Tech Heart
Harbour.Space University


Dr. Leah Isakov is a senior leader in the pharmaceutical industry with a unique combination of leadership and technical skills. She has worked in clinical trials for more than two decades and is known for delivering results. She has led NDA (New Drug Applications), PMA (Pre-Marketing Approvals) and BLA (Biologics License Applications) and has deep experience interacting with all the major regulatory bodies (FDA, EMEA, PMDA, Russian Ministry of Health, and Health Canada). She also has direct experience successfully managing cross-cultural international teams (USA, China, Japan and Canada). Her recent therapeutic areas include Oncology, Infectious Diseases, Cardiovascular, Asthma, Renal Failure and HIV for Phase II-IV clinical trials in drugs and biologics.

As a leader, Leah strives to be at the forefront of management practice. She incorporates data-driven decision making and quantitative risk management, and focuses on building internal capabilities along with external collaborations. She believes that successful management comes from understanding the full organizational stack; that is, not only high-level strategy, but also the technical aspects that enable success. Leah has a strong grasp of the technical side from two decades of hands-on experience in analytics, protocol design, sample size calculation, SAS programming, and integrated analysis (ISS and ISE), as well as strong GCP and regulatory knowledge.

Develop statistical thinking

 Interpret and critique statistical methods and results encountered in real data applications

 Design, conduct, interpret, and present basic statistical analyses.

After completing this course, students will understand the statistical language, notation and common methods. Students will have an understanding of study design, and be able to identify an appropriate statistical analysis for a study.  Students will be able to interpret results of statistical analyses presented in literature, and will be able to evaluate the methodological strength of a published paper with respect to the study design and appropriateness of statistical methods.


- Analytics

Business Strategy

Clinical Development

- Biostatistics

- Team Building

- Strategy

- Data Management

- CRO Management

- R

DATE: 12 - 30 June, 2017


LECTURES: 3 Hours per day


LOCATION: Barcelona, Harbour.Space Campus



Introductory one-module applied statistical methods course that emphasize inference and sound decision-making through extensive coverage of data collection and analysis. This course gives students the skills to perform, present, and interpret basic statistical analyses. Topics include confidence intervals and hypothesis testing; sample size and power considerations; analysis of variance and multiple comparisons; correlation and regression; multiple regression and statistical control of confounding.


We offer innovative university degrees taught in English by industry leaders from around the world, aimed at giving our students meaningful and creatively satisfying top-level professional futures. We think the future is bright if you make it so. 


DATE: 12 –30 June, 2017

DURATION:  3 Weeks

LECTURES: 3 Hours per day


LOCATION: Barcelona, Harbour.Space Campus



Session 1

Descriptive Statistics

Class Objectives:  Distinguish measurement, categorical and ordinal data; create and interpret frequency tables, proportions and percentages to describe a research sample on categorical data; create and interpret frequency distributions and histograms to describe a research sample on measurement data; calculate and interpret means and standard deviations to describe a research sample on measurement data.

Session 2

The Normal Distribution and Sampling Distributions

Class Objectives:  Determine when the normal model is appropriate to describe data; apply the characteristics of the normal model to describe the distribution of a measurement variable; apply the standard normal distribution, using statistical tables and Excel, to describe the distribution of a measurement variable.

Session 3

Estimation, sampling distributions, Introduction to confidence intervals

Class Objectives:  Understand sampling distributions; distinguish between the standard deviation and the standard error for a measurement variable; calculate and interpret a confidence interval for a mean; determine means, standard deviations, and confidence intervals using statistical computing packages.


Session 4

Introduction to hypothesis testing

Class Objectives:  Understand the logic and notation of hypothesis testing, including the null and alternative hypothesis, Type I and Type II errors, significance levels and p-values, significant and non-significant results, one and two tailed tests; carry out and interpret a one-sample test for a mean; understand the relationship between a t-test and a confidence interval; conduct and interpret a one-sample t-test using statistical packages.

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