Is Foundations: Data, Data, Everywhere beginner friendly?

Yes, this course is designed for beginners. It covers foundational concepts and requires no prior advanced knowledge in the subject area.

How long does it take to complete Foundations: Data, Data, Everywhere?

The total duration of the course is approximately 18 hours (approximately). Since it is generally self-paced, completion time depends entirely on your learning schedule and availability.

Does this course include hands-on projects?

Yes, the curriculum is highly practical and includes hands-on projects. Completing these will help you build a portfolio of work to demonstrate your new skills.

Data Science

Foundations: Data, Data, Everywhere

Name: Foundations: Data, Data, Everywhere
Availability: InStock
Rating: 4.8 (99174 reviews)

This course is part of Google Data Analytics Professional Certificate

4.8|Reviews (99K)

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Coursera Plus

₹13,999/yr

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Coursera Plus

₹2,099/mo

Single Learning ProgramStarting from ₹1,699/month

₹1,699/mo

TeamsPer user for 12 months

₹24,688

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Course Insight

Suitable for beginner learners. This course serves as an entry point into Data Science, building foundational knowledge before moving on to advanced frameworks or specialized paths.

Beginner FriendlySelf-Paced LearningProject-Based

SKILLS TO
MASTER

Analytics

Exploratory Data Analysis

Modeling

Predictive Machine Learning

SQL Querying

Relational Data Management

Pandas

Matplotlib

Statistics

Tableau

ETL

Careers:Relevant for professionals pursuing roles within Data Science.

💡This course fits perfectly into our comprehensiveData Science Learning Path. Explore the ecosystem to see how it compares to other foundational skills.

Quick Facts

18 hours (approximately)

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What You’ll Learn

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate from Google

See how this course curriculum compares with alternatives

Outcomes

Define and explain key concepts involved in data analytics including data, data analysis, and data ecosystems..
Conduct an analytical thinking self assessment giving specific examples of the application of analytical thinking..
Discuss the role of spreadsheets, query languages, and data visualization tools in data analytics..
Describe the role of a data analyst with specific reference to jobs..

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