What Does It Take To Become A Healthcare Data Scientist

Big Data in healthcare is not unheard of. The healthcare industry is generating a significant source of data in the world. People who have skill sets in health information technology and analytics, the employment rate for them is increasing.

The global pandemic has dramatically increased the need as well as the importance of data science and analytics professionals. We have seen over the months how data science professionals have come together and worked on COVID-19 healthcare data and build AI/ML models to track the outbreak and use the information for contact tracing, screening applications and vaccine development.

With all this data and the right application of tools and methods, we can improve patient outcomes and reduce health care costs. This is precisely where a healthcare data scientist comes in. The intersection of healthcare and data science is the emerging area of healthcare research and operations. Analysts, researchers and data scientists in this field can really take advantage of this increasing demand for opportunities.

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Data science blends the unique skills of unlocking insights of data and storytelling. It can combine multiple skills from several disciplines to make it relevant for various roles, including healthcare.  Talking about health data science, it refers to any data that pertains to the biomedical sciences and public health. The data might originate from observational studies, clinical trials, computational biology, electronic medical records, genetic and genomic data. A data scientist can make use of the available genomic data and upon its analysis can provide an understanding of genetic issues and can also help in studying reactions to various kinds of drugs and its effect on diseases.

How Does One Become A Data Scientist In Healthcare?

Well, it is no different from being a regular data scientist but with a very high focus on healthcare data. Till date, the majority of data science courses and degrees focus on data science in general, and using the foundational skills; one can make a data career in healthcare, bioinformatics, medical/genomic science, health economics and other associated fields. Health Data Science is a pretty new discipline, and it’s a blend of epidemiology, statistics, mathematics, informatics and computer science. It is at the intersection of a lot of different disciplines.

There are specialised data science programs offered by universities, mostly at the postgraduate level. For example, there is a Masters In Health Data Science, an eighteen months post-grad program offered by Harvard University. MSc Health Data Science will help you develop innovative skills needed to unlock knowledge from complex health data to address some of the biggest health challenges that we face across the globe today. There is also a master’s program here at Georgetown in health informatics and data science.

There are courses in India as well, such as Master of Health Data Science (SRM University), and Master of Science – Health Informatics & Analytics from The University of Trans-Disciplinary Health Sciences and Technology (TDU). The idea of such programmes is to create graduates who can manage and handle huge, messy healthcare data sets from various sources, and to bring them all together in an analysable format. It provides knowledge on analysing the data using statistical machine learning approaches and drawing useful insights from the data.

One of the other skills required is to be able to communicate results with various healthcare stakeholders. Health data scientists need to communicate to other data scientists about the methods they’ve used, talk to the clinicians to understand the disease they’re looking at, communicate to the lab scientists and most importantly be able to communicate clearly and transparently with patients and the public. Because at the heart of all health data science projects is the patient or the public, and building proper applications that can have a real impact in the Health Service and in commercial domains.

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#careers #healthcare analytics #healthcare data scientist #data science

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How to Bash Read Command

Bash has no built-in function to take the user’s input from the terminal. The read command of Bash is used to take the user’s input from the terminal. This command has different options to take an input from the user in different ways. Multiple inputs can be taken using the single read command. Different ways of using this command in the Bash script are described in this tutorial.

Syntax

read [options] [var1, var2, var3…]

The read command can be used without any argument or option. Many types of options can be used with this command to take the input of the particular data type. It can take more input from the user by defining the multiple variables with this command.

Some Useful Options of the Read Command

Some options of the read command require an additional parameter to use. The most commonly used options of the read command are mentioned in the following:

Option	Purpose
-d <delimiter>	It is used to take the input until the delimiter value is provided.
-n <number>	It is used to take the input of a particular number of characters from the terminal and stop taking the input earlier based on the delimiter.
-N <number>	It is used to take the input of the particular number of characters from the terminal, ignoring the delimiter.
-p <prompt>	It is used to print the output of the prompt message before taking the input.
-s	It is used to take the input without an echo. This option is mainly used to take the input for the password input.
-a	It is used to take the input for the indexed array.
-t <time>	It is used to set a time limit for taking the input.
-u <file descriptor>	It is used to take the input from the file.
-r	It is used to disable the backslashes.

 

Different Examples of the Read Command

The uses of read command with different options are shown in this part of this tutorial.

Example 1: Using Read Command without Any Option and variable

Create a Bash file with the following script that takes the input from the terminal using the read command without any option and variable. If no variable is used with the read command, the input value is stored in the $REPLY variable. The value of this variable is printed later after taking the input.

#!/bin/bash  
#Print the prompt message
echo "Enter your favorite color: "  
#Take the input
read  
#Print the input value
echo "Your favorite color is $REPLY"

Output:

The following output appears if the “Blue” value is taken as an input:

Example 2: Using Read Command with a Variable

Create a Bash file with the following script that takes the input from the terminal using the read command with a variable. The method of taking the single or multiple variables using a read command is shown in this example. The values of all variables are printed later.

#!/bin/bash  
#Print the prompt message
echo "Enter the product name: "  
#Take the input with a single variable
read item

#Print the prompt message
echo "Enter the color variations of the product: "  
#Take three input values in three variables
read color1 color2 color3

#Print the input value
echo "The product name is $item."  
#Print the input values
echo "Available colors are $color1, $color2, and $color3."

Output:

The following output appears after taking a single input first and three inputs later:

Example 3: Using Read Command with -p Option

Create a Bash file with the following script that takes the input from the terminal using the read command with a variable and the -p option. The input value is printed later.

#!/bin/bash  
#Take the input with the prompt message
read -p "Enter the book name: " book
#Print the input value
echo "Book name: $book"

Output:

The following output appears after taking the input:

Example 4: Using Read Command with -s Option

Create a Bash file with the following script that takes the input from the terminal using the read command with a variable and the -s option. The input value of the password will not be displayed for the -s option. The input values are checked later for authentication. A success or failure message is also printed.

#!/bin/bash  
#Take the input with the prompt message
read -p "Enter your email: " email
#Take the secret input with the prompt message
read -sp "Enter your password: " password

#Add newline
echo ""

#Check the email and password for authentication
if [[ $email == "admin@example.com" && $password == "secret" ]]
then
   #Print the success message
   echo "Authenticated."
else
   #Print the failure message
   echo "Not authenticated."
fi

Output:

The following output appears after taking the valid and invalid input values:

Example 5: Using Read Command with -a Option

Create a Bash file with the following script that takes the input from the terminal using the read command with a variable and the -a option. The array values are printed later after taking the input values from the terminal.

#!/bin/bash  
echo "Enter the country names: "  
#Take multiple inputs using an array  
read -a countries

echo "Country names are:"
#Read the array values
for country in ${countries[@]}
do
    echo $country
done

Output:

The following output appears after taking the array values:

Example 6: Using Read Command with -n Option

Create a Bash file with the following script that takes the input from the terminal using the read command with a variable and the -n option.

#!/bin/bash  
#Print the prompt message
echo "Enter the product code: "  
#Take the input of five characters
read -n 5 code
#Add newline
echo ""
#Print the input value
echo "The product code is $code"

Output:

The following output appears if the “78342” value is taken as input:

Example 7: Using Read Command with -t Option

Create a Bash file with the following script that takes the input from the terminal using the read command with a variable and the -t option.

#!/bin/bash  
#Print the prompt message
echo -n "Write the result of 10-6: "  
#Take the input of five characters
read -t 3 answer

#Check the input value
if [[ $answer == "4" ]]
then
   echo "Correct answer."
else
   echo "Incorrect answer."
fi

Output:

The following output appears after taking the correct and incorrect input values:

Conclusion

The uses of some useful options of the read command are explained in this tutorial using multiple examples to know the basic uses of the read command.

Original article source at: https://linuxhint.com/

#bash #command 

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

50 Data Science Jobs That Opened Just Last Week

Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.

In this article, we list down 50 latest job openings in data science that opened just last week.

(The jobs are sorted according to the years of experience r

1| Data Scientist at IBM

**Location: **Bangalore

Skills Required: Real-time anomaly detection solutions, NLP, text analytics, log analysis, cloud migration, AI planning, etc.

Apply here.

2| Associate Data Scientist at PayPal

**Location: **Chennai

Skills Required: Data mining experience in Python, R, H2O and/or SAS, cross-functional, highly complex data science projects, SQL or SQL-like tools, among others.

Apply here.

3| Data Scientist at Citrix

Location: Bangalore

Skills Required: Data modelling, database architecture, database design, database programming such as SQL, Python, etc., forecasting algorithms, cloud platforms, designing and developing ETL and ELT processes, etc.

Apply here.

4| Data Scientist at PayPal

**Location: **Bangalore

Skills Required: SQL and querying relational databases, statistical programming language (SAS, R, Python), data visualisation tool (Tableau, Qlikview), project management, etc.

Apply here.

5| Data Science at Accenture

**Location: **Bibinagar, Telangana

Skills Required: Data science frameworks Jupyter notebook, AWS Sagemaker, querying databases and using statistical computer languages: R, Python, SLQ, statistical and data mining techniques, distributed data/computing tools such as Map/Reduce, Flume, Drill, Hadoop, Hive, Spark, Gurobi, MySQL, among others.

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#careers #data science #data science career #data science jobs #data science news #data scientist #data scientists #data scientists india

How to Become a Data Scientist from Scratch

Want to know how to become a data scientist from scratch? This comprehensive guide will take you through every necessary step to become a successful data scientist.

A Complete Career Guide on How to Become a Data Scientist
Data science has become the hottest career option for students. It’s become one of the fastest-growing career paths. In this high-tech world, every business and organization needs data scientists to leverage their data to the fullest extent. This provides ongoing opportunities for those who want to get hired into a data scientist role. This blog post will take you through all the necessary steps you need to know to become a successful data scientist.

#learn-data-science #data-science #data-science-skills #become-a-data-scientist #data-scientist

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

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This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management