Vital Skills for Data Science Specialization

Vital Skills for Data Science Specialization

Vital Skills for Data Science Specialization Gain Exposure to Key Data Science Areas . Gain professional knowledge in the field of Data Science and learn applicable skills in Cybersecurity and Ethics. Taught in English 21 languages available Some content may not be translated Instructors: Al Pisano +3 more Close Instructors Al Pisano University of Colorado

Description

Data science is a rapidly growing field that combines statistics, computer science, and domain expertise to extract knowledge and insights from data. As businesses and organizations continue to collect vast amounts of data, the demand for skilled data scientists who can analyze and interpret this data has never been higher.

To succeed in the field of data science specialization, professionals must possess a unique set of skills that set them apart from traditional data analysts. These skills go beyond basic technical abilities and involve a combination of analytical, programming, and domain-specific knowledge.

One vital skill for data science specialization is proficiency in programming languages such as Python, R, and SQL. These languages are commonly used in data analysis and machine learning tasks, and proficiency in at least one of them is essential for working with large datasets. Data scientists should also

Vital Skills for Data Science Specialization

Gain Exposure to Key Data Science Areas . Gain professional knowledge in the field of Data Science and learn applicable skills in Cybersecurity and Ethics.

Taught in English

Some content may not be translated

Al Pisano
Bobby Schnabel
Danielle Szafir

Instructors: Al Pisano

3,945 already enrolled

Included with Coursera Plus

Specialization – 4 course series

Get in-depth knowledge of a subject

4.5

(52 reviews)

Intermediate level

Recommended experience

2 months at 10 hours a week
Flexible schedule
Learn at your own pace
Progress towards a degree

What you’ll learn

  • Identify applications of Data Science.

  • Identify the steps of the Data Science process and apply them with real world data.

  • Discuss privacy concerns.

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