Data roles are among the most in-demand and highest-paying careers in tech today, and companies are no longer just hiring "Data Scientists" — they want people who can code in Python, query SQL, build and evaluate ML models, and understand how modern Generative & Agentic AI fits into real products. This course is built exactly for that shift, taking you foundation-first through Python, statistics, machine learning, deep learning and NLP, deployment, and finally Generative & Agentic AI — with 6 mentor-reviewed portfolio projects and a capstone along the way
● Every industry — e-commerce, BFSI, healthcare, marketing, manufacturing — is hiring for data and AI
roles
● Employers increasingly expect data professionals to understand LLMs, RAG, and AI agents, not just
traditional ML — this course covers both
● Beginner-friendly: no CS degree or prior coding experience required, so career-changers and freshers can
start from zero
● A strong, in-demand skill comb
● Data Science, AI & ML roles are consistently ranked among the fastest-growing and most in-demand job
categories in India and globally
● Companies are actively hiring for hybrid profiles that combine classic ML with Generative AI / Agentic
AI skills — a combination very few institutes teach together
● A portfolio of 6 real, mentor-reviewed GitHub projects plus a capstone gives you tangible proof of skill
that recruiters can verify instantly
● 4 live mentor-led sessions per week (~12–15 hrs/week) with guided self-study
● Every week ends with a hands-on lab or a mentor-reviewed deliverable—not passive video watching
● Build 6 portfolio projects: EDA Dashboard, Churn Prediction, Sales Forecasting (XGBoost), Customer Segmentation & Recommender, NLP Text Classifier, and Model Deployment (Docker + FastAPI)
● Complete an open-ended capstone project, mentored from start to finish, and present it live to mentors and peers
● Learn from mentors experienced in real-world Data Science and AI project delivery
● Weekly concept checks to ensure you understand every stage of the curriculum
● Receive code and GitHub repository reviews focused on readability, reproducibility, and documentation—the same standards employers use to evaluate candidates
● Resume and LinkedIn profile building centered around your GitHub portfolio and capstone project
● Mock interviews covering Python, SQL, Machine Learning case studies, and GenAI/Agentic AI concepts
● Placement assistance through our employer and agency network, with continued support even after course completion
Yes. We offer dedicated placement support, including resume building, mock interviews, and referrals. Our team stays engaged with you until you're placed, with an overall placement track record of 70%+.
Yes. The course is beginner-friendly and starts with Python fundamentals in Week 1, so no prior coding experience is required. All you need is curiosity and a commitment of around 12–15 hours per week.
Yes. Employers primarily look for practical skills and a strong project portfolio. This course helps you build six mentor-reviewed GitHub projects plus a capstone, and we also prepare you to confidently explain your career gap during interviews.
Every week includes a hands-on lab or practical deliverable. From Week 7 onward, you'll build mentor-reviewed portfolio projects including EDA dashboards, churn prediction models, forecasting pipelines, customer segmentation, NLP classifiers, and deployed APIs using real datasets.
You can pursue roles such as Data Analyst, Junior Data Scientist, Entry-Level ML Engineer, Business Intelligence Analyst, and AI/ML Associate, with a clear path toward Data Scientist and AI Engineer roles as you gain experience.
Both. The course builds strong foundations in Python, Statistics, Machine Learning, Deep Learning, and NLP before progressing into Generative AI and Agentic AI topics including LLMs, Prompt Engineering, RAG, Vector Databases, and AI Agents.
Yes. Upon successfully completing all phase deliverables and the capstone project, you'll receive a Certificate of Completion in Data Science with AI & ML from Acton Engineers, along with a GitHub portfolio showcasing your projects.
Yes. Data Science and AI/ML skills, supported by a strong GitHub portfolio, are recognized and in demand globally, including across the UAE, Saudi Arabia, and other Gulf countries, making this course an excellent foundation for an international career.
You'll learn Python, Jupyter Notebook/Google Colab, Git & GitHub, SQL, Excel, NumPy, Pandas, Matplotlib, Seaborn, Tableau or Power BI, scikit-learn, XGBoost, SciPy, statsmodels, PyTorch, Hugging Face Transformers, Docker, FastAPI, AWS/GCP fundamentals, LangChain, and vector databases such as FAISS and Chroma.
Become a job-ready Data Scientist with hands-on ML, Deep Learning, and Generative & Agentic AI skills — backed by real projects, mentor support, and a team that stays with you till you're placed.
Format: 4 Live Mentor-Led Sessions per Week + Self-Study (~12–15 Hours/Week)
-> W1: Python Programming Foundations – Variables, Control Flow, Data Structures
-> W2: Functions, Files, OOP Basics & Git/GitHub Workflow
-> W3: SQL & Relational Databases – Joins, CTEs & Aggregations
-> W4: Data Acquisition – REST APIs, Excel, CSV & JSON Data
-> W8: ML Foundations, Regression & Model Evaluation
-> W9: Classification & Churn Prediction Project
-> W10: Feature Engineering, XGBoost & Sales Forecasting
-> W11: Customer Segmentation & Recommendation Systems
-> W5: Statistics, Probability & Hypothesis Testing
-> W6: NumPy, Pandas & Data Wrangling
-> W7: EDA, Data Visualization & Dashboard Project
-> W12: Deep Learning Fundamentals & CNNs using PyTorch
-> W13: NLP, Transformers & Hugging Face Text Classifier Project
-> W14: Docker, FastAPI & Model Deployment (Capstone Begins)
-> W15: LLMs, Prompt Engineering, RAG & Vector Databases
-> W16: Agentic AI, Tool-Using Agents & Capstone Showcase
An open, mentor-scoped end-to-end data science project — chosen by Week 12–13, built mainly across Weeks 14–16, and presented live in Week 16 as the centerpiece of your portfolio
6 mentor-reviewed portfolio projects, weekly hands-on deliverables, phase-end concept checks, code/repo quality reviews, and a final capstone presentation
Certificate of Completion in "Data Science with AI & ML" + a GitHub portfolio of 6 projects and a capstone to present to employers.
