Data Science Methodology in Action using Dataiku

 Buy Now
  

Price: 99.99$

With the explosive growth of unstructured data and data science techniques, we now have abundant opportunities to design, develop, and deploy AI models. While there are numerous courses that teach Data Science, what you truly need is a step-by-step guide on how to select a problem, explore data, develop and deploy models, and continuously improve them using user feedback and learning. This course follows a modified CRISP-DM methodology and provides clear instructions for your data science project using Dataiku. We categorize data scientists into two groups: clickers and coders. Clickers refer to data scientists who utilize data science tools with user interfaces to provide high-level specifications. Examples of such tools include SPSS Modeler, Excel, Alteryx, and Dataiku. In these cases, you can incorporate formulas using pre-packaged libraries without writing code. On the other hand, the second group consists of data scientists who use procedural languages with libraries to write code for their data science tasks. The primary objective of this course is to give you an introductory clicker experience in data science using Dataiku as the tool. Learning is significantly enhanced when you apply what you are learning to a project. In this course, we use a capstone project to provide hands-on experience in designing and prototyping a Data science engagement. The skills you gain from this project can be directly applied in your day-to-day life. Related courses:· If you are interested in a coding course, we offer a course using Python.· Our data science methodology course is also designed for Business Analysts and Project Managers with limited development background.· If you are interested in advanced machine learning, we have made recommendation for courses.· If you are interested in model deployment, monitoring and control, we offer a course on AI governance and control. Course starts with two critical activities· Set up Environment – step by step instructions in preparing sandbox environment for your Dataiku exercises· Data Science Methodology – to review key steps, tasks and activities associated with our data science methodology After above section, this course introduces our 7-step data science methodology and use Python to explain each step using our real-life use case example. These 7 steps include· Step 1: Describe Use Case to explain selected use case for data science work· Step 2: Describe Data to describe Data Sources and explain data sets using Dataiku· Step 3: Prepare Datasets to Prepare Data Sets using Dataiku· Step 4: Develop Model will provide hands-on exercises in applying many AI modeling techniques on data sets such as clustering, and regression using Dataiku.· Step 5: Evaluate Model will provide measurements to Evaluate your AI Model Results· Step 6: Deploy Model will provide process for deploying your AI models.· Step 7: Monitor model will provide process for continuous monitoring and evaluating your models in production In this course, we will give you an opportunity to design a use case and then work on its implementation using Dataiku. You should download all data sets and sample code. Complete all assignment in each section of the course and submit your final notebook using instructions provided.