Data Science and Machine Learning Fundamentals

Data science is about using statistics to draw insights from data to drive action and improve business performance. This course will guide you through the world of data science and machine learning, using applied examples to demonstrate real-world applications. Whether you’re an aspiring data scientist or a c-level exec, this course will bring you up to speed on everything data science. You’ll be introduced to machine learning, classification, exploratory data analysis, feature selection, and feature engineering—what they mean and how they are relevant to your business.

 We’ll start by defining the skills, tools, and roles behind data science that work together to create insights. We’ll then walk through regression and classification—the most common predictive and statistical techniques. Finally, you will understand why having a basic understanding of data science outputs is essential to all business stakeholders and how we can use those outputs to make business decisions.

 

About This Course

Data science is about using statistics to draw insights from data to drive action and improve business performance. This course will guide you through the world of data science and machine learning, using applied examples to demonstrate real-world applications. Whether you’re an aspiring data scientist or a c-level exec, this course will bring you up to speed on everything data science. You’ll be introduced to machine learning, classification, exploratory data analysis, feature selection, and feature engineering—what they mean and how they are relevant to your business.  We’ll start by defining the skills, tools, and roles behind data science that work together to create insights. We’ll then walk through regression and classification—the most common predictive and statistical techniques. Finally, you will understand why having a basic understanding of data science outputs is essential to all business stakeholders and how we can use those outputs to make business decisions.

Outline

Upon completing this course, you will be able to:

• Distinguish data science from business intelligence
• Outline the data science and machine learning process
• Describe basic data science terms, roles, skills, and applications
• Distinguish popular models used for data science and machine learning
• Identify common data preparation steps to better understand your data, better structure your data, and remove errors
• Evaluate model results

Who Should Attend
Whether you are a business leader or an aspiring analyst exploring data science, this Data Science & Machine Learning Fundamentals course will serve as your comprehensive introduction to this fascinating subject. You’ll learn all the key terminology to allow you to talk data science with your teams, begin implementing analysis, and understand how data science can help your business.

About Our Expert
		Sebastian Taylor  Seb started his career in risk management, managing the implementation and operations of quantitative energy hedging strategies. Having since worked in several different industries, from finance to retail and service, Seb’s business intelligence skills were developed within the realities of real-world business requirements. Seb has worked in a range of companies with differing levels of data maturity, from start-ups with zero data infrastructure to large multinationals with centralized global data. The constant in all these has been Seb’s drive to improve analytical decision-making through better automation, more effective models, and clarity of communication.
		Lester Leong Lester Leong is passionate about the fields of finance, machine learning, and business. Over the course of the years, he has developed skills in analyzing data and producing actionable insights for businesses. Lester’s goal is to enable students to learn data science and apply it towards finance for improved profitability, efficiency, and model interpretability. He studied in multiple fields such as economics, finance, accounting, business, and data science. His work experience includes leading a fintech company in machine learning, implementing company wide data science initiatives as a VP in Wells Fargo, and other various data consulting roles. In his spare time, Lester is an editor for the publication Towards Data Science and gives walks to his Shar-pei mix.

Continued Risk Learning Credits:  4

PRMIA Continued Risk Learning (CRL) programs provide you with the opportunity to formally recognize your professional development, documenting your evolution as a risk professional. Employers can see that you are not static, making you a highly valued, dynamic, and desirable employee. The CRL program is open to all Contributing, Sustaining, and Risk Leader members, providing a convenient and easily accessible way to submit, manage, track and document your activities online through the PRMIA CRL Center. To request CRL credits, please email [email protected].

Registration
Membership Type	Price
Members	$ 80.00
Non Members	$ 88.00

Access

Immediate access to the course is granted for 90 days after your purchase. Please complete the course within that time period.

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Support

For technical issues regarding course access, contact [email protected]

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