO APPLICATION
Your learning journey
Python & statistics
Build the foundations.
Machine learning
Practise with real tasks.
Applied GenAI
Demonstrate your work.
Company internship
Apply your learning in a company.
THE COMPLETE SYLLABUS
A structured curriculum
Three months of technical training with a progressive learning sequence, practical exercises and project reviews.
Expand each section to see the topics and practical outcome. Introductory tool coverage is identified within the syllabus.
Ask About This ProgramWeek 1 — Python Programming Foundations
Jupyter, Python syntax, data types, conditions, loops, functions, collections, file I/O, virtual environments, notebooks and introductory Git.
Practice outcome: Write a documented Python notebook to clean and summarize a small CSV dataset.
Week 2 — Python Data Toolkit
NumPy arrays, Pandas Series/DataFrames, filtering, groupby, joins, missing values, duplicates, date handling and data profiling.
Practice outcome: Prepare a reproducible data-cleaning notebook with validation checks.
Week 3 — SQL for Data Science
Relational concepts, PostgreSQL or SQLite, SELECT, WHERE, JOIN, GROUP BY, HAVING, subqueries, CTEs and basic window functions.
Practice outcome: Use SQL to extract a customer or sales analysis dataset.
Week 4 — Statistics, Probability & Business Questions
Descriptive statistics, distributions, probability, sampling, confidence intervals, hypothesis-testing intuition, correlation vs causation and KPI interpretation.
Practice outcome: Explain a data-driven business finding with appropriate caveats.
Week 5 — Exploratory Data Analysis & Visualization
EDA workflow, summary profiling, visualization with Matplotlib and Seaborn, outliers, leakage awareness, feature distributions and clear storytelling.
Practice outcome: Produce an exploratory analysis report with charts and written insights.
Week 6 — Machine Learning Workflow
Problem framing, supervised vs unsupervised learning, train/validation/test splits, baselines, scikit-learn pipelines, preprocessing and feature scaling.
Practice outcome: Train a baseline regression or classification model without leakage.
Week 7 — Regression & Classification
Linear/logistic regression, regularization overview, decision trees, random forests, class imbalance, confusion matrices, precision, recall, F1, ROC-AUC, MAE and RMSE.
Practice outcome: Compare candidate models using the right metrics and baseline.
Week 8 — Model Validation & Improvement
Cross-validation, hyperparameter search, feature engineering, overfitting/underfitting, error analysis, interpretability and fairness basics.
Practice outcome: Document model selection and limitations in a model evaluation report.
Week 9 — Unsupervised Learning & Segmentation
K-means, scaling, selecting cluster count, PCA intuition, segmentation case studies, and why clustering results require business validation.
Practice outcome: Build and explain a customer segmentation notebook.
Week 10 — Deployment & Reproducibility
Model serialization, inference workflow, Streamlit introduction, FastAPI overview, GitHub READMEs, environment files, tests and basic monitoring concepts.
Practice outcome: Build a small Streamlit prediction demo with usage instructions.
Week 11 — Generative AI & Responsible Applied AI
LLM and embeddings fundamentals, prompting, structured output, introductory retrieval-augmented generation, hallucination checks, data privacy, model evaluation and AI-assisted coding.
Practice outcome: Prototype a small document Q&A or AI-assisted analyst workflow with test cases.
Week 12 — Capstone & Internship Readiness
Problem statement, data permissions, EDA, feature engineering, models, evaluation, simple app, GitHub documentation, presentation, interview preparation and technical review.
Practice outcome: Present an end-to-end capstone with reproducible repository and limitations.
MAKE YOUR LEARNING VISIBLE
Projects and portfolio
Turn the syllabus into practical work you can explain, demonstrate and discuss in interviews.
Data preparation notebook
Clean and validate a dataset using Python and SQL.
Predictive model comparison
Compare regression or classification models using suitable metrics.
Customer segmentation
Explore clustering and explain the limits of the findings.
End-to-end ML capstone
Present a reproducible model, evaluation and simple application.
Skills you will practise
- Write Python programs and use Pandas, NumPy and SQL for data preparation and analysis.
- Apply essential statistics and exploratory data analysis to business problems.
- Select and evaluate regression, classification and clustering techniques at foundation level.
- Recognize data leakage, overfitting, class imbalance and the limitations of model metrics.
- Build a basic reproducible machine-learning workflow and demonstrate it through a simple application.
- Use introductory GenAI tools responsibly, verify outputs and explain RAG fundamentals.
- Maintain a clear GitHub portfolio, document findings, and communicate results to technical and nontechnical audiences.
AFTER COMPLETING TRAINING
An actual 3-month company internship
Apply your technical learning through supervised company work. The internship follows the three-month training phase.
Month 4: Data Preparation & EDA
Understand a business problem; inspect authorized datasets; perform cleaning, SQL extracts, EDA and data-quality validation.
Work evidence: Problem brief, data dictionary, SQL/Python notebooks, weekly mentor sign-offs
Month 5: Modeling & Evaluation
Build baseline and improved models; validate results; compare metrics; perform error analysis and prepare review demonstrations.
Work evidence: Experiment log, evaluation report, reproducible pipeline and mentor feedback
Month 6: Delivery & Communication
Package a model or analytics prototype; create documentation; test inference; present business value, risks, and limitations.
Work evidence: Final repository, demo, documentation, presentation and supervisor evaluation
Internship documentation reflects the actual period and activities completed. Enquire about company arrangements, work mode, timings and any applicable stipend.
PREPARE FOR YOUR NEXT STEP
Career opportunities
Build skills to apply for entry-level opportunities aligned with this program.
Career opportunities depend on skills, available openings and employer selection. Employment is not guaranteed.
PRESENT YOUR WORK WITH CONFIDENCE
Career preparation
Project presentation
Explain your approach, demonstrate your work and discuss technical choices.
Portfolio documentation
Organise project files, reports or repositories to show what you have built.
Interview readiness
Practise technical discussions and interview exercises included in the program.
Assessment includes practical assignments, project demonstrations and feedback. Training completion and internship participation are documented separately.
YOUR QUESTIONS, ANSWERED
Frequently asked questions
What is the program duration?
Six months: three months of training followed by three months of internship.
Is this an actual company internship?
Yes. Educora offers an actual three-month company internship after completion of training.
Do I need prior experience?
Basic computer and spreadsheet knowledge, file handling, and internet use. Comfort with school-level algebra and percentages; willingness to learn probability and statistics.
What are the fees and batch timings?
Please contact Educora for current fees, upcoming batches, delivery mode and class timings.
Is the internship paid?
Please enquire about any applicable stipend and the specific internship arrangements.
Is a job guaranteed?
No. The program includes career preparation; employment depends on vacancies, skills and employer selection.
LET’S DISCUSS YOUR GOALS
Choose your next step with Educora.
Ask about eligibility, the syllabus, fees, upcoming batches and the company internship.