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Data engineering involves a lot of decisions. And with the dozens of database solutions that we have currently, choosing a database or data storage platform is certainly one decision to make with the right knowledge and competence.
NoSQL databases offer different paradigm and capability totally different from what we know in traditional relational database management systems (RDBMS). Also, data decision are made usually during application development until when getting the value of the data it becomes an issue.
In this Hackerday, we want to go through all the classes of NoSQL that is and pick an example of the lot. This Hackerday is not an intensive review into each of them but we will do well to mention what can be offered in each example.
We will begin with the traditional or popular RDBMS, discuss the features, functionalities, and limitations. In the light of that, we will walk through all the various classes of NoSQL database and try to establish where they are the best fit.
At the end of this Hackerday, students will be able to adequately make a choice of database type given a required business specification and non-functional requirement. Also, students will be able to take on any interview to show their wide knowledge of the different database solutions in the market.
In this deep learning project, you will find similar images (lookalikes) using deep learning and locality sensitive hashing to find customers who are most likely to click on an ad.
In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.
Hive Project -Learn to write a Hive program to find the first unique URL, given 'n' number of URL's.
Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.
In this deep learning project, you will build your own face recognition system in Python using OpenCV and FaceNet by extracting features from an image of a person's face.
Estimating churners before they discontinue using a product or service is extremely important. In this ML project, you will develop a churn prediction model in telecom to predict customers who are most likely subject to churn.
In this time series project, you will forecast Walmart sales over time using the powerful, fast, and flexible time series forecasting library Greykite that helps automate time series problems.
In this spark project, you will use the real-world production logs from NASA Kennedy Space Center WWW server in Florida to perform scalable log analytics with Apache Spark, Python, and Kafka.
In this data science project, you will learn how to perform market basket analysis with the application of Apriori and FP growth algorithms based on the concept of association rule learning.
In this NLP AI application, we build the core conversational engine for a chatbot. We use the popular NLTK text classification library to achieve this.