learndatascience

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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.

Naive bayes theorem: Naive bayes theorm uses bayes theorm for conditional probability with a naive assumption that the features are not correlated to each other and tries to find conditional probability of target variable given the probabilities of features. We will use titanic survival dataset here and using naive bayes classifier find out the survival probability of titanic travellers. We use sklearn library and python for this beginners machine learning tutorial. GaussianNB is the classifier we use to train our model. There are other classifiers such as MultinomialNB Naive Bayes Part 2: we will build email spam classifier using naive bayes algorithm. We will use sklearn CountVectorizer to convert email text into a matrix of numbers and then use sklearn MultinomialNB classifier to train our model. The model score with this approach comes out to be very high (around 98%). Sklearn pipeline allows us to handle pre processing transformations easily with its convenient api. In t...

what is machine Learning Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.  Machine learning focuses on the development of computer programs  that can access data and use it learn for themselves.The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide.  The primary aim is to allow the computers learn automatically  without human intervention or assistance and adjust actions accordingly. Deep Learning: The field of artificial intelligence is essentially when machines can do tasks that typically require human intelligence. It encompasses machine learning, where machines can learn by experience and acquire skills without human involvement. Deep le...

Introduction: I am pretty sure that many of us come across the article from the Harvard Business Review back in 2012. A data scientist is a professional known as the sexiest job of the 21st century. Also, research conducted by McKinsey Global Institute back in 2013 projected that there will be approximately 425,000 and 475,000 unfilled data analytics’ positions in North America by 2018. The take-home message here is that there will be a constant stream of analytic talent will be required in all industries, where companies collect and use data for their competitive advantages. What exactly a data scientist? In an over-simplified description, a data scientist is a professional who can work with a large amount of data and extract analytical insights. They communicate their findings to the stakeholders (i.e., senior leadership, management, and clients). Thus, companies can benefit from making the best-informed decisions to drive their business growth and profitability (i.e., depe...

Introduction and Installation: Matplotlib is an amazing visualization library in Python for 2D plots of arrays. Matplotlib is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack. It was introduced by John Hunter in the year 2002.One of the greatest benefits of visualization is that it allows us visual access to huge amounts of data in easily digestible visuals. Matplotlib consists of several plots like line, bar, scatter, histogram etc. Installation of matplotlib: Format strings in plot function: you can use format string in plot function to control visual aspects of your plot such as color, marker, markersize, alpha etc. One can use keyword arguments to individually specify these properties if you dont like using format strings. Documentation for plot function formate string Individual arguments for plot alpha property of plot API axes labels,grids,legend: Bar Chart : to plot bar chart, ...

What is Pandas python and installation of Pandas..? pandas  is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the  Python  programming language . Installation: Dataframe Basics: DataFrame is a main object of pandas. It is used to represent tabular data (with rows and columns). 1) What is dataframe?                       Pandas DataFrame  is two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). A Data frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns. Pandas DataFrame consists of three principal components, the  data ,  rows , and  columns . 2) Create dataframe from csv file and python dictionary 3) Dealing with rows and columns 4) Operations: mean, max, std, describe 5) Conditional selection 6) set_index...