Business statistics

Proqramın qısa məzmunu

Module 1: Introduction to Statistics

Module 2: R Fundamentals for Analyzing and Interpret Row Data

Install R and R Studio and engage in a basic R session
Be able to read in data and write out data files from various sources
Create and execute their own user-defined functions in an R session
Understand the characteristics of different data types and structures in R
Sort, select, filter, subset, and manipulate tables of data in R
Understand how to use the apply() family of functions to execute various actions against different R data structures
Know how to use reshaping and recoding “short cuts” for changing data types and for rearranging data structures.
Basic visualization and share reports in R

Module 3: Applying Descriptive and Diagnostic Statistics in Real Business

Visualizing and Exploring Data (EDA)
- Chart absolute frequency, relative frequency, cumulative absolute frequency and cumulative relative frequency histograms, etc.
Descriptive Statistical Measures for Business
- Estimate sample skewness, sample kurtosis frequency distribution shape measures and samples correlation, samples covariance association measures, etc.
Probability Distributions
- Evaluate probability distribution goodness of fit through quantile-quantile plots and normality test, etc.
Sampling and Estimation for Decision Making
- Estimate population mean and population proportion confidence intervals assuming known or unknown population variance, etc.
Statistical Inference for Data Mining
- Capstone project from Real Business Environment

Module 4: Statistical Decision Making under Uncertainty for Business

Trendlines and Regression Analysis with Data
- with real business cases
Forecasting Techniques for Business
- with real business cases
Monte Carlo Simulation and Risk Analysis Process
- with real business cases
Statistical Decision Analysis for Driving Consumer Experience.
- with real business cases
Statistical Clustering Analysis for Customer Behavioral Segmentation
- with real business cases

Təlimçi

Təhsili:

2016 – 2017, Università degli Studi dell’Aquila, Dövlət idarəetməsi, Bakalavr, ERASMUS+ KA1
2013 – 2017, Qafqaz Universiteti, Dövlət idarəetməsi, Bakalavr
2018 – 2020, Azərbaycan Dövlət İqtisad Universiteti, Strateji idarəetmə, Maqistr
2021 – 2024, Azərbaycan Dövlət İqtisad Universiteti, İqtisadiyyat, Doktorantura

İş təcrübəsi:

2015 – 2017, QSS Analytics, SPSS üztə təlimçi, Data Analitiki
2018 – 2019, 166 Yükdaşıma və Logistika, Maliyyə hesabatlığı analitiki, Excel və SPSS təlimçisi, Baş Data Analitiki
2019, Alliance Logistics, Statistika və Hesabat bölməsinin rəhbəri
2019 – hazırda, Data SoCool, Data Modelləşmə və Hesabatlıq üzrə təlimçi, Satışlarda
Data Analitikası və Proqnozlaşdırıcı Marketing təlimçisi
2020 – hazırda, UNEC Business School, Köməkçi lector
2019 – hazırda, Kapital Bank ASC, Data Analitikası departamenti, Data Modelləşdirilməsi üzrə Menecer

Beynəlxalq sertifikatları:

2019, QueBIT Consulting, IBM SPSS Modeler: Mastering and Tuning Decision Trees
2019, Microsoft, Analyzing and Visualizing Data with Power BI, Analyzing Big Data with Microsoft R
2019, Harvard University, Data Science: R Basics, Machine Learning
2019, University of Colorado Boulder, Predictive Modeling and Analytics
2019, IBM, Machine Learning with R – Level 1
2021, LinkedIn, Learning Data Governance

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