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Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.

The term data scientist was coined as recently as 2008 when companies realized the need for data professionals who are skilled in organizing and analyzing massive amounts of data.1 In a 2009 McKinsey&Company article, Hal Varian, Googles chief economist and UC Berkeley professor of information sciences, business, and economics, predicted the importance of adapting to technologys influence and reconfiguration of different industries.2

The ability to take data to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it thats going to be a hugely important skill in the next decades.

Hal Varian, chief economist at Google and UC Berkeley professor of information sciences, business, and economics3

Effective data scientists are able to identify relevant questions, collect data from a multitude of different data sources, organize the information, translate results into solutions, and communicate their findings in a way that positively affects business decisions. These skills are required in almost all industries, causing skilled data scientists to be increasingly valuable to companies.

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Take theData Science Essentialsonline short course and earn a certificatefrom the UC Berkeley School of Information.

In the past decade, data scientists have become necessary assets and are present in almost all organizations. These professionals are well-rounded, data-driven individuals with high-level technical skills who are capable of building complex quantitative algorithms to organize and synthesize large amounts of information used to answer questions and drive strategy in their organization. This is coupled with the experience in communication and leadership needed to deliver tangible results to various stakeholders across an organization or business.

Data scientists need to be curious and result-oriented, with exceptional industry-specific knowledge and communication skills that allow them to explain highly technical results to their non-technical counterparts. They possess a strong quantitative background in statistics and linear algebra as well as programming knowledge with focuses in data warehousing, mining, and modeling to build and analyze algorithms.

They must also be able to utilize key technical tools and skills, including:

R

Python

Apache Hadoop

MapReduce

Apache Spark

NoSQL databases

Cloud computing

D3

Apache Pig

Tableau

iPython notebooks

GitHub

Glassdoor ranked data scientist as the #1 Best Job in America in 2018 for the third year in a row.4 As increasing amounts of data become more accessible, large tech companies are no longer the only ones in need of data scientists. The growing demand for data science professionals across industries, big and small, is being challenged by a shortage of qualified candidates available to fill the open positions.

The need for data scientists shows no sign of slowing down in the coming years. LinkedIn listed data scientist as one of the most promising jobs in 2017 and 2018, along with multiple data-science-related skills as the most in-demand by companies.5

The statistics listed below represent the significant and growing demand for data scientists.

28%Demand Increase by 2020

Number of Job Openings

Average Base Salary

Best Job in America 2016, 2017, 2018

Sources:GlassdoorandForbes

Data is everywhere and expansive. A variety of terms related to mining, cleaning, analyzing, and interpreting data are often used interchangeably, but they can actually involve different skill sets and complexity of data.

Data scientists examine which questions need answering and where to find the related data. They have business acumen and analytical skills as well as the ability to mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data. Results are then synthesized and communicated to key stakeholders to drive strategic decision-making in the organization.

Skills needed:Programming skills (SAS, R, Python), statistical and mathematical skills, storytelling and data visualization, Hadoop, SQL, machine learning

Data analysts bridge the gap between data scientists and business analysts. They are provided with the questions that need answering from an organization and then organize and analyze data to find results that align with high-level business strategy. Data analysts are responsible for translating technical analysis to qualitative action items and effectively communicating their findings to diverse stakeholders.

Skills needed:Programming skills (SAS, R, Python), statistical and mathematical skills, data wrangling, data visualization

Data engineers manage exponential amounts of rapidly changing data. They focus on the development, deployment, management, and optimization of data pipelines and infrastructure to transform and transfer data to data scientists for querying.

Skills needed:Programming languages (Java, Scala), NoSQL databases (MongoDB, Cassandra DB), frameworks (Apache Hadoop)

Data science professionals are rewarded for their highly technical skill set with competitive salaries and great job opportunities at big and small companies in most industries. With over 4,500 open positions listed on Glassdoor, data science professionals with the appropriate experience and education have the opportunity to make their mark in some of the most forward-thinking companies in the world.6

Below are the average base salaries for the following positions:7

Data analyst:$65,470

Data scientist:$120,931

Senior data scientist:$141,257

Data engineer:$137,776

Gaining specialized skills within the data science field can distinguish data scientists even further. For example, machine learning experts utilize high-level programming skills to create algorithms that continuously gather data and automatically adjust their function to be more effective.

1hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century. Accessed April 2018.arrow_upwardReturn to footnote reference2 http://www.mckinsey.com/industries/high-tech/our-insights/hal-varian-on-how-the-web-challenges-managers. Accessed July 2018.arrow_upwardReturn to footnote reference3www.mckinsey.com/industries/high-tech/our-insights/hal-varian-on-how-the-web-challenges-managers. Accessed July 2018.arrow_upwardReturn to footnote reference4 http://www.glassdoor.com/List/Best-Jobs-in-America-LST_KQ0,20.htm. Accessed April 2018.arrow_upwardReturn to footnote reference5 blog.linkedin.com/2018/january/11/linkedin-data-reveals-the-most-promising-jobs-and-in-demand-skills-2018. Accessed April 2018.arrow_upwardReturn to footnote reference6 http://www.glassdoor.com/List/Best-Jobs-in-America-LST_KQ0,20.htm. Accessed April 2018.arrow_upwardReturn to footnote reference7 http://www.glassdoor.com/Salaries/index.htm. Accessed April 2018.arrow_upwardReturn to footnote reference

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What is Data Science? | The Data Science Career Path

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Despite the promise of data science and huge investments in data science teams, many companies are not realizing the full value of their data. In their race to hire talent and create data science programs, some companies have experienced inefficient team workflows, with different people using different tools and processes that dont work well together. Without more disciplined, centralized management, executives might not see a full return on their investments.

This chaotic environment presents many challenges.

Data scientists cant work efficiently. Because access to data must be granted by an IT administrator, data scientists often have long waits for data and the resources they need to analyze it. Once they have access, the data science team might analyze the data using differentand possibly incompatibletools. For example, a scientist might develop a model using the R language, but the application it will be used in is written in a different language. Which is why it can take weeksor even monthsto deploy the models into useful applications.

Application developers cant access usable machine learning. Sometimes the machine learning models that developers receive are not ready to be deployed in applications. And because access points can be inflexible, models cant be deployed in all scenarios and scalability is left to the application developer.

IT administrators spend too much time on support. Because of the proliferation of open source tools, IT can have an ever-growing list of tools to support. A data scientist in marketing, for example, might be using different tools than a data scientist in finance. Teams might also have different workflows, which means that IT must continually rebuild and update environments.

Business managers are too removed from data science. Data science workflows are not always integrated into business decision-making processes and systems, making it difficult for business managers to collaborate knowledgeably with data scientists. Without better integration, business managers find it difficult to understand why it takes so long to go from prototype to productionand they are less likely to back the investment in projects they perceive as too slow.

Learn about the data science lifecycle (PDF)

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About the ProgramBachelor of Arts (BA)

The Data Science Major degree program combines computational and inferential reasoning to draw conclusions based on data about some aspect of the real world. Data scientists come from all walks of life, all areas of study, and all backgrounds. They share an appreciation for the practical use of mathematical and scientific thinking and the power of computing to understand and solve problems for business, research, and societal impact.

The Data Science Major will equip students to draw sound conclusions from data in context, using knowledge of statistical inference, computational processes, data management strategies, domain knowledge, and theory. Students will learn to carry out analyses of data through the full cycle of the investigative process in scientific and practical contexts. Students will gain an understanding of the human and ethical implications of data analytics and integrate that knowledge in designing and carrying out their work.

The Data Science major requirements includeDATAC8andDATAC100, the core lower-division and upper-division elements of the major, along with courses from each of the following requirement groups:

All students will select a Domain Emphasis, a cluster of one lower division course and two upper division courses, that brings them into the context of a domain and allows themto build bridges with data science.

Students can apply to declare the Data Science major after completing all the lower-division prerequisites (see the Major Requirements tab).For applicants with prerequisites in progress, applications will be reviewed after the grades for all prerequisites are available.

It is necessary for applicants toachieve a minimum prerequisite grade point average (GPA) in order to declare the Data Science major. Information on this GPA and the process to apply for admission to the major can be found on the Declaring the Major web page.

TheMinor in Data Scienceat UC Berkeley aims to provide students with practical knowledge of the methods and techniques of data analysis, as well as the ability to think critically about the construction and implications of data analysis and models. The minor will empower students across the wide array of campus disciplines with a working knowledge of statistics, probability, and computation that allow students not just to participate in data science projects, but to design and carry out rigorous computational and inferential analysis for their field of interest.Check the Data Science Minor program websitefor details.

VISIT PROGRAM WEBSITE

In addition to the University, campus, and college requirements listed on the College Requirements tab, students must fulfill the below requirements specific to the major program. Please check theData Science program websitefor updates.

In some cases, students may complete alternative courses to satisfy the above prerequisites. See the lower-division requirements page on the Data Science program website for more details.

Students will also be required to take one lower division course towards their choice of Domain Emphasis.

Students will be required to complete 8 unique upper-division courses for a total of 28 or more units from the following requirement categories.

Students will be required to take two upper division courses comprising 7 or more units that provide computational and inferential depth beyond that provided in Data 100and the lower-division courses.

Students will be required to take one upper-division course on probability.

Students will be required to take one upper-division course on modeling, learning, and decision-making.

Students will be required to take one course from a curated list of courses that establish a human, social, and ethical context in which data analytics and computational inference play a central role.

Students will also be required to take two upper division courses towards their choice of Domain Emphasis.

Domain Emphases that students can choose from:

The Minor in Data Science at UC Berkeley aims to provide students with practical knowledge of the methods and techniques of data analysis, as well as the ability to think critically about the construction and implications of data analysis and models. The minor will empower students across the wide array of campus disciplines with a working knowledge of statistics, probability, and computation that allow students not just to participate in data science projects, but to design and carry out rigorous computational and inferential analysis for their field of interest.

All minors must be declared no later than one semester before a student's Expected Graduation Term (EGT). If the semester before EGT is fall or spring, the deadline is the last day of RRR week. If the semester before EGT is summer, the deadline is the final Friday of Summer Sessions. For more information about declaring the minor, view the Data Science minor webpage.

All courses for the minor must be taken for a letter grade.

Students must earn a C- or better in each course, and have a minimum 2.0 GPA in all courses towards the minor.

Students may overlap up to 1 course in the upper division requirements for the Data Science minor with each of their majors (for example, a Computer Science major may count COMPSCI/DATA/STAT C100 toward both their major and the Data Science minor).

A maximum of one course offered by or cross-listed with the students major department(s) may count toward the data science minor upper-division requirements, including any overlapping course (for example, if a Computer Science major takes COMPSCI/DATA/STAT C100 toward the Data Science minor, this is the only COMPSCI, ELENG, or EECS course which may count toward the upper-division requirements for the minor).

An upper-division course used to fulfill a lower-division requirement (for example, Stat 134 to fulfill the probability requirement) will not be counted toward the maximum 1 course allowed to overlap with the major, nor will it fulfill one of the four upper division course requirements.

There is no restriction on overlap with another minor.

Courses used to fulfill the minor requirements may be applied toward the Seven-Course Breadth requirement, for Letters & Science students.

All minor requirements must be completed prior to the last day of finals during the semester in which you plan to graduate.

Complete a total of 4 upper-division courses in one of the following pathways:

Choose ONE from theApproved Elective List.

Undergraduate students must fulfill the following requirements in addition to those required by their major program.

For detailed lists of courses that fulfill college requirements, please review theCollege of Letters & Sciencespage in this Guide. For College advising appointments, please visit the L&S Advising Pages.

All students who will enter the University of California as freshmen must demonstrate their command of the English language by fulfilling the Entry Level Writing requirement. Fulfillment of this requirement is also a prerequisite to enrollment in all reading and composition courses at UC Berkeley.

The American History and Institutions requirements are based on the principle that a US resident graduated from an American university, should have an understanding of the history and governmental institutions of the United States.

All undergraduate students at Cal need to take and pass this course in order to graduate. The requirement offers an exciting intellectual environment centered on the study of race, ethnicity and culture of the United States. AC courses offer students opportunities to be part of research-led, highly accomplished teaching environments, grappling with the complexity of American Culture.

The Quantitative Reasoning requirement is designed to ensure that students graduate with basic understanding and competency in math, statistics, or computer science. The requirement may be satisfied by exam or by taking an approved course.

The Foreign Language requirement may be satisfied by demonstrating proficiency in reading comprehension, writing, and conversation in a foreign language equivalent to the second semester college level, either by passing an exam or by completing approved course work.

In order to provide a solid foundation in reading, writing, and critical thinking the College requires two semesters of lower division work in composition in sequence. Students must complete parts A & B reading and composition courses by the end of their second semester and a second-level course by the end of their fourth semester.

The undergraduate breadth requirements provide Berkeley students with a rich and varied educational experience outside of their major program. As the foundation of a liberal arts education, breadth courses give students a view into the intellectual life of the University while introducing them to a multitude of perspectives and approaches to research and scholarship. Engaging students in new disciplines and with peers from other majors, the breadth experience strengthens interdisciplinary connections and context that prepares Berkeley graduates to understand and solve the complex issues of their day.

For units to be considered in "residence," you must be registered in courses on the Berkeley campus as a student in the College of Letters & Science. Most students automatically fulfill the residence requirement by attending classes here for four years. In general, there is no need to be concerned about this requirement, unless you go abroad for a semester or year or want to take courses at another institution or through UC Extension during your senior year. In these cases, you should make an appointment to meet an adviser to determine how you can meet the Senior Residence Requirement.

Note: Courses taken through UC Extension do not count toward residence.

After you become a senior (with 90 semester units earned toward your BA degree), you must complete at least 24 of the remaining 30 units in residence in at least two semesters. To count as residence, a semester must consist of at least 6 passed units. Intercampus Visitor, EAP, and UC Berkeley-Washington Program (UCDC) units are excluded.

You may use a Berkeley Summer Session to satisfy one semester of the Senior Residence requirement, provided that you successfully complete 6 units of course work in the Summer Session and that you have been enrolled previously in the college.

Participants in the UC Education Abroad Program (EAP), Berkeley Summer Abroad, or the UC Berkeley Washington Program (UCDC) may meet a Modified Senior Residence requirement by completing 24 (excluding EAP) of their final 60 semester units in residence. At least 12 of these 24 units must be completed after you have completed 90 units.

You must complete in residence a minimum of 18 units of upper division courses (excluding UCEAP units), 12 of which must satisfy the requirements for your major.

L&S College Requirements: Reading & Composition, Quantitative Reasoning, and Foreign Language, which typically must be satisfied with a letter grade, can be satisfied with a Passed (P) grade during Fall 2020 and Spring 2021 if a student elects to take the course for P/NP. Note: This doesnotinclude Entry Level Writing (College Writing R1A).

Requirements within L&S majors and minors can be satisfied with Passed (P) grades during the Fall 2020 and Spring 2021 semesters. This includes prerequisites for majors. Contact your intended or declaredmajor/minor adviserfor more details.

Departments may create alternative methods for admitting students into their majors.

L&S students will not be placed on academic probation automatically for taking all of their courses P/NP during Fall 2020 or Spring 2021.

Sample plans for completing major coursework are included below. These are not comprehensive plans which will reflect the situation of every student. These sample plans are meant only to serve as a baseline guide for structuring a plan of study, and only include the minimum courses for meeting the L&S Data Science major requirements.

*Note: this sample plan is based on a transfer student who has completed 1 year of calculus, linear algebra and data structures, as well as IGETC/L&S 7-Course Breadth at their previous college or university, which may not reflect the reality for every transfer student. Students should consult with a Data Science Advisor to make an individualized plan based on their specific situation.

Major Maps help undergraduate students discover academic, co-curricular, and discovery opportunities at UC Berkeley based on intended major or field of interest. Developed by the Division of Undergraduate Education in collaboration with academic departments, these experience maps will help you:

Explore your major and gain a better understanding of your field of study

Connect with people and programs that inspire and sustain your creativity, drive, curiosity and success

Discover opportunities for independent inquiry, enterprise, and creative expression

Engage locally and globally to broaden your perspectives and change the world

Use the major map below as a guide to planning your undergraduate journey and designing your own unique Berkeley experience.

View the Data Science Major Map PDF.

Each semester, we recruit dozens of students to participate in our student teams as interns and volunteers. Teams include Communications, Analytics, External Relations, and Curriculum Development. Interested students can email ds-teams@berkeley.edu with questions about the opportunities. Learn more here.

The Data Scholars program addresses issues of underrepresentation in the data science community by establishing a welcoming, educational, and empowering environment for underrepresented and nontraditional students. The program, which offers specialized tutoring, advising, mentorship, and workshops, is especially suited for students who can bring diverse perspectives to the field of Data Science.Learn more here.

Students in our consulting network help make data science accessible across the broader campus community by providing technical support and tutoring. Peer consultants are available at Moffitt Library on a drop-in basis. Learn morehere.

Academic Peer Advisors are available to help fellow students choose classes, explore academic interests, and learn how to declare the Data Science major. The Peer Advising services are available on a drop-in basis at Moffitt Library. Contact the Data Science Peer Advisors at ds-peer-advising@berkeley.edu.Learn more here.

The Data Science Discovery Research program connects undergraduates with hands-on, team-based opportunities to contribute to cutting-edge research projects with graduate and post-doctoral students, community impact groups, entrepreneurial ventures, and educational initiatives across UC Berkeley. Learn more here.

The Data Science Nexus is an alliance of data science student organizations on campus that work together to build community, host industry events, and provide academic support for students. In recognition of the extraordinarily diverse and multi-faceted nature of data science, members of the Nexus come from a variety of domains. Learn more here.

Expand all course descriptions [+]Collapse all course descriptions [-]

Terms offered: Spring 2021This course engages students with fundamental questions of justice in relation to data and computing in American society. Data collection, visualization, and analysis have been entangled in the struggle for racial and social justice because they can make injustice visible, imaginable, and thus actionable. Data has also been used to oppress minoritized communities and institutionalize, rationalize, and naturalize systems of racial violence. The course examines key sites of justice involving data (such as citizenship, policing, prisons, environment, and health). Along with critical social science tools, students gain introductory experience and do collaborative and creative projects with data science using real-world data.Data and Justice: Read More [+]

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1.5 hours of discussion per week

Additional Details

Subject/Course Level: Data Science, Undergraduate/Undergraduate

Grading/Final exam status: Letter grade. Alternative to final exam.

Data and Justice: Read Less [-]

Terms offered: Prior to 2007An introduction to computational thinking and quantitative reasoning, preparing students for further coursework, especially Foundations of Data Science (CS/Info/Stat C8). Emphasizes the use of computation to gain insight about quantitative problems with real data. Expressions, data types, collections, and tables in Python. Programming practices, abstraction, and iteration. Visualizing univariate and bivariate data with bar charts, histograms, plots, and maps. Introduction to statistical concepts including averages and distributions, predicting one variable from another, association and causality, probability and probabilistic simulation. Relationship between numerical functions and graphs. Sampling and introduction to inference.Introduction to Computational Thinking with Data: Read More [+]

Objectives & Outcomes

Course Objectives: C6 also includes quantitative reasoning concepts that arent covered in Data 8. These include certain topics in: principles of data visualization; simulation of random processes; and understanding numerical functions through their graphs. This will help prepare students for computational and quantitative courses other than Data 8.C6 takes advantage of the complementarity of computing and quantitative reasoning to enliven abstract ideas and build students confidence in their ability to solve real problems with quantitative tools. Students learn computer science concepts and immediately apply them to plot functions, visualize data, and simulate random events.

Foundations of Data Science (CS/Info/Stat C8, a.k.a. Data 8) is an increasingly popular class for entering students at Berkeley. Data 8 builds students computing skills in the first month of the semester, and students rely on these skills as the course progresses. For some students, particularly those with little prior exposure to computing, developing these skills benefits from further time and practice. C6 is a rapid introduction to Python programming, visualization, and data analysis, which will prepare students for success in Data 8.

Student Learning Outcomes: Students will be able to perform basic computations in Python, including working with tabular data.Students will be able to understand basic probabilistic simulations.Students will be able to understand the syntactic structure of Python code.Students will be able to use good practices in Python programming.Students will be able to use visualizations to understand univariate data and to identify associations or causal relationships in bivariate data.

Hours & Format

Summer: 6 weeks - 4 hours of lecture, 2 hours of discussion, and 4 hours of laboratory per week

Additional Details

Subject/Course Level: Data Science, Undergraduate/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Formerly known as: Computer Science C8R/Statistics C8R

Also listed as: COMPSCIC6/STATC6

Introduction to Computational Thinking with Data: Read Less [-]

Terms offered: Summer 2021 8 Week Session, Spring 2021, Fall 2020, Summer 2020 8 Week SessionFoundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social and legal issues surrounding data analysis, including issues of privacy and data ownership.Foundations of Data Science: Read More [+]

Rules & Requirements

Prerequisites: This course may be taken on its own, but students are encouraged to take it concurrently with a data science connector course (numbered 88 in a range of departments)

Credit Restrictions: Students will receive no credit for DATAC8COMPSCIC8INFOC8STATC8 after completing COMPSCI 8, or DATA 8. A deficient grade in DATAC8COMPSCIC8INFOC8STATC8 may be removed by taking COMPSCI 8, COMPSCI 8, or DATA 8.

Hours & Format

Fall and/or spring: 15 weeks - 3-3 hours of lecture and 2-2 hours of laboratory per week

Summer: 8 weeks - 6 hours of lecture and 4 hours of laboratory per week

Additional Details

Subject/Course Level: Data Science, Undergraduate/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Formerly known as: Computer Science C8/Statistics C8/Information C8

Also listed as: COMPSCIC8/INFOC8/STATC8

Foundations of Data Science: Read Less [-]

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Data Science < University of California, Berkeley

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