We define Big Data In Marketing as a marketing process that is automated to such a degree that it can be steered by setting a business objective in a marketing software system. This implies that the marketing system should be intelligent and knowledge-able enough to understand a high-level objective, such as the acquisition of new customers or revenue maximization, to plan and execute a sequence of business actions, such as an adver-tisement campaign or price adjustment, with the aim of achiev-ing the objective, and to learn from the results to correct and optimize the actions if needed. In this course, we also use the term programmatic to refer to highly automated marketing software systems and services, and the terms algorithmic and programmatic are used interchangea-bly in most contexts.
This course aims to teach you importance of analytics in this new era of rapidly growing innovation and technologies. From identifying a business problem to building a predictive analytics model, you will taken through every possible stage of Data Science Life Cycle. While we teach you state-of-art machine learning algorithms, we take you through the tricks of fine tuning, avoiding bias and variance. Each domain has its own way of solving analytical problems, although the machine learning algorithms are the same, what differs is Feature Extraction.
We will teach you how to extract features for domains like Banking, Telecom and Retail. Curse of dimensionality is something that bothers data scientists a lot, we will teach you how to engineer the features and reduce the dimensions or pick the best. At the end of this course, you will acquire skills to even compete in Hackathons and Data Science platforms like Kaggle.
This course will take 8 weeks and you will master data science concepts from data science life cycle to neural networks.
This group coaching roundtable is for the HR practioneers, COOs and executives who wants to learn how to support their organizations to adopt Big Data. What you will learn
- The benefits of getting into big data as an organisation and developing a big data strategy
- What are the Skills Needed when hiring a Data Science Team and structure of organization to accommodate them.
- Where and how to source for Data Scientists.
- Upskilling, Retooling and Reskilling the workforce. How the workforce skills be improved to bridge the talent gap?
- Tools: Interview questions for Data Scientists and Organogram for Data Science teams.
New technologies are changing the pillars of the business world. What is the Future of Commerce and Innovation? Join us at our lab this Saturday as we host Edwin Kaduki who will share insights on this topic. How do we play our part in writing the story for the future of commerce in Kenya and beyond? Register from this link to reserve your seat.
As data scientists, we want to spend our time analyzing problems and rather than dealing with corporate politics. How can we do that when there is often a rift between executives and their data science teams. This can lead to a lack of trust, poor implementation and support of projects and thus, failed data science teams. Register from this link to reserve your seat
This coming Saturday we are honored to be hosting Fiona Makaka, a Data Protection Expert. She will share knowledge on The Implication of Data Protection Bill to Data Science Practice. Come and Learn more about the Data Protection Law and how it relates to you as a Data Scientist. Register from this link to reserve your seat