Businesses today are remarkably data-rich as compared to their predecessors. Having massive amounts of data alone does not ensure successful data-driven decision-making. How does a business ensure that it is able to utilise its data to its full potential? One of the key challenges for decision-makers and managers is to understand what makes for good data science, and how the evidence from this field should be used in evaluation and decision-making.
Data science is an interdisciplinary field that deals with processes and systems, that are used to extract knowledge or insights from large amounts of data. It combines statistical analysis, data management, computation, and substantive expertise, with the goal of improving decision-making and performance in business, government, administration, law, and just about every other field.
The training will cover every step of strategic decision making, to make optimal use of big-data investments and being able to measure and manage all legal risks that may arise from the use of big-data.
The training will cover every step of strategic decision making, to make optimal use of big-data investments and be able to measure and manage all legal risks that may arise from the use of big-data.
In the course of four, four-day modules, data experts will equip participants with a comprehensive theoretical and practical understanding of data science.
Each module provides them with theoretical inputs as well as a case-study project to practice various scenarios arising in a big-data context to create an environment that results in more accurate, timely and trustworthy decisions.
- Provide an in-depth understanding of data science and how it can be applied in business profitably and competitively
- To understand the magnitude of the Internet of Things (IoT) generated data and how big data analytics can assist in analyzing such data for better decision making
- To understand the full spectrum of the legal, regulatory and ethical issues that arise with the development and use of big data solutions
- To understand better approaches of treating disruption as a catalyst to pursue new business opportunities, and how to make optimal investments in big data
- Analysts or data scientists that are required to drive data science awareness in their collaborations with partner teams across the business.
- Senior managers from varying business functions in need of technical acumen to be more effective in improving the data science capabilities within the business.
Some of the target functions include operations, technology, quality control, finance, marketing, procurement and human resources.
Data Science Project-Based Learning
Special emphasis will be put on ensuring that participants acquire the comprehensive know-how in a highly practical way: working on projects involving case studies from their businesses will enable the future data scientists to transfer their training to the workplace while still attending the programme.
Teaching will be delivered using a learner-centred approach and complemented by case studies and guest lectures from practitioners in the industry.
Assessments will be designed using the problem-solving method to develop skills in theory application to real-world cases, improving problem-solving skills in this context, and developing a deeper sense of originality and resourcefulness.
Each module is designed to take four days, including time for group discussions and consultations.
What is Data Science?
- Overview of data science and the role it plays in various business functions including: marketing, supply chain management, production management, process management, and finance.
- Overview of the key concepts and tools used by data scientists. Includes Big Data 5Vs (Volume, Veracity, Velocity, Variety, Value) and Hadoop components.
- Case study discussions on big data processing. How to handle multiple user requests to a growing data repository (velocity) resulting from massive streams (volume) of varying data (variety), while at the same time generating accurate and trustworthy (veracity) insights in real-time (value). Includes examples from public (real-time?) datasets and social media.
- Overview of big data analytics including how it complements the work of data scientists, predictive modelers, and business intelligence experts. Discuss the required expertise/roles in solving big data problems.
- Presentation of selected data science projects and scenarios in depth.
- PROJECT ASPECT: Introduction to the team project and “meet your project coach”.
The Internet of Things (IoT)
- Overview of the relationship between IoT and Big Data. Why we should care about the growing number of devices (25 billion by 2020) and their interconnectedness.
- Case study description of how data is being acquired in sensors (embedded in multiple devices), and how data from events/transactions, facial recognition devices or sentiment analysis tools is analyzed for insights.
- Understand the relationship between data science and natural language and audio-visual content processing.
- Case studies on the various techniques being applied to solve problems in web-scale image search engines, face recognition, copy detection, mobile product search, and security surveillance.
- Presentation of selected data science projects and scenarios in depth.
- PROJECT ASPECT: Data science project kick-off and development of a project proposal based on participants’ own enterprises; preparation of a “data science problem pitch”.
Big Data, Privacy, and the Law
- Discussion on why big data equals big privacy concerns.
- Current regulations in Kenya affecting Big Data collection, release (open data) and use. Identify privacy statutes and agency regulations, possible discriminatory use of big data, potential legal issues, and limitations of existing laws and regulations.
- International legal issues in Big Data. Using a case study approach, discuss cases where international companies have been impacted by changes in privacy laws in relation to big data, and how this affects data collection, storage, consent from users/customers (transparency privacy notices), and the data such organizations can no longer collect (POPI Act). Known legal and financial consequences for privacy violations.
- Future issues in Big Data and the Law. What can we learn from current changes in law within nations and regional trade blocks such as the European Union’s General Data Protection Regulation (GDPR)?
- Discuss samples of corporate data protection policies.
- PROJECT ASPECT: Development on a project proposal on the measures a well-known publicly listed local company needs to take to be compliant, and draft a Corporate Data Protection Policy.
Planning for Big-Data Driven Innovation: Disruption vs Optimization
- Overview on how businesses are leveraging on big data for digital innovation.
- Identify the emerging big-data driven business models, services, and products in various sectors including Agriculture, Health, and Banking.
- Discuss business models that are being rendered irrelevant (i.e. postal services by email) and how this could have been mitigated.
- Discuss emerging business models that have successfully disrupted the market using big-data.
- Discuss factors to consider when conducting Cost Benefit Analysis (CBA) of a Big Data investment. This also includes key performance indicators to recognize the drivers of value from data science impacts, and suggestions on how to retain a competitive data science team from a CBA perspective..
- PROJECT ASPECT: Data science project presentations.
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