AI News, Preparing for the Transition to Data Science

Preparing for the Transition to Data Science

Before this he was a Fellow in the 2015 Summer Insight session, where he developed an app to help social scientists write in the style of the top journal articles in their field.

After a PhD spent analyzing data and studying the Higgs Boson at the Large Hadron Collider, Kevin joined the Insight Data Science team to provide unique educational experiences and help PhDs and postdocs make transitions to careers in data science.

As part of the Insight team, we attend career panels at universities across the country to speak with scientists who are considering career transitions.

One of the most common questions we get asked is: “What skills and tools should I be learning?” Since 2012 Insight has helped 400+ of the brightest PhDs and postdocs transition into top industry positions and we’ve learned a lot about how to make the transition efficient.

Python is a general purpose programming language that has a growing number of modules for data analysis, including SciPy, Numpy, Pandas, StatsModels, and Scikit-learn, as well as many visualization tools like seaborn, matplotlib, and ggplot.

Data scientists often work closely with engineering teams and being able to understand your colleagues and teammates is crucial for doing great work.

But your goal as a data scientist should be to get better at finding the right tool, the right model, and perhaps most importantly, asking the right questions of your data.

In the tech industry, one often has to continually reevaluate priorities and work in an agile manner rather than letting projects go on for long timescales with less flexibility.

One final piece of advice we suggest is immersing yourself in the data science world by keeping abreast of the latest news in the field.

Preparing for the Transition to Data Science

Before this he was a Fellow in the 2015 Summer Insight session, where he developed an app to help social scientists write in the style of the top journal articles in their field.

After a PhD spent analyzing data and studying the Higgs Boson at the Large Hadron Collider, Kevin joined the Insight Data Science team to provide unique educational experiences and help PhDs and postdocs make transitions to careers in data science.

As part of the Insight team, we attend career panels at universities across the country to speak with scientists who are considering career transitions.

One of the most common questions we get asked is: “What skills and tools should I be learning?” Since 2012 Insight has helped 400+ of the brightest PhDs and postdocs transition into top industry positions and we’ve learned a lot about how to make the transition efficient.

Python is a general purpose programming language that has a growing number of modules for data analysis, including SciPy, Numpy, Pandas, StatsModels, and Scikit-learn, as well as many visualization tools like seaborn, matplotlib, and ggplot.

Data scientists often work closely with engineering teams and being able to understand your colleagues and teammates is crucial for doing great work.

But your goal as a data scientist should be to get better at finding the right tool, the right model, and perhaps most importantly, asking the right questions of your data.

In the tech industry, one often has to continually reevaluate priorities and work in an agile manner rather than letting projects go on for long timescales with less flexibility.

One final piece of advice we suggest is immersing yourself in the data science world by keeping abreast of the latest news in the field.

Machine Learning

Supervised learning algorithms are trained using labeled examples, such as an input where the desired output is known.

The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors.

Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data.

Popular techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition.

How companies are using big data and analytics

We spoke with six senior leaders from major organizations and asked them about the challenges and opportunities involved in adopting advanced analytics: Murli Buluswar, chief science officer at AIG;

Murli Buluswar, chief science officer, AIG: The biggest challenge of making the evolution from a knowing culture to a learning culture—from a culture that largely depends on heuristics in decision making to a culture that is much more objective and data driven and embraces the power of data and technology—is really not the cost.

What I have learned in my last few years is that the power of fear is quite tremendous in evolving oneself to think and act differently today, and to ask questions today that we weren’t asking about our roles before.

And it’s that mind-set change—from an expert-based mind-set to one that is much more dynamic and much more learning oriented, as opposed to a fixed mind-set—that I think is fundamental to the sustainable health of any company, large, small, or medium.

Ruben Sigala, chief analytics officer, Caesars Entertainment: What we found challenging, and what I find in my discussions with a lot of my counterparts that is still a challenge, is finding the set of tools that enable organizations to efficiently generate value through the process.

I hear about individual wins in certain applications, but having a more sort of cohesive ecosystem in which this is fully integrated is something that I think we are all struggling with, in part because it’s still very early days.

That helps best inform the appropriate structure, the forums, and then ultimately it sets the more granular levels of operation such as training, recruitment, and so forth.

Vince Campisi, chief information officer, GE Software: One of the things we’ve learned is when we start and focus on an outcome, it’s a great way to deliver value quickly and get people excited about the opportunity.

I think the other aspect is that we recognize as a team and as a company that we ourselves do not have sufficient skills, and we require collaboration across all sorts of entities outside of American Express.

We need to put a full package together for our business colleagues and partners so that it’s a convincing argument that we are developing things together, that we are colearning, and that we are building on top of each other.

We’ve been able to take over 60 different silos of information related to direct-material purchasing, leverage analytics to look at new relationships, and use machine learning to identify tremendous amounts of efficiency in how we procure direct materials that go into our product.

So you can help a power-generating provider who uses the same wind that’s come through and, by having the turbines pitch themselves properly and understand how they can optimize that level of wind, we’ve demonstrated the ability to produce up to 10 percent more production of energy off the same amount of wind.

What we’ve focused on mostly is developing a platform that speaks to what we think is a value proposition that is important to the individuals who are looking to begin a career or to sustain a career within this field.

When we talk about the value proposition, we use terms like having an opportunity to truly affect the outcomes of the business, to have a wide range of analytical exercises that you’ll be challenged with on a regular basis.

Murli Buluswar: I have found that focusing on the fundamentals of why science was created, what our aspirations are, and how being part of this team will shape the professional evolution of the team members has been pretty profound in attracting the caliber of talent that we care about.

Otherwise, the pure data scientist will not be able to talk to the database administrator, who will not be able to talk to the market-research person, who which will not be able to talk to the email-channel owner, for example.

Data Science: Take a Data-Driven Approach to Business

Data science is required to extract value from massive volumes of data and can lead to game-changing insights.

Data scientists, who possess a blend of statistical, analytical and math skills, are a rare breed and are in high demand.

Make sure you have a plan for putting predictive models and other data science output to work solving real business challenges.

Enterprises embarking on data science must also develop ground rules for how data is used based on both ethical (non-binding) and legal (binding) standards.

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