Build a modern data science profile with stronger modelling, communication, and product context.
Months
Part-time
Projects
Portfolio
Tools
Industry-std
1:1
Mentorship
Personal
Data Science is the discipline of turning raw data into business-changing decisions using statistics, machine learning, and storytelling. From forecasting demand at Deliveroo to detecting fraud at Stripe, data scientists power the hardest decisions in modern companies. In 9 months, you will learn Python, ML modelling, experimentation, and communication so you can own analysis that actually moves the needle.
Everything you need to learn with confidence, build practical experience and move your career forward.
A GLOBAL LEARNING COMMUNITY
Live learning
Mentor-led sessions
40+ projects
Portfolio-ready work
1:1 mentorship
Guidance that is personal
Career preparation
Resume and interviews
100% placement
Career-ready support
Curriculum
Each phase moves from competence building into portfolio-visible output.
Timeline
Weeks 1-8
Establish a strong base in Python, statistics, and applied analysis workflows.
Timeline
Weeks 9-18
Learn to choose, train, and evaluate models with decision quality in mind.
Timeline
Weeks 19-28
Translate model output into business recommendations and clean narratives.
Timeline
Weeks 29-36
Package your work into visible, role-facing proof.
Explore 10 dedicated research-backed capstone systems for every domain (AI, Data, Security, Cloud) modeled after production corporate environments and SOTA literature.
Build an autonomous agent with LangGraph & RAG to transcribe clinical consultations, extract structured diagnostic observations, and auto-assign ICD-11 codes with confidence scoring.
Develop a RAG-powered knowledge graph assistant that indexes course textbooks, lecture transcripts, and slides to generate custom study roadmaps.
Train a Deep Reinforcement Learning (PPO / DDPG) agent that continuously rebalances a multi-asset portfolio to maximize Sharpe ratio under transaction fee constraints.
Develop a mobile visual search app allowing shoppers to snap a photo of any item in the physical world and instantly find matching products in catalog.
Train a Deep Reinforcement Learning agent (SAC) for a 6-axis industrial robot arm performing high-speed pick-and-place sorting of randomized parts.
Build an AI agent that inspects application architecture diagrams and OpenAPI specs to automatically generate STRIDE threat models and attack trees.
Deploy a computer vision model using CNN facial recognition to identify individual cattle, pigs, and sheep from barn camera feeds.
Build an AI agent that ingests commercial invoice PDFs and extracts product line items to auto-assign international Harmonized System (HS) tariff codes.
Build an aspect-based sentiment analysis model extracting specific product feature ratings (e.g., 'battery life', 'durability') from Amazon reviews.
Develop an AI assistant that analyzes historical judicial sentencing records to help judges identify sentencing discrepancies across similar crime profiles.
Build an interactive enterprise Power BI dashboard featuring DAX measures, automated ETL data modeling from SQL data warehouses, and dynamic KPI drill-downs for global sales, regional margins, and product profitability.
Construct an enterprise-grade Excel financial model with dynamic scenario analysis (Best/Base/Worst case), automated VBA macros for multi-department consolidation, XLOOKUP/INDEX-MATCH formulas, and interactive KPI dashboards.
Architect a real-time streaming ML pipeline processing ICU patient telemetry (heart rate, SpO2, blood pressure) to predict septic shock 6 hours prior to clinical onset.
Implement a Deep Knowledge Tracing (DKT) model using Recurrent Neural Networks to estimate a student's mastery level of concepts over time.
Architect a parallelized GPU-accelerated Monte Carlo simulation engine calibrating interest rate yield curves (Hull-White model) to evaluate liquidity risks.
Architect an automated competitor price scraping bot and dynamic pricing engine that adjusts SKU prices based on real-time market elasticity.
Build a root-cause analytics model using Decision Trees and SHAP to identify raw material lot batches causing high assembly line scrap rates.
Architect a streaming analytics engine using Apache Flink to analyze SYN flood packet velocity and IP entropy to trigger BGP Blackhole routing.
Build an ensemble machine learning model processing local weather station IoT sensors to predict localized sub-zero frost risks 12 hours ahead for fruit orchards.
Architect a real-time IoT analytics console monitoring high-speed parcel sorter chute sensors to detect package jams and balance sorting lane loads.
Design and implement an automated Zero-Trust network gatekeeper enforcing dynamic micro-segmentation policies for hospital IoT devices (infusion pumps, MRI scanners).
Develop a continuous biometric facial recognition and keystroke dynamics verifier validating student identity throughout high-stakes online exams.
Develop a specialized SIEM analytics module parsing ISO 20022 SWIFT financial transaction messages to detect unauthorized wire transfers.
Build a fraud detection engine analyzing loyalty point redemption velocity and gift card claim patterns to flag fraudulent redemptions.
Develop a static analysis tool inspecting 3D printer G-code files to detect malicious void insertions intended to weaken structural manufactured parts.
Build an inventory tool scanning enterprise source code and TLS servers to identify legacy RSA / ECC algorithms and recommend Post-Quantum Cryptography (PQC) replacements.
Build an industrial firewall module monitoring BACnet / Modbus greenhouse environment controllers to prevent malicious heating overrides.
Build a ROS2 security module enforcing DDS-Security (Data Distribution Service) encryption and node authentication for warehouse mobile robots.
Build a continuous social media account monitoring bot scanning corporate YouTube, LinkedIn, and Twitter accounts for unauthorized admin additions.
Deploy an industrial intrusion detection agent monitoring NEMA / 170 traffic light controller communications to block malicious signal manipulation.
Design and deploy a HIPAA-compliant AWS multi-region infrastructure using Terraform featuring Aurora PostgreSQL Global Database and encrypted S3 buckets.
Build an event-driven serverless video processing pipeline on AWS using Elemental MediaConvert to transcode lecture MP4s into adaptive HLS streams.
Architect a real-time financial market tick data lakehouse on GCP using Cloud Bigtable and Dataflow ingesting 1,000,000 trade events/sec.
Deploy an AWS CloudFront CDN architecture with Lambda@Edge that automatically converts product images to compressed WebP formats based on browser capabilities.
Architect a SAP HANA high-availability environment on AWS using Terraform, AWS Launch Wizard, and SLES Pacemaker clustering for manufacturing ERP.
Deploy a Gravitational Teleport Zero-Trust access gateway on AWS providing certificate-based SSH and Kubernetes access with session recording.
Build a cost-optimized machine learning model training pipeline on AWS using SageMaker Spot Instances to train satellite crop vision models.
Architect a multi-tenant SaaS carrier portal on AWS using ECS Fargate, PostgreSQL RDS, and AWS Cognito for 500 freight brokerage clients.
Build a high-performance marketing video streaming platform using AWS S3, CloudFront CDN, and AWS WAF delivering promotional video ads worldwide.
Build a SAML 2.0 / OIDC Identity Provider integration connecting AWS IAM Identity Center with US Government PIV / CAC smart card authentication.
Own analysis, experimentation, and predictive work that influences product and business decisions.
$88,000
starting pay for
Data Scientists
Bridge structured analysis with practical model implementation, deployment, and evaluation.
$95,000
starting pay for
Applied ML Engineers
Turn ambiguous business questions into measurable, data-backed recommendations and experiments.
$80,000
starting pay for
Analytics Scientists
Apply statistical modelling and machine learning to finance, risk, and forecasting problems.
$110,000
starting pay for
Quantitative Analysts
Sources: Glassdoor.in
and LinkedIn Salary Insights
Select a market benchmark to view salary estimates at different experience stages.
Global Benchmark Salary
Global Benchmark Salary
Global Benchmark Salary
Global Benchmark Salary
Source: Glassdoor.com and LinkedIn Salary Insights
A clear picture of the professional profile you will build over the program.
$88,000
Expected salary
Hard Skills
Soft Skills
Education
Projects
Customer Churn Prediction
Built an end-to-end churn model with feature engineering, validation, and stakeholder writeup for a telecom dataset.
Grouping tools by what they enable keeps the learning story cleaner and more persuasive.
Analysis
Modeling
Communication
Real-World Applications
See how the curriculum of the **Data Science** program is directly applied in solving critical challenges across global sectors. Click any industry to view practice projects you can build.
“Predictive modeling engines to classify patients at high risk of readmission or septic shock using streaming telemetry data from ICU monitors.”
“Classification models predicting student drop-out likelihood based on submission delays, forum activity, and quiz scoring.”
“Time-series forecasting models predicting equity index movements, option pricing indicators, and interest rate trends.”
“Dynamic pricing algorithms optimizing margins based on competitive price scrapers, weather, and inventory levels.”
“Vibration telemetry anomaly classifiers predicting bearing failures on turbine shafts using autoencoders.”
“Malware family classifiers categorizing binary files based on assembly patterns and API call strings.”
“Yield prediction models estimating harvest volumes based on weather cycles and soil sensor data.”
“Time-series forecasting models predicting transport demand and port congestion rates.”
“Customer clustering models grouping user segments based on navigation patterns and purchase histories.”
“Anomaly detection models flagging public benefit claims fraud based on filing patterns and credential metrics.”
“Flight path anomaly classification models predicting engine degradation from stream telemetry.”
“Time-series predictive models estimating battery health decay in electric vehicle fleets.”
“Molecular mix prediction engines estimating compound shelf life based on temperature telemetry.”
“Time-series classification models predicting structural decay in bridge support sensors.”
“Classification models predicting client churn risk based on service engagement telemetry.”
“Recommendation classification models matching viewer profiles to catalog titles.”
“Convolutional neural networks classifying fabric quality and identifying weave anomalies.”
“Time-series classification models predicting oxygen decay in aquaculture tanks.”
“Time-series classification models predicting refrigeration failure in transit vehicles.”
“Time-series forecasting models predicting hotel occupancy rates based on search telemetry.”
“Time-series forecasting models predicting server CPU load based on application traffic.”
“Classification models predicting litigation duration based on court docket data.”
“Time-series classification models predicting drill bit wear based on telemetry.”
“Classification models predicting donor churn based on engagement history logs.”
“Time-series classification models predicting solar cell decay based on telemetry.”
“Time-series forecasting models predicting rental occupancy rates based on search telemetry.”
“Molecular mix prediction engines estimating compound shelf life based on temperature telemetry.”
“Time-series classification models predicting cell tower decay based on telemetry.”
A structured path for learners who want to move from notebooks and theory into robust analysis, machine learning decisions, and portfolio-grade problem solving.
Move from dashboard support into predictive, experimental, and model-backed decisions.
Turn quantitative ability into a sharper, marketable data profile.
Build confidence with practical modeling instead of broad, unfocused theory.
“Weekly mentor feedback helped me connect analytics concepts to real education problems. The structure kept me moving every week.”
“Every module led to something visible in my portfolio. Python and SQL finally clicked because we kept applying them to real scenarios.”
“The live sessions were focused and easy to follow. I finished with a dashboard project I can confidently show in interviews.”
“The examples felt close to real business work. I now understand how to turn a vague question into a clean analysis plan.”
“The mock interviews were direct and useful. By the end of the batch, I could explain my projects clearly and answer technical questions with confidence.”
“I was working full-time, so the recordings and review checkpoints mattered. I always knew what to complete next.”
“The mentor comments were specific, not generic. That helped me improve my notebooks and explain my choices more professionally.”
“The dashboard labs were excellent. I learned how to design reports that are useful for decision-makers, not just visually busy.”
“The Agentic AI program pushed me way beyond tutorials. I shipped an actual RAG-powered assistant as my capstone, and it is now live in production.”
“The AI Business Analytics track gave me both technical depth and storytelling frameworks to step into a management position with confidence.”
“What separates Wills is that they care about proof, not completion. By the end I had five real projects and landed a lead role in 6 weeks.”
“I was a software developer looking to pivot into data. The Data Science program gave me a structured path to frame my experience into something hiring managers respond to.”
The same trust-first system used on the homepage carries through to each program detail page.
01
Refine the story you tell about your background, projects, and direction.
02
Turn assignments into portfolio assets, case studies, and stronger proof.
03
Move into applications and interviews with clearer materials and tighter narratives.
