Certifications

This certification provides training on the practical use of generative AI models (LLMs), particularly the OpenAI API. It enables you to learn how to: Create effective prompts (zero-shot, one-shot, few-shot) to generate various types of outputs. Program using the OpenAI API to develop text, image, and audio capabilities. Use Python and tools such as LangChain and LlamaIndex to build AI applications. Analyze data and automate tasks using LLMs. Develop prototypes and practical solutions for marketing, analytics, design, or business processes.

Holders of this certificate showed their ability to build cloud-native fullstack applications using JavaScript. They are prepared to take on the challenges of an entry-level full-stack developer role. Learners can build, deploy, test, run, and manage full-stack applications. They can utilize the tools, technologies, and methodologies used by full-stack developers including HTML, CSS, JavaScript, Git, GitHub, React, Node.js, Express, DevOps, Agile, CI/CD, NoSQL databases, Containers, Docker, Kubernetes, OpenShift, Microservices, and Serverless computing.

What holders of this certification can do: Analyze data within a database using SQL and Python. Create a relational database and work with multiple tables using DDL commands. Construct basic to intermediate level SQL queries using DML commands. Compose more powerful queries with advanced SQL techniques like views, transactions, stored procedures, and joins.

What holders of this certification can do: Develop a foundational understanding of Python programming by learning basic syntax, data types, expressions, variables, and string operations. Apply Python programming logic using data structures, conditions and branching, loops, functions, exception handling, objects, and classes. Demonstrate proficiency in using Python libraries such as Pandas and Numpy and developing code using Jupyter Notebooks. Access and extract web-based data by working with REST APIs using requests and performing web scraping with BeautifulSoup.

What holders of this certification can do: Describe what a data science methodology is and why data scientists need a methodology. Apply the six stages in the Cross-Industry Process for Data Mining (CRISP-DM) methodology to analyze a case study. Evaluate which analytic model is appropriate among predictive, descriptive, and classification models used to analyze a case study. Determine appropriate data sources for your data science analysis methodology.

What holders of this certification can do: Describe the Data Scientist’s tool kit which includes: Libraries & Packages, Data sets, Machine learning models, and Big Data tools Utilize languages commonly used by data scientists like Python, R, and SQL Demonstrate working knowledge of tools such as Jupyter notebooks and RStudio and utilize their various features Create and manage source code for data science using Git repositories and GitHub.