Data is the most valuable asset for an analytics professional. With web scraping, you can make the internet your data source.

Facebook acquired WhatsApp for 22 billion US dollars or 55 dollars per user in 2014. What was the reason behind this acquisition? to get more user data! Diverse, representative, good quality data is the lifeblood of an analytics pipeline. The number of websites on the internet is estimated to be around 2 billion. Web scraping turns the entire world wide web into your data set. In this webinar, we will introduce how to scrape a website using the BeautifulSoup package in Python. We will discuss how to navigate the HTML DOM to find data that interests you, some best practices, the legality of web scraping, and briefly touch on how to build and automate a web scraper on the cloud using Azure Functions.

Outlier detection is an important field of study and has a wide range of applications. Fraud detection, anomalous data, and intrusion detection are some examples.

Outliers are data points that deviate significantly from the normal distribution or projected trends within a dataset in the context of data analysis. These data points can introduce noise, modify statistical measurements, and degrade analytical model correctness. As a result, identifying and dealing with outliers is crucial for generating trustworthy insights and making data-driven decisions.

Working on a project is a great way to put your theoretical knowledge into practice.

Competing in data science competitions is a great way to build your skillset and confidence

Data Science competitions are a good way to showcase your skillset to potential employers, or for brushing up your analytical skillset. This blog talks about some competitions that you can take part in to meet your goals.

Running AI/ML solutions on the cloud can be tricky, but tools such as AutoML can make the process easier.

Model selection and tuning hyperparameters can be a tedious task. Setting up multiple runs, hyperparameter sweeps, and algorithms is usually not a Data Scientist’s favorite part of the job. AutoML is a tool in Azure ML that does all of this for you. There are a variety of algorithms, hyperparameters, and evaluation metrics available that are automatically run based on your defined criteria. In this webinar, we will look at how we can use AutoML for training machine learning models. We will also look at the evaluation metrics and see how we can pick the best model from an AutoML run.