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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Designed for: complete or near-complete programming beginners and professionals rebuilding weak or outdated Python foundations.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
Starting level
No prior programming required
Applied outcome
Personal data analyser
Get notified when applications openCheck whether this is your starting point

Starting point

Is this course right for you?

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

  • 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 personal data analyser

Process hundreds or thousands of records, surface data-quality problems and useful patterns, and demonstrate a working end-to-end data tool.

  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.

How the course works

Learn live

14 instructor-led online lectures of 90 minutes, combining explanation, demonstrations, and guided coding.

Practise and build

Short tasks between sessions progressively build the personal data analyser. Independent practice is additional to the 21 teaching hours.

Review and support

Selected work may be reviewed in class or office hours. Exact support windows and recording access will be published before applications open.

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 personal 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.

Time commitment and learner responsibility

Teaching and practice

21 instructor-led hours. Independent practice, short tasks, and capstone effort are additional; the weekly range will be confirmed before applications open.

Participation

Attendance and consistent practice affect progress. Recordings, if offered, do not replace participation or practice.

Technical setup and support

A functioning laptop, editor, current Python setup, and reliable internet are required. Support windows follow the published cohort policy.

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

Fee, application, and cohort process

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.

  1. Review course details and prerequisites.
  2. Submit a free application when applications open.
  3. The application is reviewed.
  4. Suitable learners receive an offer.
  5. Pay the deposit by the offer deadline.
  6. Pay the balance under the published schedule.
  7. The cohort proceeds when its published conditions are met.
  8. Receive setup and access instructions.

If the minimum cohort is not met, the published cancellation/refund terms apply. Receipt/invoice, certificate criteria, recording, assignment review, and support details will be finalised before applications open.

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.

Why is data used to teach beginner Python?

Structured data provides concrete problems while the focus remains core Python.

Which Python version and editor will be used?

A current Python version and supported editor will be specified in setup guidance.

Will notebooks be used?

Scripts are central; a lightweight interactive environment may support guided exploration.

What if I already know some Python?

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

How much practice is expected?

Practice and capstone work are additional to 21 teaching hours; the range will be published before applications open.

Will assignments receive feedback?

The final review and support model will be published before applications open.

Are recordings available?

No recording access is promised until the policy is published.

What if I miss a session?

Missed-session arrangements will be published before applications open.

Do I need strong mathematics?

No. School-level arithmetic is sufficient.

What is the capstone?

A core-Python personal data analyser.

What is required for a certificate?

Certificate criteria are not yet published.

Does applying confirm a seat?

No. A seat is confirmed only after an offer and required payment.

When is payment collected?

Only after acceptance and an offer; no payment is collected with an application.

What if the minimum cohort is not reached?

The cohort proceeds only under published conditions; applicable refund/cancellation terms will govern payments.

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

Start with the right foundation

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