SB
Spandana Bala
ML Engineer
Hyderabad · India
Now at · Infosys
AI Engineering · Practical career preparation
Predictive AI Engineer (Data Science)
Python, SQL, Statistics & Machine Learning
Turn data into predictions—and evidence that supports the decision. Build a complete predictive workflow: define a problem, prepare data with Python and SQL, train meaningful baselines and evaluate results without leakage. Package a model and explain its errors, uncertainty and operating limits.
12 core modules
Diagnostic-based preparation
Three guided projects
One independently assessed capstone
RoboEdify track record
10,000+
alumni transformed
1,000+
hiring partners
4.8/5
average class rating
87%
placed in 6 months
10+
years of training
Where our AI alumni work
Microsoft
Amazon
Salesforce
ServiceNow
Deloitte
Infosys
Accenture
TCS
Wipro
Capgemini
Cognizant
HCL
MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
Direct answer
What does this engineering pathway involve?
Predictive AI uses historical observations to estimate an outcome, quantity or future value. Data-science engineering connects that prediction to reliable data, statistical reasoning, validation and an application. This course emphasizes tabular modeling and a bounded forecasting exercise, with a reproducible capstone.
Learning approach
Build systems you can inspect, test and explain.
Each practical task includes an explicit requirement, a baseline and a failure investigation. You keep versions and results so another learner can reproduce your work. The final assessment includes an unfamiliar debugging exercise as well as your prepared project.
Learning format
Learn through classes, projects and individual feedback.
The program combines live mentor-led classes, guided labs, project work and support sessions. An advisor can explain the current on-campus, online or working-professional format and course-specific software/compute access before enrollment.
On-campusLive onlineWorking-professional format
Who should join
A common core, with preparation matched to your starting point.
Graduates and working professionals need Python and basic AI/data readiness. Foundations of Engineering Computing and Foundations of AI provide an entry route; equivalent experience is accepted. SQL and applied statistical reasoning are developed in the core, but broad programming gaps require preparation first.
A diagnostic identifies preparation needs. Graduates and working engineers complete the same practical exit requirements. Relevant prior learning can be recognized without repeating equivalent foundation work.
Learning outcomes
Leave with skills you can demonstrate.
Define a predictive taskConnect a target and metric to a decision.
Prepare analytical dataUse SQL/Python with explicit keys, units and quality checks.
Apply statistical reasoningExplain variation, uncertainty and experimental limitations.
Train and compare modelsUse defensible splits, baselines and preprocessing pipelines.
Evaluate forecasts and errorsInspect time dependence, class errors and threshold tradeoffs.
Package a reproducible modelValidate inputs and plan monitoring without claiming unseen production results.
Readiness areas
What you should be able to do before you start.
PythonFunctions, collections, files and basic array operations.
MathematicsAlgebra, probability intuition and reading graphs.
Data practiceIdentify labels, missing values and evaluation splits.
A diagnostic identifies preparation needs. Graduates and working engineers complete the same practical exit requirements. Relevant prior learning can be recognized without repeating equivalent foundation work.
Course curriculum
Twelve modules, from task definition to an independent capstone.
Every module includes topics, a practical task, a failure to investigate, deliverables and an assessed outcome.
01
Problem framing and analytical contracts
Engineering core
+
01
Problem framing and analytical contracts
Engineering coreTopics
•Target definition
•Prediction time
•Observation window
•Decision threshold
•Costs of errors
•Baseline
Hands-on lab
Define a bounded equipment/service prediction task and document what is known at prediction time.
Failure scenario you solve
A feature exists only after the event being predicted.
You build
Task contract and feature-availability map.
Assessed outcome: Identify leakage before training starts.
02
Python and SQL data preparation
Engineering core
+
02
Python and SQL data preparation
Engineering coreTopics
•Tables/keys
•Joins
•Aggregation
•Window functions
•Dataframes
•Schemas
•Units
•Duplicate detection
Hands-on lab
Build a dataset from supplied relational tables and verify join cardinality and row counts.
Failure scenario you solve
A many-to-many join duplicates examples and changes the apparent event rate.
You build
SQL queries, data pipeline and reconciliation report.
Assessed outcome: Explain each row and demonstrate that joins preserve the intended population.
03
Exploratory analysis and data quality
Engineering core
+
03
Exploratory analysis and data quality
Engineering coreTopics
•Missingness
•Outliers
•Distributions
•Class balance
•Visual summaries
•Group differences
•Data provenance
Hands-on lab
Audit the dataset and report quality issues with explicit inclusion rules.
Failure scenario you solve
Dropped rows systematically remove difficult examples.
You build
EDA notebook and data-quality report.
Assessed outcome: Distinguish a justified cleaning rule from convenient removal of failures.
04
Statistics and experimental reasoning
Engineering core
+
04
Statistics and experimental reasoning
Engineering coreTopics
•Sampling
•Uncertainty intervals
•Hypothesis tests
•Multiple comparisons awareness
•A/B design
•Correlation versus causation
Hands-on lab
Analyze a supplied randomized experiment and contrast it with observational evidence.
Failure scenario you solve
A correlation is reported as proof that an intervention caused an improvement.
You build
Statistical analysis and limitation statement.
Assessed outcome: Explain what the study design permits you to conclude.
05
Feature pipelines and validation design
Engineering core
+
05
Feature pipelines and validation design
Engineering coreTopics
•Training-only preprocessing
•Categorical encoding
•Grouped/time splits
•Cross-validation
•Reproducible transformations
Hands-on lab
Build a pipeline and compare a random split with a task-appropriate split.
Failure scenario you solve
Preprocessing learns from the final test set.
You build
Versioned pipeline and split audit.
Assessed outcome: Keep preprocessing and model selection inside the proper training boundary.
06
Supervised models and baseline comparison
Engineering core
+
06
Supervised models and baseline comparison
Engineering coreTopics
•Regression/classification
•Linear models
•Trees/boosting
•Regularization
•Overfitting
•Matched comparisons
Hands-on lab
Train a simple baseline and a stronger candidate under the same validation design.
Failure scenario you solve
A complex model wins on training loss but loses on held-out examples.
You build
Model comparison and selected configuration.
Assessed outcome: Justify complexity using evidence rather than model popularity.
07
Model selection and decision quality
Engineering core
+
07
Model selection and decision quality
Engineering coreTopics
•Hyperparameters
•Calibration
•Thresholds
•Precision/recall
•Regression errors
•Subgroup slices
•Final holdout
Hands-on lab
Choose a threshold using validation data and inspect errors by relevant scenario.
Failure scenario you solve
Aggregate accuracy hides a costly failure in an important group.
You build
Threshold analysis and final evaluation report.
Assessed outcome: Separate threshold selection from untouched final testing.
08
Forecasting and time-aware evaluation
Engineering core
+
08
Forecasting and time-aware evaluation
Engineering coreTopics
•Time-series features
•Seasonality
•Naive baseline
•Rolling evaluation
•Horizons
•Delayed availability
Hands-on lab
Build a small forecast and compare it with a seasonal/naive baseline using rolling-origin evaluation.
Failure scenario you solve
A random split exposes future patterns to model development.
You build
Forecasting notebook and horizon-level errors.
Assessed outcome: Explain whether the forecast beats a realistic baseline at the stated horizon.
09
Interpretation, segmentation and anomaly foundations
Engineering core
+
09
Interpretation, segmentation and anomaly foundations
Engineering coreTopics
•Permutation importance
•Local explanation limits
•Clustering
•Anomaly scores
•Sensitivity
•Prediction versus causation
Hands-on lab
Inspect model sensitivity and run a bounded segmentation/anomaly exercise on supplied data.
Failure scenario you solve
An explanation is treated as a causal mechanism or an anomaly score as proof of a defect.
You build
Interpretation report and bounded unsupervised exercise.
Assessed outcome: State the limits of the explanation and validate selected examples.
10
Model packaging and monitoring design
Engineering core
+
10
Model packaging and monitoring design
Engineering coreTopics
•Batch/API inference
•Schema validation
•Model/version registry concepts
•Training-serving consistency
•Drift versus performance
•Rollback
Hands-on lab
Package inference and simulate schema drift, missing features and delayed outcome labels.
Failure scenario you solve
A shifted input distribution is mistaken for measured model failure without labels.
You build
Inference service/script, tests and monitoring plan.
Assessed outcome: Reproduce predictions and distinguish input drift from measured predictive degradation.
11
Independent predictive-system capstone
Capstone
+
11
Independent predictive-system capstone
CapstoneTopics
•Frozen task
•SQL/data pipeline
•Baseline
•Validation
•Final metrics
•Inference
•Handoff
Hands-on lab
Deliver a bounded predictive model with a reproducible data pipeline and independent evaluation.
Failure scenario you solve
A strong result cannot be recreated from the archived queries and split.
You build
Repository, data/split manifest, model report and inference example.
Assessed outcome: Defend the data, metric and result and reproduce an unseen valid prediction.
12
Technical interviews and portfolio defense
Career preparation
+
12
Technical interviews and portfolio defense
Career preparationTopics
•SQL reasoning
•Statistical claims
•Leakage diagnosis
•Model tradeoffs
•Live debugging
Hands-on lab
Explain the capstone and diagnose an unfamiliar join, feature or evaluation defect.
Failure scenario you solve
A learner reports a score without explaining which records it covers.
You build
Case study, technical README and individual feedback.
Assessed outcome: Trace a result from source data to decision and explain its limits.
Scope note: This course emphasizes tabular predictive modeling, statistics and bounded forecasting. It does not imply mastery of every deep-learning domain, production-scale data engineering or causal discovery. GenAI and tool-using agents are separate pathways.
Tools and methods
Work with a focused, qualified stack.
Python, pandas and NumPyPrepare data and implement analytical workflows.
SQL and a teaching databaseBuild and audit relational datasets.
scikit-learnCreate preprocessing pipelines, baselines and model comparisons.
Plotting and statistical toolsInspect distributions, errors and uncertainty.
Versioned experiments and inference toolsPackage the model and reproduce results.
The core uses small-to-moderate teaching datasets and a qualified Python/SQL environment. No paid cloud subscription or large GPU cluster is required for the stated capstone. The actual environment and data licences are explained before enrollment.
Projects and portfolio
Three guided projects and one independent capstone.
The guided projects develop across the modules and are integrated into the core.
Guided project 1
SQL and data-quality investigation
Build a joined analytical dataset and reconcile counts and inclusion rules.
Portfolio evidence
QueriesData auditEDA
Guided project 2
Baseline and threshold comparison
Compare supervised models with leakage-safe validation and a task-relevant threshold.
Portfolio evidence
PipelineMetricsError slices
Guided project 3
Time-aware forecasting exercise
Evaluate a compact forecast against a realistic naive baseline.
Portfolio evidence
Rolling evaluationHorizon errorsFindings
Capstone
A reproducible predictive decision-support system
Use a supplied bounded dataset to build a prediction pipeline with SQL/data preparation, a simple baseline, justified validation and packaged inference. Explain which decisions the result can support and the limitations that remain.
What you submit
- •A frozen task specification and acceptance checklist.
- •Source, configuration and data/input manifests.
- •Baseline comparisons and retained evaluation results.
- •Tests of the required failure cases.
- •A readable handoff and individual technical defense.
What the assessor checks
- •Declare target, population and feature availability at prediction time.
- •Reconcile joins, exclusions and valid denominators; retain a reproducible data manifest.
- •Use task-appropriate grouped/time validation where needed and keep final evaluation untouched.
- •Report a baseline, decision-relevant errors and uncertainty/limits without inventing a performance guarantee.
- •Demonstrate consistent preprocessing and defined invalid-input behavior at inference.
Assessment and completion
Demonstrate practical competence.
RoboEdify · Certificate of Completion
Predictive AI Engineer (Data Science)
Presented to
Learner name
Awarded for completing the practical assessments and independently defending the course capstone within its documented scope.
Manikanta Kona
Founder · RoboEdify
ROBO
EDIFY
CERT
EDIFY
CERT
30%
Module labs
20%
Practical checkpoints
35%
Capstone
15%
Individual debugging and defense
The proposed completion standard is 70% overall, at least 60% separately in capstone and individual defense, and completion of all mandatory practical requirements. Feedback identifies gaps for remediation and reassessment. A numerical score does not compensate for missing required behavior.
This is a proposed RoboEdify course credential. External vendor certification or accreditation is not implied.
Career preparation and progression
Prepare for relevant work with evidence you can explain.
Data Science Trainee / Junior Data ScientistDevelop data preparation, modeling and evaluation skills.
Predictive Analytics DeveloperBuild reproducible prediction and reporting workflows.
ML Integration / Validation TraineePackage inference and investigate data/model failures.
Your preparation includes a reviewed repository, a technical case study, truthful resume statements and a live debugging/interview exercise. Each employer sets its own experience and eligibility requirements.
Career support
Build your portfolio. Prepare your profile. Practise your interviews.
Career support includes portfolio and profile preparation, interview practice, and role-fit introductions where available. The shared hiring-partner network includes Infosys, TCS, Deloitte, Accenture, Cognizant, NTT Data and Capgemini.
01 / PORTFOLIO
Evidence from your own work.
For this course, your portfolio centres on SQL/data pipelines, validation evidence, model comparisons and reproducible inference you complete.
02 / PROFILE
Profile and resume preparation.
A reviewed technical project summary, a readable repository and resume statements grounded in your contribution.
03 / INTERVIEWS
Interview practice and introductions.
Technical interview practice, with role-fit introductions where available.
RoboEdify does not guarantee an interview, offer, salary, employer, location or timeline.
Institute leadership
Meet the team behind RoboEdify.
MK
Manikanta Kona
Founder, RoboEdify · Enterprise AI Architect
Enterprise AI · Agentic Systems · LLM Platforms · Robotics & Edge AI
15 yrs
ENTERPRISE AI
2,400+
LEARNERS
4.9 /5
RATING
Manikanta brings 15 years of enterprise platform architecture experience from AT&T, Salesforce, Cox Communications and Broadcom. His background includes enterprise platform and AI rollouts for Fortune-500 banks, telcos and insurers, and production agentic-AI deployments for governed case handling.
Education: M.S. in Engineering, Purdue University.
RK
Ravi Krishna
Chief Technologist, RoboEdify · Implementation & Delivery Lead
Enterprise automation · Deployment · Evaluation evidence · Delivery governance
10 yrs
IMPLEMENTATION & DELIVERY
1,800+
LEARNERS
4.8 /5
RATING
Ravi leads RoboEdify's implementation and delivery practice. His background spans enterprise automation programs, deployment, evaluation evidence and delivery governance.
Industry voices from RoboEdify’s AI programs
What employers say about RoboEdify’s AI and robotics graduates.
The following testimonials retain their original program context. They describe AI, robotics and enterprise-program experience, rather than outcomes from this newly drafted course.
RoboEdify grads ramp 40% faster on applied AI projects than typical hires. Best AI engineering pipeline in India.
Aakash Mehta, Partner Programme Lead, Microsoft
We've onboarded 80+ RoboEdify alumni in 18 months. Lowest ramp time we've seen for ML plus AI agent practices.
Anita Sharma, Senior Manager, Deloitte
The programme is comprehensive — predictive ML, LLM systems, plus agentic and robotics work. Grads come pre-trained for enterprise.
Rahul Bhatt, Solutions Lead, Mphasis
Their agent + robotics track produces engineers who ship production-grade perception and control code on day one. Genuinely rare.
Deepak Pillai, Senior Architect, TCS
What sets RoboEdify apart is the simulation-to-hardware layer baked into the AI track. Our clients ask for exactly this profile.
Suresh Menon, Practice Lead, Accenture
Their fundamentals prep is rigorous, and the capstone with a real deployed system and safety case is what closes interviews for us.
Vikram Iyer, Director, Infosys
RoboEdify's AI grads get models into production twice as fast in the first 90 days. Our internal metrics back this up clearly.
Lakshmi Nair, VP Engineering, Wipro
Best AI + robotics pipeline we've sourced from in India. Their projects are production work, not toy code.
Karthik Subramanian, Engineering Director, Cognizant
Strong ML and edge-deployment foundation. Their grads need almost zero ramp time on enterprise engagements with us.
Arun Joshi, Practice Director, Capgemini
We've placed 40+ RoboEdify alumni across our AI and automation teams. Strong fundamentals, sharp on the agent stack.
Sanjay Verma, Talent Director, IBM
ITOM + Predictive Intelligence is exactly the talent gap we've been struggling to close. RoboEdify is filling it for us reliably.
Anjali Desai, Practice Head, LTIMindtree
Their AI track delivers engineers who navigate data, models and integrations on customer engagements unsupervised.
Ramesh Iyer, Senior Manager, Tech Mahindra
Hired 25+ RoboEdify graduates for our AI practice. Strong coding, strong ML depth, sharp on the agent layer.
Geetha Pillai, Talent Acquisition Lead, Cyient
RoboEdify grads who blend robotics with Azure OpenAI land production-ready on day one. Rare combination, well-trained.
Priya Reddy, Talent Lead, Microsoft
AI alumni across RoboEdify
Meet alumni featured in our AI programs.
NV
Naveen Vedala
AI Agent Engineer
Hyderabad · India
Now at · TCS
TA
Tejashwini Addla
Simulation Engineer
Hyderabad · India
Now at · Deloitte
TD
Tharunesh Dillikar
Robotics Software Engineer
Seattle · United States
Now at · Accenture
MM
Mujahed Mohammed
Edge AI Engineer
Hyderabad · India
Now at · Accenture
BK
Bhargav Kumar Murala
Physical AI Engineer
Hyderabad · India
Now at · Capgemini
SL
Sai Manasa Leburi
Autonomous Systems Engineer
New York · United States
Now at · NTT Data
RD
Rahul Dhamma
Robot Learning Engineer
Hyderabad · India
Now at · Cognizant
Our locations
Come chat with us—on campus or online.
Flagship campus
Hyderabad
2nd Floor, Hitech City Road · Above Domino's · Opp. Cyber Towers, Jai Hind Enclave ·
Hyderabad, Telangana
Call
+91 8142998866
US desk
+1 256 388 7766
Opening hours
Mon–Sun · 7 AM–9 PM
Online
Global
Live online classes and mentorship, with working-professional learning options. This course uses the software and compute arrangements described in its tools section.
Format
Live online + mentorship
Options
Working-professional
Learning support
Support when you need to catch up.
Freeze your seat for up to 90 days and rejoin the next class at no extra fee. TAs run catch-up sessions every Saturday, and recordings of every live session are available for the lifetime of your account.
FAQs
Questions about prerequisites, tools and completion.
Is SQL included?
Yes. Data preparation includes joins, aggregation, window-function concepts and checking analytical row counts.
How is this different from Foundations of AI?
The foundation introduces models and evaluation. This course develops a fuller data-science workflow with SQL, statistics, model selection, forecasting and inference handoff.
Will I learn deep learning?
The main depth is predictive tabular work and forecasting. Advanced neural-network specialization is outside the core.
Does the course guarantee a model accuracy?
No. Performance depends on the task and data. You are assessed on sound validation, useful comparisons and honest reporting.
Can graduates and working engineers both join?
Yes, with the stated readiness. Both follow the same practical core; preparation is assigned through a diagnostic.
How is the learning workload structured?
The program combines live classes, guided labs, projects and support sessions. An advisor can explain the current class format before enrollment.
Can I study online or on campus?
RoboEdify offers its Hyderabad campus, live online classes and a working-professional format. The course-specific software and compute setup is explained separately.
What if I need to pause or catch up?
You can freeze your seat for up to 90 days and rejoin the next class at no extra fee. Saturday catch-up sessions and recordings of every live session support learning. Recordings remain available for the lifetime of your account.
Is placement guaranteed?
No. Support includes portfolio/profile preparation, interview practice and role-fit introductions where available. RoboEdify does not guarantee an interview, offer, salary, employer, location or timeline.
What if I miss a practical requirement?
Feedback identifies the missing capability and reassessment work. Attendance alone does not meet the completion standard. Confirm course-specific reassessment arrangements before enrollment.
How can I ask about fees and tool access?
Contact an advisor about preparation, the current offering, fees, software/compute access and any model-usage costs before enrollment.
Still have a question?
Find your starting point in Predictive AI Engineer (Data Science).
One million AI-native professionals by 2027.
Tell us about your programming, data and application experience. We’ll help you understand the preparation you need and the practical projects this course is designed to develop.
Plan your learning
- Course
- Predictive AI Engineer (Data Science)
- Preparation
- Diagnostic-based preparation before the common core
- Level
- Specialist
- Curriculum
- 12 modules
Confirm your intake dates, delivery mode, fees, assessment and practical access with RoboEdify before enrolling. Course content describes the learning scope; an enquiry does not reserve a seat.