If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
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Tabular data is omnipresent nowadays and can provide us with meaningful insights into both business and engineering problems. A common way of extracting these insights is by applying machine learning (ML) techniques to this data. The process of applying ML to a dataset consists of various steps, e.g., data preprocessing, feature engineering, and hyper-parameter optimisation, with each of these steps often being a time consuming trial and error process in and of themselves. Additionally, one needs to be an expert in the domain of ML in order to be efficient and effective at each of these steps. It can take quite some time for an organisation to either find these domain experts externally, or grow this expertise in-house.
Knowledge of Mathematics is essential to understand the data science basics. So if you want to learn Mathematics for Data Science, this article is for you. In this article, you will find the 10 Best Mathematics for Data Science Free Courses. For these courses, You don't need to pay a single buck. Now, without any further ado, let's get started- This is a completely FREE course for beginners and covers data visualization, probability, and many elementary statistics concepts like regression, hypothesis testing, and more.
You've found the right Statistics and Probability with Excel course! This course will teach you the skill to apply statistics and data analysis tools to various business applications. How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this course on Probability and Statistics in Excel. If you are a business manager, or business analyst or an executive, or a student who wants to learn Probability and Statistics concepts and apply these techniques to real-world problems of the business function, this course will give you a solid base for Probability and Statistics by teaching you the most important concepts of Probability and Statistics and how to implement them in MS Excel.
An American University math professor and his team created a statistical model that can be used to detect misinformation in social posts. The model also avoids the problem of black boxes that occur in machine learning. With the use of algorithms and computer models, machine learning is increasingly playing a role in helping to stop the spread of misinformation, but a main challenge for scientists is the black box of unknowability, where researchers don't understand how the machine arrives at the same decision as human trainers. Using a Twitter dataset with misinformation tweets about COVID-19, Zois Boukouvalas, assistant professor in AU's Department of Mathematics and Statistics, College of Arts and Sciences, shows how statistical models can detect misinformation in social media during events like a pandemic or a natural disaster. In newly published research, Boukouvalas and his colleagues, including AU student Caitlin Moroney and Computer Science Prof. Nathalie Japkowicz, also show how the model's decisions align with those made by humans.
With the use of algorithms and computer models, machine learning is increasingly playing a role in helping to stop the spread of misinformation, but a main challenge for scientists is the black box of unknowability, where researchers don't understand how the machine arrives at the same decision as human trainers. Using a Twitter dataset with misinformation tweets about COVID-19, Zois Boukouvalas, assistant professor in AU's Department of Mathematics and Statistics, College of Arts and Sciences, shows how statistical models can detect misinformation in social media during events like a pandemic or a natural disaster. In newly published research, Boukouvalas and his colleagues, including AU student Caitlin Moroney and Computer Science Prof. Nathalie Japkowicz, also show how the model's decisions align with those made by humans. "We would like to know what a machine is thinking when it makes decisions, and how and why it agrees with the humans that trained it," Boukouvalas said. "We don't want to block someone's social media account because the model makes a biased decision."
It is very difficult to quantify how much impact does data science and software engineering has on our lives. Most of us can hardly remember the dark age of just few years ago , where you couldn’t ask Siri for the directions to the nearest restaurant. If you do remember that time, you probably wouldn’t wanna go back. Today you don’t have to drive around in your car just to find a restaurant to have a nice meal, instead you can ask your mobile assistant (developed by software engineer), which will trigger an algorithm (developed by data scientist) and will show you the location of nearest restaurant on you phone map application (developed by software engineer). And this is only regarding our personal lives. These technologies have made a much bigger impact on industries. Using software and big data, businesses are able to make data-informed decisions. This means being better able to identify an audience, anticipate their needs, give them what they want, and make bigger profits. What is data science? Data science is hard to define exactly, but you could think of it as “the use of algorithms and statistics to draw insights from structured and unstructured data”. The goal of a data scientist is going to depend quite a lot on the problem they’re examining. From organizations trying to meddle with petabytes of data, a data scientist’s role was to help them utilize this opportunity to find insights from this data pool. They will use their computer science, statistics, and mathematical skills to analyze, process, interpret and store data. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process. What is software engineering? Software engineering has two parts: software and engineering. Software is a collection of codes, documents, and triggers that does a specific job and fills a specific requirement. Engineering is the development of products using best practices, principles, and methods. So, software engineering is defined as a process of analyzing user requirements and then designing, building, and testing software application which will satisfy those requirements. In software development, the goal is to create new programs, applications, systems, and even video games. Because there’s no such thing as bug-free software, an inescapable secondary goal for software engineers is to constantly patch and iterate on existing software to make it better and ensure it performs as required. Behind every software products there are so many stages involved, which all are done by software engineers. Data Science V/S Software Enginnering Both Data Science and Software Engineering domains involve programming skills. Where Data Science is concerned with gathering and analyzing data and Software Engineering focuses on developing applications, features, and functionality for the end-users. Data Science Software Engineering Data Science focuses on gathering and processing data. Software Engineering focuses on the development of applications and features for users. Includes machine learning and statistics. Focuses more on coding languages. Deals with Data Visualisation tools, Data Analytics tools, and Database Tools. Software Engineering deals with programming instruments, database services plan instruments, CMS devices, testing devices, integration apparatus, etc. Deals with Exploratory Data. Software Engineering focuses on systems building. Data Science is Process Oriented Software Engineering is methodology-oriented. Skills include programming, machine learning, statistics, data visualization. Skills include the ability to program and code in multiple languages. Data science deals with data and prediction and it is often not obvious what a software engineer has to do with this data-centric or data-driven team. This is because: A software engineer in a data science team is only an engineer with a knowledge of data; A data scientist knows mathematics and statistics to understand the problem and the product; They also know programming languages to build the model So the question arises, how is software engineering important for data science or what does a software engineer brings in the data science team, and here is the answer: Importance of Software Engineering Software Engineer plays an important role when it comes to productization of data science application by adding hardware, enhancing the performance, so that the data science work can be provided to external customers. Some of the responsibilities are: Building APIs: Data scientist converts the models to APIs that can be easily used by other applications but a Software engineer has to ensure that the APIs created from the model is scalable, flexible and reliable. They also use the models built by data scientists and tests and deploys them. Model Examination: The final product relies totally on the software engineer. They has to make sure that the model made by the data scientist can be used as a common model and that it can be easily managed. By easy management, it means that they has to make sure that the model can be easily moderated to suit the other product requirements as well. For this reason, they need to be updated with all the changes made in the code. Model testing and deploying: Any model, big or small, complex or easy, made by data scientists must be tested. His job is to review the code or the model created by the data scientist. Unit testing, branch testing, integration testing, security testing of the model is a part of his job. After testing, they take a decision to deploy the model. And now since all the software requires basic data like customer needs, famous functionalities, etc. , data scientist are becoming an important part of software development team as well. So we can safely say that both are dependent on each other and completes each other.
The field of data science is continuously growing, subsequently, there are a ton of career roles available for one to choose from within the data science domain. This blog lists down some of the most emerging career options in data science one can opt for. Data science is a field that requires subject matter expertise (e.g., biology if you plan to do bioinformatics), programming skills, and training in mathematics and statistics. Data science as a service allows companies to get business insights leveraging advanced analytics technologies, including deep learning, without investing in in-house data science competencies. Data scientists help a company process a huge pool of information from a variety of sources.
If you are a data Scientist or learning Data science then there is nothing better than following a blog that provides the latest information. Here is a list of blogs you must follow to know more about data science, machine learnings, and AI. Simply Statistics is run by Jeff Leek, Roger Peng, and Rafa Irizarry. Simple Statistics also offers data science specialization courses. Flowing Data offers tutorials and resources for effective data visualization.
Hands-on learning of Python from beginner level so that even a non-programmer can begin the journey of Data science with ease. All the important libraries you would need to work on Machine learning lifecycle. Full-fledged course on Statistics so that you don't have to take another course for statistics, we cover it all. Data cleaning and exploratory Data analysis with all the real life tips and tricks to give you an edge from someone who has just the introductory knowledge which is usually not provided in a beginner course. All the mathematics behind the complex Machine learning algorithms provided in a simple language to make it easy to understand and work on in future. Hands-on practice on more than 20 different Datasets to give you a quick start and learning advantage of working on different datasets and problems. More that 20 assignments and assessments allow you to evaluate and improve yourself on the go. Total 10 beginner to Advance level projects so that you can test your skills.