Python Foundations for Data Science

Learn programming fundamentals through real data-oriented problems. Build the confidence to create, debug, and explain a small Python program that loads, validates, transforms, and summarises structured data.

This course builds the programming foundation for later data and AI learning. It deliberately stops before in-depth NumPy, pandas, visualisation, and machine learning.

Designed for: complete or near-complete programming beginners and professionals rebuilding weak or outdated Python foundations.

Format
Live, instructor-led online cohort
Teaching time
14 sessions × 90 minutes
Eligibility
Applicants must be 18 or older
Starting level
No prior programming required
Weekly commitment
About 5–6 hours total; up to 7 in capstone weeks
Applied outcome
Core-Python structured-data analyser
Check whether this is your starting point

Plan your participation

The practical commitment at a glance

Currently planned October–November 2026, with two 90-minute sessions each week: likely one weekday evening and one weekend session in India Standard Time (IST). Exact dates and timings are confirmed before payment.

Weekly commitment

  • Normal weeks: about 5–6 hours total — 3 hours live plus 2–3 hours of independent practice
  • Capstone weeks: up to about 7 hours total — 3 hours live plus up to 4 hours of project work
  • Approximately 8 weeks and 14 regular sessions

Language and cohort

  • Teaching primarily in English, with Hindi or Marathi clarification where useful
  • Notes, code, assignments, and capstone materials in English
  • 10–15 learners are currently planned; a published cohort’s exact minimum and capacity replace this planning range

Included

  • Live questions and 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?

A free application, followed by a clear decision

  1. Submit a free application and receive an acknowledgement and reference.
  2. We normally review it within five business days.
  3. You receive a decision: suitable, waitlisted, another course recommended, or not suitable for this intake.
  4. If selected, you receive the exact cohort schedule and offer.
  5. Pay the ₹1,000 deposit within the offer window to reserve your seat.
  6. The cohort is confirmed after the minimum group size is reached and operations are ready.
  7. The ₹3,000 balance is requested only after cohort confirmation and is normally due within seven calendar days.

A positive review may arrive before an immediate offer because the final schedule and cohort formation can take longer.

Starting point

Is this course right for you?

How the application is reviewed

This is a readiness and cohort-fit review, not a competitive programming selection process. There is no coding test, and complete beginners are not disadvantaged. We consider your learning goal, understanding of the course scope, and ability to reserve sufficient weekly time. Honest answers are more useful than impressive-sounding answers.

A good fit if you…

  • are completely or nearly new to programming;
  • want to progress toward data analysis, ML, automation, or AI;
  • prefer guided practice and feedback;
  • want to rebuild weak Python foundations;
  • will practise between sessions.

Probably not the right fit if you…

  • already structure, test, and debug small Python data programs;
  • want immediate ML, deep-learning, or GenAI development;
  • only want a certificate or placement promise;
  • do not intend to practise.

Prerequisites

  • Applicants must be 18 or older
  • No prior programming experience
  • Basic computer literacy and school-level arithmetic
  • A suitable laptop, internet, and willingness to install software
Check your current readiness

Can you already break a problem into functions, read CSV, validate records, debug a multi-function program, and write small tests?

What you will be able to do

  • Explain how data moves through a small Python program.
  • Represent numerical, categorical, textual, Boolean, date-like, and missing information.
  • Turn written rules into testable conditions.
  • Filter, count, and aggregate collections of records.
  • Clean and parse text from real datasets.
  • Model records with lists, dictionaries, tuples, and sets.
  • Structure functions with clear contracts.
  • Read and write text, CSV, and JSON safely.
  • Identify flawed or impossible values.
  • Calculate basic descriptive summaries.
  • Debug syntax, runtime, input, and logical errors.
  • Build and explain an end-to-end structured-data program.

These are intended learning capabilities, not guaranteed outcomes independent of attendance, practice, submitted work, and individual starting point.

Applied outcome

Build a Core-Python structured-data analyser

A typical project reads a CSV dataset, validates and processes values, organises logic with functions and data structures, and produces useful summaries.

  1. Question
  2. Load
  3. Validate
  4. Clean
  5. Transform
  6. Summarise
  7. Report
  8. Review

Deliverables

  • Python source code and input/output structure
  • Dataset reference and documented validation rules
  • Generated report and small confidence checks
  • README, limitations, and possible improvements

Boundary: the capstone uses core Python and the standard library. It is not a pandas project.

Examples may include an expense analyser, reading or study tracker, exercise-log analyser, household utility analyser, or public weather or transport summary. The exact project may vary by cohort while remaining aligned with the published curriculum and difficulty.

The exact brief, datasets, starter structure, and submission expectations are confirmed before payment. Do not use confidential, employer-owned, medical, financial-account, or sensitive personal data. Learner work is never published without explicit permission.

What a typical session looks like

Learn and try

Each session combines explanation, instructor demonstration, guided coding, live questions, and optional learner code-along.

Tools and setup

Jupyter Notebook is the primary beginner environment. Python scripts are introduced where appropriate; VS Code is supported but not mandatory. Windows, macOS, and Linux are supported, and 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.

Extra support and feedback

Up to two optional group clinics may be scheduled when shared demand exists. 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 attendance at 11 of 14 regular live sessions 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.

Read the recording and conduct policy

Four phases · fourteen lectures

Detailed curriculum

Phase 1 — From data questions to program logic

Establish the workflow and translate questions into computation and rules.

Lecture 1 — The data-to-answer journey and the Python workflowUnderstand how a question becomes a small program.

Programming workflow

  • Install Python/editor; scripts versus interactive execution; edit–run–inspect–correct; error messages

Data framing

  • Questions, observations, fields, datasets; input → validation → transformation → output; data forms

Practical

Inspect a dataset and report a simple result.

Capstone contribution

Frame the analyser question and workflow.

Lecture 2 — Values, variables, and the meaning of dataRepresent common data correctly.

Python values

  • Integers, floats, strings, booleans, None; variables; assignment and state

Data meaning

  • Numerical, categorical, textual, Boolean, date-like, missing; identifiers; conversion risks

Practical

Classify fields and correct type mistakes.

Capstone contribution

Define field meanings.

Lecture 3 — Expressions, calculations, input, and readable outputCalculate useful values and communicate results.

Expressions

  • Arithmetic, comparison, logic, precedence, ratios, percentages

Input/output

  • User input, formatted strings, rounding, presentation

Practical

Calculate spending ratios or study progress.

Capstone contribution

Define summaries and reporting.

Lecture 4 — Conditions and rule-based decisionsConvert written rules into explicit decisions.

Logic

  • if/elif/else, comparisons, Boolean operators, truthiness

Rules

  • Validity, thresholds, categories, missing values, decision tables, edge cases

Practical

Implement record rules.

Capstone contribution

Create validation decisions.

Lecture 5 — Loops, filtering, counting, and aggregationProcess many observations.

Iteration

  • for/while, range, enumerate, loop state

Data operations

  • Filters, counters, totals, min/max, frequencies, empty data, off-by-one checks

Practical

Aggregate a record collection.

Capstone contribution

Build the processing loop.

Phase 2 — Representing and organising data

Clean text, model records, and divide processing into functions.

Lecture 6 — Strings and text parsingClean and standardise text values.

String operations

  • Indexing, slicing, split, strip, replace, find, case, formatting, escaping

Cleaning

  • Capitalisation, whitespace, compound fields, pattern checks

Practical

Normalise names and transaction descriptions.

Capstone contribution

Clean textual fields.

Lecture 7 — Collections as records and datasetsRepresent records and datasets clearly.

Collections

  • Lists, tuples, dictionaries, sets; order, mutation, uniqueness, keys

Dataset representation

  • Record dictionaries, list of records, nesting, shape, membership, sorting, comprehensions, copying

Practical

Model a structured dataset.

Capstone contribution

Choose record structures.

Lecture 8 — Functions and data-processing stagesBreak a script into functions with clear contracts.

Mechanics and design

  • Parameters, returns, scope, defaults; responsibility, naming, assumptions, side effects

Pipeline

  • load, validate, clean, summarise, format report

Practical

Refactor a long script.

Capstone contribution

Establish function boundaries.

Phase 3 — Working with real external data

Use files, formats, quality rules, and descriptive summaries.

Lecture 9 — Files, folders, and persistent dataRead and write external data safely.

Files

  • Paths, relative/absolute, encoding, context managers, whole/line reads, writing, append/overwrite

Safe workflow

  • Preserve raw data, input/output separation, missing files

Practical

Read input and write a report.

Capstone contribution

Create safe folders.

Lecture 10 — Working with CSV and JSON datasetsLoad and save common structured formats.

CSV and JSON

  • Headers, DictReader, conversion, CSV output; objects, arrays, dictionaries, lists, load/save

Judgement

  • Flat versus nested formats and schema assumptions

Practical

Convert and validate formats.

Capstone contribution

Load the source dataset.

Lecture 11 — Data quality, validation, and cleaningHandle flawed data without silent corruption.

Quality

  • Missing, malformed, duplicate, inconsistent, impossible, outlier/error

Discipline

  • Field/record rules; reject/correct/retain/flag; rejected logs; preserve raw; document decisions

Practical

Build a quality report.

Capstone contribution

Implement validation and cleaning.

Lecture 12 — Descriptive summaries with core PythonCalculate and interpret basic summaries.

Summaries

  • Count, total, min/max, mean, median, percentages, distributions, range

Interpretation

  • Hidden detail, empty/skewed data, plausibility; built-ins and statistics module

Practical

Write reusable summary functions.

Capstone contribution

Generate useful findings.

Phase 4 — Confidence and capstone

Debug, verify, and integrate one reviewable program.

Lecture 13 — Debugging, exceptions, and confidence checksFind faults and verify trustworthy results.

Errors and debugging

  • Syntax, runtime, input, logic; tracebacks, reproduction, isolation, inspection

Confidence

  • Exceptions, assertions, tests, boundary/empty input, refactoring

Practical

Diagnose seeded faults.

Capstone contribution

Add confidence checks.

Lecture 14 — Capstone: build and review a Core-Python structured-data analyserIntegrate the course into one complete program.

Project workflow

  • Question, dataset, loading, validation, cleaning, transformation, summaries, report

Engineering review

  • Functions, readability, tests, limitations, improvements

Practical

Present the end-to-end analyser.

Journey bridge

See how NumPy and pandas scale the work in Stage 2.

Instructor

Learn with Suyog Joshi

Suyog brings more than 20 years of software engineering, architecture, and enterprise-delivery experience across banking, payments, and complex business systems. His teaching emphasises decomposition, readable code, debugging discipline, evidence, and maintainable work.

That experience matters when foundations need to remain useful beyond tutorial exercises.

About Suyog

Fees and payment journey

The configured total fee is ₹4,000 INR, inclusive of applicable taxes. A ₹1,000 deposit is requested only after an offer; the configured remaining balance is ₹3,000.

Application approved → cohort offer → ₹1,000 deposit within seven calendar days → seat reserved → cohort confirmed → ₹3,000 balance requested → balance within seven calendar days.

  • The application is free and does not reserve a seat.
  • The deposit reserves the seat.
  • The remaining balance is not requested before cohort confirmation.
  • Exact deadlines are confirmed in the cohort offer.

If the minimum cohort is not met, the published cancellation/refund terms apply. Payment confirmations are not tax invoices.

Frequently asked questions

Do I need previous programming experience?

No. Basic computer literacy and school-level arithmetic are sufficient.

Is this an AI or machine-learning course?

No. It builds the programming foundation and stops before NumPy, pandas, visualisation, and machine learning.

What if I already know some Python?

Use the readiness guidance; if you can independently build and debug data programs, review Stage 2.

What is the capstone?

A Core-Python structured-data analyser. The exact theme may vary by cohort while remaining aligned with the published curriculum and difficulty.

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.

Do I need strong mathematics?

No. School-level arithmetic is sufficient.

What if the minimum cohort is not reached?

The cohort proceeds only under the published conditions; the cancellation and refund policy governs any payment.

What course should I take next?

Applied Data Analysis with Python is the next stage.

Your place in the learning journey

Stage 1 · FoundationsStage 2 · Data analysisStage 3 · Machine learning

After this course, you should be prepared to work with NumPy, pandas, and visualisation in Applied Data Analysis with Python. This course deliberately stops before those tools.

View the complete learning journey

Continue exploring

See how these foundations connect to the wider AI and learning landscape.

Need application or enrolment help? Contact support.

Interested in this course?