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Using Declarative Programming in an Introductory Computer Science Course for High School Students

AAAI Conferences

This paper discusses the design of an introductory computer science course for high school students using declarative programming. Though not often taught at the K-12 level, declarative programming is a viable paradigm for teaching computer science due to its importance in artificial intelligence and in helping student explore and understand problem spaces. This paper describes the authors' implementation of a declarative programming course for high school students during a 4-week summer session.

How to Become a (Good) Data Scientist โ€“ Beginner Guide - KDnuggets


Probability and statistics are the basis of Data Science. Statistics is, in simple terms, the use of mathematics to perform technical analysis of data. With the help of statistical methods, we make estimates for further analysis. Statistical methods themselves are dependent on the theory of probability, which allows us to make predictions. Both statistics and probability are separate and complicated fields of mathematics.

Generate and visualize data in Python and MATLAB


Data science is quickly becoming one of the most important skills in industry, academia, marketing, and science. Most data-science courses teach analysis methods, but there are many methods; which method do you use for which data? The answer to that question comes from understanding data. That is the focus of this course. You will learn how to generate data from the most commonly used data categories for statistics, machine learning, classification, and clustering, using models, equations, and parameters.

On Education Complete Python for data science and cloud computing - CouponED


Become a true data scientist & machine learning expert with full industry knowledge Apply different predictive models and machine learning algorithms into use cases in different business areas Present analytical results to various users Master Text Mining & Natural Language Processing (NLP) using Python & Spark for sentimental analysis Work on Python with SQL on SQLite, Redshift, SAS, MongoDB, Spark and other data sources Become industry expert in banking, marketing, credit risk and product-user recommender system Collect and analyze Big Data in different systems Use AWS and Azure for Cloud Computing Master fundamental Python programming Apply generic Object Oriented Programming (OOP) Conduct real world capstone projects to build up career path Master useful data engineering knowledge and skills Convert homework and practices into your own knowledge and skills Use all famous graphics tools such as matplotlib, plotly, seaborn and ggplot into data visualization Any one should be able to use computer including being able to install software Desire to learn Python, Data Science and Cloud Computing Prior exposure to programming languages will be helpful Basic knowledge and skills of math In this nearly 50 hours course, we will walk through the complete Python for starting the career in data science and cloud computing! This is so far the most comprehensive guide to mastering data science, business analytics, statistical tests & modelling, data visualization, machine learning, cloud computing, Big data analysis and real world use cases with Python. Data science career is not just a traditional IT or pure technical game โ€“ this is a comprehensive area, and above all, you must know why you conduct data analysis and how to deploy your results to generate values for the company you are working for or your own business. Therefore, this course not only covers all aspects of practical data science, but also the necessary data engineering skills and business model & knowledge you need in different industries. Whether you are working in financing, marketing, health companies, or you are running start-up, knowing the complete application of Python for data science and cloud computing is the must to achieving various business objective and looking insights into data.

Top 20 Data Science MOOCs


Introduce yourself to the basics of data science and leave armed with practical experience extracting value from big data. This course teaches the basic techniques of data science, including both SQL and NoSQL solutions for massive data management (e.g., MapReduce and contemporaries), algorithms for data mining (e.g., clustering and association rule mining), and basic statistical modelling (e.g., linear and non-linear regression).