We have re-designed our online, accelerated data science apprenticeship: it is now available to anyone, at no cost, with no restrictions, and does not require any application nor deadlines. Data sets, a cheat sheet to get you started, real-life projects to work on, sample code, and tons of resources, are provided on DSC, and are regularly updated, including very recently. The presentation style is compact.
This is an ideal program for professionals with a quantitative background and some industry experience - in a nutshell, for anyone who understands our cheat sheet and can get started using it. This program is for self-learners. Completed projects, submitted to and reviewed by Dr. Vincent Granville, will be featured, published, and promoted on our network, reaching out to the the largest audience of data science decision makers, peers and hiring managers. Examples of such projects can be found here and here.
Ideal for people who want to change career paths, consultants, people managing data scientists, or students starting an analytic degree. In short, the easy way to become a data scientist for educated self-learners.
Free from unnecessary advanced matrix algebra or obscure, confusing, misused stats developed before the era of computers (p-value, GLM, model-based predictive analytics). Instead focusing on building automated, black-box, simple, scalable, efficient, robust, high ROI solutions for many modern applications (API's, machine-to-machine communications and real time), leveraging distributed architectures as needed, and using a unified cross-disciplinary approach.
Applications: IoT, digital (mobile) and web data, marketing, risk management, fraud detection (including spam, fake reviews, plagiarism detection), growth hacking, operations research, finance, bioinformatics, healthcare, environmental data science, engineering, business analytics, model-free predictive analytics, business optimization, big data and more.
You may learn new robust algorithms and concepts such as
as well as obtaining advice on data creation or identification, data gathering, and blending from various external / internal sources (structured / unstructured; fluid / static), defining metrics before a project starts, automated exploratory analysis with data dictionaries, and delivering insights via visuals or API's, using the DAD approach. Mostly in NoQSL environments.
Here you can connect with more than 1,500 professionals interested in our initiative, share ideas, ask questions, and get support.