• Welcome to A.I.nekova's Online Academy!

    Дария Әйнекова, PhD, Онлайн Академиясына қош келдіңіздер!

    Добро пожаловать в Онлайн Академию Дарии Айнековой, PhD!


    Empowering individuals and companies to achieve their goals through expertly crafted online and hybrid courses. Whether you're looking to upskill your team or advance your personal knowledge, my flexible, engaging courses are designed with your success in mind. 


    About Me

    My name is Dariya Ainekova.     I am a woman, a teacher, an educator, a researcher, an economist, a mathematician, an applied statistician, an athlete, and a lifelong learner, constantly exploring new facets of my identity.

    I earned my Bachelor’s degree in Mathematics from Mechanics and Mathematics Department of Kazakh National University, my Master’s degree in Economics from College of Social Sciences of KIMEP University, and my PhD in Business Administration with a specialization in Economics from the Fogelman College of Business and Economics at the University of Memphis.

    I began my career as a high school mathematics teacher during a pedagogical internship at a physics-mathematics gymnasium of Almaty. After graduating from KIMEP, I then joined KIMEP University as a full-time teaching assistant in the Department of Economics, where I advanced through the ranks to lecturer and later senior lecturer. I taught Introduction to Economics, Microeconomics, Macroeconomics, Precalculus, Calculus, Mathematical Methods, Money and Banking, World Economy, and various other courses in the Economics Department.

    In 2014, I enrolled in the PhD program at the University of Memphis. From 2014 to 2016, I worked as a research assistant (for Prof. David Kemme) and a teacher assistant (for Prof. Carmen Astorne's Game Theory and Advanced Microeconomics classes).  During the final years of my doctoral studies (January 2017 to May 2019), I served as a lecturer for a Macroeconomics course and conducted seminar workshops for PhD students for Prof. David Kemme's Econometrics III (Time Series Econometrics) class at the Fogelman College. I successfully defended my PhD in April 2019.


    From September 2019 to December 2024, I was a lecturer and later an assistant professor at KIMEP University. During this time, I taught a diverse range of courses in Economics, Statistics, Econometrics, Mathematics for Computer Science, Quantitative Analysis and Research Methods at the bachelor’s, master’s, and PhD levels, contributing to the academic growth of students across multiple programs.


    In addition to my academic work, I cherish my time with my BraveRunning.kz team, where I share my passion for running, and the April Charity Choir, where I had the privilege to perform occasionally. Composting, birdwatching, and storytelling are among my recently discovered passions and varied interests.

    You can explore some of my academic contributions on my Google Scholar page, connect with me professionally on LinkedIn, or simply enjoy learning with me in the classroom or online. 

    Explore. Learn. Grow.

    Take the next step on your learning journey today!



Available courses

Moodle для педагогов: Создайте ваш онлайн или гибридный курс

Описание: Этот курс предназначен для того, чтобы дать педагогам навыки и знания для создания, управления и запуска увлекательных онлайн- или гибридных курсов с использованием Moodle. Участники научатся разрабатывать контент курсов, интегрировать мультимедиа, использовать инструменты оценки и повышать вовлеченность студентов.

Целевая аудитория: Педагоги, тренеры или дизайнеры учебных курсов, которые впервые работают с Moodle или ищут советы по оптимизации процесса обучения.

Длительность: 4–6 недель (с предполагаемой нагрузкой от 6 до 8 часов в неделю, включая лекции, выполнение заданий и работу над созданием своего курса в Мудл).
Форма обучения: Полностью онлайн или гибридная (на русском языке)

Statistical Literacy: Understanding Econometric Methods in Peer-Reviewed Research

Course Description: This course is designed to provide learners with a comprehensive understanding of econometric methods commonly employed in peer-reviewed research. By bridging statistical theory and practical application, participants will explore essential concepts such as regression analysis, causal inference, model selection, and hypothesis testing. The course emphasizes critical evaluation of research methodologies and equips learners to interpret and assess econometric results effectively. Whether you aim to enhance your ability to critique academic studies or apply econometric techniques in your own work, this course will provide the tools and confidence you need.
Target Audience:
  • Graduate students and early-career researchers in social sciences, business, economics, humanities, education, and other related fields
  • Professionals seeking to enhance their analytical skills for academic or industry research
  • Anyone with a basic understanding of statistics who wishes to deepen their knowledge of econometric methods
Duration: 8-10 weeks (with an estimated workload of 6-8 hours per week, including lectures, readings, assignments, and project work) 
Mode of study: Online or Hybrid (in English)

Linear Algebra for Multivariate Statistical Analysis:

Course description: This course provides a comprehensive understanding of linear algebra concepts essential for advanced multivariate statistical analysis. Students will develop the theoretical knowledge and practical skills to apply linear algebra techniques to real-world data problems in fields such as economics, biology, machine learning, and social sciences. The course covers matrix algebra, eigenvalues and eigenvectors, linear transformations, and optimization problems, with a strong emphasis on their statistical applications. 
Target Audience:

  1. Graduate Students in Quantitative Fields: Students in statistics, mathematics, computer science, data science, economics, or related disciplines who need to deepen their understanding of linear algebra for advanced statistical methods.

  2. Early-Career Researchers: Professionals working in academia or industry who want to enhance their quantitative and analytical skills for applied research or multivariate data analysis.

  3. Data Analysts and Machine Learning Practitioners: Practitioners seeking to strengthen their theoretical foundation in linear algebra to better understand the algorithms and techniques they implement.

  4. Multivariate Statistics Enthusiasts: Individuals with a strong interest in exploring and understanding multivariate statistical techniques, such as PCA, factor analysis, and regression, through the lens of linear algebra.

  5. Advanced Undergraduate Students: Motivated undergraduates in their final year of study, particularly those preparing for graduate programs or advanced roles requiring robust mathematical skills.

This course is ideal for individuals with a basic understanding of calculus and linear algebra, seeking to bridge the gap between theory and practical applications in multivariate statistics.

Course Duration: 12 -14 weeks (with an estimated workload of 6-8 hours per week, including lectures and assignments)

Mode of Study:  Online or hybrid (in English).

Statistics With R

Course description: This course provides an in-depth introduction to statistical methods using the R programming language. Designed for beginners and intermediate learners, the course covers foundational concepts in statistics and data analysis. Through practical examples and hands-on exercises, participants will learn how to use R to perform descriptive statistics, hypothesis testing, regression analysis, and data visualization. By the end of the course, students will be equipped with the skills to analyze data and draw meaningful insights using R. 

Target Audience:

  • Undergraduate and graduate students in social sciences, business, and STEM fields

  • Professionals seeking to enhance their data analysis skills 

Duration of the course: 8 weeks (with an estimated workload of 6-8 hours per week, including lectures and assignments)
Mode of study: Online or Hybrid (In English)

Introduction to Econometrics

Course Description: Dive into the world of econometrics, where data meets economic theory to unlock insights and inform decision-making. This introductory course provides a foundation in econometric principles, focusing on statistical techniques to analyze and interpret economic data. Through real-world applications and hands-on learning, participants will build skills in regression analysis, hypothesis testing, and model building, gaining a deeper understanding of how econometrics shapes research, policy, and business strategies.

Target Audience:

  • Undergraduate and graduate students in economics, business, and social sciences
  • Professionals in data analytics, finance, and policy-making seeking to enhance their quantitative skills
  • Researchers and academics beginning their journey in econometrics

Duration: 12 weeks (with an estimated workload of 6-8 hours per week, including lectures, readings, assignments, and project work) 

Mode of Study: Online or Hybrid (In English)

Business Statistical Analysis