Data analytics for managers

Description:

Course Outline

Managers like to think they know their team’s business. But data science projects confront managers with new problems and unfamiliar terms, making it harder to understand what their teams do and how they can help them. Data Analytics for Managers introduces what data science teams do; explores what questions should be asked about the data, analysis, results and actions; examines how to interpret charts and data visualisation techniques and provides practical steps on how to build trust within both the analysis team and within the results.

Course Content

Data: We all use analytic techniques to help understand and make better decisions, but what impact does this have on data and the results? How can we best respond to that challenge? This interactive section of the course is designed to help you develop an understanding of what can be expected when analysing data. It will provide an overview of the key concepts behind analytics, how these are relevant to managers and how they can be applied in practice. Practical and example based

Analysis: The choice of analytic technique is crucial. It impacts the focus of the analysis, and defines what can and cannot be done with the data. Some techniques are particularly powerful when you have a lot of data and/or time series data, others are better suited to single events or count data.  We will cover the main approaches of data analysis, why this is so important for business decision making, as well as exploring relevant metrics and reporting. The session will encourage you to engage with other participants as we explore and discuss what is meant by phrases such as big data, machine learning and when more traditional approaches are valid.  Practical and example based

Results:  Graphical representations of data can be beautiful and fascinating. They can also be a pain to understand. Typically, the most interesting thing is not the actual data point itself, but rather how it changes over time, or how it compares to other data points or trends.  This session will explore how to interpret results, what questions you should always ask and gives advice on presenting findings. It will not only help you to understand the basics of visualisations (including charts and infographics), but also provide practical tips to improve your ability to help your team create effective presentations which are easy to understand.  Practical and example based

Trust: Trust is a two-way street between analysts and managers, both must be respectful towards each other. What are some practical steps you can take to ensure you have built sufficient trust with your analysts to understand limitations and context of their analysis?  This session helps you create your own checklist of practical questions that can be asked of analysts, and that they should ask of you to ensure the work meets the needs of the business. Practical and example based

Actions:  How can you translate the analysis insights into actionable recommendations that can be used to support decisions at every level of the business?  What language can be used to explain the results, actions and recommendations?  This session will help you understand the importance of the language used within the report, when to question this and how you can help your team create unambiguous reports.
 

Learning Outcomes

Attendees will develop and extend their knowledge and skills on how to review, interpret and support the work of data science teams.

Topics Covered

• Data: what do you need to know about the data used and the impact this has on the analysis and any results?

• Analysis: what techniques could be used, and crucially which one shouldn’t be used on the available data?

• Results: how to interpret results and what questions you should always ask about charts and infographics.

• Trust: before signing off or accepting analysis, what practical steps can you take to ensure you understand the limitations and context of the analysis. 

• Actions: based on the results and recommendations, how can you use the information to support decisions?

Target Audience

This course is especially designed for professionals at managerial level in businesses to develop and extend their knowledge and skills in working with data science teams. Also for those who work for a data science/technical company but in a non-technical role e.g. marketing who may have to use the outputs of data science teams within their own work.

Cost:

£427.20 to £592.80 (inc. VAT)

Website and registration:

Region:

Greater London

Keywords:

Quantitative Data Handling and Data Analysis

Related publications and presentations from our eprints archive:

Quantitative Data Handling and Data Analysis

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