What is the salary of a data analyst or data science professional in India?

The average salary for data professionals in India is: Entry-level: ₹6–12 LPA, Mid-level: ₹10–20 LPA, Experienced: ₹20–40+ LPA. Professionals with AI and data analytics skills often earn higher salaries due to increasing demand.

Is this AI Data Science course suitable for beginners?

Yes, this course is designed for beginners, freshers, and non-technical professionals. You don’t need prior experience in coding or data science to get started.

What tools and technologies will I learn in this data science course?

You will learn industry-relevant tools such as: Excel for data analysis, SQL for querying data, Python for data processing, Power BI / dashboards, AI tools for automation and insights. These tools are widely used in real-world data roles.

What is the salary of a data analyst or data science professional in India?

The average salary for data professionals in India is: Entry-level: ₹6–12 LPA, Mid-level: ₹10–20 LPA, Experienced: ₹20–40+ LPA. Professionals with AI and data analytics skills often earn higher salaries due to increasing demand.

Is this AI Data Science course suitable for beginners?

Yes, this course is designed for beginners, freshers, and non-technical professionals. You don’t need prior experience in coding or data science to get started.

What tools and technologies will I learn in this data science course?

You will learn industry-relevant tools such as: Excel for data analysis, SQL for querying data, Python for data processing, Power BI / dashboards, AI tools for automation and insights. These tools are widely used in real-world data roles.

AI Data Science Course with Machine Learning & Generative AI Certification

Learn AI-powered data analytics, dashboards, and automation systems using Python, SQL, and modern AI tools through real-world projects.

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2000+

Learners

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4.8/5

Average Rating

LEARNING WORKING ACROSS 1700+ COMPANIES

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A diverse group of technical professionals celebrating successful job placements in the AI ecosystem.

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Live Classes

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Mentor Connect

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Career Support

SOCIAL PROOF

Our Alumni Are Working Across 1700+ Top MNCs

Our learners are building AI-powered dashboards, data automation systems, and business insights across top companies in India.

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PROGRAMS ROADMAP

AI Data Science Roadmap for Working Professionals & Freshers

2,000+ learners across India are transitioning into data-driven careers by building AI-powered analytics projects and applying data science.

Transition into Data & AI Roles with a Structured Learning Path

CAREER SWITCHERS PATH

Leverage Your Existing Skills

Learn how to apply AI in data science, dashboards, and reporting systems.

Learn Data + AI Tools for Real Work

Master tools like Excel, SQL, Python, and AI-powered analytics platforms.

Build AI Dashboards & Data Systems

Create automated dashboards, reporting systems, and data pipelines.

Work on Real Industry Projects

Build data analytics projects using real datasets and business use cases.

Transition into Data Roles

Use your portfolio to move into AI-enabled analytics roles.

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PROGRAM BENEFITS

Designed for Real-World Data & AI Execution

This is not a theory-heavy course. You learn by building AI-powered dashboards, automation systems, and real business analytics projects.

Live Class

Comprehensive training curriculum layout for the AI data science and machine learning certification course.

Mentored by
Top 1% Industry Experts

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1:1 Mentorship from Data Experts

Learn directly from professionals working in data science, analytics, and AI roles.

Live Project-Based Learning

Build dashboards, analytics systems, and automation workflows in real-time.

Industry-Relevant Curriculum

Stay updated with AI in data analytics, automation trends, and real-world business use cases.

Career-Focused Learning Path

Move from learning to earning with job-ready data science skills and project portfolios.

PROGRAM CURRICULUM

What You Will Learn in This AI Data Science Course

This program covers everything from fundamentals to advanced data analytics, AI-powered dashboards, and automation systems.

Module 1: Data Analyst to Data Scientist Foundation

Learning Goal

Understand the mindset shift and mathematical foundations required for data science modeling.

Session 1: Analyst vs Data Scientist Mindset

Why dashboards ≠ data science
Problem framing vs reporting
Causal vs descriptive thinking
What hiring managers expect from DS
Case: "Why metrics didn't move after dashboard launch"

Exercise: Convert a dashboard problem into a DS modeling problem

Session 2: Data Science Lifecycle (Industry Reality)

Problem → signal → model → decision
Offline vs online ML
Failure modes of DS projects
Role of experimentation, iteration, and uncertainty

Mini Case: Churn reduction beyond reporting

Session 3: Mathematical Foundations Refresher (Applied)

Linear algebra intuition (vectors, dot products)
Probability intuition for ML
Optimization & loss functions
Why gradient descent works

Core Machine Learning

Learning Goal

Master essential algorithms and rigorous workflows used to build and validate predictive models.

Session 4: Regression Deep Dive

Linear vs non-linear regression
Regularization (L1, L2, ElasticNet)
Feature leakage & multicollinearity
Business interpretation of coefficients

Hands-on: Convert a dashboard problem into a DS modeling problem

Session 5: Classification Algorithms (Beyond Accuracy)

Logistic regression (decision boundaries)
KNN, Naive Bayes
Precision-recall tradeoffs
Threshold tuning for business impact

Hands-on: Conversion prediction

Session 6: Tree-Based Models

Decision Trees (bias control)
Random Forest intuition
Gradient Boosting (XGBoost, LightGBM)
Feature importance pitfalls

Hands-on: Credit / risk / churn modeling

Session 7: Model Evaluation & Error Analysis

Cross-validation strategies
ROC-AUC vs PR-AUC
Error slicing by cohorts
Cost-based evaluation

Exercise: Model failure diagnosis

Session 8: Feature Engineering (Most Important Skill)

Numeric, categorical, temporal features
Aggregations & lag features
Encoding strategies
Feature stores (conceptual)

Hands-on: Improve model by 20-30% via features

Session 9: ML Pipelines & Reproducibility

Train-test leakage
Pipelines & versioning
Experiment tracking (conceptual tools)
Reproducible DS workflows

Advanced & Specialized ML

Learning Goal

Expand into specialized domains and complex data types encountered in business environments.

Session 10: Unsupervised Learning

K-Means, Hierarchical clustering
DBSCAN for behavior discovery
PCA & UMAP intuition
When clustering fails

Hands-on: Behavioral segmentation

Session 11: Time Series for Business

Trend, seasonality, noise
ARIMA vs ML-based forecasting
Rolling validation
Forecast confidence intervals

Hands-on: Demand / revenue forecasting

Session 12: Experimentation & Causal Thinking

A/B testing pitfalls
Selection bias
Observational vs experimental data
Introduction to causal inference

Case Study: Why A/B test lied

Session 13: Anomaly & Fraud Detection

Statistical vs ML approaches
Isolation Forest
Rare event challenges
Evaluation without labels

Deep Learning for Data Science and Machine Learning

Neural Network foundations
Activation functions & backpropagation
Common architectures (CNN, RNN introduction)
Practical DL tools

Natural Language Processing (NLP) for Data Scientists

Embeddings vs TF-IDF
Text classification
Topic modeling
Review & feedback analysis

Generative AI, LLMs & Agentic AI for Data Science

Learning Goal

Integrate modern LLM capabilities and autonomous agents into the data science workflow.

Session 16: LLMs for Data Scientists

How LLMs actually work (high level)
Strengths vs weaknesses
DS use cases beyond chat
Hallucinations & validation

Session 17: Prompt Engineering for Analytics

Structured prompting
Chain-of-thought for reasoning
SQL & Python generation safely
Prompt templates for DS tasks

Session 18: Embeddings & Vector Search

A/B testing pitfalls
Selection bias
Observational vs experimental data
Introduction to causal inference

Hands-on: Build semantic search on user feedback

Session 19: Agentic AI for Data Science

What are agents?
Tool-calling & multi-step reasoning
Data analysis agents
Auto-EDA & Auto-modeling agents

Hands-on: Build a data analysis agent

Session 20: Human-in-the-Loop DS

Where AI should stop
Validation strategies
Trust & explainability
DS + AI collaboration patterns

Deployment, MLOps & Decision Impact

Learning Goal

Put models into production and communicate their value effectively to stakeholders.

Session 21: Model Deployment Basics

Batch vs real-time models
APIs & pipelines
Monitoring predictions
Data drift & concept drift

Session 22: Explainability & Responsible AI

SHAP & feature attribution
Model transparency
Bias & fairness checks
Regulatory awareness

Session 23: Business Decisioning with Models

Turning predictions into actions
Scorecards & decision rules
ROI estimation
Stakeholder communication

Session 24: Capstone Project (Build Phase)

Business problem framing
Feature engineering
Model selection & evaluation
GenAI-assisted analysis
Deployment plan

Session 25: Capstone Presentation

Present to "stakeholders"
Defend modeling decisions
Common DS interview questions
Portfolio & resume positioning

MENTORS

Learn from Industry Data Science Experts & AI Practitioners

Get mentored by professionals with 10+ years of experience working in data analytics and AI roles across top companies.

Himangi Sharma

Data Scientist 2 -

Piyush Pankaj

Data Scientist@Edunet -

TOOLS

Tools You Will Work With

Excel

SQL

Google Analytics

Power BI

AI Automation

AI Tools

Excel

SQL

Google Analytics

Power BI

AI Automation

AI Tools

Excel

SQL

Google Analytics

Power BI

AI Automation

AI Tools

Excel

SQL

Google Analytics

Power BI

AI Automation

AI Tools

SKILLSET

AI Data Science Skill Checklist

Discovery

Data fundamentals, identify business problems, and AI data analysis.

PHASE 01

Strategy

Learn how to approach data problems and design data-driven solutions.

PHASE 02

Design

Structure dashboards, reports, and analytical workflows.

PHASE 03

Build

Create dashboards, automate reports, and build analytics systems.

PHASE 04

Analyze

Extract insights, optimize models, and interpret results.

PHASE 05

Grow

Scale data solutions and apply advanced analytics in real scenarios.

PHASE 06

CERTIFICATION

The Certificate Recognized By The Industry

Get Your Nano-Degree in AI Data Science

Show the world your expertise in AI Data Science, stand out in a competitive Ai job market and get hired easily.

Industry-recognized Nano Degree in AI Data Science.
Verified badge + unique verification ID
Trusted by 2500+ companies and agencies
AI Data Science Projects portfolio
Lifetime exclusive alumni community access

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CAREER

Career Opportunities After This AI Course

Data Analyst

₹6L – ₹15L

Drive business decisions using data + insights

Business Analyst

₹8L – ₹18L

Build data pipelines & transformation systems

AI Data Analyst

₹10L – ₹22L

Create dashboards that drive strategy

Analytics & Automation Specialist

₹12L – ₹25L

Help companies become data-driven

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Companies are actively hiring professionals with AI-powered data skills

AVERAGE ANALYTICS

PERFORMANCE ANALYTICS

₹75L

₹60L

₹45L

₹30L

₹15L

₹0L

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PROJECTS

Build Real AI & Data Science Projects That Get You Hired

Instead of just completing assignments, you build:

AI-powered product features

Product workflows

Automation systems

Real-world product use cases

This becomes your proof of work. What recruiters actually care about.

PRICING

Choose Your AI Data Science Certification Program

Flexible pricing options designed for professionals who want to learn data analytics, AI tools, and automation.

AcceleratorX + IBM Advanced AI Data Science Program

An advanced AI Data Science program designed for learners seeking deeper expertise in analytics, automation, and real-world data systems.

₹ 35,999 + GST

ADVANCED DATA CAREER TRACK

Everything included in the Regular Program
IBM Data Science Certification
Advanced Generative AI & LLM Learning Track
Exclusive ML Ops and Model Scaling Workshops
Advanced Kaggle Case Simulations
Dedicated Career Support and Placement Assistance

Frequently Asked Questions (FAQs) About AI Data Science Course

The AcceleratorX program is a 10-week AI Data Science course in India designed to help learners build skills in data analytics, dashboards, and AI-powered automation. It focuses on real-world projects using tools like Excel, SQL, Python, and AI tools to make you job-ready.

The best data science course for working professionals is one that focuses on practical skills, real business projects, and AI-powered analytics. AcceleratorX is designed to help professionals transition into data analyst and AI-driven roles without relying on outdated theoretical content.

Yes, you can start learning data science without coding. This course begins with Excel, no-code tools, and AI-assisted analysis, and gradually introduces SQL and Python for advanced learning, making it beginner-friendly.

After completing this course, you can apply for roles such as: Data Analyst, Business Analyst, AI Data Analyst, Reporting & Dashboard Specialist, Analytics & Automation Specialist. These roles are in high demand across companies using data-driven decision-making and AI tools.

To switch into data science, you need hands-on experience, real-world projects, and a strong portfolio. This course helps you build dashboards, analytics systems, and automation workflows that demonstrate your practical skills to employers.

Start Your Data Science Career Before You’re Left Behind

Join professionals building AI-powered data careers with real-world analytics projects and automation systems.

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