13 Weeks AI Data Science Course

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

Jobs
2000+
Learners
CTC
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Average Rating
LEARNING WORKING ACROSS 1700+ COMPANIES
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IBM
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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.

Microsoft
Deloitte
Paisabazaar
Bajaj Finserv
Indian Government
Razorpay
Microsoft
Deloitte
Paisabazaar
Bajaj Finserv
Indian Government
Razorpay
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

Leverage Your Existing Skills

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

Learn Data + AI Tools for Real Work

Learn Data + AI Tools for Real Work

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

Build AI Dashboards & Data Systems

Build AI Dashboards & Data Systems

Create automated dashboards, reporting systems, and data pipelines.

Work on Real Industry Projects

Work on Real Industry Projects

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

Transition into Data Roles

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
Program Benefits

Mentored by
Top 1% Industry Experts

1:1

1:1 Mentorship from Data Experts

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

Live

Live Project-Based Learning

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

Industry

Industry-Relevant Curriculum

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

Career

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.

01

Reframing from Analyst → Data Scientist

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
02

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
03

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

Session 14: Introduction to Deep Learning

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

Session 15: NLP for Data Scientists

  • Embeddings vs TF-IDF
  • Text classification
  • Topic modeling
  • Review & feedback analysis
04

Generative AI & Agentic 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
05

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

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

Himangi Sharma
Himangi Sharma
Data Scientist 2 -
Piyush Pankaj
Piyush Pankaj
Data Scientist@Edunet -
TOOLS

Tools You Will Work With

Excel
Excel
SQL
SQL
Google Analytics
Google Analytics
Power BI
Power BI
AI Automation
AI Automation
AI Tools
AI Tools
Excel
Excel
SQL
SQL
Google Analytics
Google Analytics
Power BI
Power BI
AI Automation
AI Automation
AI Tools
AI Tools
Excel
Excel
SQL
SQL
Google Analytics
Google Analytics
Power BI
Power BI
AI Automation
AI Automation
AI Tools
AI Tools
Excel
Excel
SQL
SQL
Google Analytics
Google Analytics
Power BI
Power BI
AI Automation
AI Automation
AI Tools
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

AI Data Science Certificate

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

Career Opportunities

Companies are actively hiring professionals with AI-powered data skills

AVERAGE ANALYTICS
PERFORMANCE ANALYTICS
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PROJECTS

Build Real AI Products 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 Learning Path

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

MOST POPULAR

AcceleratorX + IBM

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

FAQs

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.