Weekly commitment
- Normal weeks: about 6–7 hours total — 3 hours live plus 3–4 hours of independent practice
- Capstone weeks: up to about 8 hours total — 3 hours live plus up to 5 hours of project work
- Approximately 8 weeks and 14 regular sessions
Applied Data Analysis with Python
Learn to clean, explore, visualise, and explain unfamiliar tabular datasets through reproducible workflows and defensible analytical reasoning.
This course focuses on descriptive and exploratory analysis. Predictive-model training, model selection, classification or regression workflows, threshold setting, and machine-learning evaluation belong to Stage 3.
Designed for: learners who can independently write and debug small Python programs using functions, collections, files, and basic error handling.
- Format
- Live, instructor-led online cohort
- Teaching time
- 14 sessions × 90 minutes
- Eligibility
- Applicants must be 18 or older
- Starting level
- Python fundamentals required
- Weekly commitment
- About 6–7 hours total; up to 8 in capstone weeks
- Applied outcome
- Reproducible real-data investigation
Plan your participation
The practical commitment at a glance
Currently planned A future 8-week cohort with two 90-minute live sessions each week in India Standard Time (IST). Exact dates and timings are confirmed before payment.
Language and cohort
- Teaching primarily in English, with Hindi or Marathi clarification where useful; written materials are in English
- Jupyter Notebook is the primary environment
- 10–15 learners are currently planned; a published cohort’s exact minimum and capacity replace this planning range
Included
- Between-session support
- Individual checkpoint and capstone feedback
- Planned regular-session recordings and an eligibility-based Certificate of Completion
Confirmed means a durable course commitment. Currently planned is guidance while a cohort is forming. Confirmed before payment means you will see the exact detail before paying.
What happens after you apply?
Review first, payment only after an offer
- Applications will open for a published cohort.
- Submit a free readiness and course-fit application.
- Receive the review decision and, if selected, the exact cohort offer.
- Pay the deposit within the offer window to reserve a seat.
- Pay the remaining balance only after cohort confirmation.
Registering for updates is not an application and does not reserve a seat.
Starting point
Is this course right for you?
A good fit if you…
- write functions and use lists, dictionaries, and files independently;
- can trace and fix common errors;
- want disciplined data investigation rather than model training;
- value reproducibility and evidence;
- will practise between sessions.
Probably not the right fit if you…
- are new to programming;
- cannot yet organise a small program into functions;
- want predictive ML, deep learning, or production MLOps;
- only want a certificate or placement promise.
Prerequisites
- Applicants must be 18 or older
- Variables, conditions, loops, functions, and collections
- CSV/JSON files and simple exceptions
- Basic debugging and small multi-function programs
Check your Python readiness
Can you explain control flow, write functions, use record collections, read CSV/JSON, handle a simple exception, debug logic, and organise multiple functions?
What you will be able to do
- Frame bounded questions and reproducible environments.
- Use NumPy arrays and vectorised operations.
- Load and inspect unfamiliar pandas datasets.
- Select, filter, sort, and derive columns precisely.
- Diagnose missing, malformed, duplicated, and unsuitable data.
- Build transparent cleaning workflows.
- Group, aggregate, pivot, join, concatenate, and reshape data.
- Work correctly with dates, time, and text.
- Describe distributions with appropriate measures.
- Reason about sampling, uncertainty, bias, and comparisons.
- Investigate relationships without implying causation.
- Create purposeful, non-misleading visualisations.
- Communicate findings, caveats, and next questions.
- Produce a reproducible end-to-end investigation.
These are intended learning capabilities, not guaranteed outcomes independent of prior Python fluency, attendance, practice, submitted work, and individual starting point.
Applied outcome
Investigate and explain a real dataset
Discover and communicate a non-obvious pattern in messy real-world data while showing why the finding may or may not generalise.
- Question
- Inspect
- Diagnose quality
- Clean
- Explore
- Visualise
- Check
- Explain
Deliverables
- Reproducible notebook and supporting scripts
- Environment definition and dataset documentation
- Processed data and quality/assumption log
- Visual summary, analytical report, limitations, and next steps
Boundary: this is not a predictive-modelling exercise. Model comparison, thresholds, and ML evaluation belong to Stage 3.
What a typical session looks like
Explain and demonstrate
Sessions combine explanation, instructor demonstration, guided analysis, live questions, and optional learner code-along.
Tools and setup
Jupyter Notebook is the primary environment. VS Code is supported but not mandatory. Windows, macOS, and Linux are supported; GitHub is not required.
Comfort and access
Screen sharing is voluntary and cameras are optional. A setup guide and pre-course setup support are provided. The meeting platform is confirmed before payment.
Support and feedback
Help while you learn, with clear boundaries
Questions and channels
The cohort WhatsApp group is optional. Joining may expose your phone number and profile information to other members. Email remains available if you do not join, and no essential announcement or support information is available only through WhatsApp. Assignments and capstones are not submitted through WhatsApp. Questions are normally answered within two business days.
Feedback
Every learner receives individual feedback on at least one structured checkpoint and a brief review of a submitted capstone, normally within seven business days of the published deadline.
Recordings
Regular-session recording is planned and formally confirmed in the cohort offer and recording notice. Successful recordings remain available until 90 days after the final regular session; optional clinics are not guaranteed to be recorded. Recordings support catch-up but do not replace practice or count toward certificate attendance. If a regular recording fails, relevant materials and a written summary or replacement walkthrough are provided.
Certificate
Eligibility requires at least 75% attendance at regular live sessions (11 of 14) plus capstone submission. Optional clinics and recording views do not count. An agreed capstone extension preserves eligibility. No grade or pass mark is required. Eligible certificates are issued manually, normally within 10 business days after the final eligibility check.
Four phases · fourteen lectures
Detailed curriculum
Phase 1 — Establishing an analytical workflow
Move from ordinary Python programs to reproducible analysis and vectorised thinking.
Lecture 1 — From Python programs to analytical workflowsSet up an end-to-end reproducible analysis process.
Workflow and environment
- Question, acquisition, inspection, cleaning, exploration, communication
- Virtual environments, packages, notebooks versus scripts, project folders
Reproducibility and practical
- Requirements, randomness, sources, assumptions
- Set up the capstone repository and question
Lecture 2 — NumPy and vectorised thinkingUse arrays for efficient numerical operations.
Arrays
- Creation, shape, dimensions, dtypes, indexing, slicing
Vectorisation
- Arithmetic, masks, aggregations, broadcasting
Practical
Compare core-Python loops with NumPy for readability and performance.
Capstone contribution
Prepare numerical inspection utilities.
Phase 2 — Working confidently with DataFrames
Inspect, transform, clean, combine, and reshape real tables.
Lecture 3 — pandas Series and DataFramesUnderstand labelled tables and inspect new datasets.
Objects and loading
- Series, DataFrame, index, columns, dtypes; CSV, JSON, spreadsheet awareness, parsing
Inspection
- head, tail, sample, info, describe, shape, unique values, memory
Profile the capstone dataset.
Lecture 4 — Selecting, filtering, sorting, and deriving columnsExpress analytical questions as precise DataFrame operations.
Selection and filtering
- Columns, loc/iloc, labels/positions, masks, conditions, membership, missingness
Transformation
- Sorting, renaming, derived and conditional columns, chained-assignment caution
Create analysis-ready variables.
Lecture 5 — Data types, missing values, and quality diagnosisDiagnose data quality before analysis.
Types and missingness
- Numbers as text, dates, categories, nullable types; patterns; drop/retain/impute
Quality profile
- Duplicates, ranges, categories, summaries, assumption logs
Produce a capstone quality report.
Lecture 6 — Cleaning and transforming messy dataPreserve evidence through transparent cleaning.
Cleaning
- String normalisation, conversion, mapping, dates, duplicates
Design and audit
- Raw/interim/processed layers, idempotence, validation, rejected rows, rules, before/after checks
Lecture 7 — Grouping, aggregation, and analytical summariesSummarise segments while recognising hidden risks.
Group operations
- groupby, multiple/named aggregations, pivot tables, counts, rates, weights
Interpretation
- Small groups, aggregation bias, Simpson’s paradox, denominators
Lecture 8 — Combining and reshaping datasetsJoin sources correctly and validate relationship structure.
Joins
- Inner/left/right/outer, keys, cardinality, unmatched rows, duplicate keys, row counts, indicators
Reshaping
- Wide/long, melt, pivot, concatenation
Lecture 9 — Working with dates, time, and textAnalyse temporal and textual fields correctly.
Dates and time
- Parsing, components, durations, sorting, resampling awareness, time zones
Text and questions
- Vectorised strings, patterns, token/length summaries, trends, cohorts, extraction
Phase 3 — Reasoning responsibly from data
Describe distributions, uncertainty, relationships, and visual evidence.
Lecture 10 — Descriptive statistics and distributionsChoose summaries that fit the distribution.
Centre and spread
- Mean, median, mode, robustness; range, variance, standard deviation, quartiles, IQR
Distributions
- Histograms, skew, percentiles, outliers, business meaning, units, scale
Lecture 11 — Sampling, uncertainty, and responsible comparisonAvoid overconfident conclusions from observed data.
Samples and uncertainty
- Methods, selection/non-response/survivorship bias; variability, confidence-interval intuition, practical significance
Comparisons
- Absolute/relative change, denominators, confounding, causal caution
Lecture 12 — Relationships, correlation, and analytical hypothesesInvestigate association without claiming causation.
Relationships
- Cross-tabs, group comparisons, scatter plots, covariance, correlation
Hypotheses and cautions
- Observation/explanation, alternatives, segments, confounding, spurious patterns, leakage preview
Lecture 13 — Data visualisation and evidence-based storytellingCreate clear charts that answer a question.
Selection and implementation
- Bar, line, histogram, box, scatter; matplotlib, pandas plotting, labels, scales, legends, annotations
Communication
- Hierarchy, misleading axes, clutter, accessibility, evidence-led narrative
Phase 4 — Complete data investigation
Integrate question framing, quality, cleaning, exploration, visuals, and communication.
Lecture 14 — Capstone: investigate and explain a real datasetCommunicate defensible findings, limitations, and next questions.
Workflow
- Problem, documentation, quality assessment, cleaning, exploration, visualisation
Deliverables
- Notebook, processed data, visual summary, analytical report
Outcome
- Discover a non-obvious pattern, check it multiple ways, explain generalisability
Journey bridge
Move from descriptive to predictive questions in Stage 3.
Instructor
Learn with Suyog Joshi
Suyog brings more than 20 years of software engineering, architecture, and enterprise-delivery experience across banking, payments, and complex systems. His teaching emphasises decomposition, debugging, evidence, and maintainable work.
That background supports clear questions, reproducible workflows, explicit assumptions, quality checks, and defensible conclusions.
About SuyogFee, application, and cohort process
The configured total fee is ₹8,000 INR, inclusive of applicable taxes. A ₹2,000 deposit is requested only after an offer; the configured remaining balance is ₹6,000.
- Review fit, prerequisites, schedule, and policies.
- Complete the readiness guidance and submit a free application when open.
- The application is reviewed.
- Suitable learners receive an offer.
- Pay the deposit by the offer deadline.
- Pay the balance under the published schedule.
- The cohort proceeds when its published conditions are met.
- Receive environment-preparation and access instructions.
If the minimum cohort is not met, published cancellation/refund terms apply. Payment confirmations are not tax invoices. Recording and certificate commitments match the summaries above and are formally confirmed in the cohort offer and applicable versioned policies.
Frequently asked questions
How much Python do I need?
You should independently use functions, collections, files, exceptions, and basic debugging. Completing Stage 1 is not mandatory when you meet these prerequisites.
What if I am not ready?
Start with Python Foundations for Data Science.
Does this course teach machine learning?
No. It teaches descriptive and exploratory analysis; predictive modelling is Stage 3.
How much statistics is required?
Basic numerical interpretation helps; the course teaches the descriptive and uncertainty concepts it uses.
What if I miss a session?
A successful regular-session recording may help you catch up, but it does not replace practice, count toward certificate attendance, or create a refund entitlement.
What if the minimum cohort is not reached?
The cancellation and refund policy governs any payment.
What course should I take next?
Practical Machine Learning Foundations is Stage 3.
Your place in the learning journey
After this course, you should be ready to frame prediction problems, establish baselines, and evaluate models in Practical Machine Learning Foundations. Predictive modelling is deliberately deferred until then.
Continue exploring
Related reading and systems
Connect practical data work to the concepts and learning systems behind the journey.