Professional skills

Python for Data Analysis

Name: Python for Data Analysis
Price: 2800.00 USD
Availability: InStock

Three-day Python for Data Analysis course where you clean, transform, visualise, and explore datasets using pandas, NumPy, and matplotlib with practical labs.

24 hours (3 days) Beginner Data Science & AI

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THE BIG PICTURE

About this course

The Python Data Analysis Certificate recognises practical ability to use Python and core libraries to prepare, analyse, and present data. There is no single awarding body for this title; providers and employers commonly set their own criteria. Over three days, you will work hands-on with Python, NumPy, pandas, and visualisation libraries to ingest, clean, transform, and explore real-world datasets. Guided labs use Jupyter Notebooks and focus on reproducible workflows, from data wrangling through to clear charts and summary insights. There is no single, standard external exam for “Python data analysis”. Where assessments are offered, they are provider-run; this course includes exam-style questions and a timed mock to help you evidence your skills and plan next steps. The awarding body owns the certification scheme. MindClick provides training and exam preparation and is not an accredited or authorised partner.

What you’ll learn

Use Python, Jupyter, and virtual environments to set up a reliable analysis workspace
Ingest CSV/JSON files and APIs and validate schema, types, and missing data
Apply NumPy and pandas for indexing, joins, reshaping, aggregation, and time-series handling
Clean, transform, and engineer features with vectorised operations and robust pipelines
Create clear visualisations with matplotlib and seaborn, using effective styles and annotations
Perform exploratory data analysis and basic descriptive statistics to derive insights
Export analysis outputs to reusable notebooks and reports with code, visuals, and narrative
Practise exam-style tasks and plan next steps towards suitable assessments or employer evaluations

A CLEAR PATH FORWARD

3-day Python for Data Analysis agenda

3 days · 24 contact hours · 09:00–17:30

3 instructor-led days (24 contact hours) mapped to the exam domains. Each day combines short concept sessions, hands-on practice on a running case study and exam-style questions.

Learning route: 3 recommended days
Practical work: 3 guided activities
Finish with: Mock exam + certification exam

Take a look at each day. Open a section to see the topics and practical work.

Day 1Python foundations for data handlingPython basics, files, NumPy, and data ingestion

What we’ll cover

09:00–10:30 · Orientation, environment check (Jupyter/Anaconda), Python refresher: types, control flow, functions
10:45–12:15 · Working with files: paths, CSV/JSON, encoding, data ingestion patterns and validation
13:15–14:45 · NumPy fundamentals: arrays, dtypes, broadcasting, vectorised computation
15:00–16:00 · Practical data parsing: dates, categoricals, handling missing and malformed values
16:00–16:45 · Project setup: structuring a reproducible analysis repo and notebook conventions
16:45–17:15 · Exam-style practice questions (foundations and ingestion) and group debrief
17:15–17:30 · Daily wrap-up and Q&A

Put it into practice

Build a small ingestion pipeline that loads multiple CSV files, validates schema, and consolidates them into a clean NumPy-backed structure. Document assumptions and edge cases directly in a Jupyter Notebook.

You’ll take away
Ingestion notebook with validated, consolidated dataset

Day 2Data wrangling with pandaspandas structures, joining, reshaping, cleaning, feature engineering

What we’ll cover

09:00–09:30 · Recap of Day 1 and Q&A
09:30–10:45 · pandas Series and DataFrame essentials: indexing, selection, assignment, and views vs copies
11:00–12:15 · Joins and merges, concatenation, and handling duplicated or inconsistent keys
13:15–14:30 · Reshaping data with stack/unstack, melt/pivot, and groupby aggregations
14:45–15:45 · Data cleaning pipelines: missing data strategies, outliers, type coercion, and feature engineering
15:45–16:45 · Performance tips: vectorisation, apply vs map, categoricals, and memory profiling
16:45–17:15 · Exam-style practice questions (wrangling and pipelines) with solutions
17:15–17:30 · Daily wrap-up and Q&A

Put it into practice

Create a reusable pandas pipeline that merges disparate tables, reshapes to a tidy format, engineers features, and outputs a clean DataFrame. Save intermediate steps and sanity checks to support auditability.

You’ll take away
Reusable pandas cleaning and transformation pipeline

Day 3Visualisation, EDA, and reportingVisualisation, EDA, basic stats, reporting, and assessment readiness

What we’ll cover

09:00–09:30 · Recap of Day 2 and Q&A
09:30–10:45 · Exploratory data analysis: distributions, correlations, and summary statistics
11:00–12:15 · Visualisation with matplotlib and seaborn: charts, styles, facets, and annotations
13:15–14:15 · Time series and date handling: resampling, rolling windows, and seasonal patterns
14:15–15:15 · Communicating results: notebook structure, narrative, and exporting to HTML/PDF
15:30–16:30 · Mock exam (timed, mixed multiple-choice and short practical tasks)
16:30–17:15 · Review of mock exam, feedback, and individual action plans
17:15–17:30 · Guidance on selecting and booking suitable assessments or compiling a portfolio

Put it into practice

Conduct an end-to-end EDA of a real dataset, producing clear visuals and concise commentary. Package the notebook and figures into a shareable report for stakeholder review.

You’ll take away
EDA notebook and visualisation report

Bring it all together

Certification exam: Python for Data Analysis. Confirm the current exam format with the awarding body when you book. Every attendee also receives a MindClick certificate of completion for 24 contact hours.

Sample MindClick certificate of completion for Python for Data Analysis, marked SAMPLE — What you receive
You earn two separate credentials. The sample shows the MindClick training certificate.
MindClick certificate of completion: 3 days, 24 contact hours, issued to every attendee who completes the course.
Python for Data Analysis: issued by the awarding body when you meet its requirements and pass the exam.

Before you choose your learning route

Certification route: There is no single external voucher-based exam for a 'Python Data Analysis' certificate; providers typically assess via their own tests or portfolios.

Session timings are indicative; the trainer may adjust them for the group. Exam details reflect the awarding body’s published information at the time of writing; confirm the current version when you book.

The awarding body owns this certification scheme. MindClick provides training and exam preparation and is not an accredited or authorised partner of the awarding body.

Fees

Fee per learner outside India. Exam fee included.
Delivery format	Fee per learner	Exam fee
Live online	$2,800 USD	Included
Classroom	$4,500 USD	Included

One awarding-body certification exam attempt is included in the training fee. Confirm the current fee, any applicable tax and what the fee covers in writing before you pay.

Prerequisites

No formal prerequisites. Recommended: basic programming or spreadsheet analysis experience; bring a laptop with Python 3.x (Anaconda or venv), Jupyter, and permission to install packages.

FIND YOUR START

Confirmed batch starts

Choose a published start, then review your offer in the same enrollment flow. A start date is not the full timetable or a seat reservation.

Choose your country & month

Nov142026

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