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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.

Learn basic concepts of theory and application for statistical inference. Get an introduction to statistical and critical thinking, including descriptive statistics, probability, sampling distributions, interval estimation, hypothesis testing and regression.

SKILLS:

- Biostatistics

Clinical Development

- Clinical Trials

- Biotechnology

- GCP

Drug Development

- Pharmaceutical Industry

- Oncology

- Regulatory Submissions

DATE: 11 - 29 Jun, 2018

DURATION: 3 Weeks

LECTURES: 3 Hours per day

LANGUAGE: English

LOCATION: Barcelona, Harbour.Space Campus

COURSE TYPE: Offline

WHAT YOU WILL LEARN
COURSE OUTLINE
ABOUT LEAH
HARBOUR.SPACE 

INTRODUCTION TO
STATISTICS

This is introductory course in statistics. Topics discussed include descriptive statistics and statistical inference procedures of estimation, confidence intervals and hypothesis testing. The first part of the course will include introduction to mathematical statistics, in particular point estimations, theory of measures of the  quality of estimators, and theory of statistical tests, including introduction to uniformly most powerful test and likelihood ratio tests. 

The second part concerns applied methods of hypothesis testing, in particular t-test, paired t-test, non-parametric statistics and simple and linear multiple regressions, as well as basics of regression diagnostics. The course will also include applications of data analysis in R package.

LEAH  ISAKOV
RESERVE MY SPOT

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.

HARBOUR.SPACE UNIVERSITY

DATE: 11 – 29 Jun, 2018

DURATION: 3 Weeks

LECTURES: 3 Hours per day

LANGUAGE: English

LOCATION: Barcelona, Harbour.Space Campus

COURSE TYPE: Offline

INTRODUCTION TO STATISTICS

Session 2

• Distributions. Property of Distribution Function

• Some Special distribution

• Moment generating function

Session 3

• Point Estimation

• Confidence Intervals for Means

• Confidence interval for Difference in Means

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Session 1

• Introduction/ review, data types, probability and laws of probability. Random data types

• Conditional probability, marginal probability, check for independence

• Statistical computing with R (time permitting)

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Session 4

• Measures of Quality of Estimators

• Sufficient Statistics

• Completeness and Uniqueness

In this module, the beautiful and powerful results of linear algebra will be explored. One of the most important tools in the study of matrices is Jordan canonical form which allows to simplify many calculations (e.g. exponentiation).

We will learn how to find such a canonical form and apply that knowledge to special classes of matrices. Furthermore, bilinear forms will be introduced together with the classical notion of a Euclidean space. An algorithm called Gram–Schmidt Process will provide a useful technique for constructing orthonormal bases of a Euclidean space and its subspaces.

In this module, the beautiful and powerful results of linear algebra will be explored. One of the most important tools in the study of matrices is Jordan canonical form which allows to simplify many calculations (e.g. exponentiation).

We will learn how to find such a canonical form and apply that knowledge to special classes of matrices. Furthermore, bilinear forms will be introduced together with the classical notion of a Euclidean space. An algorithm called Gram–Schmidt Process will provide a useful technique for constructing orthonormal bases of a Euclidean space and its subspaces.

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