Data Science with Agentic AI & Gen AI

Data science is the practice of using statistics, programming and machine learning to turn data into insights, predictions and decisions. A data scientist collects and cleans data, explores it, builds models and explains what the results mean for the business.

A data scientist frames a business question, gathers and prepares the data, explores it, builds and validates statistical or machine-learning models, and communicates the findings so people can act on them. The exact mix varies by company.

It is challenging but learnable with steady practice. The hardest parts for most people are statistics and getting comfortable with Python. Learning in a structured order, from Python and statistics to machine learning, makes it much easier.

Yes. The course starts with an introduction to data science, statistics and Python basics, so beginners can join. Expect to spend extra time practising Python and statistics in the first weeks.

Nine modules: data science and big data foundations; Python with NumPy and Pandas; functions, OOP and statistics; machine learning with Python, including time series; an ML project on feature selection, ensembles and tuning; deep learning and CNNs; RNNs, autoencoders, TensorFlow and Keras; a Generative AI program covering LLMs, prompt engineering, RAG and AI agents; and a real-time project with resume preparation.

Python from the basics: variables, strings, flow control, lists, tuples, dictionaries and sets, comprehensions and generators, functions, lambda functions, classes and OOP, then NumPy and Pandas for data analysis and plotting.

Yes. You learn K-Means clustering, association rules and recommendation engines, decision trees, random forest, Naive Bayes, linear and logistic regression, support vector machines and time series forecasting with ARIMA and ETS, then feature selection, ensembles such as AdaBoost and gradient boosting, cross-validation and GridSearchCV tuning.

Yes. Deep learning covers neural networks, gradient descent and backpropagation, multi-layer perceptrons, CNNs for image classification, RNNs and LSTMs, autoencoders, TensorFlow, TensorBoard, Keras and transfer learning.

Yes. The course covers TF-IDF and the vector space model, text mining and sentiment analysis, RNNs and LSTMs for sequences, and large language models in the Generative AI module.

Case studies run through every module, such as a recommendation engine, time series forecasting, an end-to-end ML project and CNN image classification. The Generative AI module adds an AI chatbot, a document Q&A system using RAG and workflow automation with AI agents, and the course ends with a real-time project and a GenAI capstone.

The course has 120+ hours of instructor-led training plus assignments and project work, usually completed in about 5 to 6 months. The exact schedule depends on the batch (weekday or weekend, classroom or online), so ask a career advisor for current batch timings.

Yes. Module 8 is a Generative AI program covering LLMs, prompt engineering, RAG with vector databases, AI agents and multi-agent systems, LLM APIs, LangChain and LlamaIndex, and responsible AI, with hands-on projects.

Generative AI is a type of AI that creates new content, such as text, code, images or audio, by learning patterns from large amounts of existing data. Large language models (LLMs) are the best-known example.

Generative AI builds on the same foundations as data science, including statistics, machine learning, deep learning and NLP. Many data professionals now use generative AI tools, but it is a specialised area, not a replacement for core data science skills.

Large language models (LLMs) are deep-learning models trained on very large amounts of text to predict the next word. That lets them answer questions, summarise, translate, write code and follow instructions. They can also be confidently wrong, so outputs need checking.

Retrieval-augmented generation (RAG) is a technique where relevant information is first retrieved from a document store, often using embeddings and a vector database, and then given to an LLM so its answer is grounded in that source material.

Prompt engineering is the practice of writing and structuring instructions for an LLM, such as role, context, examples and output format, so it produces more accurate and useful responses.

Agentic AI refers to AI systems designed to pursue a goal through multiple steps by reasoning or planning, using tools, keeping state or memory where appropriate, and taking actions within defined boundaries.

Generative AI produces content in response to a prompt. Agentic AI uses a generative model as one component inside a system that can plan steps, call tools such as search or code, keep track of progress and act towards a goal. Agentic AI is not simply "better AI"; it is a different way of using models.

An AI agent is a program, usually built around an LLM, that decides which steps to take and which tools to use to complete a task, then checks the results and continues until the task is done or it needs human input.

Through tool or function calling: the developer describes the available tools (for example a search, a database query or a calculator), the model chooses a tool and its inputs, the application runs it, and the result is fed back to the model for the next step.

Multi-agent AI uses several specialised agents, for example a researcher, a writer and a reviewer, that pass work between each other under some form of orchestration. It can help with complex tasks but adds cost and makes behaviour harder to test.

Agent memory is how an agent keeps information between steps or sessions: short-term state such as the current plan and tool results, and longer-term memory such as stored notes or documents it can retrieve later.

In agentic systems, RAG is often one of the tools an agent can call: the agent decides when it needs information, retrieves relevant documents, and uses them to plan or answer, rather than relying only on what the model memorised.

Not reliably for important tasks. Agents can hallucinate, call tools incorrectly or be manipulated by malicious inputs, so well-designed systems limit permissions, log actions, evaluate outputs and keep a human in the loop for consequential decisions.

It can be, if you build strong fundamentals and practical projects. Freshers often start as data analysts or junior data scientists. Competition is real, so skills you can demonstrate matter more than a certificate alone.

Common starting roles include Data Analyst, Junior Data Scientist and Junior ML Engineer. With experience and further skills, people move into Data Scientist, ML Engineer, NLP Engineer or AI Engineer roles. Role fit depends on your background and demonstrated skills.

Yes, many non-IT graduates learn data science, especially those comfortable with numbers. Plan extra time for Python and statistics at the start; a career advisor can suggest whether to begin with a foundation course.

Data analytics focuses on describing what happened and why, using SQL, spreadsheets, statistics and dashboards. Data science goes further into building predictive and machine-learning models. Many data scientists start as analysts.

AI is the broad field of building systems that perform tasks that normally need human intelligence. Data science is the practice of extracting insights and predictions from data. They overlap heavily through machine learning, which is used in both.

Learn the data science foundations first: Python, statistics, machine learning and NLP. Generative AI and agentic AI build on these, and employers increasingly expect people working with LLMs to understand data, evaluation and model limitations.

Yes. Classroom training is held at #96, 2nd Floor, 100 Ft Ring Road, near Mico Layout, above Apollo Pharmacy, BTM 1st Stage, Bengaluru, Karnataka – 560029.

Yes. Live instructor-led online training is available, as well as one-to-one, fast track, customised and corporate training.

Yes. Sessions are 30% theory and 70% practical, with case studies in each module, a real-time project, a Generative AI capstone and resume preparation.

Yes. You can book a free demo class, in the classroom or online, at a time that suits you. Call 080-4219 1321 or message 90350 37886 on WhatsApp.

Yes. Placement and Career Support includes resume building, interview preparation, mock interviews, career counselling and sharing job opportunities. Placement assistance does not guarantee a job; hiring decisions are made by employers.

Ascent Software Training Institute is at #96, 2nd Floor, 100 Ft Ring Road, near Mico Layout, above Apollo Pharmacy, BTM 1st Stage, Bengaluru, Karnataka – 560029, close to Vega City Mall and Jayadeva Hospital.