learndatascience

Make sure you understand that Data Scientist and Data Engineer are not the same thing. A Data Scientist builds models using mathematics, statistics and machine learning to explain and predict complex behavior, and codifies those models into real-world software. A Data Engineer designs and builds data architectures for ingestion, processing, and surfacing data for large-scale data-intensive applications. Often the Data Scientist and Data Engineer will work together to build an end-to-end solution for companies requiring advanced analytical models that are operationalized at scale. The Data Scientist is interested in large scale architecture only insomuch as it allows the "science to scale." Thus any Big Data project should have a Data Scientist alongside the Data Engineer to ensure that what gets built is analytically sound (no point in engineering a big data architecture that doesn't prepare and process data in a way that supports the specific models built by ...

BIN():  It converts a decimal number to a binary number. Syntax: SELECT BIN(18); Output: BINARY():  It converts a value to a binary string Syntax: SELECT BINARY "GeeksforGeeks"; Output: COALESCE():  It returns the first non-null expression in a list. Syntax: SELECT COALESCE(NULL,NULL,'GeeksforGeeks',NULL,'Geeks'); Output: CONNECTION_ID():  It returns the unique connection ID for the current connection. Syntax: SELECT CONNECTION_ID(); Output: CURRENT_USER():  It returns the user name and host name for the MySQL account used by the server to authenticate the current client. Syntax: SELECT CURRENT_USER(); Output: DATABASE():  It returns the name of the default database. Syntax: SELECT DATABASE(); Output: IF():  It returns one value if a condition is TRUE, or another value if a condition is FALSE. Syntax: SELECT IF(200<500, "YES", "NO"); Output: LAST_INSERT_ID():  It returns the first AUTO_INCREMENT value that ...

SQL Basics: Relational Databases A relational database is a database that stores related information across multiple tables and allows you to query information in more than one table at the same time. It's easier to understand how this works by thinking through an example. Imagine you're a business and you want to keep track of your sales information. You could set up a spreadsheet in Excel with all of the information you want to keep track of as separate columns: Order number, date, amount due, shipment tracking number, customer name, customer address, and customer phone number. This setup would work fine for tracking the information you need to begin with, but as you start to get repeat orders from the same customer you'll find that their name, address and phone number gets stored in multiple rows of your spreadsheet. As your business grows and the number of orders you're tracking increases, this redundant data will take up unnecessary space and generally ...

Real Estate price Prediction Steps: 1.introduction 2.Data Cleaning 3.Feature Engineering 4.Outliers Removal 5.Model Building 6.Python Flask server 7.Website or UI 8.Deploy machine learning model  to production AWS Introduction: This data science project series walks through step by step process of how to build a real estate price prediction website. We will first build a model using sklearn and linear regression using banglore home prices dataset from kaggle.com. Second step would be to write a python flask server that uses the saved model to serve http requests. Third component is the website built in html, css and javascript that allows user to enter home square ft area, bedrooms etc and it will call python flask server to retrieve the predicted price. During model building we will cover almost all data science concepts such as data load and cleaning, outlier detection and removal, feature engineering, dimensionality reduction, gridsearchcv for hyper...

Logistic Regression-Binary Classification: Logistic regression is used for classification problems in machine learning. This tutorial will show you how to use sklearn logisticregression class to solve binary classification problem to predict if a customer would buy a life insurance. At the end we have an interesting exercise for you to solve. Usually there are two types of machine learning problems (1) Linear regression where prediction value is continuous (2) Classification where predicted value is categorical. Logistic regression is used for classification problems mainly. Logistic Regression -MultiClass Classification: This tutorial will show you how to use sklearn logisticregression class to solve multiclass classification problem to predict hand written digit. We will use sklearn load_digits to load readily available dataset from sklearn library and train our classifier using that information.