Hyperparameter optimization is an essential component in many data science pipelines and typically entails exhaustive time and resource-consuming computations in order to explore the combinatorial search space. Similar to this problem, other key operations in data science pipelines exhibit the exact same properties. Important examples are: neural architecture search, where the goal is to identify the best design choices for a neural network, and query cardinality estimation, where given different predicate values for a SQL query the goal is to estimate the size of the output. In this paper, we abstract away those essential components of data science pipelines and we model them as instances of tensor completion, where each variable of the search space corresponds to one mode of the tensor, and the goal is to identify all missing entries of the tensor, corresponding to all combinations of variable values, starting from a very small sample of observed entries. In order to do so, we first conduct a thorough experimental evaluation of existing state-of-the-art tensor completion techniques and introduce domain-inspired adaptations (such as smoothness across the discretized variable space) and an ensemble technique which is able to achieve state-of-the-art performance. We extensively evaluate existing and proposed methods in a number of datasets generated corresponding to (a) hyperparameter optimization for non-neural network models, (b) neural architecture search, and (c) variants of query cardinality estimation, demonstrating the effectiveness of tensor completion as a tool for automating data science pipelines. Furthermore, we release our generated datasets and code in order to provide benchmarks for future work on this topic.

Calls to use open generative language models in academic research have highlighted the need for reproducibility and transparency in scientific research. However, the impact of generative AI extends well beyond academia, as corporations and public interest organizations have begun integrating these models into their data science pipelines. We expand this lens to include the impact of open models on organizations, focusing specifically on fact-checking organizations, which use AI to observe and analyze large volumes of circulating misinformation, yet must also ensure the reproducibility and impartiality of their work. We wanted to understand where fact-checking organizations use open models in their data science pipelines; what motivates their use of open models or proprietary models; and how their use of open or proprietary models can inform research on the societal impact of generative AI. To answer these questions, we conducted an interview study with N=24 professionals at 20 fact-checking organizations on six continents. Based on these interviews, we offer a five-component conceptual model of where fact-checking organizations employ generative AI to support or automate parts of their data science pipeline, including Data Ingestion, Data Analysis, Data Retrieval, Data Delivery, and Data Sharing. We then provide taxonomies of fact-checking organizations' motivations for using open models and the limitations that prevent them for further adopting open models, finding that they prefer open models for Organizational Autonomy, Data Privacy and Ownership, Application Specificity, and Capability Transparency. However, they nonetheless use proprietary models due to perceived advantages in Performance, Usability, and Safety, as well as Opportunity Costs related to participation in emerging generative AI ecosystems. Our work provides novel perspective on open models in data-driven organizations.

While the field of NL2SQL has made significant advancements in translating natural language instructions into executable SQL scripts for data querying and processing, achieving full automation within the broader data science pipeline - encompassing data querying, analysis, visualization, and reporting - remains a complex challenge. This study introduces SageCopilot, an advanced, industry-grade system system that automates the data science pipeline by integrating Large Language Models (LLMs), Autonomous Agents (AutoAgents), and Language User Interfaces (LUIs). Specifically, SageCopilot incorporates a two-phase design: an online component refining users' inputs into executable scripts through In-Context Learning (ICL) and running the scripts for results reporting & visualization, and an offline preparing demonstrations requested by ICL in the online phase. A list of trending strategies such as Chain-of-Thought and prompt-tuning have been used to augment SageCopilot for enhanced performance. Through rigorous testing and comparative analysis against prompt-based solutions, SageCopilot has been empirically validated to achieve superior end-to-end performance in generating or executing scripts and offering results with visualization, backed by real-world datasets. Our in-depth ablation studies highlight the individual contributions of various components and strategies used by SageCopilot to the end-to-end correctness for data sciences.

The rapid advances of large language models (LLMs), such as ChatGPT, are revolutionizing data science and statistics. These state-of-the-art tools can streamline complex processes. As a result, it reshapes the role of data scientists. We argue that LLMs are transforming the responsibilities of data scientists, shifting their focus from hands-on coding, data-wrangling and conducting standard analyses to assessing and managing analyses performed by these automated AIs. This evolution of roles is reminiscent of the transition from a software engineer to a product manager. We illustrate this transition with concrete data science case studies using LLMs in this paper. These developments necessitate a meaningful evolution in data science education. Pedagogy must now place greater emphasis on cultivating diverse skillsets among students, such as LLM-informed creativity, critical thinking, AI-guided programming. LLMs can also play a significant role in the classroom as interactive teaching and learning tools, contributing to personalized education. This paper discusses the opportunities, resources and open challenges for each of these directions. As with any transformative technology, integrating LLMs into education calls for careful consideration. While LLMs can perform repetitive tasks efficiently, it's crucial to remember that their role is to supplement human intelligence and creativity, not to replace it. Therefore, the new era of data science education should balance the benefits of LLMs while fostering complementary human expertise and innovations. In conclusion, the rise of LLMs heralds a transformative period for data science and its education. This paper seeks to shed light on the emerging trends, potential opportunities, and challenges accompanying this paradigm shift, hoping to spark further discourse and investigation into this exciting, uncharted territory.

Over the years there has been exponential growth in data science applications, fueled by data collected from a wide variety of sources. In the last 10 years alone we have seen the implementation of data science, machine learning and deep learning. Although we hear a lot more about machine learning and deep learning, it is the core data science technique that a lot of companies focus on as this is where they make money and save a lot of money. However, studies show that 68% of data studies go unused and 90% of data is left unstructured. This is due to companies failing to focus on the data analytical processing phase, as it can take a lot of time, money and resources.

In recent years, we have witnessed a growing interest in data science not only from academia but particularly from companies investing in data science platforms to analyze large amounts of data. In this process, a myriad of data science artifacts, such as datasets and pipeline scripts, are created. Yet, there has so far been no systematic attempt to holistically exploit the collected knowledge and experiences that are implicitly contained in the specification of these pipelines, e.g., compatible datasets, cleansing steps, ML algorithms, parameters, etc. Instead, data scientists still spend a considerable amount of their time trying to recover relevant information and experiences from colleagues, trial and error, lengthy exploration, etc. In this paper, we, therefore, propose a scalable system (KGLiDS) that employs machine learning to extract the semantics of data science pipelines and captures them in a knowledge graph, which can then be exploited to assist data scientists in various ways. This abstraction is the key to enabling Linked Data Science since it allows us to share the essence of pipelines between platforms, companies, and institutions without revealing critical internal information and instead focusing on the semantics of what is being processed and how. Our comprehensive evaluation uses thousands of datasets and more than thirteen thousand pipeline scripts extracted from data discovery benchmarks and the Kaggle portal and shows that KGLiDS significantly outperforms state-of-the-art systems on related tasks, such as dataset recommendation and pipeline classification.

… AI software suite that bundles various AI and machine learning tools … for the new Microsoft Azure NVads A10 v5 series virtual machines.

In my daily routine I have to deal with a lot of the same situations from loading csv files to visualizing data. So, to help streamline my process I created the habit of storing snippets of code that are helpful in different situations from loading csv files to visualizing data. I think snippets of code are super valuable, rewriting code can be a real waste of time so having a complete toolkit with all the simple solutions you need to streamline your data analysis process can be of great help. If you liked this post connect with me on Twitter, LinkedIn and follow me on Medium. Thanks and see you next time!

Data science and machine learning can be practiced with varying degrees of efficiency and productivity. Let's imagine somebody is teaching a "Productive Data Science" course or writing a book about it -- using Python as the language framework. What should the typical expectations be from such a course or book? The course/book should be intended for those who wish to leapfrog beyond the standard way of performing data science and machine learning tasks and utilize the full spectrum of the Python data science ecosystem for a much higher level of productivity. Readers should be taught how to look out for inefficiencies and bottlenecks in the standard process and how to think beyond the box.

In the digital transformation of any company, AI/ ML is one of the crucial tools that can be leveraged. At Digité, we are keenly aware of the need for AI/ ML to assist and enhance capabilities our solutions provide to our customers in all possible manner. We look at AI in two distinct categories. There's Ubiquitous AI which includes applications that solve short, highly contextual objectives using simple data science pipelines. These applications serve as a way to improve the quality of user's experience of any product they use.