Table 4 Datathon applicant and participant characteristics.

From: Advancing data science research education in Africa through datathon-driven innovations

 

Applicants

(N = 92)

Hybrid participantsa

(n = 49)

In-person participantsb

(n = 15)

p-valuec

Gender

Female

21 (23)

13 (27)

4 (27)

0.638

Male

71 (77)

36 (73)

11 (73)

African regiond

Central

9 (10)

8 (16)

0 (0)

 < 0.0001*

Eastern

22 (24)

4 (8)

0 (0)

Northern

1 (1)

1 (2)

0 (0)

Southern

3 (3)

2 (4)

0 (0)

Western

56 (61)

34 (69)

15 (100)

Highest degreee

Bachelor’s

13 (14)

1 (2)

0 (0)

0.0003*

Master’s

52 (57)

26 (53)

9 (60)

Doctorate

27 (29)

22 (45)

6 (40)

Data science expertise

Basic

33 (36)

22 (45)

5 (33)

0.020*

Intermediate

56 (61)

24 (49)

8 (53)

Advanced

3 (3)

3 (6)

2 (13)

Current professionf

Master’s student

12 (13)

6 (12)

1 (7)

0.035

Doctoral student

21 (23)

9 (18)

1 (7)

Postdoctoral student

10 (11)

8 (16)

1 (7)

Professor or lecturer

8 (9)

6 (12)

2 (13)

Research assistant

34 (37)

16 (33)

6 (40)

Other

6 (7)

4 (8)

4 (27)

  1. All results expressed as frequency (%).
  2. a Participants for the hybrid foundational phase of the datathon training.
  3. b Fifteen participants attended in person at the Bamako training site for the program’s foundational and datathon phases.
  4. c Comparing differences across the applicant, hybrid, and in-person groups. Non-participant frequencies were determined as the differences between the comparison group columns. Calculations performed using Fisher’s Exact Tests.
  5. d One applicant resided in the United States.
  6. e Enrolled in or completed the degree program at the time of application.
  7. f Response was unavailable for one applicant.
  8. *p < 0.05.
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