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Your Data Science Toolbox -- What is Inside?


Data science is a very broad multi-disciplinary field that includes several subdivisions such as data visualization, machine learning, and artificial intelligence. Due to the broadness of the field and because data science is constantly changing due to technological innovations and the development of new algorithms, a successful data scientist has to maintain a big and updated toolbox at all times. Keep in mind that as a data scientist, you can only perform tasks that you have the right tools for. This article will discuss several tools that one can include in their data science toolbox. Knowledge-based tools can be grouped into three main categories based on the level of data science tasks involved: level 1 (basic level); level 2 (intermediate level); and level 3 (advanced level). Basic tools are tools that would enable one to perform level 1 tasks.

50 Must-Read Free Books For Every Data Scientist in 2020


This includes causal models, regression models, factor models and so on. The sample programs are in R.

Contextual Bandits for adapting to changing User preferences over time Artificial Intelligence

Contextual bandits provide an effective way to model the dynamic data problem in ML by leveraging online (incremental) learning to continuously adjust the predictions based on changing environment. We explore details on contextual bandits, an extension to the traditional reinforcement learning (RL) problem and build a novel algorithm to solve this problem using an array of action-based learners. We apply this approach to model an article recommendation system using an array of stochastic gradient descent (SGD) learners to make predictions on rewards based on actions taken. We then extend the approach to a publicly available MovieLens dataset and explore the findings. First, we make available a simplified simulated dataset showing varying user preferences over time and how this can be evaluated with static and dynamic learning algorithms. This dataset made available as part of this research is intentionally simulated with limited number of features and can be used to evaluate different problem-solving strategies. We will build a classifier using static dataset and evaluate its performance on this dataset. We show limitations of static learner due to fixed context at a point of time and how changing that context brings down the accuracy. Next we develop a novel algorithm for solving the contextual bandit problem. Similar to the linear bandits, this algorithm maps the reward as a function of context vector but uses an array of learners to capture variation between actions/arms. We develop a bandit algorithm using an array of stochastic gradient descent (SGD) learners, with separate learner per arm. Finally, we will apply this contextual bandit algorithm to predicting movie ratings over time by different users from the standard Movie Lens dataset and demonstrate the results.

Towards a Flexible Embedding Learning Framework Artificial Intelligence

Representation learning is a fundamental building block for analyzing entities in a database. While the existing embedding learning methods are effective in various data mining problems, their applicability is often limited because these methods have pre-determined assumptions on the type of semantics captured by the learned embeddings, and the assumptions may not well align with specific downstream tasks. In this work, we propose an embedding learning framework that 1) uses an input format that is agnostic to input data type, 2) is flexible in terms of the relationships that can be embedded into the learned representations, and 3) provides an intuitive pathway to incorporate domain knowledge into the embedding learning process. Our proposed framework utilizes a set of entity-relation-matrices as the input, which quantifies the affinities among different entities in the database. Moreover, a sampling mechanism is carefully designed to establish a direct connection between the input and the information captured by the output embeddings. To complete the representation learning toolbox, we also outline a simple yet effective post-processing technique to properly visualize the learned embeddings. Our empirical results demonstrate that the proposed framework, in conjunction with a set of relevant entity-relation-matrices, outperforms the existing state-of-the-art approaches in various data mining tasks.

Ethical Machine Learning in Health Care Artificial Intelligence

The use of machine learning (ML) in health care raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML in the advancement of health care. Specifically, we frame ethics of ML in health care through the lens of social justice. We describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to post-deployment considerations. We close by summarizing recommendations to address these challenges.

Probabilistically Sampled and Spectrally Clustered Plant Genotypes using Phenotypic Characteristics Artificial Intelligence

Clustering genotypes based upon their phenotypic characteristics is used to obtain diverse sets of parents that are useful in their breeding programs. The Hierarchical Clustering (HC) algorithm is the current standard in clustering of phenotypic data. This algorithm suffers from low accuracy and high computational complexity issues. To address the accuracy challenge, we propose the use of Spectral Clustering (SC) algorithm. To make the algorithm computationally cheap, we propose using sampling, specifically, Pivotal Sampling that is probability based. Since application of samplings to phenotypic data has not been explored much, for effective comparison, another sampling technique called Vector Quantization (VQ) is adapted for this data as well. VQ has recently given promising results for genome data. The novelty of our SC with Pivotal Sampling algorithm is in constructing the crucial similarity matrix for the clustering algorithm and defining probabilities for the sampling technique. Although our algorithm can be applied to any plant genotypes, we test it on the phenotypic data obtained from about 2400 Soybean genotypes. SC with Pivotal Sampling achieves substantially more accuracy (in terms of Silhouette Values) than all the other proposed competitive clustering with sampling algorithms (i.e. SC with VQ, HC with Pivotal Sampling, and HC with VQ). The complexities of our SC with Pivotal Sampling algorithm and these three variants are almost same because of the involved sampling. In addition to this, SC with Pivotal Sampling outperforms the standard HC algorithm in both accuracy and computational complexity. We experimentally show that we are up to 45% more accurate than HC in terms of clustering accuracy. The computational complexity of our algorithm is more than a magnitude lesser than HC.

AI and Wargaming Artificial Intelligence

Recent progress in Game AI has demonstrated that given enough data from human gameplay, or experience gained via simulations, machines can rival or surpass the most skilled human players in classic games such as Go, or commercial computer games such as Starcraft. We review the current state-of-the-art through the lens of wargaming, and ask firstly what features of wargames distinguish them from the usual AI testbeds, and secondly which recent AI advances are best suited to address these wargame-specific features.

Urban Traffic Flow Forecast Based on FastGCRNN Artificial Intelligence

Traffic forecasting is an important prerequisite for the application of intelligent transportation systems in urban traffic networks. The existing works adopted RNN and CNN/GCN, among which GCRN is the state of art work, to characterize the temporal and spatial correlation of traffic flows. However, it is hard to apply GCRN to the large scale road networks due to high computational complexity. To address this problem, we propose to abstract the road network into a geometric graph and build a Fast Graph Convolution Recurrent Neural Network (FastGCRNN) to model the spatial-temporal dependencies of traffic flow. Specifically, We use FastGCN unit to efficiently capture the topological relationship between the roads and the surrounding roads in the graph with reducing the computational complexity through importance sampling, combine GRU unit to capture the temporal dependency of traffic flow, and embed the spatiotemporal features into Seq2Seq based on the Encoder-Decoder framework. Experiments on large-scale traffic data sets illustrate that the proposed method can greatly reduce computational complexity and memory consumption while maintaining relatively high accuracy.

Optimal Sepsis Patient Treatment using Human-in-the-loop Artificial Intelligence Artificial Intelligence

Sepsis is one of the leading causes of death in Intensive Care Units (ICU). The strategy for treating sepsis involves the infusion of intravenous (IV) fluids and administration of antibiotics. Determining the optimal quantity of IV fluids is a challenging problem due to the complexity of a patient's physiology. In this study, we develop a data-driven optimization solution that derives the optimal quantity of IV fluids for individual patients. The proposed method minimizes the probability of severe outcomes by controlling the prescribed quantity of IV fluids and utilizes human-in-the-loop artificial intelligence. We demonstrate the performance of our model on 1122 ICU patients with sepsis diagnosis extracted from the MIMIC-III dataset. The results show that, on average, our model can reduce mortality by 22%. This study has the potential to help physicians synthesize optimal, patient-specific treatment strategies.

Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learning Artificial Intelligence

Job Title Benchmarking (JTB) aims at matching job titles with similar expertise levels across various companies. JTB could provide precise guidance and considerable convenience for both talent recruitment and job seekers for position and salary calibration/prediction. Traditional JTB approaches mainly rely on manual market surveys, which is expensive and labor-intensive. Recently, the rapid development of Online Professional Graph has accumulated a large number of talent career records, which provides a promising trend for data-driven solutions. However, it is still a challenging task since (1) the job title and job transition (job-hopping) data is messy which contains a lot of subjective and non-standard naming conventions for the same position (e.g., Programmer, Software Development Engineer, SDE, Implementation Engineer), (2) there is a large amount of missing title/transition information, and (3) one talent only seeks limited numbers of jobs which brings the incompleteness and randomness modeling job transition patterns. To overcome these challenges, we aggregate all the records to construct a large-scale Job Title Benchmarking Graph (Job-Graph), where nodes denote job titles affiliated with specific companies and links denote the correlations between jobs. We reformulate the JTB as the task of link prediction over the Job-Graph that matched job titles should have links. Along this line, we propose a collective multi-view representation learning method (Job2Vec) by examining the Job-Graph jointly in (1) graph topology view, (2)semantic view, (3) job transition balance view, and (4) job transition duration view. We fuse the multi-view representations in the encode-decode paradigm to obtain a unified optimal representation for the task of link prediction. Finally, we conduct extensive experiments to validate the effectiveness of our proposed method.