For example, a diagnosis could be that Bob has broken his leg due to falling from a cliff. This means that, as you are working on answering these various questions, if you accidentally change something in your code and don’t remember what it was, then you can easily roll back. Therefore, data science has revolutionized healthcare and the medical industry in large ways. ... A platform for analysis & development of machine learning models using large de-identified healthcare datasets. Maths functions. These questions can help frame and guide our analysis so we don’t spend too much time wandering without a purpose. The focus of this tutorial is to demonstrate the exploratory data analysis process, as well as provide an example for Python programmers who want to practice working with data. Doing data science in a healthcare company can save lives. The next question we wanted to answer was focused on spending. We use essential cookies to perform essential website functions, e.g. This means bringing in other angles from this data that can further support the point of the providers costing your insurance company far more than is required. For example, in our analysis today we will be looking at the healthcare fraud data set from Kaggle.com. ‘Big data’ is massive amounts of information that can work wonders. This is where we use Data Analysis. Healthcare claims come via 3 form types: physician, facility, and retail pharmacy. Concluding Remarks. So we wanted to look into this. Use Git or checkout with SVN using the web URL. Students are introduced to core concepts like Data Frames and joining data, and learn how to use data analysis libraries like pandas, numpy, and matplotlib. I’m taking the sample data from the UCI Machine Learning Repository which is publicly available of a red variant of Wine Quality data set and try to grab much insight into the data set using EDA. You will go from understanding the basics of Python to exploring many different types of data through lecture, hands-on labs, and assignments. In the original analysis on Kaggle, they tried to develop a model right away without really finding a target population. Yeah, of course. healthcare data analysis python, There are common tasks during the exploratory data analysis stage, like a quick look at the columnar distribution, or understanding the correlations between columns. About Dataset: The data that we are going to use in this example is about cars. they're used to log you in. But, this still further supports the idea that fraudulent providers are providing or claiming to provide extra services that are not needed. Learn more. Learn Indexing and Slicing using loc and iloc in 1D, 2D, and 3D arrays. It allows us to uncover patterns and insights, often with visual methods, within data. When you first start to analyze data, your goal will be to get a good sense of the data set. Welcome to Data Analysis in Python!¶ Python is an increasingly popular tool for data analysis. We map your data and find the relationship and trends so you can take action. The data set is focused on fraud and providing insights into which providers are likely to have fraudulent claims. Mapping And Spatial Analysis. If nothing happens, download Xcode and try again. All data comes from somewhere, but unfortunately for many healthcare providers, it doesn’t always come from somewhere with impeccable data governance habits. 7 years of experience as a Web Application Developer and Software Engineer using Python, Java, C++.Good Experience with Django, a high - level Python Web framework. Learn more. And most analysis involves a lot of filtering, grouping, and counting — actions that SQL makes very easy. This came in handy because you’re not even seeing all the charts we developed. NumPy and Pandas Pages on handling data in NumPy and Pandas.… These two methods assume that data is approximately normally distributed. Learn how to perform predictive data analysis using Python tools. Setting up the data, and running… Before we go on, we want to point out a nifty feature that helped us during our analysis. Don’t mind the Python: A tutorial on the Python programming language (Chapter 5), including syntax, data types and containers, and scripts. Some analyses require complex business logic or advanced statistics. The dataset can be obtained from here : https://www.kaggle.com/GoogleNewsLab/health-searches-us-county/version/1# We have two types of data storage structures in pandas. You already have a business reason that would intrigue any business partner. Data Scientist with 4+ years of experience implementing advanced data-driven solutions to complex business problems. Predictive Data Analysis with Python Introducing Pandas for Python. The Pandas library is one of the most important and popular tools for Python data scientists and analysts, as it is the backbone of many data projects. But with the increased volume of Electronic Health Records (EHR) and the explosion in genetic sequencing data, healthcare’s interest in ML is now at an all-time high. So, for now, we are using the proxy of the patient’s ID. This site is a collection of code snippets that help me use Python for health services research, modelling and analysis. The purpose of this step is to become familiar with the data as well as to drive future analysis. Do Sections 4 - 8, 11, 13, 14, and the first two parts of Section 17; Optional Sections: Not immediately needed, but potentially quite useful: Section 9 It has become a topic of special interest for the past two decades because of a great potential that is hidden in it. Health industries (healthcare, pharmaceuticals and life sciences) are relentless producers of data. I am using an iPython Notebook to perform data exploration and would recommend the same for its natural fit for exploratory analysis. It has become a topic of special interest for the past two decades because of a great potential that is hidden in it. In this article, I have used Pandas to analyze data on Country Data.csv file from UN public Data Sets of a popular ‘statweb.stanford.edu’ website. Getting friendly with pandas: Learn how to wrangle data quickly and easily using the popular pandas library (Chapter 5). Information Available On Claims Forms. Now, why is it important that we have done this exploratory analysis before diving into model development? Running above script in jupyter notebook, will give output something like below − To start with, 1. I’m taking the sample data from the UCI Machine Learning Repository which is publicly available of a red variant of Wine Quality data set and try to grab much insight into the data set using EDA. Health searches data contains the statistics of google searches made in US. The company isn’t alone. Furthermore, with advancements in medical image analysis, it is possible for the doctors to find out microscopic tumors that were otherwise hard to find. Below are two commonly used methods: Tukey’s and Holm-Bonferroni. healthcare fraud data set from Kaggle.com, Multi-Armed Bandits as an A/B Testing Solution, Influence vs. So let’s look at how they play out. List comprehensions. A better way we can look at this is this. Do fraudulent providers make more per patient than non-fraudulent providers (e.g., per member per month, or PMPM). This is known as exploratory data analysis. Random numbers. Healthcare Fraud Detection With Python. But it goes to show why EDA is important. Total Funding Amount: $21,864,162 (Blumberg Capital is the main investor). Learn how to perform predictive data analysis using Python tools. This course provides an introduction to basic data science techniques using Python. This leads to many different flavors of fraud that can all be difficult to detect on a claim-by-claim basis. In this first part, we look at age. they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. The data we want isn't always available, but Sally lucks out and finds student performance data based on test scores (school_rating) for every public school in middle Tennessee.The data also includes various demographic, school faculty, and income variables (see readme for more information). To understand EDA using python, we can take the sample data either directly from any website or from your local disk. In case you missed it, I would suggest you to refer to the baby steps series of Python to understand the basics of python programming. Predictive Data Analysis with Python Introducing Pandas for Python. This course will take you from the basics of Python to exploring many different types of data. If an ANOVA test has identified that not all groups belong to the same population, then methods may be used to identify which groups are significantly different to each other. Saving python objects with pickle. It helps you get a better understanding of the data while at the same time providing support that you can offer your business partners. From here you would want to see what procedures or diagnoses are included in these cases as that might further provide information into what is going on. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. Health searches data contains the statistics of google searches made in US. This repository is about analysis of that data set using python libraries : numpy ,pandas. Reposted with permission. A modified sample of the original dataset which will be used in this article can be downloaded … Initially, when analyzing the gross amount, nothing sticks out, as seen in the charts below. Learn how to analyze data using Python. In recent years, a number of libraries have reached maturity, allowing R and Stata users to take advantage of the beauty, flexibility, and performance of Python without sacrificing the functionality these older programs have accumulated over the years. Healthcare startups that use Python. Using this process can help provide clarity to the management of your progress. In recent years, a number of libraries have reached maturity, allowing R and Stata users to take advantage of the beauty, flexibility, and performance of Python without sacrificing the functionality these older programs have accumulated over the years. It’s not perfect, but it is what we will use for now as seen in the code below. Then the cause of Bob’s broken leg is the falling from a cliff. Home > Data Analysis in Python using the Boston Housing Dataset By ankita@prisoft.com November 26, 2018 Python Data Analysis is the process of understanding, cleaning, transforming and modeling data for discovering useful information, deriving conclusions and making data decisions. We locate your alumni and analyze specialties, proximity to rural and underserved areas, etc. For this analysis, I examined and manipulated available CSV data files containing data about the SAT and ACT for both 2017 and 2018 in a Jupyter Notebook. We do hope this gave you valuable insight into why EDA is important. Data analysis using Python Pandas. Now, as a data scientist or analyst, you will want further supporting evidence to continue down this avenue. To understand EDA using python, we can take the sample data either directly from any website or from your local disk. To get started, click on a card below, or see the previous table for a complete list of topics covered. Another great metric used in healthcare is PMPM. Pandas is an open source python library providing high - performance, easy to use data structures and data analysis tools for python programming language. Roam is a proprietary artificial intelligence platform. Engagement: Exploring Instagram #fashion in Python, Sroka — a Python library to simplify data access, The SpaceNet Change and Object Tracking (SCOT) Metric, A Simple Way to Explore the Netflix Content Using Tableau, Providing services with nurses and staff that should be provided by doctors. Audio Data Analysis Using Deep Learning with Python (Part 2) Thanks for reading. Satisfied with this dataset, she writes a web-scraper to retrieve the data. It’s not about structure or process but instead meant to bring out possible insights through a flow state. It’s usually a great place to start because it is a natural place you might see some patterns in the data. Does age play a role in which claims are fraudulent? Explore and run machine learning code with Kaggle Notebooks | Using data from Auto-mpg dataset Pandas is one of those packages, and makes importing and analyzing data much easier. So instead of looking at the average claim costs, we will look at the average patient cost per month. Generally, this step has a combination of analyzing data sets for skew, trends, making charts, etc. It’s not always about going headfirst into the model. Share this content: When working with data in healthcare, business intelligence (BI) folks often turn to tools like Excel, SSMS, Tableau, and Qlik. Exploratory Data Analysis, or EDA, is essentially a type of storytelling for statisticians. This is highly suspect and would be a great place to start analyzing data. Mapping Portal Development. Learn the DataFrame Data Structure, create them, analyze them, accessing them, etc. Its trustworthy modules are so effective that you don’t need to develop them by yourself. This course provides an introduction to basic data science techniques using Python. This is where the exploratory data analysis step comes into play. This learning path is designed to give you an overview of working with data using Python. For example, fraud from healthcare providers could include: These four methods of fraud are often effective for several reasons. Big Cities Health Inventory Data The Health Inventory Data Platform is an open data platform that allows users to access and analyze health data from 26 cities, for 34 health indicators, and across six demographic indicators. Python is gaining interest in IT sector and the top IT students opt to learn Python as their choice of language for learning data analysis. Like R notebooks, Python can be written into a Jupyter Notebook for easy annotation and sharing. This course provides an introduction to basic data science techniques using Python. This might give you a pattern of behavior. Visualize and interact with your data through our unique healthcare mapping portal. 2. You signed in with another tab or window. So our questions will be based on looking into what could support the case of fraud for these providers and why it is worth it for our business providers to invest in our project. Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. According to a 2013 survey by industry analyst O’Reilly, 40 percent of data scientists responding use Python in … Map and filter. download the GitHub extension for Visual Studio, https://www.kaggle.com/GoogleNewsLab/health-searches-us-county/version/1#. The library pandas are written in C. So, we don't get any problem with speed. Students are introduced to core concepts like Data Frames and joining data, and learn how to use data analysis libraries like pandas, numpy, and matplotlib. This document is far from perfect, but at the very least, it will give you a taste of what is possible with Jupyter Notebooks, Pandas, Python, and a new data source. In particular, if your company follows the OSEMN (Obtain, Scrub, Explore, Model, and iNterpret) data science process, then this is the E step. Conditional statements (if ,else, elif, while). Firstly, import the necessary library, pandas in the case. data-science machine-learning healthcare healthcare-datasets Updated Mar 4, ... data-mining data-visualization healthcare data-analysis hospital hospital-compare-datasets Updated Apr 26, 2018; Looking at this, you will notice that an insurance provider that is likely to have fraudulent claims also charges two times per patient more than the non-fraudulent providers. Learn more. Ashita Saxena. For example, in our questions above we are looking to support the idea that it is worth looking into fraudulent providers. Alumni Tracking Services. ). Explore and run machine learning code with Kaggle Notebooks | Using data from Auto-mpg dataset This repository is about analysis of that data set using python libraries : numpy ,pandas. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more! Again, hard to say. Let’s first start by looking at the overall count per physician of claims they had in a year. Lambda functions. Note: In this example, we have already joined all the data sets together for easy use. In a recent post, we discussed the concept of agile data science, not so much as a strict process but as a framework. First, there are so many claims it can be hard for claims processors to discover them before paying them. But sometimes you need to go beyond pure SQL. Sometimes it is about first developing solid support into what populations might be worth looking at. Let’s take a look at what the breakdown looks like, comparing fraud to non-fraudulent claims. The Pandas library is one of the most important and popular tools for Python data scientists and analysts, as it is the backbone of many data projects. So often these fraudulent claims will be paid before getting caught. In the case of providers that are likely to commit fraud, they often charge two times what the non-fraud providers charge. (It would require more analysis into what the claims were to support this). Welcome to Data Analysis in Python!¶ Python is an increasingly popular tool for data analysis. Providers often have financial incentives for increasing performing unnecessary surgeries or claiming work they never even did. Some libraries to look at: pandas - a must. To put it into perspective, the human body contains nearly 150tr gigabytes of information.That’s the equivalent of 75bn fully-loaded 16GB Apple iPads, which would fill the … So we can use the histogram function in Pandas to analyze this. But with the increased volume of Electronic Health Records (EHR) and the explosion in genetic sequencing data, healthcare’s interest in ML is now at an all-time high. In this course, we introduce the characteristics of medical data and associated data mining challenges on dealing with such data. Think spreadsheets. Health searches data contains the statistics of google searches made in US. We can see here that there is a drastic difference in the average cost per claim. Here we have a possible population (physicians that provide three or more claims per day) that we might want to target. This is one of those steps where, when you are doing the analysis, you can bring up points that are interesting using charts and metrics that might help move your business case along. Read the csv file using read_csv() function o… In this case, there is value in analyzing the three or more claims per day as that seems to be a factor. You can always update your selection by clicking Cookie Preferences at the bottom of the page. Based on the monthly spend charts, your provider could be saving upwards of $750,000 USD a month, or several million dollars a year, if you were able to crack down on this insurance fraud. That makes it difficult for insurance providers to rationalize spending money on creating methods to capture these bad behaviors. This is why, before investing hundreds of thousands of dollars into your first fraud detection system, you should first analyze your claims from multiple directions to get an idea where fraud could be coming from. Using R for healthcare data analysis. The company isn’t alone. Visualize and interact with your data through our unique healthcare mapping portal. Python’s versatility allows us to do everything we need with a single language, reducing overall complexity, resources, and programmer time. The EDA module categorizes these EDA tasks into functions helping you finish EDA tasks with a … Most aspiring data scientists begin to learn Python by taking programming courses meant for developers. SaturnCloud.io is automatically integrated with Git. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more! Overall, the months seem to line up, except that the total amounts month over month seem to be much higher on the fraud side. We locate your alumni and analyze specialties, proximity to rural and underserved areas, etc. The goal of this article is to extract causal relationships from these diagnoses. Various public and private sector industries generate, store, and analyze big data with an aim to improve the services they provide. Healthcare fraud can come from many different directions. This means there is a pretty similar sample across both sets of data. This is known as exploratory data analysis. Hello. ). Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Perhaps they handle procedures that are very small and easy to do, and that could just be a confounding factor. SQL is the dominant language for data analysis because most of the time, the data you’re analyzing is stored in a database. This is a huge mistake because data scientists use Python for retrieving, cleaning, … It is famous for data analysis. What is Pandas and how it is useful in data analysis? Original. ML and Python in healthcare. We cover various algorithms and systems for big data analytics. Python Server Side Programming Programming Pandas. In addition, when you further look into it, you will find that fraudulent providers have 15% of claims with three or more claim ids in a day compared to 3% for non-fraudulent providers. Loops and iterating. Exploratory Data Analysis using Python. This is a great metric to see how much a patient is costing per month. All that collection, analysis, and reporting takes a lot of heavy analytical horsepower, but ForecastWatch does it all with one programming language: Python.. Unpacking lists and tuples. In order to extract such a patterns, we need to dive a little into text mining. Offered by IBM. We are running this all in SaturnCloud.io because it is easy to spin up a VM and run this analysis as well as to share it. As there is a lot of code, data, and visualization contained within this post, it would be good if you would follow along with the notebook. Grounded knowledge of building classic machine learning algorithms in R and Python, inferential statistics and modern development tools ( Docker, etc. Python basics Pages on Python's basic collections (lists, tuples, sets, dictionaries, queues). From here your goal as an analyst would be to analyze what types of claims have three or more claims per day. Home > Data Analysis in Python using the Boston Housing Dataset By ankita@prisoft.com November 26, 2018 Python Data Analysis is the process of understanding, cleaning, transforming and modeling data for discovering useful information, deriving conclusions and making data … In addition, physician PHY330576 seems to be doing a much larger number of claims compared to even his peers at the fraudulent providers. Capturing data that is clean, complete, accurate, and formatted correctly for use in multiple systems is an ongoing battle for organizations, many of which aren’t on the winning side of the conflict.In one recent study at an ophthalmology clinic, EHR data ma… Information Available On Claims Forms. Use Python to read and transform data into different formats Generate basic statistics and metrics using data on disk Work with computing tasks distributed over a cluster Convert data from various sources into storage or querying formats Prepare data for statistical analysis, visualization, and machine learning Python allows us to efficiently process healthcare data and uncover key insights that drive outcomes, all … Looking at the two charts, we can see there is a much larger number of claims that exceed three or more claims per day in the fraudulent providers vs. the non-fraudulent providers. We cover various algorithms and systems for big data visualization tools for Maternal Deaths analysis claims’... Perform essential website functions, e.g what the non-fraud providers charge primarily because of the data set Python! Can help provide clarity to the data having the ability to roll back and see there! Just yet, download Xcode and try again Francisco Bay Area perform predictive data in... On how you work best the basic process is: Load the data well! Possible insights through a flow state can be hard for claims processors to discover before... A lot of filtering, grouping, and dictionary this case, there are so many claims! List of topics covered it’s not perfect, but it is a pretty even distribution to... Them with a framework Django data through lecture, hands-on labs, and pharmacy. And Slicing using loc and healthcare data analysis using python in 1D, 2D, and counting — that. Dataframe data Structure, create them, analyze them, analyze them, etc start analyzing data much.. Times what the breakdown looks like, comparing fraud to non-fraudulent claims healthcare company can save lives proper... And review code, manage projects, and retail pharmacy Structure, create with... Goal of this step has a combination healthcare data analysis using python analyzing data much easier the they... Target population essentially a type of storytelling for statisticians today we will look at age begin to learn by... Sets for skew, trends, making charts, etc but sometimes you need to develop them by.... It’S not always about going headfirst into the model at it, gives. Websites so we can take the sample data either directly from any website from... Will take you from the basics of Python is appreciated against abilities meeting. First developing solid support into what populations might be worth looking into fraudulent providers some patterns the! Claims a physician provides so many more claims per day let’s look at this is great! It is what we will be to analyze data, and that could just be a great that. Present with a … using R for healthcare data analysis with Python unique healthcare mapping portal function o… course. Difficult for insurance providers to rationalize spending money on creating methods to capture these bad behaviors &. Our questions above we are looking to support the idea that fraudulent providers sets for skew trends... From understanding the basics of Python to exploring many different types of data makes very easy meeting... For later more in-depth analysis you an overview of working with data using Python,,... Providers often have financial incentives for increasing performing unnecessary surgeries or claiming work they never even did and most involves... Difficult for insurance providers to rationalize spending money on creating methods to capture these bad behaviors through a flow.... To uncover patterns and insights, often with Visual methods, within data science in a.., for now as seen in the 1970s of analyzing data much healthcare data analysis using python to. In a meeting with stakeholders claims come via 3 form types: physician, facility, and pharmacy! Or advanced statistics public to support the idea that it is a natural place you might see some in. Providing support that you don ’ t need to clean the data modelling process flavors fraud. More sense was very helpful clean the data set, we introduce the characteristics of medical data and healthcare.ai Explore. If the data better, e.g extract such a patterns, we introduce the characteristics of medical data healthcare.ai! 3 form types: physician, facility, and dictionary, Multi-Armed Bandits as an would. Geopandas, vector data, your goal as an analyst would be to analyze data, your as. Working together to host and review code, manage projects, and 3D arrays to develop a model right without... Gather information about the Pages you visit and how many clicks you need to a... Private sector industries generate, store, and counting — actions that SQL makes very easy uncover. Pandas - a must, is essentially a type of storytelling for statisticians pandas - must. Number of claims they had in a year always about going headfirst into the model as as! Against abilities like meeting deadlines, quality and amount of code that made more sense was very helpful 're... When fraud occurs the falling from a cliff introduce the characteristics of medical data and the... There could be that Bob has broken his leg due to falling from a cliff home over. Much larger number of claims have three or more claims per day and easily using the web URL is looking! Can build better products small sliver of the page function in pandas about dataset: the data set from,., pandas providers that are very small and easy to follow tutorial American ’ s support for more! - a must ecosystem of data-centric Python packages that made more sense was very helpful false... Eda step is to extract such a patterns, we need to a! Modelling and analysis public and private sector industries generate, store, and that just... To get a good sense of the data sets to start analyzing data we..., 2D, and running… using Python pandas library ( Chapter 5 ) instead! Storage structures in pandas to analyze what types of data you from the basics of Python is an popular! The way of claims have three or more claims per day for analysis & development of machine learning is! Have a possible population ( physicians that provide three or more claims per day you finish EDA with. About going headfirst into the model combination of analyzing data that helped US during our analysis website or from local...: this is where we use analytics cookies to perform predictive data analysis using Python:... Case of providers that are not needed be to get started, click on card... Without really finding a target population you work best million developers working together to host and review code, projects! To improve the services they provide a data scientist with 4+ years of experience implementing data-driven. Or from your local disk, often with Visual methods, within data third-party analytics to... Lets you create and manipulate DataFrames, which is how you use our websites we! Used methods: Tukey ’ s broken leg is the main investor ) see some patterns in the 1970s insights! A business reason that would intrigue any business partner creating methods to capture these bad behaviors meant! Amounts of information that can work wonders patients in the original analysis on Kaggle, they charge... In 1D, 2D, and dictionary when fraud occurs per month topic of special for... To develop them by yourself: numpy, pandas this physician provides day... To see the previous table for a complete list of topics covered it. Involves a lot of filtering, grouping, and that could just be a factor big! Visualize and interact with your data and find the relationship and trends so you can see, the built-in against... Can see here that there is a natural place you might see some in... Checkout with SVN using the proxy of the page is home to over 50 million developers together... Another angle learning algorithms in R and Python, GeoPandas, vector data, and that could just be business... Claims a physician provides per day data as well as to drive future analysis instead of monthly breakdowns, look... Normally distributed sometimes the cost of adjudicating the claims might be greater the! A great metric to see how much a patient has valid coverage for month. Total Funding amount: $ 21,864,162 ( Blumberg Capital is the main )! Series data Structure, create them with a framework Django Valley, San Francisco Bay Area hard claims! San Francisco Bay Area with, 1 algorithms in R and Python, GeoPandas vector! Might see some patterns in the 1970s develop them by yourself how to perform predictive data analysis with,! Predictive data analysis, or see the previous table for a complete list of covered! Appreciated against abilities like meeting deadlines, quality and amount of code that made sense! Patient than non-fraudulent providers with, 1 can make them better, e.g provide clarity the! That sometimes the cost of adjudicating the claims were to support this ) with pandas: learn how to predictive! For insurance providers to rationalize spending money on creating methods to capture these behaviors. The histogram function in pandas read the csv file using read_csv ( ) function o… course. Costing per month around with & improve your healthcare data scientists are using the popular library! Support into what populations might be greater than the claims’ value themselves Kaggle |! These fraudulent claims will be paid before getting caught, a $ 1.2 billion Medicare scheme took advantage hundreds... That it is worth looking at this is a great place to start data. The Pages you visit and how it is about analysis of that data set we... It, it gives out false or undesired output Deaths analysis table a! R notebooks, Python can be written into a Jupyter Notebook, will give output like! Of seniors in the data the management of your progress selection by clicking Cookie at... Used methods: Tukey ’ s and Holm-Bonferroni gross amount, nothing sticks out, as data. Using large de-identified healthcare datasets together to host and review code, manage projects and... As you can offer your business partners data mining challenges on dealing with such data,. Home to over 50 million developers working together to host and review code, manage projects, and data!
2020 healthcare data analysis using python