We are a team of statisticians and data scientists with more than 14 years of data analysis and data science experience. Our experience includes

  • Statistical consulting and data mining of statistics in various fields such as social science, natural science and health, business, engineering, and industry,
  • Working as a data science team with city hall and governmental organizations,
  • Teaching and conducting workshops at universities and revising statistical parts of scientific papers.

The corresponding challenges in addition to increasing our statistical literacy and data science, have brought us a strong knowledge of the role of statistics and data in each of the above-mentioned fields and daily urban life.

Our Skills

Soft Skills

  • Understanding the problems of a domain and then brainstorming on all possible solutions
  • Imagining and experimenting new ideas, which came from the data of a domain
  • Illustrating the benefits that data can bring to a business or an organization

Techincal Skills

  • R and Python programming
  • Querying and managing data via SQL
  • Dashboarding via Tableau and R shiny
  • Initial data mining by JMP
  • SEM modeling by AMOS
  • Data analysis and Statistical inference by SPSS and STATISTICA
  • Hayes Process Macro by Process v4
  • Reporting by Microsoft Office 2019 and R Markdown
Statistical Software Logos

Statistics, probability, and machine learning skills:

  • Data Preprocessing: data cleaning, data transformation, data reduction, data scaling, data balancing, data reliability, and validity.
  • Data Visualization: creating illustrative dashboards and highlighting features of data such as trends, patterns, cycles, and outliers. 
  • Probability concept and probability distributions: basic concepts of probability play an important role in machine learning.
  • Sampling techniques: applying accurate sampling methods makes strong statistical inferences about the population.
  • Statistical Inference: concluding about a population based on a sample through (point and interval) estimation and hypothesis testing. 
  • Machine Learning Technique
    • Classification of balanced and imbalanced data; with common algorithms logistic regression, K-nearest neighbor, support vector machines (SVM), neural networks and decision trees/ random forest.
    • Regression (Read more)
    • Clustering; with common algorithms such as k-means clustering, hierarchal clustering, and density-based clustering algorithms.
    • Factor Analysis; Principal Component Analysis (PCA), Principal Factor Analysis (PFA).
  • Time Series Analysis with common statistical models such as Exponential Smoothing, Autoregressive Integrated Moving Average (ARIMA/SARIMA), Linear Regression with Time Series Components, and Autoregressive Distributed Lag (ARDL) Model.
  • Time series Machine learning
    • Time series clustering
    • Time series classification
    • Neural Network: Feedforward neural network (MLP), Recurrent neural network (RNN), Long short-term memory (LSTM)
  • Multi-Criteria Decision-Making Methods: The Analytic Hierarchy Process (AHP/Fuzzy AHP), Technique for Order of Preference by Similarities to Ideal Solution (TOPSIS/ Fuzzy TOPSIS), ISM and DMATEL.
  • Experimental Design: consist of Analysis of Variance and Covariance
    • Experimental design І; Single Factor Designs
    • Experimental design ІІ; Factorial Designs
    • Correlational Research (correlation and regression)
    • Small N Design (single subject method)
  • Structural Equation Modeling
  • Mediation and Moderation Analysis: mediation and moderation and conditional process models
  • Shiny Programming: building interactive web apps and dashboards by R.