I've seen a few free alternatives to Cursor suggested around here lately, but I haven't seen this group yet: VS Code, Aider, and Supermaven. This setup uses the free tier of the Gemini models, which include up to 15 requests per minute, 1 million tokens per minute, and up to 1,500 requests per day. Supermaven provides very quick, unlimited code completion on the free tier. I've linked a Youtube video, which shows the steps involved.

Predictive modelling is a process used in data science to create a mathematical model that predicts an outcome based on input data. It involves using statistical algorithms and machine learning techniques to analyze historical data and make predictions about future or unknown events. Table of Content - What is predictive modelling? - Importance of Predictive Modeling - Applications of Predictive Modeling - What are dependent and independent variables? - How to select the Right model? - What is training and testing data? - Types of Predictive Models - What is predictive modelling? - Predictive modelling is a process used in data science to create a mathematical model that predicts an outcome based on input data. It involves using statistical algorithms and machine learning techniques to analyze historical data and make predictions about future or unknown events. - In predictive modelling, the goal is to build a model that can accurately predict the target variable (the outcome we want to predict) based on one or more input variables (features). The model is trained on a dataset that includes both the input variables and the known outcome, allowing it to learn the relationships between the input variables and the target variable. - Once the model is trained, it can be used to make predictions on new data where the target variable is unknown. The accuracy of the predictions can be evaluated using various metrics, such as accuracy, precision, recall, and F1 score, depending on the nature of the problem. - Predictive modelling is used in a wide range of applications, including sales forecasting, risk assessment, fraud detection, and healthcare. It can help businesses make informed decisions, optimize processes, and improve outcomes based on data-driven insights.

While not easy, bootstrapping your data science career is possible. Here's an overview of the most important skills and resource suggestions for learning them. https://www.kdnuggets.com/bootstrapping-your-data-science-career-a-guide-to-self-learning-pathways

EdX has eight online courses in data analytics available at no cost to the student. A KDnuggets article (https://www.kdnuggets.com/free-courses-that-are-actually-free-data-analytics-edition) lists what each student will learn in each course. While free, these courses offer no certificate of completion, unless the student wants to pay a fee for that certificate, which includes graded assignments and tests, and students have unlimited access to the training materials. Otherwise, the free courses offer the online training materials only for the duration of each course. However, if you have your sights on those certificates, you can use the coupon code EDXLEARN24 for a 30% discount. The discount offer ends October 1, 2024. The fee for each course certificate ranges from $59 for the AWS course to $379 for the Hong Kong University of Science and Technology's Foundations of Data Analytics course. Prices are in US dollars. Here is a list of the online courses and their prices for the certificates of completion: IBM: Data Analytics Basics for Everyone, $99 for certificate IBM: Analyzing Data with Excel, $99 for certificate HKUSTx: Foundations of Data Analytics, $379 for certificate HarvardX: Introduction to Data Science with Python, $299 for certificate ASUx: Business Intelligence and Data Analytics, $149 for certificate AWS: Getting Started with Data Analytics on AWS, $59 for certificate AdelaideX: Big Data Analytics, $199 for certificate ASUx: Data Analytics Methods, $149 for certificate

My goal is to get a job in something related to E-commerce, so I thought about doing 3 projects that could get me closer to my goal, I would like to know your opinion, the projects will go from simpler to more complex. 1) A simple payment, customer management and inventory system. It would consist of a sql database of no more than 20 - 30 tables with things like customers, employees, products, stock, payment methods, etc. and their respective transactions in stored procedures, triggers, security management to avoid sql injections etc, as well as views to filter sensitive customer information etc., and a simple streamlit web app that allows a person to simulate a purchase with their email, payment method (simulated), product, quantity etc. and returns an order detail that can be downloaded, and updates the database automatically. Maybe I could add something here 2) An in-depth analysis of an e-commerce site and generation of models, for things like predicting sales based on historical data, creating recommendation systems, looking for purchasing patterns, bottlenecks etc and analysis on the site, things like churn rate analysis, purchases by device, abandoned carts etc, that kind of thing. Finally show all this in an interactive dashboard in power bi 3) An in-depth trend analysis using big data with pyspark and other tools. Let me know what you think, is this a good idea or should I try something more innovative?