Science learning software

Their educational materials provide teachers with several potential entry points into the study of earthquakes. AlgebraLAB is designed to address the overlap between math and science by helping students apply their basic math skills to problems in science. This is done through lessons, practice problems and exposing students to real-world application of the skills. Highlights for Children Goes Beyond the Magazine Highlights is a very popular educational magazine for kids that provides stories, games and lots of great content children love.

The online offerings at the Highlights Magazine website provides kids with everything from picture stories, coloring pages, crafts, science experiments and so much more. RADLab - Teach Radioactivity in the Classroom Installed on a classroom computer or as a freeware application that your students can install on their own computer, the RADLab provides a way to perform radiation experience without risking actual radiation exposure in the classroom.

The interactive software provides students with detailed information about each element. Images of the elements in their natural state are also included. Brightstorm is designed to help students by offering free math, science and English videos from top teachers. The website also offers paid programs to help students with their math homework and provide test prep materials. The program literally simulates an actual microscopic world where living things - unicellular organisms - travel, collide, reproduce and die all in front of your eyes.

The Blobz Guide - Teach Simple Electronic Circuits To Children The Blobz guide takes the sort of simple electronic circuits that electrical engineering students first learn about, and presents them in a fun and entertaining way for a much younger audience.

In much the same way, they'll also see how the many parts of the human nervous system are connected together and work together as one entire system. Logisim - Simulate Digital Logic Circuits Logisim is free sortware for designing and simulating digital logic circuits. I have been thinking about this a lot over the past few years, as my college like so many others spends more and more money on online proctoring services.

We used ProctorU a few years ago, and the student- and faculty-led outcry was so significant that the college cancelled their contract with the company the following term. I am fully behind most if these points. But how do we balance them with the completely warranted concerns with cheating and plagiarism. There is so much of it, in online courses. Is there a more practical approach than these services, which also keeps in mind the science of learning?

I agree that this is a difficult line to walk, but I think the answer lies in alternative assessments—assignments that move beyond exams. That is fair, and as an English Professor I do not typically assign exams regularly. However, there are many instances when regular exams are indispensable as ways for gauging how students are doing. For example, a professor might assign question individual quizzes as a way of setting the stage for Team Based Learning TBL.

Then, the professor might give the same quizzes as a foundation for group discussion and team-building. It seems like cutting out quizzes altogether might be taking a shortcut.

But not always. Pingback: Recommended reads Small Pond Science. R users typically come from science, education, and various other industries that need statistical computing and design in their processes. Big companies that utilize R in their analytics operations, such as Google, Facebook, and LinkedIn , usually are finance and analytics-driven, as R has proved to be the top mechanism for data analysis, statistics, and machine learning.

R is platform-independent, meaning it can be easily applied in each operating system. Many users also report its power in constructed-in capabilities and libraries, data manipulation, and reporting. Whether the company needs a comprehensive financial analytics strategy or process, R has become one of the most used data science tools to explore and manage data.

We continue our list of the top data scientist tools with Python. Together with R, this programming language makes the state of the art towards data science tools and techniques. In an ideal world, learning both would be a perfect solution, but we will not concentrate much on that.

Instead, we will talk about how Python enables data scientists to begin their journey into this exciting field, but also want to explore the world of programming since Python was primarily developed as a programming language. It offers a wide range of libraries attractive for both programmers and data scientists such as seaborn or TensorFlow. But its popularity within data science is also based on the possibility to clean, manipulate, and analyze data, just like R. They do have differences , and the user has to finally decide which one fits better in their needs to work with data, but Python has emerged as one of the most prominent data scientist tools out there.

In fact, there are numerous tools built with or connected with Python, such as SciPy, Dask, HPAT, and Cython , among others, which makes this programming language among the top choices of striving data scientists who would love to grow in the field.

The industry loves Python since scientists are usually looking for tools that will enable them a simple programming experience, without much hassles and potential complexities. That only confirms that Python is one of the top data science software on our list. As a matter of fact, Python was declared as one of the top 3 most popular languages in , and it will surely grow in the future as well. Business intelligence has developed into one of the most powerful solutions for companies that look for smart data analysis, predicting the future, and utilizing BI tools for generating actionable insights.

There are many differences between business intelligence and data science, but with the recent development of BI tools, both became closely interconnected and dependent on each other. The use of machine learning, predictive analytics, and various data connectors that enable the user to work with enormous amounts of databases, flat files, marketing analytics, CRM, etc.

There are also numerous business intelligence examples that illustrate what kind of value it can bring to the business bottom line. To put this into perspective, let's take a look at an example:. The example above shows us a visual of the drag and drop interface created in datapine for a 6 months forecast based on past and current data.

This is extremely useful in scenarios where future predictions can provide a backbone for defining forthcoming business strategies and decisions which would, otherwise, be based on possible human errors and "hunches. It is a fact that companies have become more data-driven, require a deep dependence on information, but need someone who can own the process of managing their data, often also of sensible nature. Business intelligence solutions not only provide the possibility to manipulate the data but also create powerful dashboards and reports that translate the work of data scientists to real business scenarios protected with high-security levels.

These tools for data science offer additional aspects in dealing with information. They might implement a MySQL report builder to relieve the IT department from carrying out SQL queries and, therefore, save enormous amounts of resources and create a cost-effective business environment. The possibilities within business intelligence are endless, and by using modern data science visualization tools in the form of BI software, scientists can become the backbone of a successful business strategy.

Jupyter, know as a computational notebook, is one of the open-source data science tools that was born out of the Python Project back in and, since then, became renowned for its possibilities to combine software code, support scientific computing across all programming languages such as Python, Julia, R, and Fortran, among dozens of others more than 40, to be exact. This notion definitely confirms that the data migration power of the tool is undeniable. The tool works as a computational notebook, as mentioned, that contains live code, equations, visualizations, and text.

It's consisted of the language of choice, sharing notebooks, interactive outputs such as images, videos, HTML, or custom MIME types, and integration with other data analysts tools and big data solutions such as Apache Spark.

They also provide a hub for pluggable authentication, centralized deployment, and container friendly features so it's not unusual to see giants such as IBM, Google, or Soundcloud that are currently using it.