Online Course – Google Certified Professional Internship in Data Science, University of Washington

Tackle real-world data challenges. Master computational, statistical, and informational data science in three courses.

Suggested by: Coursera (What is Coursera?)

Start your Coursera.com free trial today
קורס אונליין - התמחות מקצועית מוסמכת במדעי הנתונים של Google

Professional Certificate

Intermediate level

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

  • SQL and NoSQL-based data management
  • Data search algorithms
  • Practical principles in statistics and machine learning
  • Data presentation and results communication
  • Legal and ethical issues when working with big data

What you will learn in the course

Courses for which the course is suitable

  • Data Analyst
  • Data Scientist
  • Data Engineer
  • Big Data Project Manager
  • Data Application Developer
  • Big Data Analyst
  • Machine Learning Expert
  • Information Technology Consultant

Internship – a series of 4 courses

Learn extensive data management, evaluate big data technologies, and design effective visualizations.

Topics covered:

  • SQL and NoSQL-based data management
  • Data search algorithms
  • Practical principles in statistics and machine learning
  • Data presentation and results communication
  • Legal and ethical issues when working with big data

In the final project, developed in collaboration with the digital internship platform Korsolev, you will apply your new skills to a real data science project.

Details of the courses that make up the specialization

Course 1: Data Manipulation at Scale: Systems and Algorithms

Duration: 20 hours

Rating: 4.3 (766 ratings)

What you’ll learn:

The course will teach you the landscape of relevant systems, the principles they are based on, and their trade-offs. You will also learn about the history and context of data science, the skills, challenges, and methods the term encompasses, and how to structure a data science project.

Learning objectives:

  • Describe common patterns and challenges in data science projects.
  • Identify and use programming models related to manipulating data at scale.
  • Use database technologies optimized for large-scale analytics.
  • Evaluate NoSQL systems and describe their trade-offs.
  • Think in terms of MapReduce to write algorithms in Hadoop and Spark.
  • Describe the landscape of big data systems specialized for graphs, arrays, and streams.

Skills you will gain:

  • Relational algebra
  • Python programming
  • SQL
  • MapReduce

Course 2: Practical Predictive Analytics: Models and Methods

Duration: 6 hours

Rating: 4.1 (317 ratings)

What you’ll learn:

In this course, you will design statistical experiments and analyze the results using modern methods. You will also learn about the common pitfalls in interpreting statistical arguments, especially those related to big data.

Learning objectives:

  • Design effective experiments and analyze the results.
  • Use sampling methods to formulate clear statistical arguments.
  • Explain and apply different classification methods.
  • Explain and apply concepts and methods of unsupervised learning.
  • Describe the common idioms of large-scale graphical analytics.

Skills you will gain:

  • Random arrangement
  • Predictive Analytics
  • Machine learning
  • R programming

Course 3: Communicating Data Science Results

Duration: 7 hours

Rating: 3.4 (142 ratings)

What you’ll learn:

You will learn how to design and control visualizations, explain the current state of privacy, ethics, and governance of big data, and use cloud computing to analyze big data in a reproducible way.

Learning objectives:

  • Design and control visualizations.
  • Explain the current state of big data privacy, ethics, and governance.
  • Use cloud computing to analyze big data in a reproducible way.

Course 4: Data Science at Scale – Final Project

Duration: 6 hours

Rating: 3.8 (25 ratings)

What you’ll learn:

In the final project, students will engage in a real project that requires them to apply skills from the full data science pipeline: preparing, organizing, and transforming data, building a model, and evaluating results.

Skills you will gain:

  • Data processing
  • statistics
  • Data Analytics
  • Python programming
  • R programming

Related products