₹0 for students · Certificate · Beginner

Data Science for Beginners

20 lessons: from defining data to analysing, visualising and communicating it

20 lessons · ~14 hours · 30-question final exam · pass mark 70%

About the course

What you’ll learn

A complete first course in data science: what data science is and the ethics that come with it, statistics and probability, relational and non-relational data, cleaning data with pandas, charts for quantities, distributions, proportions and relationships, the data science lifecycle, data science in the cloud and real-world uses. Each lesson has a study guide, the full lesson with notebooks and assignments, and a quiz. Pass every quiz and the final exam to earn a verifiable certificate.

Data analysisStatisticspandasData cleaningData visualisationSQLNoSQLData ethicsStorytelling with data

Part of the career tracks: Data Analyst, Machine Learning Engineer

How the certificate works

Read each lesson’s study guide, then the full lesson and any notebooks or exercises.
Pass every lesson quiz with 70% or more.
Pass the final exam: 30 questions in 45 minutes.
Get a certificate with a public verification link, and add it to LinkedIn.

Syllabus

20 lessons in 6 parts

Introduction to data science

Defining Data Science

40 min · Data science combines statistics, computing and domain knowledge to extract insight for decisions.

Introduction to Data Ethics

40 min · Principles: accountability, transparency, fairness, reliability, privacy, inclusiveness.

Defining Data

30 min · A dataset is a collection of data points.

A Brief Introduction to Statistics and Probability

60 min · Probability ranges from 0 to 1; random variables follow distributions (discrete or continuous).

Working with data

Working with Data: Relational Databases

40 min · Relational databases store data in tables linked by keys.

Working with Data: Non-Relational Data

40 min · NoSQL trades fixed schemas for flexibility and scale.

Working with Data: Python and Pandas

60 min · Series is one labelled column; DataFrame is a table of Series; NumPy underneath.

Working with Data: Data Preparation

50 min · Explore first: info(), describe(), value_counts().

Data visualisation

Visualising Quantities

45 min · matplotlib draws charts; seaborn adds statistical plots with less code.

Visualising Distributions

40 min · Distributions show spread, skew and peaks; understand them before modelling.

Visualising Proportions

35 min · Proportion charts show parts of a whole; group and count first.

Visualising Relationships

40 min · Scatter plots show two variables per observation; hue adds a third.

Making Meaningful Visualisations

45 min · Start from the question and audience; match the chart to the data.

The data science lifecycle

The Data Science Lifecycle

35 min · Stages: capturing (problem and data), processing, analysing/modelling, communicating, maintaining.

Analysing Data

40 min · EDA explores data with statistics, charts and questions before modelling.

Communicating Insights

35 min · Insights matter only when people understand and act on them.

Data science in the cloud

Introduction to Data Science in the Cloud

30 min · The cloud provides computing resources over the internet on a pay-as-you-go basis.

Low-Code Machine Learning in the Cloud

45 min · Azure ML studio supports low-code training and deployment.

Machine Learning in the Cloud with the SDK

50 min · The Azure ML Python SDK gives full, scriptable control over ML workflows.

Data science in the wild

Data Science in the Real World

35 min · Finance: fraud detection, credit risk; healthcare: imaging, risk prediction.

Course content is adapted from Microsoft's "Data Science for Beginners" curriculum (https://github.com/microsoft/Data-Science-For-Beginners), used under the MIT License, Copyright (c) Microsoft Corporation. Study guides and quiz questions are written by Vikosha. Certificates are issued by Vikosha and are not issued or endorsed by Microsoft. Licence: MIT License, Copyright (c) Microsoft Corporation. View the original curriculum