Program Overview
Today, nearly all companies need people who understand data and AI. Consider hospital staff scheduling, fraud spotting by banks and recommendations by online stores. Every one of them uses these systems. If you want to learn these skills by building things, our program at AcceleratorX gives you a clear path to getting certified.
We begin where spreadsheets conclude. In this course, you’ll write code for neural networks, set up data pipelines and run predictive models in the cloud. You will create real projects and work with mentors to showcase your capabilities to hiring managers.
What is Artificial Intelligence Data Science?
Standard data analysis involves looking at past events. Data science in AI goes further to utilize machine learning to predict what might come next and automate tasks we do every day.
People in this field use algorithms and automated data pipelines to work with large datasets using natural language processing and deep learning tools. They create real systems that address common business challenges.
What’s so Special about AcceleratorX Data Science Course?
This program aims to teach the specific skills companies look for when they hire people, significantly adding to the utility value of the product.
Weekly Mentorship
Meet working professionals weekly for mentorship check-ins.
Real-World Projects
Projects can help you build a portfolio of real-world work.
IBM Certification
You Will Get An IBM Certificate Upon Completion.
Interview Prep
Mock interview and resume review in practice aids interview preparation.
Practical Tasks
Tasks which will allow students to practice coding concepts.
Job Opportunities
Assistance with finding and applying for job opportunities.
Make Portfolio of Work
When making hiring decisions, most employers don’t put a lot of weight on your paper qualifications. During the program, you’ll code machine learning models, build data pipelines, and deploy your work on the internet. It gives you a public portfolio to showcase during your interviews.
Course Syllabus
The way we designed the syllabus makes it easier for students to start with basic knowledge and finish with an industry-ready skill set.
| Phase 1 | Phase 2 | Phase 3 | Phase 4 | Phase 5 |
|---|---|---|---|---|
| From Analyst to Data Scientist | ML & Feature Engineering | Advanced Modeling & Experiments | Generative AI & Agentic Data Science | MLOps & Implementation |
| Mathematics, statistics, linear algebra, probability, loss functions, gradient descent. | XGBoost, LightGBM, linear/logistic regression, random forests, feature engineering, data cleaning. | K-Means, DBSCAN clustering, ARIMA forecasting, A/B testing, fraud/suspicious behavior detection. | Large Language Models (LLMs) integration, automated agent workflows using CrewAI. | Model API deployment, data drift monitoring, model interpretability (SHAP). |
| Phase 1: Week 1-3 | Phase 2: Week 4-6 |
|---|---|
| From Analyst to Data Scientist | ML & Feature Engineering |
| Math, statistics, linear algebra, probability, loss functions, gradient descent. | XGBoost, LightGBM, linear/logistic regression, random forests, feature engineering. |
| Phase 3: Week 7-9 | Phase 4: Week 10-11 |
|---|---|
| Advanced Modeling & Experiments | Generative AI & Agentic Data Science |
| K-Means, DBSCAN, ARIMA trend forecasting, A/B testing, fraud detection. | Large Language Models (LLMs), automated agent workflows with CrewAI. |
| Phase 5: Week 12-13 | |
|---|---|
| MLOps & Implementation | |
| Model APIs deployment, data drift monitoring, explainable AI (SHAP). |
Phase 1: From Analyst to Data Scientist
Mathematics, statistics, linear algebra, probability, loss functions, gradient descent.
Phase 2: ML & Feature Engineering
XGBoost, LightGBM, regression models, random forests, feature engineering, data cleaning.
Phase 3: Advanced Modeling & Experiments
K-Means, DBSCAN, ARIMA time-series forecasting, A/B testing, fraud detection.
Phase 4: Generative AI & Agentic Data Science
Large Language Models (LLMs), automated agent workflows with CrewAI.
Phase 5: MLOps & Implementation
Model API deployment, data drift monitoring, explainable AI (SHAP).
Phase one: How to go from analyst to Data Scientist
We begin with the mathematics and statistics behind these systems. You will study linear algebra, probability, loss functions and gradient descent. This will help you understand how algorithms find patterns.
Step 2: The Making of Machine Learning and Feature Engineering
You will utilize XGBoost and LightGBM to code up classic models like linear regression, logistic regression, random forests, and gradient boosting. You’ll also discover how to clear messy data and prepare features to make your models become more accurate.
The third stage of advanced modeling and experimentation
You will be able to learn the concepts of K-Means and DBSCAN that will help you group data. Furthermore, you will be using ARIMA models to forecast trends over some time. You will also be setting up A-B tests to see how the changes affect users. You'll learn to create systems that identify suspicious behaviour in transactions.
Phase 4 is Generative AI and Agentic Data Science
You will construct systems that leverage the capabilities of Large Language Models. You will also create workflows using CrewAI to automate complex tasks.
Stage 5: Implementation, MLOps and Decision Influence
"A model that just sits on your laptop doesn’t do much at all."
You will create APIs that execute your models, automate monitoring that watches out for changes in your data over time, and use SHAP or a similar tool to explain how your models decide.
Tools Utilized by AI Data Scientists
You will employ the identical instruments that experts utilize daily.
Data Analysis
- Jupyter Notebooks
- Pandas
- NumPy
Machine Learning
- Scikit-Learn
- XGBoost
- LightGBM
Data Engineering
- dbt
- BigQuery ML
Collaboration & Agentic
- Jira
- Git
- CrewAI
Skills You Will Learn
Skills In Technology & Technical Skills
Analytical Abilities
Expert Abilities
Career Opportunities and Pay Scales in India
Salaries are varied on the basis of experience and location. This is how much typical roles pay in India.
| Function and Average Yearly Salary | Typical Pay Scale (LPA) |
|---|---|
| Artificially intelligent data scientist | ₹8 - 20 LPA |
| ML Engineer | ₹10 - 25 LPA |
| Growth Analyst | ₹6 - 15 LPA |
| MLOps Expert | ₹12 - 30 LPA |
| Data Science Consultant | ₹10 - 35 LPA |
For anyone preparing for applications, read the guide to the technical interview for Data Science.
Who Can Take Up This Course?
- GraduatesIndividuals looking to commence their journey in machine learning or analytics.
- TransitionersPeople who want to transition into technical roles.
- ProfessionalsProfessionals that want to use software to build machine learning systems.
- Analytical Minds & StudentsAnyone looking to enhance their analytical skills, business students, and math.
- Beginners Without Coding BackgroundPeople without a coding background are beginners. Before we start writing code in Python, let us start with the basic concepts to build solid analytical confidence.
Frequently Asked Questions
What is AcceleratorX’s AI Data Science course?
A 13-week training program that covers machine learning, predictive analytics, and generative AI. By creating real projects, you gain practical experience and earn a certificate.
Is coding a necessity for learning data science?
Indeed. We start from the basics of logical thinking and a simple analysis before moving to python. This gives you confidence before you start coding in a machine.
Which data science program in India is the best for experts?
Search for courses that offer hands-on projects, portfolio development, and job readiness training. The IBM certificate, mock interviews, and weekly check-ins with mentors are all part of AcceleratorX’s programming to prepare you for the market.
How much does an AI Data Scientist Earn in India?
The freshers offered a salary of around ₹8 LPA whereas the experienced ones can earn above ₹30 LPA. The wage you receive is determined by your code and portfolio and ML MLOps.
DO CERTIFICATIONS IN DATA SCIENCE HELP YOU GET A JOB?
Certainly. A credential indicates you have finished a structured training. It works best when you couple it with a public portfolio that shows you can write clean, working code.