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Explore CoursesA practical Data Science course in Bangalore that takes you from Python and statistics to machine learning, deep learning and NLP, then into Generative AI: LLMs, prompt engineering, RAG and AI agents. Built for students, fresh graduates, IT professionals and career switchers who want skills they can demonstrate in projects and interviews.
Data Science with Agentic AI & Gen AI is a Data Science course by Ascent Software Training Institute, taught in the classroom at BTM 1st Stage, Bangalore, or live online. It covers Python, statistics, machine learning, deep learning, NLP and Generative AI (LLMs, RAG and AI agents) over 120+ hours of instructor-led training, with case studies, a real-time project and Placement & Career Support.
Fees: ask a career advisor for the current fee · Sources: the Data Science with AI & Generative AI brochure (Oct 2026) of Ascent Software Training Institute
See batch timingsA traditional data science course stops at analysing data and building predictive models. Data Science with Agentic AI & Gen AI keeps those foundations and adds the next two steps: how generative models create content, and how agentic systems combine those models with tools, memory and workflows to complete multi-step tasks.
Data → Analysis → Models → Insights
Use statistics and programming to understand data and support decisions.
Examples → Patterns → Predictions
Models learn patterns from data instead of following hand-written rules.
Prompt → Model → New content
Deep-learning models, such as LLMs, generate text, code, images or other content.
Goal → Plan → Tools → Check → Result
Systems use models, tools, memory and workflows to accomplish multi-step tasks, within limits set by people.
Not a ranking: Agentic AI is not simply "better AI". Each layer solves different problems, and most real systems still depend on solid data, statistics and model evaluation.
| Aspect | Traditional Data Science course | Data Science with Agentic AI & Gen AI |
|---|---|---|
| Foundations | Python, statistics, ML, deep learning | Same foundations: Python, statistics, ML, deep learning, NLP |
| What models do | Predict, classify, cluster, forecast | Also generate content and drive multi-step AI workflows |
| Language skills | Classic NLP: text cleaning, TF-IDF, sentiment | Classic NLP, plus how LLMs, embeddings and RAG build on it |
| Typical outputs | Models, dashboards, reports | Models and reports, plus AI assistants and agent workflows |
| Career direction | Data Analyst, Data Scientist | Data Scientist, plus a path towards AI Engineer and GenAI roles |
Data science is the practice of using statistics, programming and machine learning to turn raw data into insights, predictions and better decisions.
Records of what happened: sales, clicks, calls, transactions, text and images.
Tools to summarise data and tell real patterns from random noise.
Python, NumPy and Pandas to clean, analyse and model data at scale.
Algorithms that learn patterns from past data to make predictions.
Every project starts from a question, such as "which customers might leave?"
Estimating what is likely to happen next: churn, demand, risk.
Turning results into recommendations people can act on.
General definitions. Course coverage of these topics is listed in the syllabus below.
Generative AI is a type of artificial intelligence that creates new content, such as text, code, images or audio, by learning patterns from large amounts of existing data.
Traditional ML usually predicts or classifies: "Will this customer churn? Yes or no." Generative AI produces something new: "Write a retention email for this customer." Both learn from data, but the output is different.
A large language model (LLM) is a deep-learning model trained on very large amounts of text to predict the next word. That simple training goal lets it answer questions, summarise, translate and write code. It can also be confidently wrong.
Text and summaries, source code, images, audio, structured data such as JSON, and embeddings: numerical representations of meaning used for search and retrieval.
Document question-answering, customer-support assistants, report and email drafting, code assistance, search over internal knowledge, and summarising calls or tickets.
Agentic AI refers to AI systems designed to pursue a goal through multiple steps by reasoning or planning, using tools, maintaining state or memory where appropriate, and taking actions within defined boundaries.
Understands the goal and decides what to do next.
Calls search, a database, code or an API to get facts or do work.
Keeps track of results so far and relevant context.
Breaks the goal into steps and adjusts the plan.
Takes the next step within the permissions it has been given.
Checks the result; repeats, finishes, or asks a human.
↻ The loop repeats until the goal is met, a limit is reached, or a person needs to decide.
| Concept | What it does | How Agentic AI differs |
|---|---|---|
| Chatbot | Replies to messages in a conversation | An agent can take actions and use tools across several steps, not just reply |
| Prompt engineering | Writes better instructions for one model call | Agentic systems manage many calls, tools and decisions; prompts are one ingredient |
| Traditional automation | Follows fixed, pre-written rules | An agent chooses its next step based on the situation, so it is more flexible but less predictable |
| Generative AI | Generates content in response to a prompt | Agentic AI uses generative models inside a loop of planning, tool use and checking |
AI agents are useful, but they are not fully autonomous or infallible. Good agentic systems are designed with these risks in mind.
General industry explanation, not a description of a specific Ascent Software Training Institute module.
Each layer depends on the one before it. The data science foundations come first; Generative AI and Agentic AI build on top of them.
The course suits freshers, job seekers and working professionals who are comfortable with numbers and willing to practise coding regularly. People from analytics, software, testing or engineering backgrounds usually progress fastest; others can succeed with extra practice on Python and statistics.
BE, BTech, BSc, BCA, MCA and other graduates starting a data career.
Add ML and AI application skills to your programming experience.
Move from reporting to predictive modelling and machine learning.
Use your scripting and testing skills in data and AI work.
Weekday and weekend batches fit around work.
Move into data from finance, operations, marketing or other fields.
Understand how modern AI actually works, from data to agents.
If these apply to you, spend extra time on basics before or alongside the course. A career advisor can suggest where to start.
The syllabus begins with an introduction to data science and teaches Python from the basics before statistics, so beginners can start from Python fundamentals. You will still need time to practise every week, especially in the first modules.
The Data Science syllabus runs from statistics and Python to machine learning, deep learning and NLP, then a full Generative AI program, ending with a real-time project and resume preparation. Open any module to see what you learn, what you practise and why it matters.
How data science, analytics, machine learning and AI relate, and how a data project runs from sourcing data to insight.
Evaluating a dataset for quality and planning the steps to prepare it.
Most of a data scientist's time goes into understanding and preparing data.
Python fundamentals and the NumPy and Pandas skills used in every later module.
Loading, cleaning, grouping and plotting real datasets in Pandas.
Python is the main language of data science and AI.
Clean, reusable Python and the statistics behind every model.
Summarising data and running hypothesis tests on a sample.
Statistics is how you judge whether a pattern is real or noise.
The core machine learning algorithms, when to use each, and how to judge their results.
A case study for each algorithm, from clustering customers to forecasting a time series.
These algorithms solve most everyday business prediction problems.
How to take a model from working to well-tuned, and how to compare approaches fairly.
An end-to-end ML project with feature selection, ensembles and tuning, plus a sentiment analysis case study.
Good features and tuning often matter more than the choice of algorithm.
How neural networks learn, and how CNNs recognise images.
Building a digit classifier and a CNN for image classification.
Deep learning powers image, speech and language AI, including LLMs.
Sequence models, unsupervised deep learning and the frameworks used to build them.
Building an autoencoder and retraining an Inception model on your own images.
TensorFlow and Keras are widely used to build deep learning models in industry.
Part B builds on the data science foundations: LLMs, prompt engineering, RAG, AI agents and GenAI application development, taught in two parts with hands-on projects.
About this syllabus: All topics come from the Data Science with AI & Generative AI brochure. Module 9 is a real-time project with resume preparation (see Projects below). The "you practise" examples describe typical lab exercises.
The course uses Python with NumPy, Pandas and scikit-learn, TensorFlow, Keras and TensorBoard for deep learning, and LLM APIs, LangChain, LlamaIndex and vector databases for generative AI, with an introduction to Hadoop.
Write, run and document analysis step by step.
Load, clean, reshape and summarise data.
Charts for exploration and model monitoring.
Regression, classification, clustering, tuning and forecasting.
Neural networks, CNNs, RNNs and autoencoders.
Text mining, sentiment and sequence models.
Prompting and comparing leading LLMs.
How large datasets are stored and processed.
Building chatbots, RAG systems and AI agents.
Tool names are used only to describe the training. Ascent Software Training Institute does not claim any partnership with these vendors.
Each topic is taught with a presentation, then practised hands-on with assignments, with one-on-one mentorship for each topic. Sessions are 30% theory and 70% practical.
Concept explained with real business examples.
Assignments for each topic in Python, plus a case study per module.
Apply it in a project on real-world data.
Validate models and check results honestly.
Present insights and answer interview questions.
# The kind of workflow practised in the machine-learning modules
import pandas as pd
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.ensemble import RandomForestClassifier
df = pd.read_csv("sales.csv").dropna()
X, y = df.drop(columns="churn"), df["churn"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = RandomForestClassifier().fit(X_train, y_train)
print(cross_val_score(model, X_train, y_train, cv=5).mean())These projects and case studies come from the course syllabus. They cover machine learning, deep learning and generative AI, and the course ends with a real-time project.
A store wants "customers also bought" suggestions from past purchases.
A business needs to forecast values for a future period from historical data.
Find the best model for a business dataset and tune it.
Classify images with deep learning, including your own dataset.
Let users ask questions about a set of documents and get grounded answers.
Automate a multi-step task with an agent that uses tools and APIs.
Gen AI and Agentic AI exercises follow the same discipline as data science projects: a clear problem, the right data, a sensible model choice, and honest evaluation before anything is called finished.
These are example applications to show what each skill set is used for. They are not a list of Ascent Software Training Institute projects.
A Data Scientist works with data, statistics and models to produce insights. An ML Engineer builds and deploys ML systems. An AI Engineer builds applications on top of AI models such as LLMs. An Agentic AI Developer builds systems that use models, tools and workflows to complete multi-step tasks. Job titles vary by company.
| Role | Primary focus | Core skills | Typical outputs |
|---|---|---|---|
| Data Scientist | Data, statistics, ML and insights | Python, statistics, ML, communication | Analyses, models, recommendations |
| ML Engineer | Building and deploying ML systems | ML, software engineering, data pipelines | Production models, ML services |
| AI Engineer | Building AI-powered applications | LLM APIs, RAG, evaluation, software skills | AI features, assistants, search |
| Agentic AI Developer | AI systems that use tools and workflows for multi-step tasks | LLMs, tool calling, orchestration, guardrails | Agents and automated workflows |
General industry overview. Responsibilities overlap and differ between companies. Completing a course does not automatically qualify you for every role.
Most people build skills in this order and start in an entry-level data or AI role. Actual progression depends on your skills, projects, experience and what employers need.
Cleans data, builds reports and answers business questions.
SQL · Python · statistics · visualizationBuilds and evaluates models under senior guidance.
Python · ML · statisticsHelps prepare data pipelines and package models.
Python · scikit-learn · TensorFlowOwns analysis and modelling for business problems.
ML · experimentation · communicationBuilds, deploys and monitors ML systems.
ML · software engineering · deploymentBuilds language and text-processing systems.
NLP · deep learning · LLMsBuilds applications on top of LLMs, including RAG and agents.
LLM APIs · RAG · evaluationRole fit depends on your background and demonstrated skills. Senior roles usually need several years of experience.
Career support runs alongside the course. Classes are followed by commonly asked interview questions, and the career team helps you present your projects when you start applying.
Placement assistance does not guarantee a job. Hiring decisions are made by employers based on the candidate's skills and interview performance.
Guidance on which data or AI role suits your background.
Present your projects, tools and results clearly.
Simulated technical interviews with feedback.
FAQs and real-time interview questions with each topic.
Openings shared with eligible students; internship programs for students and trainees.
Online classes are live and instructor-led, with the same level of guidance as classroom training. Missed a class? You can attend the same topic in another batch within the course duration.
At the BTM 1st Stage centre in Bangalore.
Live sessions with a trainer, from anywhere.
Individual sessions at your own pace.
A shorter, more intensive schedule.
Topics tailored to your goals.
Team training for organisations.
Book a free demo, in the classroom or online, to see the teaching style before you enrol.
| Days | Batch timings |
|---|---|
| Mon – Fri | 8–10 AM12–2 PM6–8 PM7–9 PM |
| Sat – Sun | 8–10 AM12–2 PM |
| Next batch | Contact us for the next batch date Reserve on WhatsApp |
Batch timings as listed on the Data Science course page. Ask a career advisor for the next start date or for different timings.
The course is taught by industry experts: certified working professionals with current industry knowledge, as described by Ascent Software Training Institute.
Feedback from Data Science students, as published by Ascent Software Training Institute. Shown as written.
I completed a Data Science course at Ascent, and their faculty provided invaluable support for my success. Thanks to them, I secured a job. Highly recommended!
I am delighted to announce my selection for a Data Science position at Med ML. I extend my gratitude to the Ascent faculty for their invaluable assistance.
Source: Data Science course page of Ascent Software Training Institute. Outcomes are as stated by the students.
Real students and real classrooms, from the institute's official YouTube channel.
These videos cover the student experience across courses at Ascent Software Training Institute.
Independent reviews from the Google Business Profile of Ascent Software Training Institute, covering all courses. Shown exactly as written.
I took SQL and Power BI training. The trainer is excellent with deep knowledge in both subjects. He explains every concept from basic to advanced in a very simple and…I took SQL and Power BI training. The trainer is excellent with deep knowledge in both subjects. He explains every concept from basic to advanced in a very simple and practical way with real-time projects. Very patient in clearing doubts.Overall,It was a great learning experience and very helpful for my career growth.
Accent Software Training Institute is a very good place to learn IT skills like Power BI, SQL, PowerApps, and Power Automate and other IT skills as well. Even if you…Accent Software Training Institute is a very good place to learn IT skills like Power BI, SQL, PowerApps, and Power Automate and other IT skills as well. Even if you come from a non-technical background, it does not matter because the trainers explain everything in a simple and easy-to-understand manner from scratch. Khader Basha is highly knowledgeable and helpful throughout the learning journey. He explains concepts very clearly in simple terms and makes learning comfortable for beginners as well. The classes are inteactive and engaging. The staff here are also very helpful and guide you through each and every step of the learning process. Highly recommended for anyone looking to build IT skills and grow their career.
I had a very good learning experience at Ascent Software Training Institute. The trainers explained Java, Selenium, Manual Testing and SQL concepts clearly with practical examples. They were patient while…I had a very good learning experience at Ascent Software Training Institute. The trainers explained Java, Selenium, Manual Testing and SQL concepts clearly with practical examples. They were patient while explaining doubts and made the concepts easy to understand. The practical sessions were really helpful for improving my technical knowledge and confidence. Overall, I am satisfied with the training and the learning environment.
Ascent Software Training Institute is an IT training institute at #96, 2nd Floor, 100 Ft Ring Road, BTM 1st Stage, Bengaluru, and a unit of Ascent NextGen Technologies. It offers classroom and live online training in data science, data analytics, cyber security, cloud and DevOps, and full stack development.
Direct answers to the questions students ask most, grouped by topic.
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.
Ascent Software Training Institute is at #96, 2nd Floor, 100 Ft Ring Road, near Mico Layout, above Apollo Pharmacy, BTM 1st Stage, close to Vega City Mall, Bannerghatta Road, Jayadeva Hospital and BTM Water Tank. Students join the Data Science classroom batches from across South Bangalore, and live online batches are available for students elsewhere.
Book a free demo class, download the curriculum, or ask a career advisor about batches, fees and whether the course fits your background.