Sarah Masud is a fifth-year PhD scholar at the Laboratory for Computational Social Systems (LCS2) at the Indraprastha Institute of Information Technology, Delhi (IIIT-D). She holds the prestigious Google PhD Fellowship (2023-present) was previously awarded the Prime Minister's Doctoral Fellowship (2020-2023). As part of her PhD, she has authored publications in top-tier venues, addressing the analysis of hateful content in online forums. AI Membership Committee and is a Journal of Open Source Software reviewer. Before her academic pursuits, Sarah worked as a data scientist in developer tooling at Red Hat, Bangalore, for 2.5 years.

Learn how to build and deploy data science applications in Python. Analyzing data and building machine learning models is one thing. Packaging these analyses and models such that they are sharable is a different ball game altogether. This course aims at teaching you the fastest and easiest way to build and share data applications using Streamlit. You don't need any experience in building front-end applications for this.

Data Science applications are everywhere in our regular life. Every sector is revolutionizing Data Science applications, including Healthcare, IT, Media, Entertainment, and many others. Today, healthcare industries are utilizing the power of Data Science successfully, and today we are going to disclose the use of Data Science in Healthcare. If technology is to improve care in the future, then the electronic information provided to doctors needs to be enhanced by the power of analytics and machine learning. This course is designed for both beginners & experienced with some python & machine learning skills.

Almost all industries can benefit from data science, which is reflected in the fact that currently most businesses significantly increase their data analytic capabilities. It is easy to find many high-level descriptions of possible data science applications like fraud detection, predictive maintenance or recommendation systems. These descriptions mostly use buzz words such as AI, data science, machine learning and deep learning along with superlatives about their business potential. However, when it comes to real, tangible data science applications, business units often struggle with the evaluation and prioritization of use case ideas. Use case evaluation should always start with understanding the problem from an end-user perspective.

Data science and artificial intelligence are the revolutionary technologies that are changing the modern era. We are currently witnessing the advantages of fast-paced computers and game-changing business models and ideas in our daily lives. Various industries like manufacturing, finance, e-commerce, and education, to name a few, have implemented data science as a part of their daily activities. Data science has become an integral part of every industry. It is the extraction of meaningful data from organized and disorganized raw data sets using statistical algorithms and scientific techniques.

Data Science applications are everywhere in our regular life. Every sector is revolutionizing Data Science applications, including Healthcare, IT, Media, Entertainment, and many others. Today, healthcare industries are utilizing the power of Data Science successfully, and today we are going to disclose the use of Data Science in Healthcare. If technology is to improve care in the future, then the electronic information provided to doctors needs to be enhanced by the power of analytics and machine learning. This course is designed for both beginners & experienced with some python & machine learning skills.

Everyone is talking about Python's capability as a programming language for data science. Besides web development, Python is taking over big data analytics and the Artificial Intelligence industry. Python programming language is now surpassing R as the topmost choice for data science applications. There are various reasons for Python to be one of the best data science languages. It is the third most popular programming language, according to TIOBE's index.

I have always appreciated the unusual, unexpected, and surprising in science and in data. As famous science author Arthur C. Clarke once said, "The most exciting phrase to hear in science, the one that heralds new discoveries, is not'Eureka!' (I found it) but'That's funny!'" This is the primary reason that I motivated most of the doctoral students that I mentored at GMU to work on some variation of Novelty Discovery (or Surprise Discovery) for their Ph.D. dissertations. "Surprise discovery" for me is a much more positive, exciting phrase than "outlier detection" or "anomaly detection", and it is much richer in meaning, in algorithms, and in new opportunities. Finding the surprising unexpected thing in your data is what inspires our exclamation "That's funny!" that may be signaling a great discovery (either about your data's quality, or about your data pipeline's deficiencies, or about some wholly new scientific concept). As famous astronomer, Vera Rubin said, "Science progresses best when observations force us to alter our preconceptions."

What do I need to learn to make my first data science application? Do I need to learn Flask or Django for web applications? Do I need to learn TensorFlow to make a deep learning application? How should I make my user interface? Do I need to learn HTML, CSS, and JS too?

We recently heard from a number of C and C experts talk about its merits with data science. Cristiano L. Fontana of OpenSource.com talked about some of these benefits in a recent article. "While languages like Python and R are increasingly popular for data science, C and C can be a strong choice for efficient and effective data science. It is the language I use the most for number crunching, mostly because of its performance. I find it rather tedious to use, as it needs a lot of boilerplate code, but it is well supported in various environments. The C99 standard is a recent revision that adds some nifty features and is well supported by compilers."