Trending December 2023 # How Big Data Is Shaping Healthcare To Make It Further Affordable, Accurate & Intelligent # Suggested January 2024 # Top 12 Popular

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This article was published as a part of the Data Science Blogathon

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Big data is a sub-domain of Data Science that commits to applying specific tools and methods to learn and extract detailed insights into massive volumes of data.

Big data does a similar thing. Besides having data from a handful of colleagues, the file comprises thousands also millions of reviews from people.

Health Is Especially Well Fitted To Profit From Big Data

For many years, hospitals, researchers, and state agencies have diligently assembled an enormous kind of health data, from the completion rates of drug cases to the value of an ordinary medical plan to patients’ demographic data to the expected waiting period in emergency rooms.

Recently a report was published by the research and consulting firm McKinsey & Company. They discovered that there are four “provisions” in which the data is present in the health care domain:

Pharmaceutical analysis data-(e.g. clinical trial outcomes)

Clinical data-(e.g. patient reports)

Action and cost data-(e.g. expected procedure expenses)

Subject behavior data-(e.g. health investments history)

Big data appears in collecting all this data collectively in one place, sometimes from multiple heterogeneous data storehouses, and applying it to obtain insights into whereby our health care system can be more beneficial.

To distinguish which drugs are least probable to produce side effects?

Which private doctors have the most beneficial results?

Which methods are best and cost-effective?

Big data could clarify these issues and more numerous.

Three Active Units Where Big Data Is Remodelling Health Care

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1.Healthcare Providers Frequently Utilize Extensive Data To Recognize Patients At Significant Risk For Particular Healing Conditions Before Significant Difficulties Happen.

A diverse provider has been practicing patient record data to produce predictive models throughout intervention successes. The report provided by the data has enormously cut down on hospital readmissions by 49%.

Google is also taking part in recognizing health hazards. Applying data from user exploration histories, the tech giant company can follow the extent of the flu worldwide incoming real-time.

It is where the authority of big data matches the strength of data. By operating on prominent data specialists like Siemens Healthcare, providers can apply results that automate healthcare data acquisition.

Experts use big data to aggregate and normalize the data beyond an industry, thereby adopting predictive analytics techniques to recognize populations at risk better while controlling execution at all levels of an association at the boom.

2.Big Data Is Related To Enhance The Quality Of Care Experienced By Patients.

One way this is happening is by practicing data to produce a “clinical decision support software,” which is a mechanism health care providers can practice estimating their suggested practices — for instance, recognizing medical failures before they occur.

In a different case, a health care company in Delhi (the capital city of India) applied clinical data on the efforts of staff doctors to discover that one physician was working on a particular antibiotic considerably more frequently than the rest of the crew. They were possibly raising the venture of drug-resistant bacteria.

3.Big Data Is Helping Overcome The Mounting Expenses Of Health Care.

Health care is one of the most influential areas in Indian economics and uses a meaningful measure of the country’s total domestic goods. At the equivalent time, there is sufficient confirmation that each dollar’s outstanding balance contributed to health care is misused, whether by bloated expenses or redundant reports and treatments.

Big data has a significant part in bringing these charges down. In one example, big data experts applied clinical data to determine which doctors charge the most cash in methods and other procedures.

By examining their activities, the health care provider could recognize and lessen duplicate tests and additional procedures. The movement not only dropped expenses but also enhanced patient results.

The National Health Service of India utilizes data on the hospitals and cost-effectiveness of different drugs to better negotiate drug rates with pharmaceutical manufacturing.

In Bangalore, one healthcare system handles data of 40,000 patients and 6,000 workers to recognize people expected to demand costly health care co-operations in the future. They manage the data and identify who to target with preventative care before the expensive health issue appears.

Conclusion

McKinsey & Company predicts that the application of big data in health care could generate profits of up to approximately a trillion dollars in 2023.

The private domain is not the exclusive field taking note of the influence of big data in the fate of health. In Jan 2023, the National Institutes of Health declared $50 million in yearly funding to produce some of the “Data Centers of Excellence for Big Data In Health Care.”

The hubs will improve the health care study and clinic community thoroughly learn how it can apply big data to develop its health care system.

Summing-Up

Health care is all-around people, whether performance statistics or generative data but not figures. Big data’s increasing role in health care does not substitute that. By exerting the power of the wealth of health data possible through operating with notable data experts, providers can classify regions where growth is likely and work to accomplish more beneficial results, increased productivity, and a further sustainable healthcare environment.

About Author

Mrinal Walia is a professional Python Developer with a computer science background specializing in Machine Learning, Artificial Intelligence, and Computer Vision. In addition to this, Mrinal is an interactive blogger, author, and geek with over four years of experience in his work. With a background working through most areas of computer science, Mrinal currently works as a Testing and Automation Engineer at Versa Networks, India. My aim to reach my creative goals one step at a time, and I believe in doing everything with a smile.

The media shown in this article are not owned by Analytics Vidhya and are used at the Author’s discretion

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Data Science Resume: How To Make It More Appealing?

Know the best way to write your Data Science Resume and make a statement to top-tier tech companies.

The concept of life is simple, you need oxygen to live and a resume to get a job. It is essential to write an eye-catching resume to be first in a race, especially if you are applying for a

data science

job. Even if you are not a fan of writing resumes, you cannot ignore the fact that most companies require a resume in order to apply to any of their open jobs, and it is often the first layer of the interview process.

So does it matter how you write personal, educational, and professional qualifications and experience details in a resume? Yes, it does, and here are some tips about how to make your resume more appealing that will catch the eye of a recruiter or interviewer.

1. Always write a resume in brief

Rule number 1, always keep your resume short and engaging. Try to get all your details on one page because recruiters receive thousands of resumes every day and have a minute to look over someone’s resume and make a decision. Therefore make sure your resume speaks on your behalf and makes an impression.

2. Customize your resume according to the job description

While you unquestionably can make a solitary

resume and send that to each job you apply for, it would be a smart move to attempt to add customized changes depending upon the job description would positively intrigue the recruiter.

This doesn’t mean you have to do rework and upgrade your resume each time you go after a position. However, if you notice significant skills mentioned in the work posting (for example, skills like

Data Visualization

or Data Analysis) you should be certain about the resume you’re sending focuses on those skills and increase your chances of getting that job.

3. Pick a right layout

While each resume will consistently incorporate data like past work insight, abilities, contact data, and all, you ought to have a resume that is remarkable to you. That starts with the visual look of the resume, and there are various approaches to achieve a one-of-a-kind design.

Remember that the type of resume layout you pick is also significant. In case you’re applying to

with a more customary feel attempt to focus on a more traditional, curbed style of resume. In case you’re focusing on an organization with more of a startup vibe, you can pick a layout or make a resume with more colors and graphics.

4. Contact Details

After the selection of your resume’s layout next step is to add contact detail. Here are some important things you need to remember about your contact details and what to put there in the context of a data science resume specifically:

If you are applying for a job in a different city and don’t want to relocate it is better not to add your entire physical address, only put in your city and state you live.

The headline underneath your name: reflects the job you’re looking to get rather than the job you currently have. If you’re trying to become a data researcher, your headline should say “Data researcher” even if you’re currently working as an event manager.

5. Data Science Projects/Publications area

Quickly following your name, feature, and contact data ought to be your Projects/Publications area. In any resume, particularly in the technology business, you should focus on highlighting the things you have created.

For a data science resume, this may incorporate machine learning projects, AI projects, data analysis projects, and more. Hiring organizations need to perceive what you can do with your mentioned skills. This is the segment where you can flaunt.

6. Highlight your skills

At the point when you portray each project, be pretty specific about your abilities, tools, and innovations you utilized, how you made the project. Indicate the coding language, any libraries you utilized, and more. The more talk about your skills and key tools the better.

7. Professional Experience 8. About Education

If you have relevant work experience to showcase, it is better to add your educational details closer to the bottom. But if you are fresher and applying for your first job then, in that case, you have to highlight your qualification.

9. Last thing to do

While you unquestionably can make a solitary data researcher Remember that the type of resume layout you pick is also significant. In case you’re applying to tech companies

An Accurate Approach To Data Imputation

This article was published as a part of the

Data Science Blogathon

Introduction

In order to build machine learning models that are highl to a wide range of test conditions, training models with high-quality data is essential. Unfortunately, a large part of the data collected is not readily ideal for training machine learning models, this increases the need for pre-processing steps such that the data is pipelined as how a machine learning model would expect.

In the process of data cleaning, one of the most crucial steps is to deal with/fill missing values (Imputing missing data or simply data imputation) accurately such that the machine learning model learns the patterns in data as expected. Some of the most commonly practiced methods of dealing with missing values are:

This method is one of the most commonly used techniques to eradicate the inconvenience of dealing with missing data in the training phase if in case, the training data available is huge.

This method is rarely used in the machine learning community due to its less promising results. However, the method is subject to yield good results in certain situations.

Fill in the NULL values with statistically determined values based on the statistics of the training data such as training distribution mean, variance, etc.

This method is the most generally used method to fill the missing values

4. Predict the NULL values with Machine Learning Algorithms using the entire training data (without NULL values).

Article Focus

Throughout this article, we dive completely into “How to deal with/fill the missing data” using ML algorithms.

Resources for the Statistical Methods

Since this article focuses on predicting the missing values instead of inferring them from the distribution of the dataset, the reference to the three methods mentioned above that either deal with/fills the missing values are gathered below. Consider checking them out to better understand where and when to use each of these methods.

Introduction to “Understanding and Tackling Missing Values” talks from scratch, all the way up to dealing with the most complex techniques with examples.

Furthermore “Dealing with Missing Values in Python” talks about the fundamentals and important ideas in dealing with missing values.

and Furthermore in “Dealing with Missing Values in Python“, and “Statistical Imputation for Missing Value“.

Data Imputation Using ML Algorithms

Fundamentally, the problem of data imputation using ML algorithms is broadly classified into two types, using the classification algorithm and the regression algorithm. Based on the type of training data, we need to use it to categorize the problem. In this article we take look at using a classification algorithm, however, using a regression algorithm is identical (refer to an example of a regression algorithm here). In order the solve the problem of missing values in the datasets using ML algorithms, data need to undergo certain steps including pre-processing and modelling. Below are the 5 most commonly categorized steps to fill the missing values with accurate data using ML algorithms.

0. Overview

On the high level, a dataset, by dropping the labels or y column, is considered and divided into two sets, one called the training set and another one the test set. The division takes place based on the rows with NULL values and rows without NULL values. Each of these datasets is further divided into X, and y such that we have X_train, y_train, X_test, and y_test. y_train and y _test are column(s) containing the missing values (to be predicted). Upon training, we predict the missing values using the test data. More on how this works in the below steps.

Throughout this article, we use the most popular Kaggle dataset on the regression problem statements. The Google Colab notebook with detailed code and documentation is available here.

1. Preparing the Data

After dropping the label or y column, the dataset is divided into training and testing. The division takes place with reference to the presence of NULL values in individual rows.

1. Import the data using pandas CSV:

Reading the CSV file using pandas read_csv() function

# Importing all the required packages import pandas as pd import numpy as np import matplotlib.pyplot as plt import sklearn # Reading the csv file using pandas read_csv df = pd.read_csv('/content/drive/MyDrive/TrainAndValid.csv/TrainAndValid.csv')

I am importing data from my Gdrive but you can get access by downloading from Kaggle or from the following link:

2. Investigate the dataset using methods such as info(), describe(), etc:

EDA (Exploratory Data Analysis) is a crucial step in understanding the data and the very first few functions used to initiate this process are info(), describe(), isna(), etc.

# Let's learn about the dataset df.info() # Check out the df.describe() in a new cell to learn more.

3. Split the dataset based on NULL values in the dataset:

We drop the NULL values in our dataset and use that as a training set and then use the complete dataset to test on. Since we don’t have the true values for missing data it is a better option to use the complete dataset to evaluate the performance of the model.

from sklearn.model_selection import train_test_split X_test = df.drop('UsageBand', axis=1) y_test = df['UsageBand'] df_train = df.dropna() X_train = df_train.drop('UsageBand', axis=1) y_train = df_train['UsageBand']

2. Modelling

We use the Random Forest Classifier model for modelling to impute the data.

# Training the model based on the X_train and y_train data from sklearn.ensemble import RandomForestClassifier rfc = RandomForestClassifier() # fit the model rfc.fit(X_train, y_train)

3. Predict the Missing Values

Using the trained model Random Forest Classifier, fill/predict the class values of categorical column

# predict the values y_filled = rfc.predict(X_test)

4. Substitute the Data & Use the clean data

Replace the y_predicted with the column consisting of missing data in the original data set and continue the modelling.

# now we have the missing values filled so replace the column in original dataset and use the predicted one df['UsageBand'] = y_filled # Proceed and use the dataset for modelling the actual problem 🙂

In this article, we have seen, how to impute the missing data using ML algorithms? Also gathered some of the resources to learn various Importantly, we have seen how to use the existing training data to train and infer missing values using a statistical machine learning model (Random Forest Regressor in this case). We have also explored some of the standard methods followed while predicting the missing values using machine learning models such as split data based on NULL values in a row, training a model based on training datatype, and predicting missing data based on the missing data density in the training data. We barely scratched the surface of EDA (Exploratory Data Analysis) in this article through functions such as drop(), dropna(), etcetera.

tual problem i.e., in this case, predicting automobile prices.

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Surfshark Vpn Review: Affordable, But Is It Reliable And Secure?

The digital world is essentially a slippery slope in 2023. One wrong step online, and before you know it, your data and your identity might be at risk. However, many rely on Virtual Private Networks (or VPNs) to mask their identity online. But in a market full of attractive VPN service offerings, it’s difficult to get behind a VPN company that does what it says without making false promises. One such contender in this market is Surfshark, a VPN service provider promising total security while being light on the wallet. Does it live up to these claims? Well, I put that to the test and bought a membership recently. I’ve been using Surkshark VPN for a few days now, and here’s what I think about the service. I will be talking about the VPN’s best features, speed and performance, the ability to unlock certain streaming services, privacy policy, and pricing in detail.

Surfshark VPN Review (2023)

While my review will cover Surfshark’s offerings in detail, you can always look at it all from a glance using the section below. However, I strongly suggest reading the entire review to find out if this VPN service is a good fit for your needs or not.

Surfshark VPN: Cut to the Chase

Poised to protect an online user’s privacy and security, Surfshark is a VPN service that creates a secure connection and anonymizes all the browsing activity of the user. Using the fastest and secure WireGuard Protocol, Surfshark gives no substantial drop in the VPN speed while maintaining privacy with a prejudice online.

With a slew of added features, including Multi-Hop, Split Tunneling, Kill-Switch, no device limit, and a friendly privacy policy, Surfshark is an impressive VPN service. Top that off with prices that start at as low as $2.5 per month, recommending Surfshark is an easy call to make.

Surfshark VPN: Pros and Cons

PROSCONSConsistent speed and performanceBrowser extension could use more settingsFast WireGuard Protocol with capable alternativesOccasional server-loadFriendly privacy policyCleanWeb feature doesn’t work well on all browsersRock-bottom pricesSome users might not like the VPN’s shift to the NetherlandsEasy to use platform appsKnowledgable and responsive customer support

Surfshark VPN: Plans and Pricing

When it comes to VPNs, it’s always better to go in for the long haul compared to paying out monthly. Since Surfshark isn’t the only kid on the block, I decided to check out the prices of its competitors as well. However, I was pleased to find out that Surfshark has one of the most affordably priced VPN services on the market. Moreover, Surfshark unlocks all the premium features once you get a subscription instead of holding back some of them (via a different ultra-premium plan) to extort more money from you.

Compared to players such as ExpressVPN, NordVPN, and CyberGhost, Surfshark offers the most value for money in its 2-year plan. Moreover, the company itself regularly holds sales that offer discounts going as high as 81%. And if you’re someone anxious about dumping a chunk of money on a VPN at once, Surfshark also has a 30-day money-back guarantee. With that said, here’s how much you will have to pay to get a Surfshark VPN subscription:

Subscription Plan TenurePrice1-month plan$12.951-year plan$47.88 or $3.99/month2-year plan$59.76 or $2.49/monthWith a healthy variety of affordable plans, Surfshark can be for everyone

Surfshark VPN User Experience

As a typical online user who is always short on time, I look for apps that are easy to use and still do the job effectively. Surfshark nails that right from the very beginning. A simple download and sign-in got me up and running within just 2 minutes. The VPN comes with a unified scheme of apps available on PC, macOS, iOS, Android, and even as extensions for your favorite browsers.

Mac and Windows Desktop App

Surfshark incorporates a simple two-column design on its apps and extensions. Connecting to a server itself is as simple as tapping on a country’s name. That’s it. Within a few seconds, your connection will be established, and you will be located in New York. While I would have appreciated a map selection to sort through countries, the list itself is to the point, and you have flags to represent the countries.

Android and iOS Mobile App

The Surfshark VPN mobile apps on Android and iOS share the same simplicity that is demonstrated by a clean and sleek design that just works. A quick tap on a server got me to the country, and I could see the finer details at a glance, including my new IP address, encryption status, and more.

Like the PC apps, the Settings tab holds a lot of options, but if you’re someone not wanting to touch anything, you can simply connect and forget about everything else. Throughout my time using these apps, I didn’t encounter any major bugs or issues that caused me grief. While my iOS app did have trouble connecting to a Boston server twice, that little annoyance didn’t happen after that.

If you’re a daily joe who wants to connect to a VPN server without jumping through hoops, you will love Surfshark’s no-nonsense approach. It makes a potentially frustrating process really simple.

Supported Countries and Server Locations

When it comes to location, Surfshark has made sure to spread out its servers across the world. Whether you are in Europe, the Americas, Asia-Pacific or the Middle East, and Africa, there are a plethora of servers distributed. While you might encounter a loaded server once a week, the healthy variety of servers means you can easily switch to a different one and still have a fast and secure experience.

Speed and Performance

No matter how well a company packages a product, it is no good if it cannot perform. To see if the Surfshark VPN can actually give me a decent connection speed, I decided to connect to 5 different servers around the globe (London, New York, Tokyo, Singapore, and Hong Kong). Using a connection that offers me native download and upload speeds of 387 Mbps and 304 Mbps, respectively, it was now time to put the shark to the test.

The VPN service handled them all on the chin as my connection maintained extremely fast connectivity. The download speed never went below 320 Mbps, and I actually gained 10Mbps on upload speed in Singapore. The connection itself happened within seconds and was stable. I also checked the connection for packet loss by running the VPN through multiple games like Apex Legends, Fortnite, and even GeForce NOW. I encountered no packet loss, which was something I was afraid might happen.

Consistent Performance Across Protocols

One of the reasons for the high speed and stable connection is Surfshark using the new, fast, and secure WireGuard Protocol. This allowed me to connect to any server of my choosing and still retain the Internet performance I have come to know and love.

With fast speeds around the globe and over 3200 server locations, Surfshark is a VPN made for speed. While using the different protocols might affect your experience, I loved the general consistency and stability that Surfshark offers when it comes to connecting and expecting good bandwidth from a VPN service.

Is Surfshark Good for General Browsing?

When it comes to general browsing, Surfshark is as plug-and-play as you would expect. By simply choosing the country of your liking, you can open a web browser and get on the Internet. As you would expect, websites recognize you from the virtual country you’re now in and show settings based on that. Furthermore, if you’re a user living in a place that blocks VPN ports, you need not worry as Surfshark’s Camouflage aka Obfuscation mode can grant you access while using the OpenVPN protocols.

However, Surfshark performs exceedingly well when it comes to using the Internet casually and accessing websites and apps that have been geo-locked.

How Good Is Surfshark for Streaming?

Surfshark has a reputation for unblocking over 30 Netflix libraries around the world and a plethora of other streaming sites. As a Netflix user, I decided to put that to the test and connected to a few different VPN servers, including Japan, Canada, Australia, France, the US, and the UK. Surfshark performed exceptionally well when it came to streaming Netflix movies and TV series from around the globe. There was a glitch on the PC version that didn’t allow me to connect initially, but I fixed that by restarting the app.

I experienced no drop in quality or performance, and Netflix did not detect the presence of a VPN during the streaming. With geo-locked shows now at my disposal, I had fun watching Japan-exclusive Netflix shows and anime while sitting in India.

However, I didn’t stop there. I tested Surfshark out on other services including Disney+, AppleTV, BBC iPlayer, HBO Now, and even YouTube TV. My overall experience was amazing, and like before, I had no drop in performance or having to sit for the video to buffer. I managed to do that for all three of my devices simultaneously, considering Surfshark has no device limit.

How Secure Is Surfshark VPN?

Surfshark by itself is designed in a way where you don’t have to worry about security. No matter which server you’re connected to, nobody can see what you’re up to online. It is powered by Surfshark’s audited no-logs policy. This means that VPN itself keeps no records about your activity online, including what you do, download, and even your IP address. Surfshark has been audited twice by Cure53 testers to see if these claims stand true, and they still do.

The company used to operate under the jurisdiction of the British Virgin Islands, but has since shifted its HQ to the Netherlands. While the Netherlands is under the Nine-Eyes Alliance, the VPN company maintains it’s under no obligation to log any of our data which means the no-logs policy stands. Users get the added benefit of RAM Only servers, which means no data can be physically taken and is wiped right after use. Besides the excellent no-logs policy, some of the Safety Surfshark features that impressed me were:

Encryption and Tunneling Protocols

Encryption is the process of ciphering data, so it’s unreadable to third parties. Like other VPN services, Surfshark also has data encryption support. However, what’s refreshing is that instead of the standard AES-256 encryption, the Surfshark VPN offers AES-256-GCM, which is an updated and more secure version of encryption. So if you’re worried about what little data you share not being secure, don’t be.

Even though I did mention the various protocols Surfshark offers, it’s easy to recount them because of their sheer performance. The most modern and fastest remains WireGuard which Surfshark uses in most cases. However, the company claims that your mileage may vary, so experiment with other protocols to find the best fit if you do end up getting a VPN subscription.

Multi-Hop Makes Things More Secure

A thing I was concerned about was the VPN leaking DNS queries, so I resorted to checking it out myself. Using a DNS Leak Test, I found that Surfshark VPN was extremely airtight and did not leak any of my information.

Kill-Switch

Like other VPNs, Surfshark too has a kill-switch feature that will completely disable your Internet unless you’re connected to a VPN. This is especially helpful when it comes to protecting your real digital identity from being leaked.

There was a time when this feature used to cut off the Internet even if one deliberately turned the VPN off. Surfshark has since fine-tuned Kill-Switch to only turn it on when the connection drops. The kill-switch is a must-have if you dabble in sensitive work online.

Clear Privacy Policy for Once

To sum it up, Surfshark promises that it does not collect important data like IP Addresses, your browsing history, traffic, or your overall network usage. This means you are free to use this VPN without being worried about timestamps or data leaks. However, the company does collect some information, including anonymized analytical data and some account information.

This is necessary to get the service working but never more than that. You even contact Surfshark and have them modify or further restrict what little data they have. There are also no third-party cookies that the company uses, which is good to hear. So yeah, as far as services go, Surshark is pretty transparent about it, and it shows on their website and offerings.

User Reviews and Customer Support

If you feel I’m being too positive about Surfshark, you’re not alone. Do some digging online, and you will see this VPN regularly receives praise from users and reviewers alike. That’s hardly a surprise given this VPNs performance. But does Surfshark help you out if you run into a problem?

Surfshark has wonderful customer support, which includes a full-scale knowledge base, 24/7 live chat, and E-Mail support should you need it. My experience of browsing through the knowledge base was swift as the info dump had answers to all sorts of questions.

Want Even More? Get Surfshark One

While the Surfshark VPN itself is a pretty good deal, there is even more in store if you’re willing to pay just a little extra. Introduced a little while ago, Surfshark One is an added list of services that can help you better protect your data and stay safe online. Surfshark One contains three added offerings – Surfshark Alert, Surfshark Search, and the new Surfshark Antivirus.

Surfshark Alert lets you protect your most important credentials, including your personal identity numbers, ID, passwords, and even credit card. In the event of a breach that involves your data, it will inform you of the same immediately, and you can take the necessary action.

Surfshark Search is essentially a search engine free of trackers that only displays organic results. This search engine is on its way to becoming one of the best Google alternatives. And finally, Surfshark Antivirus protects you from viruses and malware system-wide.

Frequently Asked Questions (FAQ) Q. What Is a VPN and What Can I Do with It?

A Virtual Private Network (VPN) is a software that creates a secure connection between your computer and the Internet. A VPN does that by encrypting and running your data through a virtual tunnel on servers based in remote locations. This encryption allows you to hide your identities, including your IP Address, browsing activity, and other connection details.

You can do all sorts of things with a VPN. However, most casual VPN users get it to unblock geo-locked content such as Netflix and Apple TV libraries from other countries. They also use it to visit websites their ISPs have blocked and engage in P2P file-sharing (Torrenting). A VPN keeps your data hidden while you go about your business.

Q. Why Can’t I Just Get a Free VPN?

You can, but you shouldn’t. Most free VPNs that offer their service do so with low data quotas. Once you exceed that, your connection is terminated, and you cannot use it until it’s reset. However, what’s concerning is that many free VPN services have shady terms of service that might dabble in sharing your data with third parties. It exposes you to all sorts of malicious things. Always try to opt for a paid VPN service.

Q. Should I Read the Privacy Policy Myself?

Absolutely. While Surfshark has a really user-friendly policy written in plain English, your luck might be different with other companies. Nevertheless, whenever you decide to go for a VPN service, take some time to read their policy and usage contracts. This will not only give you a better idea of what to expect, but you can see for yourself if the company does in the fine print what it claims on the website banner.

Q. Are VPNs Even Legal? Q. Can I Use a VPN on My Phone?

Should I Get Surfshark VPN?

I had always heard of Surfshark VPN but didn’t try it out until now. Now that I have, I can safely say that I am pleased by the experience Surfshark VPN has to offer. With extreme safety features like multi-hop, split tunneling, RAM Only servers, and a privacy policy I could actually understand, I felt safe using this service.

Get Surfshark VPN (starts at $2.5/ month for a 2-year plan, 82% off)

Why Big Data Needs To Become Smart Data?

This article was published as a part of the Data Science Blogathon.

Businesses have always sought the perfect tools to improve their processes and optimize their assets. The need to maximize company efficiency and profitability has led the world to leverage data as a powerful tool. Data is reusable, everywhere, replicable, easily transferable, and has exponential benefits for the business. It can provide useful business insights on customer lifecycle, anomaly or issue detection, real-time data analysis, etc. However, even if data could be a fantastic tool, it is limited if you can extract and interpret the knowledge from the information.

The question now relies on how to process, understand data, and infer useful insights more efficiently and acceleratedly.

This article looks into Big Data and how it develops into Smart Data. Additionally, we will look into the concept of Smart Data and its benefits for businesses.

Source: shapelets.io

What is Big Data?

Five main characteristics often describe big data: volume, value, veracity, velocity, and variety, aka the five V’s. Many experts also consider an additional one: variability. All these attributes compose what we know as “Big Data.” Each of them is key for understanding and analysis of the data.

This concept is not new for companies, as they collect a great volume of information that increases daily. As I understand it, we collect and analyze large amounts of data to obtain actionable insights businesses use to enhance their processes. This is why Big Data is so important for any industry sector.

Did you know that it is estimated that the volume of data generated worldwide will exceed 180 zettabytes in 2025? According to Seagate’s report, that same year, 6 billion consumers, or 75% of the world population, will interact every day with data, and each connected person will have at least one data interaction every 18 seconds. In other words, the volume and the velocity of information will force businesses to increase their data processing speed. Consequently, over the next few years, Big Data will continue to be a key support for strategic development, decision making, enhanced streamlining operation/ business operations, and customer relationships.

Nevertheless, the volume, value, veracity, velocity, and variety of information will force companies to focus on adapting and starting to use tools that help them process the data quicker and smarter. This is where the concept of “Smart Data” emerges.

Source: shapelets.io

What is Smart Data?

Smart Data tools help pre-process the data when ingested to reduce the time of the analysis. What makes “smart data” smart is that the data collection points are intelligent enough to understand the data immediately. Not all data provides the same value to companies; in this scenario, the quality of the information will prevail over the amount of stored data. For example, it allows a device sensor to output useful human-readable data before sending it to a database for storage and/or detailed analysis.

Consequently, Smart Data analytics is the natural evolution of Big Data that aims to treat volumes of data intelligently, as it allows companies to obtain, among others, the following key benefits:

Time-saving

The volume of information that companies ingest doesn’t have any value raw. It needs to be cleaned and then curated to extract any knowledge. By implementing smart software, the data stream or batch will already come partially curated, which could be extremely important when there is a time restriction. For example, a self-driving car can’t afford to wait for data to be sent to the cloud, analyzed, and sent it back. It requires the data to be gathered through a sensor considered “smart,” so the data can be immediately analyzed and then sent to actuators (all internally) who are going to take whatever decision is required at this moment.

It is a great opportunity for SMEs.

Variety of data is as important as the volume and the velocity because many different types of data are available; it can be challenging to treat it if the data quality is not nearly perfect. When creating a smart data strategy, businesses must be careful about the type and quality of data ingested. Bad data quality can cost 12% of the business revenue. Here, Smart Data helps to improve the quality of the information by pre-cleaning it.

For this reason, if small and medium-sized companies use a vast amount of data in a short period, implementing a smart data strategy will help them carefully select the data they are looking for and have a better quality of analysis.

Better customer or consumer service

Traditionally in analytics, the data was amassed, groomed, and then processed at a fixed time (during the week or the day). That workflow means that the data was already obsolete because of the time series analysis.

Prevent problems A step toward automation

Tools that automate the collection and transformation of data are vital, and the need will only grow as you try to extract value from the ever-growing data volumes coming from an ever-increasing number of sources. Smart data is the tool that will allow you to automate your collection and let you focus on more important tasks.

Know the competition better

Intelligent data analysis allows companies to obtain information about the market, the sector in which they operate, and the competitive situation, providing them with useful tools to improve their position, such as price monitoring or change trends.

Source: shapelets.io

In short, Smart Data is a complementary value to Big Data, enabling it to make faster analyses with better data quality and automating data collection and processing. Smart data solutions and strategies will be time-saving thanks to their decentralized data collection and analysis. Additionally, it is an opportunity for SMEs because it will help to select and clean data for a better quality of analysis. It will improve customer service thanks to the hyper-personalization of clients’ data. It will help to detect anomalies before it even occurs, and its automation will let the business focus on more important tasks.

In this article, we dive into the concept of Smart Data and its value in the business sector and data science world. In essence, this feature covers the following:

– Concept of Big Data and why it’s important.

– Concept of Smart data.

– Key benefits of Smart Data in business and data science.

Remember: business intelligence is now key to development and success! Don’t wait, and start reshaping your world!

If you wish to understand more about the application of smart data, big data, or data science, I recommend you have a quick look at the following articles:

Shapelets Data Science studies and applications.

A quick introduction to Big Data.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

How Nosql Skill Helps To Build Big Data Career

What Are NoSQL Skills?

Hadoop, Data Science, Statistics & others

What are the Important NoSQL skills needed for surviving in the big data industry?

If you want to enter the field of big data analytics, it is important to learn the tools of the trade-in a proper manner: Some of the tools involving data analytics includes SQL, R, SPSS, and SAS. Start with tools that you have access to. Sometimes it is good to work around with multiple tools so that you can understand data analytics in a better manner. Learning does not just entail knowing about all the complex and intricate details but at least forming a basic knowledge about the basic functioning of the tools. From there, you can go ahead and gain proficiency in just one tool. It is also better to master one tool and then learn about other tools, as this will help you perform your tasks in a better manner.

Learn the tricks of the tools in data analytics: Understanding the tools is extremely important if you want to learn the tricks of the trade. There might be two possible options in this case scenario. The first is to learn through the experience and knowledge of another experienced person. The second way is through professional curriculum’s that are available in the market. While self-help tutorials may not help individuals to gain an in-depth knowledge of data analytics, learning about models is essential. This is because outputs from the running proc in SAS or models in SPSS tend to deal with a lot of statistics. Knowing which statistics to look up and which one to ignore is critical, and only a good and experienced analytic professional will be able to make that choice effectively.

Big data today encompasses a lot of buzzwords, acronyms, and terminologies. Among these terms, one that is attracting a lot of attention is NoSQL. This is especially true because brands are today trying to cope with a large amount of data on a regular basis. As more and more companies are trying to adopt Big Data at a rapid pace so that they can get ahead of their competition. Now companies aww striving to adopt Big data at such a rapid pace that deployment of these technologies needs to be understood in a comprehensive manner. One such technology is NoSQL which is essential for professionals who want to break into the field of big data and be employed within this sector. As this is a lucrative industry, understanding the most important aspect of technology that governs it is very important to stand out in the crowd. In short, if there is one thing that can help you get ahead in terms of skills is a proper understanding of NoSQL skills, which is the basis of the entire big data industry.

What is NoSQL skills & NoSQL database types their importance in Big Data?

What is NoSQL database is currently one of the main ways in which brands can manage data and databases in large quantities? That is why NoSQL is gaining a lot of prominence, as it can deal with large sets of data in an effective fashion. With a large and comprehensive range of architectures and technologies, NoSQL can help brands resolve issues of performance and scalability that are related to Big data. These issues cannot be addressed by relational databases in an effective manner, as brands today have a lot of unstructured and raw data with them that are stored on multiple servers on a cloud. Since these data sizes are huge in size, they need technology that is capable of handling this huge amount of data in an effective manner. As of now, there is a specific definition of what exactly NoSQL but there are some characteristics that define it in an effective manner. These include that NoSQL skills must not use the relational model, run well on clusters, are open source, are built for 21st-century web estates and must be schema-less as well.

An important part of this is the four types of databases. what are NoSQL databases that are uncomplicated data stores that provide clients with the perspective of an API? In this database, the client can enter a value for a key, obtain a value for the key or even delete the entire key from the data store. The key values of NoSQL databases provide primary key access, thereby allowing brands to perform better and ensure better scalability as well. Some of the popular key-value NoSQL databases include Memcached, Riak, Redis, and Couchbase. The second one is called document database, which is an amazing way in which brands can store and manipulate documents in a simple and easy manner. Documents of the company can be stored in multiple formats like XML, BSON, JSON and also retrieved from database stores. Most of these documents are very similar to each other. With hierarchical data structures, these documents are self-descriptive and consist of scalar values, collections, and maps.

How to choose the right NoSQL format?

As mentioned above, there are four different NoSQL formats and choosing the right format might seem like a tricky business. There are a few guidelines that brands might consider in case they want to invest in one of the above four formats. The key-value database is extremely useful in the following case scenario. A key-value database is ideal for storing user profiles, session information, online details like shopping cart information and consumer preferences. In case brands need to deal with queries related to data and find a relationship between the data concerned, this system is best left alone. A document database is ideal for a situation where companies need to manage content management systems, web analytics, blogging platforms, e-commerce platforms and real-time analytics.

In case brands need to work with data that have complex NOSQL transactions that cover multiple queries, this system is not of much use. On the other hand, a column family database is ideal for companies that want to monitor their blogging platforms, require content management systems, maintain counters, among other functions. This system is best avoided in situations that are newly constructed and require changing patterns of data queries. Finally, a graph database is ideal for problems spaces that are connected with data such as spatial data, social networks, routing information for money and recommend search engine-related functions.

Knowledge of what is NoSQL database is extremely important in current times.

What is NoSQL database is, therefore, one of the most important aspects of big data and knowledge of this is poised to help professionals to take their careers to the next level. It is one of the most important components in the skill set of any data analyst. Some of the reasons why they are important to include the following: a. Knowledge about These skills will help to improve the productivity of the data analysts as they will have the required skills to meet the demands of the application b. what is NOSQL database can enhance the performance of data as it can effectively combine large sets of data, along with the reduction of latency and improve the entire output as well c. NoSQL specialists are in high demand in the market today because companies need them in an urgent manner. This high demand is rightly reflected in the salaries of these individuals, which is some of the highest and lucrative across all categories and companies. Being a NoSQL specialist is today one of the most sought-after jobs in the information technology sector is a growing reality.

Another technology that is sweeping the Big data industry is Hadoop. It can easily be said that Big data has two sides that are extremely lucrative and popular among analysts. If one of them is Hadoop, the other is what is NOSQL, without any doubt. With so much data available in the world today, professionals who can manage NoSQL skills in a competent manner along with documents and files are in high demand across various companies. That is why these professionals needed to be skilled in addressing these tasks in a fast and effective manner, without adding any extra stress on the functioning of their brands.

According to Payscale, a professional skilled in what is NoSQL is close to one lakh dollars, and there is a good chance that this might increase in the future as well. Among all the industries that have opportunities for NoSQL specialists, the healthcare industry pays their professionals among the best rates. This is followed by the software development industry and the information technology service industry, and IT consulting.

What are the major careers that require NoSQL Database Types Skills?

Many industries require professionals who are adept at dealing with data in a proficient manner. There is hardly any field that does not require people who have in-depth knowledge about what is NoSQL and its related fields. Some of the areas in which they are required include the following:

Database Administrator: A database administrator is a highly qualified individual whose main task is to use specialized and highly technical software to store and organize data in a comprehensive manner. Some of their responsibilities include capacity planning, installation, configuration, migration, performance monitoring, backup, and data recovery. A good database administrates should be capable of working with a number of database platforms which includes Oracle, MongoDB, and Cassandra, among others. It goes without saying that the more experience you have, the more salary you can enjoy!

Data Architect: A data architect is another job opportunity that awaits individuals who have in-depth knowledge of NoSQL database types techniques. Their responsibilities include the creation of data models, analyze data, data warehouse, and migration of data.

Software/Application Developer: A really high profile job, the job of a software/application developer is highly sought after in the industry. These professionals are responsible for creating applications like games and word processing programmes on the one hand and also enjoy a lot of freelance work on the other hand. For this job, programming skills are really important.

Data Scientist: Another job that is quite popular in the Big data industry, a data scientist needs to possess a wide range of data-driven skills. Data science employs techniques and theories that are drawn from many fields, including statistics, mathematics, pattern recognition, data mining, among many others. According to the Harvard Business Review and Forbes, the job of a data scientist is the ‘sexiest job of the 21st century, meaning their demand will continue to rise in the future as well.