
Introduce data analytics, what data is, and how insights guide decisions. Cover data types, data monetization, data engineering, and AI ethics shaping practical analytics.
Explore how data analytics drives revenue and reduces costs by elevating data strategy at the executive level, and trace the data science maturity curve from descriptive to prescriptive analytics.
Explore the roles in a data science team, from data scientists building models to data engineers designing pipelines, and learn how analytics translators bridge business and analytics.
Master data visualization fundamentals, from Excel charts to D3 and Python libraries such as seaborn, while embracing ethical, accessible storytelling that clearly communicates insights to diverse audiences.
Explore the economics of data monetization and how organizations capture value through internal cost savings, better decisions, and external methods like selling data, APIs, and market reports.
Explore high frequency data and real-time insights for now costing and forecasting. See dashboards that combine spend data with employment and vaccinations trends to reveal grocery and entertainment trends.
Explore how alternative data broadens decision making beyond finance, using geolocation, satellite imagery, traffic, and credit card insights. Assess its timeliness and edge against costs, data quality, and vendor reliance.
It’s one thing having data governance, but we need governance across the whole data value chain – from data capturing to data use and presentation. I won’t delve too much into the ethics of data presentation and visualisation. I will instead point you in the direction of some fantastic books on the topic, such as How Charts Lie: Getting Smarter about Visual Information by Alberto Cairo. It was one of my top Data Analytics Book Recommendations.
If there is one aspect of data analytics that I’ve realised in exceptionally important, it is the ability to tell a story with data.
These books are my go-to guides for beginner to advanced data heads (to steal a phrase from Jordan Goldmeier).
Podcast recommendations to learn more while listening.
A Data Science Training course for Executives is a course designed to provide business leaders with an understanding of the fundamental concepts and applications of data science. The course covers key topics such as data analysis, data visualization, machine learning, and big data technologies. The goal of the course is to equip executives with the skills and knowledge they need to make informed decisions about data-driven initiatives within their organizations.
Data Analytics is one of the most sought-after skills globally. Business Insider has even rated both Chief Digital Officers and Data Science Executives as two of the top six most in-demand executive jobs.
However, analytics knowledge isn’t just crucial for data scientists and analysts, but for anyone looking to navigate today’s world, make data-informed decisions and be able to predict and better react to market trends.
This Data Science for Executives course explains:
1. What Data Analytics is and why it is one of the most in-demand skills today.
2. The history of Data Analytics in helping companies become market leaders and how companies came to recognise data as an asset rather than a by-product of operations.
3. Why ethics is key for trusted analytics both for the organisation and for clients.
4. How to scale analytics across the organisation using data scientists and data engineers.
5. The core principles of data analytics, from exploratory data analysis and data visualisation to high-frequency analytics and alternative data.
This course covers analytics at a high level without any coding examples, by focusing on the various aspects of analytics and how they can be applied in practice. This course will enable executives and beginners alike to understand the power of analytics, what skills data science teams need, and why analytics needs to be broadly adopted to enable a data-driven organisation.
This course was developed by DMSA and is presented by Data Science leader Matthew Bernath.