Prof. Yonathan Mizrachi

Prof. Yonathan Mizrachi (Harvard Ph.D. 1992 in Anthropology)

Google Scholar, https://cybertraits.com.

@ Senior lecturer (formally Department Head), Sociology and Anthropology + Information Systems departments, YVC Academic College.

@ The Laboratory for AI, Machine Learning, Business & Data Analytics (LAMBDA) at Tel-Aviv University, Israel

 Background as a CIO and Software product development leader. Prof. of Information Systems focused on Data Science and Predictive Analytics.

 A Data and Social Scientist, Data Science Evangelist, Data addict, loves complexity and figuring-out blurry riddles via 'Data sense making' (modeling) and by 'painting data' (data visualization).

 Mail: Yoni1961@post.harvard.edu; Phone: +972544660556

1. Research Methods & Data Science Skills (over 20 years experience)

Methods

• Strong background in statistics and quantitative reasoning – sampling, descriptive, hypothesis testing and significance tests (teaching and practicing research methods SPSS and Qualitative research methods).

• Strong understanding of Experimental Design, good understanding of Causal Inference with Observational Data (Matching, Difference in Difference, Regression Discontinuity), Hierarchical Modeling, Exploratory Data Analysis (EDA), Non-linear Models, Conjoint/Choice Modeling, Quantitative and Qualitative research methods.

• Surveys: Questionnaire Design, Sampling and Bias Correction, Hands-on execution research experience.

Analytics, modeling, visualization and Database tools and background

• Excellent data intuition (background in Library and Information Science, Inventive engineering, sense making of complexity with 20 years' experience of Social Science applied & academic research).

• Machine learning modeling: Most common Structured (Classification, Regression etc.) and Unstructured (Clustering techniques). Familiar with Deep and Reinforced learning modeling/predictive approaches.

• Data Visualization & Communication: Excellent Data-story telling abilities and a Tableau Data "painter".

• Data Wrangling: Proficient in Data Wrangling tools and methods such as Tableau Prep, Rapid Miner Data Prep module, Trifacta Wrangler.

• IBM SPSS statistical Software / AMOS – SEM and Direct Marketing module – Highly proficient.

• Tableau (and Tableau Prep) & MS Power BI – Highly proficient.

• Rapid Miner Data Science Platform – Highly proficient

• Programming: SQL (Fair), Python (Weak).

• Databases and Cloud: AWS, PostgreSQL