Is UCSanDiegoX: Big Data Analytics Using Spark beginner friendly?

No, this is an advanced level course. It is recommended for learners who already have foundational knowledge and practical experience in the domain.

How long does it take to complete UCSanDiegoX: Big Data Analytics Using Spark?

The total duration of the course is approximately 10 weeks. 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.

Do I get a certificate after completing UCSanDiegoX: Big Data Analytics Using Spark?

Yes, a certificate of completion is included, which you can add to your professional profile or resume to showcase your newly acquired skills.

What careers is this course relevant for?

The skills taught in this course are highly relevant for professionals pursuing roles within Computer Science and adjacent technical fields looking to upskill.

Computer Science

UCSanDiegoX: Big Data Analytics Using Spark

Name: UCSanDiegoX: Big Data Analytics Using Spark
Price: 29050 INR
Availability: InStock
Rating: 4.4 (11 reviews)

Learn how to analyze large datasets using Jupyter notebooks, MapReduce and Spark as a platform.

4.4|Reviews (11)

radio_button_checkededX Subscription

₹12,510/yr

Get this course and thousands more with a edX Subscription subscription.

radio_button_uncheckedIndividual Course

₹290500

Keep the course forever with lifetime access and receive a certificate.

TeamsBilled annually • 14-day free trial

₹33,026

EnterpriseFor organizations with 50+ learners

Custom Pricing

Why choose Essentials (Small Teams)?

check

1 Academy,16 Self-Paced Courses

check

Self-Paced Learning ,Employee Upskilling

Compare all plansarrow_forward

verifiedGet this course for free with the Coursera Plus subscription.

✓ Compare courses before making a decision

Check Latest Price →

Price may vary. Check latest price on provider site.

Course Insight

Designed for advanced/expert practitioners. Designed for experienced practitioners. We recommend having a solid grasp of Computer Science fundamentals before starting this specialization.

Advanced LevelCertification IncludedSelf-Paced LearningHands-On Learning

SKILLS TO
MASTER

Computer Science Basics

Fundamental principles and concepts

Practical Application

Real-world project implementation

Best Practices

Industry standard workflows and guidelines

Problem Solving

Core Concepts

Implementation

Workflow Integration

Optimization

Careers:Data Scientist, Data Analyst, Machine Learning Engineer.

Quick Facts

10 weeks

Below sections are verified from last major sync. For real-time updates and today's latest lectures, Check official page here.

What You’ll Learn

Programming Spark using Pyspark.
Identifying the computational tradeoffs in a Spark application.
Performing data loading and cleaning using Spark and Parquet.
Modeling data through statistical and machine learning methods.

See side-by-side differences in what you’ll learn

Description

In data science, data is called "big" if it cannot fit into the memory of a single standard laptop or workstation.

The analysis of big datasets requires using a cluster of tens, hundreds or thousands of computers. Effectively using such clusters requires the use of distributed files systems, such as the Hadoop Distributed File System (HDFS) and corresponding computational models, such as Hadoop, MapReduce and Spark.

In this course, part of the Data Science MicroMasters program, you will learn what the bottlenecks are in massive parallel computation and how to use spark to minimize these bottlenecks.

You will learn how to perform supervised an unsupervised machine learning on massive datasets using the Machine Learning Library (MLlib).

In this course, as in the other ones in this MicroMasters program, you will gain hands-on experience using PySpark within the Jupyter notebooks environment.

See how this course compares with alternatives