Data Insights, Trends, and Best Practices to Achieve Organizational Excellence
Is your organization looking to become a leader in digital trends and innovation? Then you’ll need a plan to implement a successful data strategy. We’ve recently published some great content on data strategy, trends, organizations, and more. Explore these resources below.
Scaling Data Science at Your Organization
In this webinar series, DataCamp’s Vice President of Product Research Ramnath Vaidyanathan will go over our IPTOP framework (Infrastructure, People, Tools, Organization and Processes) for building data strategies and systematically scaling data science within an organization.
- Part 1 provides an overview of the IPTOP framework, and how each element in the framework fits together to enable scalable data strategies.
- Part 2 focuses on the tools and infrastructure required to scale and democratize data science, along with best practices in implementation.
- Part 3 provides a deeper look at the tradeoffs with adopting different organizational structures, key data roles for the 21st century, and more.
Data science and machine learning have fundamentally changed how we make decisions, build products, and automate workflows and processes. In this fireside chat, Zach Deane-Mayer, Vice President of Data Science at DataRobot, will walk us through the use-cases where data science and machine learning have driven the most value for organizations. He will also share his insight on new developments in machine learning, like Open-AI's GPT-3 model and the use of deep-learning for tabular data, and comment on the rise of automated machine learning with his work on DataRobot.
The data science revolution makes the possible widespread, enabling data fluent organizations and societies, where everyone is equipped with the necessary skills they need to be informed, citizens, and employees. In this webinar, DataCamp’s Vice President of Product Research Ramnath Vaidyanathan, Curriculum Architect Richie Cotton, and Data Science Evangelist Adel Nehme will go over eight major trends in data infrastructure, skills, and tooling for the next year and beyond.
The automotive industry is one of the oldest industries of the modern world, but it’s much more than just making cars. Every single business function from manufacturing to mobile application development exists within the automotive industry. In this webinar, Lead Data Scientist at Ford Michael Crabtree will review a wide variety of data science use cases that will inspire you to challenge ideas in an old industry with new inventive ways to shape the future of manufacturing and mobility.
Large organizations are inherently different from young startups, or even medium-sized companies. They have usually been around longer, have stricter compliance requirements and company cultures that may go back generations. In this webinar, Maksim Pecherskiy, former CDO of the City of San Diego and lead data engineer for the Development Data Partnership at the World Bank will demystify the unique challenges to operationalizing data science within large organizations, how large organizations can get their data science practice off the ground as quickly as possible, and what are the best practices that facilitate a data-driven culture within large organizations.
“Data democratization” is one of the most popular terms surrounding the development of organizational data maturity. Join Alex Scriven, a Senior Data Scientist from the New South Wales Government in Sydney, Australia as he breaks down what data democratization means in a government environment. He will also provide specific, actionable best practices for growing data democratization in government and outline some interesting case studies for what can happen when democratization in government begins to take hold.
Data science and machine learning have fundamentally changed how we make decisions, build products, and automate workflows and processes. In this fireside chat, Quinn Lathrop, Head of Data Science and Psychometrics at DataCamp, walks us through the use cases of data science in education and how data science is powering education technology, best practices when balancing product goals with research goals, the data team’s role in creating a data culture, and an overview of the data science powering DataCamp.
As data science teams look to scale their impact across the organization, data scientists often spend a long time optimizing and refining models. All too often, they end up neglecting what may be the most important selection criterion for scaling data science: acceptance by the end-user. In this webinar, we outline how storytelling enables data teams to bring the same facts to the table, but provide a clear framework that helps formulate the most impactful aspects of their work. We cover some tips and tricks using real case examples of how to convince the most skeptical end-users.
In this webinar, DataCamp’s VP of Product Research, Ramnath Vaidyanathan, breaks down the four stages of data maturity organizations will go through, from data reactive to data scaling, data progressive, and data fluent. He also demystifies the defining characteristics of each stage of data maturity in terms of Infrastructure, People, Tools, Organization, and Processes. Finally, he uncovers the steps organizations need to take to transition from one maturity stage to another.
While there could be many culprits for the failure of digital transformation programs, a crucial reason is not recognizing that having sustainable organization-wide data transformation is a prerequisite for successful digital transformation. It's relatively straightforward to invest in tools and infrastructure that enable organization-wide data transformation, but the most challenging pillar to address is the growing skills gaps in data analytics and data fluency. In this webinar, we'll address all the key data fluency competency areas within organizations. We will also break down crucial data personas found in every organization, allowing learning and development teams to scale personalized learning paths for their people systematically.
The future of work is data. As the amount of data in the world grows at an exponential pace, learning data science skills is increasingly important for professionals in all industries to make data-driven business decisions. To meet this need, learning professionals need to rapidly develop scalable, high-quality data science training programs and use data to measure the effectiveness of their training programs. Find out how Bloomberg partnered with DataCamp to deliver a blended learning program focused on teaching data technologies to hundreds of employees in Bloomberg’s Global Data organization around the world.
This year, we stand on the precipice of a great acceleration. Organizations worldwide are looking to increase their digital resilience and become more data-driven in the process. The data science revolution has always made the impossible possible. However, the real data science revolution makes the possible widespread, enabling data fluent organizations and societies, where everyone is equipped with the necessary skills they need to be informed, citizens, and employees.
Over the past few decades, digital technologies have completely transformed our way of life. From how we communicate to the way we conduct business, software has disrupted how value is generated today. One of the most significant catalysts to the adoption and development of digital technologies is open-source software, which will empower organizations to make the most of their data and create transformative solutions, processes, and products with machine learning and data science. Find out all you need to know about open-source in data science in our exclusive new guide.
The key differentiators between the disruptors and the incumbents is not technology-based but in their data-driven culture, the insights they draw from data while examining and iterating upon their services, and the data fluency skills they foster. To become data fluent, organizations are engaging in upskilling for data fluency and L&D leaders are becoming integral to many organizations’ long-term strategy. In this white paper, we demystify data tools used by data professionals across any data-driven organization, and propose a persona-driven learning and development strategy with eight key personas to consider.
To become data fluent, organizations are engaging in upskilling for data fluency and data leaders are integral to organizations’ long-term strategy. In this white paper, we demystify data tools used by data professionals across any data-driven organization, and propose a persona-driven learning and development strategy with eight key personas to consider for upskilling in data roles.
While compliance, data quality, and infrastructure are key challenges standing in the way of operationalizing data science at scale, no challenge is more pressing than the data fluency skills gap. In this white paper, we outline the unique challenges when upskilling for data fluency in finance, the highest impact data science use cases financial services organizations can start implementing today, and the skills needed to get them off the ground.