Agenda

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  • Day 1

    March 05, 2019

  • Day 2

    March 06, 2019

  • Attend this session to learn about how Big Data Analytics is informing decisions on the development activities of Smallholder poultry in Ethiopia, Nigeria, and Tanzania. Farmers are constantly exposed to various climatic and environmental factors that limit the productivity of their farms, threaten household livelihoods and food security. Improving the performance and productivity of smallholdings through climate-smart practices require prediction, made possible by harnessing the huge data resources from mobile phones, satellites, and terrestrial statistics. The process of capturing huge amounts of data, their cleaning and analysis, and integration into appropriate predictive models must be dynamic, real-time, and specific to the prevailing agro-ecological and climatic conditions. The establishment of an innovation platform and adoption of a farmer-first approach can mitigate potential challenges, and allay fears associated with Big Data. Development partners and non-governmental organisations should deploy Big Data towards the design and implementation of their intervention programmes. These ideas are illustrated by data collected in the African Chicken Genetic Gains Project through a baseline survey of 1,200 households and on-farm testing of six tropically adapted and more productive chicken breeds in 2100 households in Nigeria.
    Agriculture

  • With the buzz of big data and data analytics, a lot of companies are using advanced analytics to make informed strategic business decisions. Some companies have successfully launched data analytics in their organizations while some have failed to launch same at their organizations. Those that have been successful are gaining a competitive advantage over those have not. Some organizations have not started at all while some are just afraid to start. Join this panel discussion led by data analytics thought leader, Yemi Keri to gain some critical and powerful lessons learned on how to launch data analytics in your organization.
    Panel Discussions
    Strategy

  • Workshops - Day 1

    March 05, 2019

  • To successfully model good machine learning problems, you need to first gather the data whether structured or unstructured. Then you need to clean the data and do efficient feature engineering to get the data ready for modeling. In this workshop, we will take you on a journey from data generating insights using datasets from the education industry. Some of the topics covered include the following: Introduction to data analytics using Python Importing data sets Tidying data Data Manipulation Data analysis Exploratory data analysis Data visualisation Drawing conclusions from data analytics
    Workshop

  • To successfully model good machine learning problems, you need to first gather the data whether structured or unstructured. Then you need to clean the data and do efficient feature engineering to get the data ready for modeling. In this workshop, we will take you on a journey from data generating insights using datasets from the healthcdare industry. Some of the topics covered include the following: Introduction to data analytics using R Importing data sets Tidying data Data Manipulation Data analysis Exploratory data analysis Data visualisation Drawing conclusions from data analytics
    Workshop

  • Workshops - Day 2

    March 06, 2019

  • Join Emeka Okoye as he walks you though novel ways in connecting small data to big data. Knowledge Graphs are graph structures that capture knowledge in the form of entities and the relationships between them in a domain. In this presentation, I will cover the following topics: – What is a graph – The big data problem – Why Enterprise Knowledge Graph in an Organization? – Relationship matters: connecting entities – Database Hugging Disorder (DBHD) – Deriving Knowledge from Insight – Connected Graph: Building Our Map – Semantic Tech Effects on Businesses and Industries – And more
    Workshop

  • To successfully model good machine learning problems, you need to first gather the data whether structured or unstructured. Then you need to clean the data and do efficient feature engineering to get the data ready for modeling. In this workshop, we will take you on a journey from data generating insights using datasets from the population space. Some of the topics covered include the following: Introduction to data analytics using Python Importing data sets Tidying data Data Manipulation Data analysis Exploratory data analysis Data visualisation Drawing conclusions from data analytics
    Workshop