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(Content sponsored by Open Data Science, LLC) Open Data Science, LLC will bring their popular applied data science and artificial intelligence conference, ODSC West 2018 , to San Francisco this October 31 to November 3. At this event, attendees will learn about the latest insights and tools and topics in artificial intelligence and data science, how to apply these skill to their work, and hear from some of the brightest minds in the data science field. Keynotes include: Andrej Karpathy , Director of AI at Tesla. Professor Daphne Koller , co-founder of Coursera. Virginia Eubanks , Associate Professor of Political Science at the University at Albany Carrie Grimes Bostock , PhD, Distinguished Engineer at Google’s Technical Infrastructure Group Katherine Gorman , founder and co-host of the machine intelligence podcast Talking Machines, will be a guest MC. Additional speakers  are industry experts from IBM, Google, OpenAI, GE, Uber, NYU, Columbia University, Salesforce, DataRobot, Amazon, and more. Learn the latest Over two intense days of full day training sessions and workshops, attendees will get hands-on with the latest data science platforms, tools, and techniques. Forge a connection with these rock stars from industry and academic, who are passionate about teaching data science skills to those who will build the future of industry. The best way to learn is by doing. That’s why ODSC West features more than 40 hands-on workshops in addition to 10 premium training sessions, specifically designed to boost your data science skills and help you land your next job. ODSC West provides a unique, well-rounded learning experience. Attendees get access to more than 100 incredible talks, given by some of the biggest names in data science, on machine learning, deep learning, data visualization, open data science, and more! Accelerate your career There’s also the ODSC West Career Fair in one of the hottest job markets for data scientists. Data science is a huge field and it takes time to find the right role for you. This is your chance to meet face-to-face with recruiters from over 50 leading tech companies, filling more than 400 positions, from interns to experienced. ODSC West is colocated with Accelerate AI , an event that brings together the top industry executives, managers, and CxOs to help you understand how AI and data science can transform your business, regardless of industry. You will hear from fellow CxOs, executives, chief data scientists, and thought leaders. Learn how AI and data science techniques are transforming business and prepare your company for the next wave of innovation. Register Now > See the Schedule > Content sponsored by Open Data Science, LLC Image credit by Open Data Science, LLC Want more? For Job Seekers | For Employers | For Influencers
There are lots of analysts. Few good ones – and excellent ones are like hens teeth. Analysts are the interface between data and action – the insight of the organization. They are important. They can change your strategy. They can leverage and exploit your big data into smart data. They transform data to insight and let you take evidence-based action. The most cited gripe I hear about analysts working in the industry is " so what? ” and " I don't trust the data ." Why are there not more good analysts? The bottom line comes down to being ultra clear on what you want your analyst to do and the skills and behaviours required to do it. Then you need to recruit to this and deploy the principles throughout your organization. Let's call them hallmarks of a great analyst. What are the 7 magic hallmarks? Great analysts need to be inquiring. This means 'seeking stones to turn' and asking questions to help shape hypothesis to test. They don’t jump to the answers; they spend the time on the questions. They need to understand the business processes, people, and systems. Spending time observing and working with operational and enabling departments. Building credibility with business leads is key. Trust is critical. They need to have the right 'hard' skill sets, including technical, scientific, and mathematical. Understanding bias, causation, forecasting, and significance testing is a must. The appropriate balance and understanding of quantitative and qualitative methodology is a given. They need to be a top communicator. Building and influencing front-line business operations. Presentations and writing ability should not be compromised. They need to focus on context and storytelling. Deploying appropriate ways of visualization techniques. Articulating the ‘big picture.’ People connect with a story; so must your analyst. They need to drive a positive learning and improvement culture. It's all about the challenging issues, right? No, focus on finding good practice, too. They need to be scalpel sharp on connecting outcome and indicator metrics. The pinch points of delivery. This is essential for base lining and the framework for benefits realization and evaluation later on. How do you find an analyst like this? It all starts with good recruiting and hiring. Thorough tests and interviews against these tips is a must. Just because they have listed a load of experience does not make them right for the job. Put them through a tough process proportionate to what you what them to achieve. If you can't do it, find someone who can. When great analysts are in the workplace, they need to be nurtured. Help them learn the business and support a culture of innovation, but challenge their delivery and ensure they operate within these seven hallmarks for analysts. Your business depends on it. Article written by Sean Price Want more? For Job Seekers | For Employers | For Influencers
When it comes to data science, the C-Suite needs to be involved. There are a number of factors that lead to success with analytics, but without you, none of those really matter. Many executives get involved with analytics projects once they have overrun their budgets or failed to meet ROI goals. I have been called in to fix a number of derailed projects and we all say the same thing, “I wish we’d have gotten involved sooner.” You don’t need to run it, and you don’t have time to run it. The right roles for the C-Suite are strategy, oversight, and sponsorship. The wrong role is active, day-to-day management. I see a number senior executives, most often CMOs, CIOs, and CTOs, get dragged into running data science initiatives. That is a full-time job in and of itself, not one that a senior executive can focus on with everything else they are tasked with doing. What the C-Suite needs to know revolves around building a smart data strategy, insuring the goals are met and evangelizing a data-driven culture. Let’s start with the motivation and biggest question – Why? There are three key groups with expectations – customers, employees, and investors. These three drive data science adoption in business. Customers, both B2C and B2B, expect personalized, consistent experiences and responsive service across a number of channels . Those expectations are met with advanced analytics and data-driven automation. Employee expectations are rising, especially in those with highly sought after skill sets. Talent analytics shows the business how to attract, engage and retain their talent. This keeps productivity on the rise while keeping the cost of talent stable. Investors have come to expect a well-defined data strategy from executive leadership. Their opinions on company valuation, competitive standing, and future performance are impacted by the business’s ability to develop and execute that strategy. Data science isn’t an optional capability from the investor’s standpoint. Building a realistic data strategy Data science realism 101: You need a plan for responsibly collecting and securing data. The business needs a plan for building the capabilities and infrastructure to turn that data into insights to drive positive business outcomes. The plan needs to have measurable ROI that stakeholders agree upon up front. You need a campaign to teach the talent what to expect and why the migration to data driven will improve the business. Everyone needs to be accountable for their part in executing the data strategy. Only collect data that will not damage the company’s image and credibility when it is revealed that data is being collected. Only collect what the business is capable of securing because the cost of a breach is massive. Data governance is the term used to describe elements of data strategy like: Data Quality – Making sure the data collected is accurate and useful in data science initiatives Data Security – Keeping data safe from both internal and external loss Data Compliance – Handling the complex legal and ethical implications of data science Data Management – Where to keep it, how to transmit it and how to process it Data is like any other capital resource. Businesses exist to turn capital resources into revenue streams, and data is no exception. The capabilities to handle that transformation fall into two categories – infrastructure and talent. Infrastructure is where the data is physically stored and processed as well as the software tools needed to, process, and consume the insights generated from the transformation. Talent are the people who add value to data. Build this around a leadership team which is experienced in understanding business needs and how to meet those needs with data science solutions. The talent war in data science is real. While there are roughly 150,000 to 200,000 people in the world claiming to be data scientists, the top tier of talent is less than 20,000 people worldwide. One of these “unicorns” is worth the cost and effort to hire because they can produce systems on their own with high impacts to business performance. Give your HR and talent acquisition teams the flexibility and tools they need to attract and retain top talent as well as outsourcing where it makes sense. Overseeing data science without being dragged into managing it The difference between being pulled into the day-to-day operations and keeping a responsible level of oversight comes down to translating data science activities into KPIs that the C-Suite can track. The jargon surrounding data science is thick. An easy way to cut through the buzzwords is with a clear focus on ROI and business goals. Data science departments should be revenue generators and not cost centers. After the initial investment to ramp up, data science departments should be like any other – a contributor to the bottom line. Many data scientists will argue that analytics departments are different and need to be run or measured differently. In my experience, this is an argument to skirt oversight at best and an outright lie at worst. The rationales against oversight come down to a lack of trust between the data science group and leadership. Executive involvement from day one is a key to remove this barrier. Building trust allows senior executives to delegate the daily operations of what will be one of the business’s most important groups while maintaining a responsible level of oversight. The layer of translation between business goals and data science methods is also critical. Stakeholders need to be onboard with what is expected of the data science team as well as how success will be defined and reported. Evangelizing data-driven business Accountability needs to extend to other organizations, as well, the consumers of analytics. Data science teams cannot operate in a silo. To connect the team with business outcomes, they have to be integrated into each organization they support. That requires a level of accountability for turning the insights that data science provides into outcomes the business expects. The data science team cannot do that alone, and other business units will not act on insights they don’t trust. Again we come back to establishing trust, which is a key function of the C-Suite in a data-driven business. Accountability has to be a two-way street for a data-driven culture to take hold. The data science team is accountable for accurate, actionable insights. The consuming organization is accountable for making their business needs clear and acting on the insights they are handed. That does not work in companies where the teams do not build trust by working together closely. That working relationship starts with executive sponsors. Clear expectations and clear goals lead to success with data science as it does with any other organization or initiative. When your leadership team gets vocal about data-driven priorities, the organization gets on board with the changes needed to make that a reality. Article written by Vin Vashishta Want more? For Job Seekers | For Employers | For Influencers
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