CIS serves a collaborative platform for students and cloud technology experts to collaborate, exchange insights, and offer mutual support. Online cloud resources are sought for students to equip with the latest cloud technologies and enable them to launch their innovative ideas.
AWS DeepRacer
AWS DeepRacer Student helps high school and college-enrolled students around the globe develop their ML skills in a fun, hands-on autonomous racing league. Any student age 16 or older, can leverage 20 hours of ML educational material and 10 hours of monthly model training compute resources for free. Put your new found skills to the test for your chance to win prizes by becoming one of the top racers in the global student league.
| Course Name | Category | Level | Duration | Platform | What you will learn | Link |
|---|---|---|---|---|---|---|
| Introduction to Artificial Intelligence (IBM) | AI | Beginner | 8 hours | Coursera | In this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI. You will also demonstrate AI in action with a mini project. This course does not require any programming or computer science expertise and is designed to introduce the basics of AI to anyone whether you have a technical background or not. | here |
| Conversational AI on Vertex AI and Dialogflow CX | Conversational AI | Intermediate | 5 hours | Cloudskillsboost.google | In this course you will learn how to use the new generative AI features in Dialogflow CX to create virtual agents that can have more natural and engaging conversations with customers. Discover how to deploy generative fallback responses to gracefully handle errors and omissions in customer conversations, deploy generators to increase intent coverage, and structure, ingest, and manage data in a data store. And explore how to deploy and maintain generative AI agents using your data, and deploy and maintain hybrid agents in combination with existing intent-based design paradigms. | here |
| Applied Data Science Capstone | Data & Data Science | Intermediate | 13 hours | Coursera | Demonstrate proficiency in data science and machine learning techniques using a real-world data set and prepare a report for stakeholders Apply your skills to perform data collection, data wrangling, exploratory data analysis, data visualization model development, and model evaluation Write Python code to create machine learning models including support vector machines, decision tree classifiers, and k-nearest neighbors Evaluate the results of machine learning models for predictive analysis, compare their strengths and weaknesses and identify the optimal model | here |
| Data Analysis with Python | Data & Data Science | Beginner | 15 hours | Coursera | Develop Python code for cleaning and preparing data for analysis - including handling missing values, formatting, normalizing, and binning data Perform exploratory data analysis and apply analytical techniques to real-word datasets using libraries such as Pandas, Numpy and Scipy Manipulate data using dataframes, summarize data, understand data distribution, perform correlation and create data pipelines Build and evaluate regression models using machine learning scikit-learn library and use them for prediction and decision making | here |
| Data Analysis with R Programming | Data & Data Science | Beginner | 36 hours | Coursera | Describe the R programming language and its programming environment. Explain the fundamental concepts associated with programming in R including functions, variables, data types, pipes, and vectors. Describe the options for generating visualizations in R. Demonstrate an understanding of the basic formatting in R Markdown to create structure and emphasize content. | here |
| Data Science Methodology | Data & Data Science | Beginner | 6 hours | Coursera | 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. | here |
| Data Visualization with Python | Data & Data Science | Intermediate | 19 hours | Coursera | Implement data visualization techniques and plots using Python libraries, such as Matplotlib, Seaborn, and Folium to tell a stimulating story Create different types of charts and plots such as line, area, histograms, bar, pie, box, scatter, and bubble Create advanced visualizations such as waffle charts, word clouds, regression plots, maps with markers, & choropleth maps Generate interactive dashboards containing scatter, line, bar, bubble, pie, and sunburst charts using the Dash framework and Plotly library | here |
| Databases and SQL for Data Science with Python | Data & Data Science | Beginner | 20 hours | Coursera | 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. | here |
| Excel Basics for Data Analysis | Data & Data Science | Beginner | 11 hours | Coursera | Display working knowledge of Excel for Data Analysis. Perform basic spreadsheet tasks including navigation, data entry, and using formulas. Employ data quality techniques to import and clean data in Excel. Analyze data in spreadsheets by using filter, sort, look-up functions, as well as pivot tables. | here |
| Fundamentals of Scalable Data Science | Data & Data Science | Beginner | 27 hours | Coursera | Apache Spark is the de-facto standard for large scale data processing. This is the first course of a series of courses towards the IBM Advanced Data Science Specialization. We strongly believe that is is crucial for success to start learning a scalable data science platform since memory and CPU constraints are to most limiting factors when it comes to building advanced machine learning models. In this course we teach you the fundamentals of Apache Spark using python and pyspark. We'll introduce Apache Spark in the first two weeks and learn how to apply it to compute basic exploratory and data pre-processing tasks in the last two weeks. Through this exercise you'll also be introduced to the most fundamental statistical measures and data visualization technologies. | here |
| Google Data Analytics Professional Certificate | Data & Data Science | Beginner | 6 months (10 hours / week) | Coursera | Gain an immersive understanding of the practices and processes used by a junior or associate data analyst in their day-to-day job Learn key analytical skills (data cleaning, analysis, & visualization) and tools (spreadsheets, SQL, R programming, Tableau) Understand how to clean and organize data for analysis, and complete analysis and calculations using spreadsheets, SQL and R programming Learn how to visualize and present data findings in dashboards, presentations and commonly used visualization platforms | here |
| IBM Data Analyst Professional Certificate | Data & Data Science | Beginner | 4 months (10 hours / week) | Coursera | Master the most up-to-date practical skills and tools that data analysts use in their daily roles Learn how to visualize data and present findings using various charts in Excel spreadsheets and BI tools like IBM Cognos Analytics & Tableau Develop working knowledge of Python language for analyzing data using Python libraries like Pandas and Numpy, and invoke APIs and Web Services Gain technical experience through hands on labs and projects and build a portfolio to showcase your work | here |
| IBM Data Science Professional Certificate | Data & Data Science | Beginner | 5 months (10 hours / week) | Coursera | Master the most up-to-date practical skills and knowledge that data scientists use in their daily roles Learn the tools, languages, and libraries used by professional data scientists, including Python and SQL Import and clean data sets, analyze and visualize data, and build machine learning models and pipelines Apply your new skills to real-world projects and build a portfolio of data projects that showcase your proficiency to employers | here |
| Introduction to Data Analytics | Data & Data Science | Be |