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WHAT ARE SOME KEY SKILLS AND QUALIFICATIONS THAT COMPANIES LOOK FOR WHEN HIRING DATA SCIENTISTS

Data scientists work at the intersection of business strategy, analytics, and engineering. As data and analytics become more central to business success, companies are actively recruiting people who can transform data into insights to help drive strategic decision making. When hiring for data scientist roles, companies seek well-rounded candidates who have strong technical abilities combined with business and problem-solving skills.

From a technical perspective, companies value candidates who have experience and skills working with large, diverse datasets. Proficiency with statistics, machine learning, data mining, and predictive modeling are at the top of most hiring managers’ lists. In-depth knowledge of programming languages like Python, R, SQL, and NoSQL databases are essential for manipulating and analyzing data. Experience with Hadoop, Spark, and other big data tools is also attractive for those working with extremely large datasets. Understanding data visualization techniques and reporting best practices is important as well to effectively communicate insights to stakeholders.

Beyond technical prowess, companies seek data scientists who can bridge the gap between analytics and business objectives. Strong business acumen and an understanding of the industry are critical for data scientists to determine which problems are most worthwhile to solve and to effectively partner with business teams. Problem-solving, critical thinking, and strategic recommendation skills help data scientists identify patterns, determine root causes, and develop solutions with measurable impact. Excellent communication and collaboration abilities are valued for ongoing engagement with key business leaders and functional areas across the organization.

Educational background varies, with many companies open to candidates from a variety of disciplines including statistics, computer science, engineering, math, information systems, and related quantitative fields. A master’s degree is commonly preferred but not always required. Bootcamp or self-study experience can make up for lack of formal education if paired with robust hands-on projects. Ongoing learning and willingness to adapt to new technologies are also attractive traits that demonstrate a data scientist’s commitment to continuous skill development in a rapidly changing field.

Work experience is another key consideration for most employers. While some organizations hire entry-level data scientists right out of school, most seek 2-5 years of relevant, hands-on analytics experience. Exposure to real-world business problems and demonstrated success with end-to-end data science projects helps candidates hit the ground running in their new role. Experience in a specific industry is valued by companies that require domain expertise, such as healthcare, finance, retail, manufacturing, and more. Working knowledge of the full data science life cycle from business understanding to deployment of results is ideal.

Qualities like intellectual curiosity, strong work ethic, and team player attitudes are important soft skills employers look for in data science candidates. Attention to detail and quality assurance skills are crucial considering the high-stakes nature of many decisions informed by data analysis. Project management and ability to multi-task on simultaneous projects and priorities are also beneficial traits. Hiring managers aim to identify well-rounded candidates who combine business and technical dexterity to become a trusted, value-added partner within their function or department.

Top criteria companies evaluate when hiring data scientists include strong proficiency in statistics, machine learning, programming, and big data tools. Business acumen, problem-solving abilities, and experience applying analytics to real-world problems are equally as important. Coupled with soft skills like communication, collaboration, and continuous learning mindsets, these well-rounded qualifications and experiences help candidates stand out for roles that require technical prowess put to strategic use. As data and analytics become further ingrained in business operations, the demand for data scientists who fulfill these technical and experiential requirements will only continue growing across all industries.

WHAT ARE SOME OF THE SPECIFIC SKILLS THAT STUDENTS GAIN THROUGH PARTICIPATING IN NIKE’S CAPSTONE PROGRAM

Nike’s Capstone program provides high school students with an opportunity to develop important hard and soft skills that will serve them well both in future educational pursuits and career paths. Through this program, students work in teams on a real-world project proposed by Nike to help solve a business challenge. This hands-on experience allows students to gain valuable project management, collaboration, communication, and problem-solving abilities.

Some of the key skills students are able to hone through the Capstone program experience include:

Project Management Skills – Students learn what it takes to successfully plan and execute a complex project from start to finish. They have to define project goals and scope, develop a work plan with timelines and assign responsibilities, track progress, and ensure the project is delivered on schedule and meets requirements. This teaches skills like priority setting, resource allocation, and adapting to changes that are critical for any career.

Collaboration Skills – As members of multidisciplinary teams, students learn effective collaboration techniques for working together toward a common goal. They practice clear communication, active listening, consensus building, handling conflicts constructively, and tapping the diverse strengths each person brings. Participating in team-based problem solving readies students for the many collaborative work environments they will likely face.

Communication Skills – Both oral and written communication skills are sharpened through delivering project presentations and documentation. Students practice organizing information logically, adapting messages for different audiences like clients or stakeholders, and using various media like slides, reports and demonstrations. Delivering persuasive recommendations enhances presentation and public speaking confidence.

Problem Solving Skills – The real-world business challenges provided by Nike require innovative thinking. Students have to analyze complex problems from multiple angles, brainstorm creative solutions, conduct research, test ideas, and iterate based on outcomes. This strengthens critical thinking, research proficiencies, and the ability to tackle open-ended problems—skills integral to any career path.

Design Thinking Skills – Many Capstone projects involve designing new product concepts, prototypes or user experiences. This immerses students in the full iterative design process of empathizing with user needs, defining the problem, ideating solutions, prototyping, testing, and refining based on feedback. Students not only strengthen creative design skills but also learn human-centered approaches through practicing design thinking methodologies.

Research Skills – To thoroughly understand business challenges and solution spaces, students extensively research topics through literature reviews and primary data gathering like surveys, interviews and contextual inquiries. This improves their abilities to efficiently gather, assess validity of, synthesize and apply information from diverse sources—all key attributes of any research-driven career.

Time Management Skills – With tight deadlines and competing priorities across classes, activities and personal lives, students experience the importance of self-discipline, prioritization, planning and organizational abilities needed to effectively manage workload and schedules. The program cultivates time management proficiencies central to work-life balance.

Leadership Skills – While working as a team, students alternate facilitating meetings, motivating others, resolving conflicts, delegating responsibilities, setting examples and driving projects forward under constraints and ambiguity. Even those who may not be formal group leaders gain exposure to developing leadership presence and guiding successful team efforts.

Perseverance – Taking on open-ended challenges that may encounter setbacks along the way builds students’ perseverance, willingness to learn from mistakes/failures, and determination to find solutions—all qualities needed to progress in uncharted problem spaces. The hands-on work gives students confidence to push through obstacles and iterative approaches to continuous improvement.

The diverse hard and soft skills strengthened through participating in Nike’s high-impact Capstone program provide a strong foundation for whatever future studies or careers students may pursue. The real-world, collaborative project experience equips students to become flexible, resourceful problem solvers ready to excel in dynamic, fast-paced work environments of the future.

HOW CAN COLLEGES ENSURE THAT AI TECHNOLOGIES ARE IMPLEMENTED RESPONSIBLY AND ETHICALLY

Colleges have an important responsibility to develop and utilize AI technologies in a responsible manner that protects students, promotes ethical values, and benefits society. There are several key steps colleges should take to help achieve this.

Governance and oversight are crucial. Colleges should establish AI ethics boards or committees with diverse representation from students, faculty, administrators, and outside experts. These groups can develop policies and procedures to guide AI projects, ensure alignment with ethical and social values, and provide transparency and oversight. Regular reviews and impact assessments of AI systems should also take place.

When developing AI technologies, colleges need processes to identify and mitigate risks of unfairness, bias, privacy issues and other harms. Projects should undergo risk assessments and mitigation planning during design and testing. Approval from ethics boards should be required before AI systems interact with or impact people. Addressing unfair or harmful impacts will help build student, faculty and public trust.

Colleges should engage students, faculty and the public when developing AI strategies and projects. Open communication and feedback loops can surface issues, build understanding of how technologies may impact communities, and help develop solutions promoting fairness and inclusion. Public-facing information about AI projects also increases transparency.

Fairness and non-discrimination must be core priorities. Colleges should establish processes and guidelines to identify, evaluate, and address potential unfair biases and discriminatory impacts from data, algorithms or system outcomes during the entire AI system lifecycle. This includes monitoring deployed systems over time for fairness drift. Diverse representation in AI teams can also help address some biases.

Privacy and data security are also critical to uphold. Clear and careful management of personal data used in AI systems is needed, including obtaining informed consent, limiting data collection and sharing to authorized uses only, putting security safeguards in place, and providing options for individuals to access, correct or delete their data. Anonymizing data where possible can further reduce risks.

Accountability mechanisms need implementation as well. Colleges should take responsibility for the proper development and oversight of AI technologies and be able to explain systems, correct errors and address recognized harms. Effective auditing of AI systems and documentation of processes helps ensure accountability. Whistleblower policies that protect those who report issues also support accountability.

Transparency about AI technologies, their capabilities and limitations is important for building understanding and managing expectations. Colleges need to clearly communicate with stakeholders about the purpose of AI systems, how they work, what data they use, how decisions are made, limitations and potential risks. Accessible explanations empower discussion and help ensure proper and safe use of technologies.

Workforce considerations are also important. As AI adoption increases, colleges play a key role in preparing students with technical skills as well as an understanding of AI ethics, biases, fairness, transparency, safety and human impacts. Curricula, certificates and training in these fields equip students for careers developing and overseeing responsible AI. Colleges also need strategies to help faculties and staff adapt to changing roles and responsibilities due to AI.

Partnerships can amplify impact. Colleges collaborating with companies, non-profits and other educational institutions on AI responsibility multiplies their capacity and influence. Joint projects, research initiatives, policy development and resources promote best practices and ensure new technologies serve public good. Partnerships also strengthen ties within communities and help address societal AI challenges.

Through proactive governance, risk assessment, public engagement, accountability mechanisms and workforce preparation, colleges can help realize AI’s promise while avoiding potential downsides. Integrating ethics into technology development supports student and community well-being. With leadership and vigilance, colleges are well-positioned to establish frameworks supporting responsible and beneficial AI.

CAN YOU RECOMMEND ANY OTHER RETAIL DATASETS THAT ARE SUITABLE FOR CAPSTONE PROJECTS

Kaggle Retail Dataset: This dataset contains over 10 years of daily sales data for 45,000 food products across 10 stores. It includes fields like store, department, date, weekly sales, markup, and more. With over 500,000+ rows, it provides a lot of rich data to analyze retail sales patterns, perform forecasting, explore department performance, and get insights into pricing and promotion effectiveness. Some potential capstone projects could be building predictive sales models, optimizing inventory levels, detecting anomalies or outliers, comparing store or department performance, etc.

Online Retail II Dataset: This dataset from the UCI Machine Learning Repository contains transactions made by a UK-based online retail between 01/12/2009 and 09/12/2011. It includes fields like InvoiceNo, StockCode, Description, Quantity, InvoiceDate, UnitPrice, CustomerID, and Country. With over 5,000 unique products and around 8,000 customers, it allows examining customer purchasing behaviors, product categories, sales trends over time. Capstone ideas could be customer segmentation, recommendation engines, predictive churn analysis, promotion targeting, assortment optimization, etc.

European Retail Study Dataset: This dataset was collected between 2013-2015 across 24 countries in Europe to study omni-channel retail. It provides information on over 42,000 customers, their purchase transactions, demographic details, online/offline shopping behaviors, returns etc. Some dimensions covered are age, gender, income-level, product categories purchased, channels used, spend amounts. This rich dataset opens up opportunities for multi-channel analytics, personalized experiences, loyalty program design, understanding cross-border trends at a continental scale.

Instacart Market Basket Analysis Dataset: This dataset collected over 3 million grocery orders from real Instacart customers. It includes anonymized order data with product names, quantities, added or removed from basket, purchase or cancellation. This provides scope for advanced market basket or transactional analysis to determine complementary or frequently bought together products, influencing factors on abandoned cart recovery, incremental sales from personalized recommendations, effects of out-of-stock items etc.

Walmart Sales Forecasting Dataset: This dataset contains daily sales data for 45 Walmart stores located in different regions collected over 3 years. Features include Store, Dept, Date, Weekly_Sales, Markup, etc. It can be leveraged to build statistical or deep learning models for short and long term demand forecasting across departments, developing automatic outlier detection capabilities, scenarion analysis during special events etc.

Target Customer Dataset: This dataset contains purchasing profiles for over 5000 anonymous Target customers encompassing their transactions over a 6 month period. It includes features like age, gender, marital status, home ownership, number of dependents, income, spend categories within Target like grocery, personal care, electronics etc. This could enable identifying high lifetime value segments, developing micro-segmentation strategies, testing personalization and targeted promotions approaches.

Kroger Customer Analytics Dataset: This dataset contains anonymous profiles of over 30,000 Kroger customers including their demographics, surveyed household & lifestyle characteristics, shopping behaviors and purchasing basket details. Variables provided are age, ethnicity, family status, income level, ZIP code, preferences like organic, wellness focused etc along with purchases across departments. Potential projects include customer churn analysis, propensity modeling for private label brands, targeted loyalty program personalization at scale.

These datasets offer rich retail data that span various dimensions – from transactions, customers, banners to omni-channel behavior. They enable diving deep into opportunities like forecasting, recommendations, segmentation, promotions analysis, supply chain optimization at scale suitable for many capstone project ideas exploring insights for retailers. The datasets are publicly available and of a good volume and variety to power meaningful analytical modeling and drive actionable business recommendations.

WHAT ARE SOME EXAMPLES OF REAL WORLD ISSUES OR PROBLEMS THAT STUDENTS CAN ADDRESS IN THEIR CAPSTONE PROJECTS

Community access to resources – A lack of access to resources is a problem faced by many communities. For their capstone project, students could research the resources needed by a specific local community and develop solutions to improve access. For example, they could analyze transportation options and propose routes to improve mobility, or identify gaps in access to healthcare and develop partnerships with local clinics. This type of project directly tackles real barriers faced by real people.

Environmental sustainability – Issues surrounding environmental sustainability and promoting green practices are very relevant today. Students could research sustainability practices on their campus or in their city and propose initiatives to reduce waste, pollution, or carbon emissions. Examples may include conducting an audit of a building’s energy usage and developing recommendations for upgrading systems to be more efficient, or creating an educational campaign to promote recycling or alternative forms of transportation among the campus or local community. Addressing environmental challenges provides tangible benefits.

Supporting vulnerable populations – Many communities struggle to meet the needs of vulnerable groups such as low-income families, the elderly, people with disabilities, etc. For their capstone, students could partner with a local organization that supports one of these populations to identify unmet needs and develop programs or services to have a meaningful positive impact. For example, students may create an app or website to help homebound seniors schedule rides to medical appointments or facilitate check-ins, or they could implement an after-school tutoring program for low-income elementary school children. Projects like these directly serve those in need.

Improving public/civic engagement – Getting community members more civically involved and participating in community decision making is important for strong, vibrant communities. Students could analyze voter turnout, volunteer rates, or civic group membership in their city and develop strategies to increase participation, such as creating a bike-based get-out-the-vote effort or holding civic forums/meetings in more neighborhood locations. The goal would be empowering community voices and strengthening civic discourse.

Bridging cultural understanding – In diverse communities, greater cultural understanding can help foster togetherness and equality. As their capstone, students may organize cultural exchange events, workplace cultural sensitivity training sessions, or cross-cultural mentoring programs between local schools. They could also research how two specific cultural groups interact to identify tensions and develop recommendations for improvement, such as through community mediation. Projects that facilitate cultural appreciation and inclusion can make real impacts.

Leveraging technology for social good – Technology continues to rapidly change the world, and students can leverage new technologies to address social issues. For example, they could build a mobile app to connect volunteers with local non-profits needing assistance, create an online platform for reporting uncared for neighborhood properties like overgrown lots to the city, or develop an online job training and placement program for unemployed young adults. Harnessing technology opens up many possibilities for driving positive change.

Public health initiatives – Promoting good public health is crucial. Students could assess a community’s nutrition and exercise levels to identify at-risk groups and plan interventions like community gardens or walking groups. Or they may conduct research on a serious local health issue like opioid abuse and propose evidence-based prevention and treatment programs. Public health focused projects aim to tackle critical needs and improve residents’ well-being.

The key aspects of a successful capstone project are that it addresses an authentic problem or need, provides tangible benefits, and involves active partnership with community stakeholders. The examples outlined here represent just a sampling of the meaningful, impactful projects students could undertake that have real world applications. By choosing to take on an issue they’re passionate about and that affects real people, students can create capstones that drive positive change and make a difference.