Data Science Intern
صاحب العمل نشط
حالة تأهب وظيفة
سيتم تحديثك بأحدث تنبيهات الوظائف عبر البريد الإلكتروني
شارك هذه الوظيفة
حالة تأهب وظيفة
سيتم تحديثك بأحدث تنبيهات الوظائف عبر البريد الإلكتروني
الوصف الوظيفي
TOPIC 1: Ridge Regression for MMM
Description:
To deal with multicollinearity and overfitting issues commonly encountered in traditional predictive analytics based on linear regression Ridge Regression can be used to reduce variance. This aims to improve the predictive capabilities of Marketing Mix Models (MMM).
Ridge regression puts additional constraints on the linear model parameters s. In this case instead of just minimizing the residual sum of squares we also have a penalty term on the s.
Main competencies
- Experience with Java and ObjectOriented coding.
- Familiarity with predictive analytics and linear regression techniques such as LASSO and Ridge regression.
- Excellent research and analytical skills.
- Excellent writing and presentation skills.
Learning outcomes
- Understand and implement linear regression techniques for predictive modeling.
- Understand regularization in regression models.
- Build Lasso and Ridge regression models.
TOPIC 2: Bayesian Linear Regression
Description:
Bayesian regression has considerably gained popularity among data analysts in recent years because of its capability to incorporate prior knowledge to estimate the model parameters.
In addition Bayesian analysis can also estimate the full distribution of these parameters (as opposed to a simple point estimate) hence allowing to quantify the uncertainty of the model.
The objective of this project is to apply this novel Bayesian regression approach to Marketing Mix Modeling (MMM)
Main competencies:
- Familiarity with supervised learning algorithms and regression models
- Experience with Java coding
- Excellent research and analytical skills.
- Excellent writing and presentation skills.
Learning outcomes
- An understanding of the Bayesian approach to machine learning
- The ability to implement Bayesian Linear Regression in Java.
- The ability to interpret the output of Bayesian Linear Regression models and compare it with other machine learning methods.
TOPIC 3: Expectation Maximization Algorithm
Description
The ExpectationMaximization (EM) algorithm is a very important machine learning technique used in various applications. We use this algorithm to estimate the parameters of the so called MixedEffects Models (Random & Fixed effects) often needed to solve various complex business problems.
The objective of this project is to analyze the convergence and performance properties of the EM algorithm in various conditions and modelling requirements. Based on the outcome of this analysis areas to improve the stability and efficiency of the algorithm will be addressed.
Main competencies
- Familiarity with Expectation Maximization Algorithm and Regression Analysis
- Experience with Java coding
- Excellent research and analytical skills.
- Excellent writing and presentation skills.
Learning outcomes
- understanding of the EM algorithm and its application to unsupervised learning problems.
- The ability to implement the EM algorithm in JAVA.
- The ability to evaluate the performance of EMbased unsupervised learning models on MMM projects.
نوع التوظيف
دوام كامل
