Category Archives: Data Science
How to Build an Efficient Data Team to Work with Public Web Data – ReadWrite
The topic of how to assemble an efficient data team is a highly debated and frequently discussed question among data experts. If youre planning to build a data-driven product or improve your existing business with the help of public web data, you will need data specialists.
This article will cover key principles I have observed throughout my experience working in the public web data industry that may help you build an efficient data team.
Although we have yet to find a universal recipe for assisting public web data the good news is that there are various ways to approach this subject and still get the desired results. Here we will explore the process of building a data team through the perspective of business leaders who are just getting started with public web data.
A data team is responsible for collecting, processing, and providing data to stakeholders in the format needed for business processes. This team can be incorporated into a different department, such as the marketing department, or be a separate entity in the company.
The term data team can describe a team of any size, from one to two specialists to an extensive multilevel team managing and executing all aspects of data-related activities at the company.
Theres a straightforward principle that I recommend businesses working with public web data to follow: an efficient data team works in alignment with your business needs. It all starts with what product you will build and what data will be needed.
Simply put, every company planning to start working with web data needs specialists who can ingest and process large amounts of data and those who can transform data into information valuable for the business. Usually, the transformation stage is where the data starts to create value for its downstream users.
The first hire can be a data engineer with analytical skills or a data analyst with experience working with big data and light data engineering. When building something more complex, its essential to understand that public web data is essentially used for answering business questions, and web data processing is all about iterations.
Further iterations may include aggregated data or enriching your data with data from additional sources. Then, you process it to get information, like specific insights. As a result, you get information that can be used in processes that follow, for example, supporting business decision-making, building a new platform, or providing insights to clients.
Looking from a product perspective, the answer to what data team you need is connected to the tools you will be using, which also depends on the volumes of data you will be using and how it will be transformed. From this perspective, I can split building a data team into three scenarios:
Ultimately, the size of your data team and what specialists you need depend on your product and vision for it. Our experience building Coresignals data team taught us that the key principle is to match the teams capabilities with product needs, despite the seniority level of the specialists.
The short answer to this question is It depends. When it comes to the classification of data roles, there are many ways to look at this question. New roles emerge, and the lines between existing ones may sometimes overlap.
Lets cover the most common roles in teams working with public web data. In my experience, the structure of data teams is tied to the process of working with web data, which consists of the following components:
In her article published in 2017, a well-known data scientist Monica Rogati introduced the concept of the hierarchy of data science needs in an organization. It shows that most data science-related needs in an organization are related to the parts of the process at the bottom of the pyramid collecting, moving, storing, exploring, and transforming the data. These tasks also make a solid data foundation in an organization. The top layers include analytics, machine learning (ML), and artificial intelligence (AI).
However, all these layers are important in an organization working with web data and require specialists with a specific skill set.
Data engineers are responsible for managing the development, implementation, and maintenance of the processes and tools used for raw data ingestion to produce information for downstream use, for example, analysis or machine learning (ML).
When hiring data engineers, overall experience working with web data and specialization in working with specific tools is usually at the top of the priority list. You need a data engineer in scenarios 2 and 3 mentioned above and in scenario 1, if you decide to start with one specialist.
Data analysts primarily focus on existing data to evaluate how a business is performing and provide insights for improving it. You already need data analysts in scenarios 1 and 2 mentioned above.
The most common skills companies seek when hiring data analysts are SQL, Python, and other programming languages (depending on the tools used).
Data scientists are primarily responsible for advanced analytics that are focused on making future predictions or insights. Analytics are considered advanced if you use them to build data models. For example, if you will have machine learning or natural language processing operations.
Lets say you want to work with data about companies by analyzing their public profiles. You want to identify the percentage of the business profiles in your database that are fake. Through multiple multi-layer iterations, you want to create a mathematical model that will allow you to identify the likelihood of a fake profile and categorize the profiles youre analyzing based on specific criteria. For such use cases, companies often rely on data scientists.
Essential skills for a data scientist are mathematics and statistics, which are needed for building data models, and programming skills (Python, R). You will likely need to have data scientists in scenario three mentioned above.
This relatively new role is becoming increasingly popular, especially among companies working with public web data. As the title suggests, the role of an analytics engineer role is between an analyst who focuses on analytics and a data engineer who focuses on infrastructure. Analytics engineers are responsible for preparing ready-to-use datasets for data analysis, which is usually performed by data analysts or data scientists, and ensuring that the data is prepared for analysis in a timely manner.
SQL, Python, and experience with tools needed to extract, transform, and load data are among the essential skills required for analytics engineers. Having an analytics engineer would be useful in scenarios 2 and 3 mentioned above.
As there are many different approaches to the classification of data roles, theres also a variety of frameworks that can help you assemble and grow your data team. Lets simplify it for an easy start and say that there are different lenses through which a business can evaluate what team will be needed to get started with web data.
Im referring to the web data in this article is big data. Large amounts of data records are usually delivered to you in large files and raw format. It would be best to have data specialists with experience working with large data volumes and the tools used for processing it.
When it comes to tools, you should consider that tools that your organization will use for handling specific types of data will also shape what specialists you will need. If you need to become more familiar with the required tools, consult an expert before hiring a data team or hire professionals to help you select the right tools depending on your business needs.
You may also start building a data team by evaluating which stakeholders the data specialists will work closely with and deciding how this new team will fit into your vision of your organizational structure. For example, will the data team be a part of the engineering team? Will this team mainly focus on the product? Or will it be a separate entity in the organization?
Organizations that have a more advanced data maturity level and are building a product that is powered by data will look at this task through a more complex lens, which involves the companys future vision, aligning on the definition of data across the organization, deciding on who and how will manage it, and how the overall data infrastructure will look as the business grows.
The data team is considered efficient as long as it meets the needs of your business, and almost in every case, the currency of data team efficiency is time and money.
So, you can rely on metrics like the amount of data processed during a specific time or the amount of money you spend. As long as you track this metric at regular intervals, the next thing you want to watch is the dynamics of these metrics. Simply put, if your team is managing to process more data with the same amount of money, it means the team is becoming more efficient.
Another efficiency indicator that combines the aforementioned is how well your team is writing code because you can have a lot of resources and perform iterations quickly, but errors equal more resources spent.
Besides the metrics that are easy to track, one of the most common problems that companies experience is trust in data. Trust in data is precisely what it sounds like. Although there is a way to track the time it takes to perform data-related tasks or see how much it costs, stakeholders may still question the reliability of these metrics and the data itself. This trust can be negatively impacted by negative experiences like previous incidents or simply the lack of communication and information from data owners.
Moreover, working with large volumes of data means spotting errors is a complex task. Still, the organization should be able to trust the quality of the data it uses and the insights it produces using this data.
It is helpful to perform statistical tests allowing the data team to evaluate the quantitative metrics related to data quality, such as fill rates. By doing this, the organization can also accumulate historical data that will allow the data team to spot issues or negative trends in time. Another essential principle to apply in your organization is listening to client feedback regarding the quality of your data.
To sum up, it all comes down to having talented specialists in your data team who can work quickly, with precision, and build trust around the work they are doing.
To sum everything up, here are helpful questions to help you assemble a data team:
I hope this article helped you gain a better understanding of different data roles that are common in organizations working with public web data, why they are essential, which metrics help companies measure the success of their data teams, and finally, how it is all connected to the way your organization thinks about the role of data.
Featured Image Credit: Photo by Sigmund; Provided by Author; From Unsplash; Thanks!
Karolis Didziulis is the Product Director at Coresignal, an industry-leading provider of public web data. His professional expertise comes from over 10 years of experience in B2B business development and more than 6 years in the data industry. Now Karolis's primary focus is to lead Coresignal's efforts in enabling data-driven startups, enterprises, and investment firms to excel in their businesses by providing the largest scale and freshest public web data from the most challenging sources online.
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How to Build an Efficient Data Team to Work with Public Web Data - ReadWrite
MLOps & Quality Data: The Path to AI Transformation – Spiceworks News and Insights
Data-driven approaches and sound MLOps strategies enable organizations to unlock the full potential of AI and ML. Abhijit Bose of Capital One discusses that while
AI and ML are being used to transform enterprises and improve customer experiences, incomplete machine learning operationalization prevents the full potential of AI strategies.
Its an incredibly exciting time to be working in the field of AI and ML. AI is in the headlines daily, permeating culture and society and creating capabilities and experiences we have never witnessed before. And importantly, AI can transform how organizations evolve to reach decisions, maximize operational efficiency, and provide differentiated customer experience and value. But scaling AI and machine learning to realize its maximum potential is a highly complex process based on a set of standards, tools, and frameworks, broadly known as machine learning operations or MLOps. Much of MLOps is still being developed and is not yet an industry standard.
The quality of an organizations data directly impacts machine learning deployments effectiveness, accuracy, and overall impact. High-quality data makes ML models more resilient, less expensive to maintain, and dependable. It offers the agility to react to data and model score drifts in real-time and makes refitting the model easier so it can re-learn and adjust its outputs accordingly. This requires organizations to create and execute a comprehensive data strategy incorporating data standards, platforms, and governance practices.
This starts with making sure that data scientists and ML engineers have standard tools, ML model development lifecycle (MDLC) standards, and platforms; making sure data is secure, standardized, and accessible; automating model monitoring and observability processes; establishing well-managed, human-centered processes like model governance, risk controls, peer review, and bias mitigation.
See More: The Growth of MLOps and Predictions for Machine Learning
MLOps has a set of core objectives: develop a highly repeatable process over the end-to-end model lifecycle, from feature exploration to model training and deployment in production; hide the infrastructure complexity from data scientists and analysts so that they can focus on their models and optimization strategies; and develop MLOps in such a way that it scales alongside the number of models as well as modeling complexity without requiring an army of engineers. MLOps ensures consistency, availability, and data standardization across the entire ML model design, implementation, testing, monitoring, and management life cycle.
Today, every enterprise serious about effectively driving value with AI and ML is leveraging MLOps in some capacity. MLOps helps standardize and automate certain processes so engineers and data scientists can spend their time on better optimizing their models and business objectives. MLOps can also provide important frameworks for responsible practices to mitigate bias and risk and enhance governance.
Even as businesses increasingly acknowledge what AI can do for them, a seemingly relentless wave of adoption since 2017 began to plateau last year at around 50% to 60% of organizations, according to McKinseys latest State of AI reportOpens a new window . Why? I argue that MLOps programs that standardize ML deployment across organizations are beset by too many data quality issues.
Data quality issues can take several forms. For example, you often see noisy, duplicated, inconsistent, incomplete, outdated, or just flat-out incorrect data. Therefore, a big part of MLOps is to monitor data pipelines and source data because, as most of us know, AI and ML are only as good as the collected, analyzed, and interpreted data. Indeed, the most misunderstood part of MLOps is the link between data quality and the development of AI and ML models. Conversely, incomplete, redundant, or outdated data leads to results nobody can trust or use effectively.
Unfortunately, with so much data being created every second of the day, organizations are losing the ability to manage and track all the information their ML models use to arrive at their decisions. A recent Forrester surveyOpens a new window revealed 73% of North American data management decision-makers find transparency, traceability, and explainability of data flows challenging. Over half (57%) said silos between data scientists and practitioners inhibit ML deployment.
See More: The Competitive Advantage of Quality Data
Data transparency is a persistent challenge with ML because to believe an algorithms insights or conclusions, you must be able to verify its accuracy, lineage, and freshness of data. You must understand the algorithms, the data used, and how the ML model makes decisions.
Doing all those things requires data traceability, which involves tracking the data lifecycle. Data can change as it moves across different platforms and applications from the point of ingestion. For example, multiple variations of merchant names or SKUs could be added to simple transaction data that must be sorted and accounted for before being used in ML models. Data must also be cleansed and transformed before reaching that point.
Rigorous traceability is also important for ensuring that data is timely and relevant. It can quickly degrade or drift when real-world circumstances change, leading to unintended outcomes and decisions. During the pandemic, for instance, demand-planning ML models couldnt keep up with supply chain disruptions, leading to inventory shortages or excesses in various industries.
Successful companies also deploy sophisticated technology platforms for testing, launching, and inspecting data quality within ML models. They supplement those platforms with modern data quality, integration, and observability tools. They bolster everything with policies and procedures like governance, risk controls, peer review, and bias mitigation. In short, they give data scientists, data and ML engineers, model risk officers, and legal professionals the tools, processes, and platforms to do their jobs effectively.
When we have integrated data, governance tools, and AI platforms, MLOps processes work remarkably well. When someone builds an enterprise ML model and pushes it to production, they can begin tracking its entire lifecycle. They can monitor how and where data moves and where it lives, preventing data quality and drift issues. As such, they are more confident their ML models can guide business and operational decisions.
See More: The Evolution of Data Governance
Engineers, data scientists, and model developers understand this. But its up to them to help senior business leaders understand why investing in data tools, technologies, and processes is critical for MLOps and, ultimately, ML. Business depends on the technology imperatives of data and ML teams, and no enterprise organization can hope to compete without an AI/ML roadmap. As Forrester saysOpens a new window , AI is an enterprise essential and is becoming critical for enterprises of all shapes and sizes. Indeed, the analyst firm predicts one in four tech executives will report to their boards on AI progress this year.
Part of that conversation must involve letting senior leadership know they cannot take their feet off their collective data and MLOps gas pedals. Today, many businesses success is tied to MLOps and the technologies data science and ML teams deploy. Leaders must understand the importance of building around a foundation of data and a modern cloud stack. If they dont, they are likely to be outperformed by competitors that do.
What data-driven considerations and approaches should organizations consider to get the most out of MLOps? Let us know on FacebookOpens a new window , TwitterOpens a new window , and LinkedInOpens a new window . Wed love to hear from you!
Image Source: Shutterstock
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MLOps & Quality Data: The Path to AI Transformation - Spiceworks News and Insights
Curiosity finds evidence of wet and dry seasons on ancient Mars – The Register
The Mars Curiosity rover continues to make discoveries that shed light on the early days of the Red Planet, this time having found evidence that the unforgiving dust world once experienced seasonal weather patterns and flooding.
The evidence came from photographs snapped by the NASA bot of the dry, dusty Martian surface marked by a series of hexagonal shapes that indicate mud covered the surface before drying and cracking.
The patterns Curiosity spotted showed junction angles of around 120 degrees, otherwise known as Y junctions, that only occur after repeat cycles of wet and dry.
Shapes in the mud on Mars that suggest wet and dry seasons ... Click to enlarge. Source: NASA/JPL-Caltech/MSSS/IRAP/Rapin et al/Nature
"In experiments, using clay layers, joint angles progressively tend towards 120 after 10 consecutive dryings with more cycles required to reach a homogeneous distribution centered at 120 and mature patterns of hexagonal shapes," scientists studying the snaps noted in a paper, which was published in Nature this week.
The cracks themselves are mere centimetres deep, which the boffins said suggests short wet-dry cycles "were maintained at least episodically in the long term," which would be yet another favorable condition for the past emergence of life on Mars.
"Wet periods bring molecules together while dry periods drive reactions to form polymers. When these processes occur repeatedly at the same location, the chance increases that more complex molecules formed there," said paper coauthor Patrick Gasda of the Los Alamos National Laboratory's Space Remote Sensing and Data Science group.
If the right organic molecules were present, "it's the perfect place for the formation of polymeric molecules required for life, including proteins and RNA," Gasda said.
Curiosity has made numerous water-related discoveries since arriving in the sulfate-bearing region of Mount Sharp in Mars' Gale Crater last year.
Shortly after it reached that area in October, Curiosity spotted popcorn-textured nodules containing minerals that suggested the prior presence of water, and then in February it clocked rippled rocks in the region that suggested the area was once lapped by waves. Further evidence of landslides potentially signaled rivers flowed down Mount Sharp from elevations beyond where Curiosity has traveled.
More recently, the newer Perseverance Mars rover found evidence of large, high-energy rivers flowing through the Jezero Crater area - another sign that Mars was once wet and possibly filled with life of some kind.
Repeated wet-dry cycles like those discovered by Curiosity, the scientists said, is another nail in the coffin for theories that Mars experienced a "monotonically declining water supply in the aftermath of an asteroid impact or a single volcanic eruption."
Recent theories have suggested that early Martian microbes may have changed the atmosphere drastically enough that Mars cooled until no longer able to support life. Drastic cooling in turn caused Mars' core to freeze, its magnetic field to dissipate, and its atmosphere to evaporate, or so the theory goes.
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Curiosity finds evidence of wet and dry seasons on ancient Mars - The Register
Unleashing GenAI: Course5’s Innovative Approach to Data Analytics … – CXOToday.com
CXOToday has engaged in an exclusive interview with Jayachandran Ramachandran, Senior VP (AI Labs), Course5 Intelligence
Q 1. How does Course5 leverage AI and GenAI technologies to enhance its services?
Course5 leverages AI and GenAI technologies to enhance its services by using advanced analytics, natural language processing, computer vision, and generative AI to provide data-driven insights and solutions for various business challenges and opportunities.
Course5 uses OpenAIs GPT models to power its enterprise analytics platforms, such as Course5 Compete and Course5 Discovery. Compete is a market and competitive intelligence platform that helps brands drive business growth and strategy. Discovery is an Augmented Analytics Platform that helps users to ask questions in natural language and consume insights from multiple data sources. Course5 also has an AI Lab that focuses on research and product development in artificial intelligence.
Q 2. What sets Course5 apart from other data analytics companies in the market?
We are a pure-play data analytics and insights company focused on helping organizations drive digital transformation using artificial intelligence (AI), advanced analytics, and insights.
Course5s understanding of the omnichannel customer journey, digital business models, and its clients businesses combined with experience across analytical disciplines, application of AI technologies, and consultative problem-solving capabilities help create powerful competitive advantage for Course5 and its clients. The integration of Course5s solutions with its clients critical business workflows and focus on driving end-user adoption and impact for the client organizationrather than just deploying Analytics and Insights initiativesensures there is significant and sustainable value addition for clients.
With a multi-pronged AI strategy and focus on creating IP-based products and accelerators, Course5 is able to scale impact, drive non-linear growth, and compete effectively across the entire data and advanced analytics landscape. Course5 is also one of the few companies to get IP-based recognition from leading industry analysts like Gartner and Forrester.
The Course5 business model of driving the services business with AI-based IP incorporates domain-specific and functional nuances to provide contextualized business solutions. Course5s competitive advantage comes from a mix of factors:
Course5 Intelligence has been recognized by leading industry analysts like Gartner and Forrester for its Analytics and AI capabilities and is one of the few companies to be recognized for its proprietary AI-based platforms. Continuous investments in Course5s AI Labs and planned deep tech acquisitions will continue to help the company drive incomparable value and competitive advantage for clients.
Q 3. Are there any specific industries or domains where Course5 has strong expertise? Are you exploring new sectors to venture into at the moment?
Course5 has deep domain knowledge and expertise across various industries, such as technology, media and telecom, manufacturing, life sciences, consumer packaged goods, and retail.
Besides innovation and customer-centricity, Course5 strives to create synergies for business-oriented solutions. Along with the industries and domains mentioned above, we also offer solutions to banking and financial services, travel and hospitality, automotive, and education.
Q 4. How do you approach data quality and data cleansing to ensure accurate and reliable results?
Course5 is a data science company that provides solutions for various industries and domains. We have a rigorous approach to data quality and data cleansing to ensure accurate and reliable results.
To ensure data quality, we follow several steps:
Q 5. Recently, Course5 integrated GenAI into its product Discovery. How difficult or easy is it to integrate GenAI into a companys existing data analytics process?
Course5 Discovery brings the power of generative AI technologies, advanced AI and analytics models into a platform that delivers actionable insights, to make faster and accurate data backed decisions for businesses that drives substantial and direct impact on business KPIs. A combination of deployment approaches such as prompt engineering and model fine-tuning are required to achieve best results. Generative AI is still evolving and requires adequate guardrails to be put in place so that insights are trustworthy. Robust process is the need of the hour to validate data, models, prompt input and completions so that Generative AI solutions does not induce new risks into the business process.
Q 6. Course5 has its own AI Labs R&D. Could you elaborate on some of the noteworthy work being done there?
AI Labs is at the core of Course5s product and research strategy, allowing us to pursue strategic bets and create a True North for the company and its clients. By incorporating the newest artificial intelligence technologies into our products and solutions such as Course5 Compete, Course5 Discovery and multiple other accelerators, we aim to automate, gain actionable insights, improve experiences, gain competitive advantage, and differentiate our clients businesses from the competition.
We accomplish this by meticulously scanning a plethora of AI frameworks, models, and algorithms, choosing the appropriate ones for rapid experimentation, curating them for business alignment, and making them compatible with enterprises. We apply best practices from design thinking and lean and agile frameworks in our product development methodology to bring in client-centricity, quick market validation, and reduced time-to-market.
Q 7. How can solutions providers such as yourself ensure the ethical use of AI in data analytics processes?
The Course5 team understands the influence and impact of AI decisions on peoples lives, as well as the enterprises responsibility to manage possible ethical and sociotechnical implications as a result. The Course5 Governance framework ensures the models are built with Responsible AI practices covering security, privacy, fairness, explainability, and interpretability.
Course5 is committed to the AI community for the safe and responsible development and use of generative AI models that are overall more powerful than any released technologies. Our ethical and Responsible AI practices are strongly focused on creating an ecosystem of AI for Good. These commitments build on an approach to responsible and secure AI development, that will help pave the way for a future that enhances AI benefits and minimizes its risks.
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Unleashing GenAI: Course5's Innovative Approach to Data Analytics ... - CXOToday.com
Best Python Tools for Building Generative AI Applications Cheat Sheet – KDnuggets
KDnuggets has released an insightful new cheat sheet highlighting the top Python libraries for building generative AI applications.
As readers are no doubt aware, generative AI is one of the hottest areas in data science and machine learning right now. Models like ChatGPT have captured public imagination with their ability to generate remarkably high-quality text from simple prompts.
Python has emerged as the go-to language for developing generative AI applications thanks to its versatility, vast ecosystem of libraries, and easy integration with popular AI frameworks like PyTorch and TensorFlow. This new cheat sheet from KDnuggets provides a quick overview of the key Python libraries data scientists should know for building generative apps, from text generation to human-AI chat interfaces and beyond.
For more on which Python tools to use for generative AI application building, check out our latest cheat sheet.
There are many open source Python libraries and frameworks available that enable developers to build innovative Generative AI applications, from image and text generation to Autonomous AI.
Some highlights covered include OpenAI for accessing models like ChatGPT, Transformers for training and fine-tuning, Gradio for quickly building UIs to demo models, LangChain for chaining multiple models together, and LlamaIndex for ingesting and managing private data.
Overall, this cheat sheet packs a wealth of practical guidance into one page. Both beginners looking to get started with generative AI in Python as well as experienced practitioners can benefit from having this condensed reference to the best tools and libraries at their fingertips. The KDnuggets team has done an excellent job compiling and visually organizing the key information data scientists need to build the next generation of AI applications.
Check it out now, and check back soon for more.
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Best Python Tools for Building Generative AI Applications Cheat Sheet - KDnuggets
Lecturer (Education Specialist) A/B – Computer Science job with … – Times Higher Education
Level A ($75,888 to $102,040) or Level B ($107,276 to $126,894) per annum plus an employer contribution of up to 17% superannuation may apply.
Flexible work arrangements can be negotiated with the right candidate.
The School of Computer and Mathematical Sciences is seeking an outstanding individual, passionate about teaching, to support the teaching of undergraduate and postgraduate programs in the rapidly growing Discipline of Computer Science. In this education-focused role, you will be responsible for the delivery of a range of courses at all levels of the Computer Science curriculum with an emphasis on cyber security, optimisation, data science and/or software engineering. You will join a large team of committed educators and researchers engaged in education, research and industry collaboration across a broad spectrum of computer science specialisations.
The position is available from 1st January 2024.
To be successful you will need:
Lecturer (Level B)
Lecturer (Level A)
Enjoy an outstanding career environment
The University of Adelaide is a uniquely rewarding workplace. The size, breadth and quality of our education and research programs - including significant industry, government and community collaborations - offers you vast scope and opportunity for a long, fulfilling career.
It also enables us to attract high-calibre people in all facets of our operations, ensuring you will be surrounded by talented colleagues, many world-leading. Our work's cutting-edge nature - not just in your own area, but across virtually the full spectrum of human endeavour - provides a constant source of inspiration.
Our core values are integrity, respect, collegiality, excellence and discovery. Our culture is one that welcomes all and embraces diversity. We are firm believers that our people are our most valuable asset, so we work to grow and diversify the skills of our staff.
The Faculty of Sciences, Engineering and Technology aims to increase the diversity of its staff. Applications from women are particularly encouraged. To support our staff and students in their academic lives, the faculty celebrates diversity and has a range of programs available including:
In addition, we offer salary packaging; high-quality professional development programs and activities; and an on-campus health clinic, gym and other fitness facilities.
Learn more at: adelaide.edu.au/jobs
Your faculty's broader role
The Faculty of Sciences, Engineering and Technology is a thriving centre of learning, teaching and research across a broad range of disciplines, including the mathematical sciences. Many of its academic staff are world leaders in their fields, and graduates are highly regarded by employers.
We proudly support gender and cultural diversity and are passionate about creating a more vibrant and enriching community of staff and students.
Learn more at: set.adelaide.edu.au
How to apply
Click on the link below to view the Selection Criteria and to apply for the opportunity.
careers.adelaide.edu.au/cw/en/job/512543/lecturer-education-specialist-ab-computer-science
Please ensure you submit a resume and upload a document that includes your responses to all of the selection criteria for the position as contained in the position description or selection criteria document.
***Application closes at 11:55pm, Monday 18th September 2023***
For further information
For a confidential discussion regarding this position, contact:
Until 5 September 2023:
Professor Finnur LrussonInterim Head of the School of Computer and Mathematical SciencesP: +61 (8) 8313 3528E: finnur.larusson@adelaide.edu.au
From 6 September 2023:
Professor Abelardo PardoHead of the School of Computer and Mathematical SciencesE: abelardo.pardo@adelaide.edu.au
The University of Adelaide is an Equal Employment Opportunity employer. Women and Aboriginal and Torres Strait Islander people who meet the requirements of this position are strongly encouraged to apply.
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Lecturer (Education Specialist) A/B - Computer Science job with ... - Times Higher Education
Predictive Analytics : Know Everything About Predictive Analytics … – NASSCOM Community
In todays data-driven world, organizations are faced with an ever-increasing influx of information. This abundance of data holds the potential to transform the way businesses operate, but only if they can extract meaningful insights from it. This is where predictive analytics solutions come into play. By harnessing advanced algorithms and machine learning techniques, predictive analytics empowers businesses to not only understand historical trends but also foresee future events and make informed decisions. In this article, we will delve into the world ofpredictive analytics solutions, exploring their benefits, applications, and challenges.
Predictive analytics is a branch of data analysis that focuses on using historical data and statistical algorithms to predict future outcomes. It goes beyond descriptive analytics, which simply summarizes past events, and diagnostic analytics, which aims to identify the causes of past events. Instead, predictive analytics aims to forecast what might happen in the future based on patterns and trends discovered in historical data.
Predictive analytics solutions are advanced tools and techniques used to analyze historical data and identify patterns, trends, and relationships that can be used to make informed predictions about future events or outcomes. These solutions employ various statistical, machine learning, and data mining methods to extract valuable insights from large datasets. Here are some key components and benefits of predictive analytics solutions:
1. Data Collection and Preparation: Predictive analytics starts with gathering relevant data from various sources, such as databases, spreadsheets, or external APIs. The data is then cleaned, transformed, and organized to ensure accuracy and consistency.
2. Feature Selection and Engineering: Selecting the right features (variables) to include in the analysis is crucial. Additionally, engineers might create new features that better represent the underlying patterns in the data, enhancing the accuracy of predictions.
3. Algorithm Selection: Different algorithms, such as regression, decision trees, neural networks, and ensemble methods, can be applied based on the nature of the data and the prediction task. The choice of algorithm can significantly impact the accuracy of predictions.
4. Model Training: During this phase, the predictive model is trained using historical data. The model learns patterns and relationships within the data to make predictions.
5. Validation and Testing: Predictive models need to be validated and tested on new, unseen data to assess their performance and ensure they generalize well to different situations.
6. Prediction and Forecasting: Once the model is trained and validated, it can be used to predict future outcomes based on new input data. This could include predicting sales, customer behavior, stock prices, disease outbreaks, and more.
7. Continuous Monitoring and Updating: Predictive models are not static. They should be regularly monitored to ensure they remain accurate and relevant. As new data becomes available, the models can be retrained and updated to reflect changing patterns.
Benefits of Predictive Analytics Solutions:
1. Improved Decision-Making: Predictive analytics provides insights that empower organizations to make informed decisions, optimizing operations, resource allocation, and strategies.
2. Risk Management: By identifying potential risks and anomalies in advance, businesses can take proactive measures to mitigate them.
3. Enhanced Marketing and Sales: Predictive analytics helps companies understand customer behavior, enabling personalized marketing campaigns and improving sales forecasts.
4. Supply Chain Optimization: Predictive analytics can forecast demand and optimize inventory management, reducing costs and ensuring products are available when needed.
5. Healthcare and Medicine: Predictive analytics aids in disease outbreak predictions, patient outcomes, and treatment effectiveness, ultimately improving healthcare delivery.
6. Financial Forecasting: The finance industry uses predictive analytics for credit risk assessment, fraud detection, and investment strategies.
7. Industrial Maintenance: Predictive analytics can predict equipment failures, allowing timely maintenance and minimizing downtime.
In conclusion, predictive analytics solutions leverage historical data and advanced algorithms to make accurate predictions about future events and outcomes. These solutions have the potential to transform various industries by optimizing processes, minimizing risks, and driving innovation.
Predictive analytics servicesoffer businesses a powerful tool to gain insights into the future. By analyzing historical data, these solutions enable organizations to make informed decisions, improve processes, and deliver enhanced customer experiences. From healthcare to finance and beyond, the applications of predictive analytics are vast and continually expanding. However, to fully leverage the potential of these solutions, organizations must address challenges related to data quality, privacy, and complexity. As technology advances and data science evolves, predictive analytics will undoubtedly play an increasingly vital role in shaping the business landscape of tomorrow.
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Predictive Analytics : Know Everything About Predictive Analytics ... - NASSCOM Community
Never again: is Britain finally ready to return to the office? – The Guardian
Working from home
With even the big internet firms warning staff they need to show up more often, is working from home over? Or have the attitudes and expectations of employees changed for ever?
Sat 12 Aug 2023 11.15 EDT
The Office is back. Not just the Ricky Gervais sitcom, which is getting an Australian makeover with a female lead (filming began last month). No: the office is back. Amazon has issued a warning to staff who are not spending at least three days a week in the office. Meta wants its workers to do the same from next month. And if further proof were needed that working from home has officially been replaced by return to office, it was provided by Zoom. The firm, whose revenues jumped 300% during the first year of the pandemic, last week asked employees to come in for at least two days a week.
If only it were so simple for the UKs David Brents. People still like working from home and forcing them to return can have unforeseen repercussions: for instance, research in the UK by the CIPD, the association of HR professionals, found that about 4 million people 12% of employees had changed careers due to a lack of flexible working, and 2 million (6%) had left their job in the last year.
Big tech firms are not the only ones insisting on more office time for white-collar workers than they would like. Osborne Clarke, the international law firm, has told its staff that they must be in the office three days a week if they want to get a performance bonus, although it later clarified that some staff might have valid reasons for not doing so. Luminaries of the Tory right, such as John Redwood and Jacob Rees-Mogg, have campaigned for civil servants to return to the office five days a week, the latter even leaving sorry you were out notes on desks.
Chief executives are very keen to get workers back more often, according to Mark Freebairn, a partner at Odgers Berndtson, an executive search firm.
The chief executive community, the board community that I speak to the majority would want more time in the office at the moment, and when one breaks cover and starts becoming more dictatorial about it, the rest will follow, he said.
The main reason, Freebairn said, was a genuine problem presented by the shift to remote working: the pipeline of talent is drying up.
I could probably teach someone bright the technical aspects of a recruitment job in an hour. But could they understand how to influence and persuade and navigate a situation to their advantage in a subtle and nuanced way? No. You have to watch someone to do that.
There was alarm among some recruiters about the impact of working from home, Freebairn added.
A large investment fund had been targeting graduates with two years experience working for firms such as McKinsey or Accenture and tempting them away by quadrupling their salary, he said.
They do this every two years. And every time they come back and say you cant believe how much better this crop is. But in 2022, it was the worst crop theyd ever seen. Not because of intellect, but because they just didnt know how to engage with people. Theyve never had the learning by osmosis that you get in an office experience.
So will the boardroom Canutes manage to turn the tide on their workforces, as Brent might put it? What are the facts about working from home?
First, most people cant do their job remotely almost 60% of US workers are fully on site, according to research by Professor Nick Bloom of Stanford University, because they have frontline jobs. The working from home debate only concerns the rest the 29% with hybrid working arrangements, mostly professionals and managers, and the 11% who are fully remote mostly specialists in IT or human resources roles. Home workers tend to be well-paid graduates.
The amount of home working they do appears to have stabilised in the last year. Research shows about 25% of work days in the US are done from home, according to Bloom and his colleagues. Full-time employees in the UK, Australia, Canada and other English-speaking countries work about 1.4 days a week at home on average a figure that has not changed much since 2021.
Those trends are also clear in job listings, according to Adzuna, the job search engine. The proportion of vacancies in the UK advertised as hybrid has gone above 11% this year 123,341 in June and the number of jobs listed as fully remote has hit 159,627, or 15.1%.
This is driven by employee demand, according to CIPD research. More than four-fifths of organisations in the UK have some sort of hybrid working policy, and 71% of workers view flexible working as important to them when considering a new role, said Claire McCartney, a senior policy adviser at CIPD.
She added: Its likely that organisations are going to struggle to attract and keep talent if they want people in the office full-time, five days a week. People do have different expectations around workplace flexibility.
Nowhere is this clearer than in the City of London. Friday mornings at Liverpool Street station used to be thronged with financial sector workers on the way to work, but are now noticeably quieter. The few people coming out of Bank station are mostly wearing casual clothing, and plenty are tourists rather than City workers.
Data from Transport for London shows that the number of passengers tapping out at Bank was about 35,000 on a typical Friday this year, about half the level of January 2020. At 15 central London tube stations, about 100,000 fewer people are arriving each day.
Abby, an employee at a construction firm, commutes two days a week from Brighton. I prefer working hybrid, she said. Its a privilege working from home overall Im saving more money that way. She and her colleague John are clear that five days a week is not an option. Never again, he said.
The lack of people means plenty of shops have shut and mobile coffee stands and sandwich sellers do not bother to turn up on Fridays.
In the short term, office demand in London is down by about 20%, said Lee Elliott, a commercial real estate expert at Knight Frank, although he said some of that related to the economic climate. Knight Frank research shows about half of multinationals plan to reduce their office space within the next three years.
But over the long term theres no doubt that all the UK office markets are going to be impacted by obsolescence, he added buildings that are dated or fall foul of energy efficiency rules that are coming into force in 2027. About 60% of London office space would either need to be upgraded or demolished, he said.
In the meantime, companies are choosing smaller, more attractive spaces. Cubicles are out and meeting spaces are in. And smaller spaces are usually cheaper.
People arent focused on cost-per-desk now, Elliott added. The metric theyre thinking about is footfall a bit like retail. Theyre aiming for about 60% to 70% of the building being used.
Meanwhile, the trend towards enticing workers back into offices that began with providing an office barista or a pool table has turned into a torrent of perks.
One of the most popular benefits among employers is free meals, said James Neave, head of data science at Adzuna. More than 5,000 job adverts in July mentioned free food a 48% rise offered by big names such as Sainsburys, Wagamama and Dominos Pizza. Others are offering free gym memberships, tax-free childcare, mental health days, an extra day off for your birthday, language lessons, duvet days and even pawternity leave (time off to look after your pet).
But employers should be thinking about more than just remote or office working, according to Maria Kordowicz, an associate professor in organisational behaviour at the University of Nottingham and director of its centre for inter-professional education and learning.
How meaningful is the work that our organisation produces? she said. How is it contributing to societal betterment? In a post-Covid landscape, weve increasingly been asking the question about how we can look after one another, how we can look after the environment, how can we ensure that the businesses that we run are sustainable. And that, to me, is what attracts talent which is a vague phrase, and what we should be talking about is diverse teams that carry a range of abilities.
Perhaps the emphasis on persuasion misses the point about why people may prefer to work from home or in an office: peace and quiet, and no commute.
Were seeing a lot of people going back into an office environment whove got used to being able to focus and concentrate. And theyre going into an open-plan office and things are suddenly amplified, said Leah Steele, an executive coach and founder of Searching for Serenity.
Neurodivergent people with conditions such as ADHD may not have realised until the pandemic why they were struggling, she said. It was normal for them to be distracted and tired all the time, and they didnt need to commute two or three hours a day. Suddenly an open-plan office feels overwhelming.
More leisure time has been another plus weekday afternoon golf became a thing in the US, according to Stanford University research.
That sort of finding has allowed critics such as Rees-Mogg to point out that productivity from fully remote working is lower than that achieved in the office about 10% lower, according to Blooms research. Hybrid working may provide the best of both worlds it seems not to have a negative effect, and may provide a modest boost.
Still, the longer-term effect on younger workers careers is harder to assess, and Freebairn said it was fair to compare modern remote workers with freelancers and consultants who work as contractors. They risk being seen as lacking ambition, and find it harder to advance their careers.
During the pandemic, Steele had a lot of calls from younger professionals who were anxious about not being in the office. For someone who is more junior and is struggling, who feels impostor syndrome, and wants to run something past their boss or bounce an idea off a colleague, that wasnt possible.
There may be some technological solutions to these problems, fuelled in part by a growth in the number of startups since the pandemic it seems to be easier to set up a new company without the overheads of an office.
Outfits such as Kadence create virtual office spaces that might make it easier for people to have those watercooler moments with their boss, while Scoop hopes to make it easier to coordinate days in the office with colleagues. And people who dont have space for a desk in their bedroom can use Radious, an Airbnb-like service where you can work from someone elses home.
The challenge for the producers of the new Australian version of The Office will be to reflect how different the working environment is now from 20 years ago. The 2024 version will feature Felicity Ward as the Brent-like manager told that her office is being shut down, and everyone will need to work from home. A tragedy for her. Perhaps not for her staff.
Additional reporting by Donna Ferguson and Maximilian Jenz
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Never again: is Britain finally ready to return to the office? - The Guardian
NVIDIA AI Workbench Speeds Adoption of Custom Generative AI for … – NVIDIA Blog
New Developer Toolkit Introduces Simplified Model Tuning and Deployment on NVIDIA AI Platforms From PCs and Workstations to Enterprise Data Centers, Public Clouds and NVIDIA DGX Cloud
SIGGRAPHNVIDIA today announced NVIDIA AI Workbench, a unified, easy-to-use toolkit that allows developers to quickly create, test and customize pretrained generative AI models on a PC or workstation then scale them to virtually any data center, public cloud or NVIDIA DGX Cloud.
AI Workbench removes the complexity of getting started with an enterprise AI project. Accessed through a simplified interface running on a local system, it allows developers to customize models from popular repositories like Hugging Face, GitHub and NVIDIA NGC using custom data. The models can then be shared easily across multiple platforms.
Enterprises around the world are racing to find the right infrastructure and build generative AI models and applications, said Manuvir Das, vice president of enterprise computing at NVIDIA. NVIDIA AI Workbench provides a simplified path for cross-organizational teams to create the AI-based applications that are increasingly becoming essential in modern business.
A New Era for AI DevelopersWhile hundreds of thousands of pretrained models are now available, customizing them with the many open-source tools can require hunting through multiple online repositories for the right framework, tools and containers, and employing the right skills to customize a model for a specific use case.
With NVIDIA AI Workbench, developers can customize and run generative AI in just a few clicks. It allows them to pull together all necessary enterprise-grade models, frameworks, software development kits and libraries from open-source repositories and the NVIDIA AI platform into a unified developer toolkit.
Leading AI infrastructure providers including Dell Technologies, Hewlett Packard Enterprise, HP Inc., Lambda, Lenovo and Supermicro are embracing AI Workbench for its ability to augment their latest generation of multi-GPU-capable desktop workstations, high-end mobile workstations and virtual workstations.
Developers with a Windows or Linux-based NVIDIA RTX PC or workstation will also be able to initiate, test and fine-tune enterprise-grade generative AI projects on their local RTX systems, and easily access data center and cloud computing resources to scale as needed.
New NVIDIA AI Enterprise 4.0 Software Advances AI Deployment To further accelerate the adoption of generative AI, NVIDIA announced the latest version of its enterprise software platform, NVIDIA AI Enterprise 4.0. It gives businesses the tools needed to adopt generative AI, while also offering the security and API stability required for reliable production deployments.
Newly supported software and tools in NVIDIA AI Enterprise that help streamline generative AI deployment include:
NVIDIA AI Enterprise software which lets users build and run NVIDIA AI-enabled solutions across the cloud, data center and edge is certified to run on mainstream NVIDIA-Certified Systems, NVIDIA DGX systems, all major cloud platforms and newly announced NVIDIA RTX workstations.
Leading software companies ServiceNow and Snowflake, as well as infrastructure provider Dell Technologies, which offers Dell Generative AI Solutions, recently announced they are collaborating with NVIDIA to enable new generative AI solutions and services on their platforms. The integration of NVIDIA AI Enterprise 4.0 and NVIDIA NeMo provides a foundation for production-ready generative AI for customers.
NVIDIA AI Enterprise 4.0 will be integrated into partner marketplaces, includingAWS Marketplace,Google Cloud and Microsoft Azure, as well as through NVIDIA cloud partner Oracle Cloud Infrastructure.
Additionally, MLOps providers, including Azure Machine Learning, ClearML, Domino Data Lab, Run:AI, and Weights & Biases, are adding seamless integration with the NVIDIA AI platform to simplify production-grade generative AI model development.
Broad Partner SupportDell Technologies and NVIDIA are committed to helping enterprises build purpose-built AI models to access the immense opportunity of generative AI. With NVIDIA AI Workbench, developers can take advantage of the full Dell Generative AI Solutions portfolio to customize models on PCs, workstations and data center infrastructure. Meghana Patwardhan, vice president of commercial client products at Dell Technologies
Most enterprises do not have the expertise, budget and data center resources to manage the high complexity of AI software and systems. We look forward to NVIDIA AI Workbenchs potential to simplify generative AI project creation with one-click training and deployment on the HPE GreenLake edge-to-cloud platform. Evan Sparks, chief product officer for AI at HPE
As a workstation market leader offering the performance and efficiency needed for the most demanding data science and AI models, we have a long history collaborating with NVIDIA. HP is embracing the next generation of high-performance systems, coupled with NVIDIA RTX Ada Generation GPUs and NVIDIA AI Workbench, and bringing the power of generative AI to our enterprise customers and helping move AI workloads between the cloud and locally. Jim Nottingham, senior vice president of advanced computing solutions at HP Inc.
Lenovo and NVIDIA are helping customers overcome deployment complexities and more easily implement generative AI to deliver transformative services and products to the market. NVIDIA AI Workbench and the Lenovo AI-ready portfolio enable developers to leverage the power of their smart devices and scale across edge-to-cloud infrastructure. Rob Herman, vice president and general manager of Lenovo Workstation & Client AI
The longstanding VMware and NVIDIA partnership has helped unlock the power of AI for every business by delivering an end-to-end enterprise platform optimized for AI workloads. Together, we are making generative AI more accessible and easier to implement in the enterprise. With AI Workbench, NVIDIA is giving developers a set of powerful tools to help enterprises accelerate gen AI adoption. With the new NVIDIA AI Workbench, development teams can seamlessly move AI workloads from the desktop to production. Chris Wolf, vice president of VMware AI Labs
Watch NVIDIA founder and CEO Jensen Huangs SIGGRAPH keynote address on demand to learn more about NVIDIA AI Workbench and NVIDIA AI Enterprise 4.0.
AI Workbench is coming soon in early access. Sign up to get notified when it is available.
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NVIDIA AI Workbench Speeds Adoption of Custom Generative AI for ... - NVIDIA Blog
An integrated approach to training data scientists – Stanford Report – Stanford University News
Every day, data scientists are analyzing vast amounts of information about the world, using computational methods to find new ways to understand a problem or phenomenon, and deciding what to do about it.
But its not enough to use data on its own it must be understood within its social and political context as well, according to Stanford political scientist Jeremy Weinstein. This year, Weinstein, along with Stanford statisticians Guenther Walther and Chiara Sabatti, has launched two new degrees: a Bachelor of Science in Data Science and a Bachelor of Arts in Data Science & Social Systems.
Jeremy Weinstein is the faculty director of the BA in Data Science and Social Systems and a professor of political science. Mallory Nobles, right, is the programs associate director. (Image credit: Andrew Brodhead)
Theres basically no new technological frontier that doesnt depend on or engage in some important way with human behavior or a political or social institution, explained Weinstein, a professor of political science in the School of Humanities and Sciences who serves as faculty director of the BA program in Data Science & Social Systems. For example, when staffing the tech industry of the future, you want people who can effortlessly move between the technical team, the policy team, and the trust and safety team. The Data Science and Social Systems program is designed to prepare professionals who can work at those intersections.
This past spring, over 90 students took the new gateway course for the major, DATASCI 154: Solving Social Problems with Data. Throughout the course, which Weinstein co-taught with Mallory Nobles, the programs associate director, students developed skills in quantitative analysis, modeling, and coding, but also honed their ability to frame problems, choose appropriate designs, and interpret data as it relates to its social and political environment.
The course brought two mindsets together: a social science approach, rooted in an understanding of causal inference, and an engineering approach, based in learning algorithmic design and optimization.
As Weinstein and Nobles emphasized to their students, these perspectives are interconnected.
When you ask and answer causal questions about a social problem, youre deepening your understanding of the underlying causes, which can give you clues about how you might go about solving it, and when you design an algorithmic solution, you then want to understand its effect when deployed in the world, which brings you back to causal inference, said Weinstein, who is also the faculty director of Stanford Impact Labs.
Students explored the value of these different approaches through modules designed with scholars from different fields at Stanford.
For example, Jennifer Pan in the Communication Department introduced students to the role of data science and causal inference techniques in studying the impact of social media on polarization and the spread of disinformation. Marshall Burke from the Department of Earth System Science engaged students in thinking through how machine learning approaches can help measure a changing climate, while social scientific methods are critical for understanding the impact of mitigation and adaptation policies. Ramesh Johari from the Department of Management Science and Engineering, along with David Scheinker from the School of Medicine, exposed students to the challenge of delivering equitable access to healthcare and how algorithmic approaches can improve delivery of patient care through the lens of their work on diabetes at Stanford Medicine.
Students learned how they too can be at ease shifting their perspective between engineering and social sciences. Class assignments emphasized statistics, computer science, and math in tandem with topics in the social and behavioral sciences, like psychology, sociology, economics, and political science. Their final project was to write a research proposal to tackle a social problem of their choosing.
As Josh Orszag learned, getting the data is the easy part. Data cant get you very far unless you have a meaningful research question.
Josh Orszag is a Data Science and Social Systems major who took Solving Social Problems with Data this past spring. (Image credit: Andrew Brodhead)
If you dont have the right research question, youre not going to get anywhere, said Orszag, a Data Science and Social Systems major interested in issues related to democracy and governance. The challenge is figuring out what problem or predicament you want your data to answer.
Orszag teamed up with Ava Kerkorian, a prospective Data Science and Social Systems major, to think about how to build trust in the election process.
Throughout their research design process, Kerkorian said she and Orszag went back and forth with each other as they figured out how such a complex issue could be tackled in a way that was specific, scalable, and also actionable.
So many times during this project, we had to take a pause and ask ourselves, how do we measure trust? What would success look like? What is confidence? Are we even sure this is something we want? Kerkorian said.
What they ended up proposing was studying whether a nudge a concept from behavioral economics that sways behavior through small suggestions or positive reinforcement explaining how redistricting works from an Independent Redistricting Commission could influence peoples attitudes about the fairness of an election.
The course made Serena Lee, also a data science and social systems major, think critically about what it means to be a responsible data scientist.
Serena Lee, who took Solving Social Problems with Data, presented her final project at a poster presentation at the end of the quarter.
This class taught me that the work starts with how to collect data because that has a lot of value-laden decisions, from whom to involve in the dataset to what questions to ask, what wording to use, and how far in the past to look at the data, Lee said. For her final project with Annie Zhu, they wanted to explore the influence of video-based misinformation in comparison to text-based misinformation. Specifically, they proposed studying different ways platforms could flag potentially harmful posts.
Eva Gorenburg, who also took the class this quarter, said learning the ins and outs of research design has changed how she now sees data.
I think its really easy to take numbers as objective fact, but what we learned is even in studies that seem super quantitative and objective, there are tons of choices in the study design that impact results, Gorenburg said.
Students also learned that what they choose to measure and not measure and how they use their data all have social consequences.
If you just rely on observational study, observation or opinion, therere so many essential experiences that youre leaving out, said Emily Winn, an environmental systems engineering major. Solving social problems with data allows us to see things on a much broader scale than previously before.
Winn and Gorenburg worked together for their final project, which was a proposal to study the social impacts of arsenic poisoning on women in Rural Bangladesh, where little data on its effects exists. Specifically, they wanted to know whether arsenicosis would lower a womans likelihood of marriage, which is essential for the economic and social security of women living in the region.
Understanding social problems is not the same as solving them.
Social problems exist for complex reasons, said Weinstein. Solving problems involves significant stakeholder consultation and understanding what the pathway is from a new insight or a new tool to actual change in the world.
Esha Thapa was one of over 90 students who took Solving Social Problems with Data this past spring. (Image credit: Andrew Brodhead)
For Esha Thapa, a Data Science and Social Systems major, the class marks the beginning of an interesting journey examining these dynamics in greater depth.
Its definitely not a process that ends with the quarter ending, said Thapa. Its something that we need to take with us for the rest of our careers and this is a great gateway course in that respect.
Following Solving Social Problems with Data, students in the major will continue to take a range of core classes in data science, ethics, and social sciences. In their senior year, students will take a capstone practicum where they will apply computational and statistical methods to address a social issue in a real-life setting.
Data Science majors can pursue one of two tracks: either a Bachelor of Science being overseen by Walther and Sabatti, or a Bachelor of Arts, which Weinstein and Nobles direct.
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An integrated approach to training data scientists - Stanford Report - Stanford University News