Data Science in 2020: Computing, Curricula, and Challenges for the Next 10 Years

Figures & data

Fig. 1 Distribution of undergraduate and graduate-level courses and programs described in our survey.

Fig. 2 The number of years teaching data science for each respondent (blue), or years until the first planned teaching of a data science course (light blue).

Fig. 3 Axial coding results for challenges facing data science instructors and resources needed to teach data science effectively.

Fig. 4 Challenges for teaching introductory data science, grouped by respondent department.

Fig. 5 Programming languages and software used in introductory data science courses.

Table 1 Programming languages used in introductory data science courses by discipline.

Language	Mathematics	Computer Science	Statistics	Combined	Other	Total
Java	0	1	0	0	0	1
Julia	0	0	1	0	0	1
Python	7	5	6	1	5	24
R	14	1	11	5	5	36
SQL	2	1	0	0	0	3

Table 2 Topics most often taught in introductory data science courses for all disciplines.

Category	Description	Frequency of inclusion
Data visualization	Exploratory data analysis: multivariate plots and graphs, summary statistics.	56
Data cleaning	Data cleaning and wrangling (i.e., restructuring data tables, creating new variables).	51
Professional practices	Data ethics and responsible data use.	43
Data management	Data curation and data quality.	36
Statistical methods	Regression models: simple linear regression, least squares regression, logistic regression.	36
Professional practices	Reproducible research: computational reproducibility, empirical reproducibility, statistical reproducibility.	35
Professional practices	Data lifecycle and data collection, data quality evaluation.	34
Statistical methods	Research methods: research cycle, hypothesis definition and testing.	28
Data architecture	Data architecture, data types, and data formats.	27
Machine learning	Text mining: natural language processing, topic modeling, text visualization (i.e., word clouds, frequency plots).	27
Visualization	Customizing data visualizations: color and composition, accessibility, “grammar of graphics.”	27
Machine learning	Supervised learning: decision trees, Naive Bayes classification, neural networks, support vector machines (SVM), ensemble methods.	26

Table 3 Topics that are most often omitted from a data science curriculum for all faculty respondents.

Category	Description	Frequency of omission
Advanced computing	Big data infrastructures: high-performance networks, infrastructure and services management and operations.	30
Machine learning	Reinforced learning: Q-learning, TD-learning, genetic algorithms.	25
Advanced computing	Big data technologies: batch and parallel processing, streaming systems.	24
Advanced computing	Cloud computing.	24
Advanced computing	Systems engineering and software engineering principles.	21
Data management	Data governance: overall management of the availability, usability, integrity and security of data used in an enterprise.	21
Information technologies	Systems and data security: data anonymization, federated access control.	21
Data storage	Data storage systems, archive services, digital libraries, and their operational models.	20

Fig. 6 Computational topics relevant to statistics as indicated by Nolan and Temple Lang (2010). Topics covered in more than 75% of data science curricula in our survey are boxed in blue (solid line). Topics included in more than 50% of data science curricula are boxed in orange (dotted line). Topics not addressed in our survey are greyed out.

Table

			Not			Percent	Percent
Description	Intro	Program	covered	Unknown	N	covered	in intro
Regression models: simple linear regression, least squares regression, logistic regression.	36	27	1	2	66	95%	55%
Exploratory data analysis: multivariate plots and graphs, summary statistics.	57	5	2	2	66	94%	86%
Data cleaning and wrangling (i.e., restructuring data tables, creating new variables).	52	7	6	0	65	91%	80%
Research methods: research cycle, hypothesis definition and testing.	28	33	3	3	67	91%	42%
Traditional statistical inference: t/z-based hypothesis tests, confidence intervals.	16	45	4	3	68	90%	24%
Data ethics and responsible data use.	44	15	3	4	66	89%	67%
Linear algebra: matrix manipulation, eigenvalues, singularity.	8	50	8	2	68	85%	12%
Simulation-based inference: bootstrapping, randomization tests.	22	36	7	3	68	85%	32%
Data architecture, data types, and data formats.	28	28	9	2	67	84%	42%
Reproducible research: computational reproducibility, empirical reproducibility, statistical reproducibility.	36	18	5	7	66	82%	55%
Experimental design, modeling, and planning.	17	39	9	3	68	82%	25%
Machine learning (supervised): decision trees, Naive Bayes classification, neural networks, support vector machines (SVM), ensemble methods.	27	28	9	4	68	81%	40%
Relational and non-relational databases; SQL, NoSQL, data warehouse solutions, ETL, OLTP, OLAP.	12	40	10	4	66	79%	18%
Customizing data visualizations: color and composition, accessibility, “grammar of graphics.”	28	25	8	6	67	79%	42%
Data curation and data quality.	36	15	7	7	65	78%	55%
Machine learning (unsupervised): clustering algorithms, principal components analysis (PCA), singular value decomposition (SVD), independent components analysis (ICA).	20	32	14	2	68	76%	29%
Text mining: natural language processing, topic modeling, text visualization (i.e., word clouds, frequency plots).	28	23	15	2	68	75%	41%
Advanced data visualizations: creating animated or dynamic visualizations, maps.	22	27	11	6	66	74%	33%
Data acquisition through web scraping and/or API calls.	24	25	9	9	67	73%	36%
Applications software requirements and design.	10	38	11	7	66	73%	15%
Graph analysis: paths, social networks.	15	33	12	8	68	71%	22%
Data lifecycle and data collection, data quality evaluation.	35	11	12	8	66	70%	53%
Optimization and numerical algorithms (EM).	10	36	17	5	68	68%	15%
Data protection, backup, and privacy.	24	18	14	10	66	64%	36%
Information systems and collaborative systems.	7	33	16	10	66	61%	11%
Systems engineering and software engineering principles.	4	35	21	7	67	58%	6%
Systems and data security: data anonymization, federated access control.	13	26	21	7	67	58%	19%
MCMC simulation.	5	32	20	11	68	54%	7%
Cloud computing.	6	28	24	9	67	51%	9%
Data storage systems, archive services, digital libraries, and their operational models.	8	25	20	12	65	51%	12%
Big data technologies: batch and parallel processing, streaming systems.	2	31	25	9	67	49%	3%

Nolan, D., and Temple Lang, D. (2010), “Computing in the Statistics Curricula,” The American Statistician, 64, 97–107, DOI: 10.1198/tast.2010.09132.

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