#Data_Privacy

[edujournalblogs]

AI in Cyber Security

The integration on Artificial Intelligence into cybersecurity marks as a pivotal breakthrough in the battle against cyber threats.The key is to identify the vulnerables within the system thus effectively negating the possibility of a potential attack , bolstering security , and safeguarding both corporate and customer data. By offering a cutting edge technology to detect and mitigate cyber crimes effectively, AI and ML has shaped the way we think and respond to cyber security challenges. As the digital landsccape is continuously evolving, the demand for people with Artificial Intelligence and Machine Learning Expertise is on the rise.

Benefits :

Malware Detection

Identifying potential Phishing attacks

Monitoring System Performance such as data usage, processor, memory usage etc

Maintaining Data Privacy

Identifying Unauthorized user behavior pattern.

Check out our master program in Data Science and ASP.NET- Complete Beginner to Advanced course and boost your confidence and knowledge.

URL: www.edujournal.com

[phonemantra-blog]

The Growing Scrutiny of Worldcoin: A Closer Look at the Crypto Project As Worldcoin, a crypto project co-founded by OpenAI CEO Sam Altman, gains global attention, it's also facing increasing scrutiny from governments. Launched in July, the project offers free cryptocurrency in exchange for iris scans through its "orb" devices, aiming to provide a digital ID that can validate human identity in an AI-driven world. However, privacy concerns have arisen regarding Worldcoin's data collection practices. While the project claims to delete or encrypt the biometric data, privacy advocates remain skeptical. Here's a closer look at the actions taken by governments in response: Argentina's Investigation Argentina, where Worldcoin has garnered significant interest, has initiated an investigation by the Agencia de Acceso an Informacion Publica (AAIP) data regulator. The AAIP is scrutinizing Worldcoin's collection, storage, and use of personal data. The regulator seeks information about risk mitigation and the legal basis for processing personal data related to the project. [caption id="attachment_51053" align="aligncenter" width="600"] Scrutiny of iris-scanning crypto project Worldcoin grows[/caption] France's Checks France's data watchdog, CNIL, conducted checks at Worldcoin's Paris office recently. CNIL had previously expressed concerns about the legality of Worldcoin's biometric data collection. The regulator's actions indicate a growing interest in evaluating the project's compliance with data protection laws. Germany's Ongoing Investigation In Germany, a data watchdog has been investigating Worldcoin since late last year due to concerns surrounding the extensive processing of sensitive biometric data. Additionally, Germany's financial regulator, Bafin, has initiated an investigation into the digital currency project, underlining the multi-faceted scrutiny it faces. Kenya's Suspension Kenya has temporarily suspended Worldcoin's local activities while conducting a government assessment of potential risks to public safety. Preliminary reviews have raised concerns, including questions about consumer consent in exchange for monetary rewards, which authorities deem close to inducement. Portugal's Involvement Portugal's data regulator, the CNPD, has inspected Worldcoin's local data collection operations. The regulator has also been in contact with the Bavarian data protection authority in Germany, indicating a collaborative approach to addressing concerns related to Worldcoin's data practices. FAQs About Worldcoin's Scrutiny Q1: What is Worldcoin, and why is it facing scrutiny? Worldcoin is a crypto project offering free cryptocurrency in exchange for iris scans. It's under scrutiny due to concerns about its data collection practices and the use of biometric data. Q2: How does Worldcoin address privacy concerns regarding biometric data? Worldcoin claims to either delete or encrypt biometric data. However, privacy advocates remain cautious about data security. Q3: What actions have governments taken regarding Worldcoin? Several governments, including Argentina, France, Germany, Kenya, and Portugal, have initiated investigations and checks into Worldcoin's data collection and practices. Q4: Is Worldcoin available globally, and how can users participate? Worldcoin is accessible to users worldwide through its "orb" devices, which require iris scans in exchange for cryptocurrency. Q5: Are there alternative methods to participate in Worldcoin without sharing biometric data? As of now, the project primarily relies on iris scans for participation. However, it remains essential to monitor updates and potential alternatives.

[joostrap]

Gravity Forms Encrypted Fields Nulled 5.9.2 Keep your data safe and secure with Enc...

[biedexcom]

Andrew Yang takes lead in California data privacy measure#stockmarkets#andrew #business #california #campaign_contributions #campaign_finance #campaigns #consumer_affairs #consumer_privacy #data #data_privacy #elections #general_elections #general_news #government_and_politics #human_rights_and_civil_liberties #lead #measure #political_fundraising #privacy #right_to_privacy #social_affairs #social_issues #state_elections #takes #technology #technology_issues #united_states_general_election #yang

[smestreet]

WhatsApp Went Down in India and Faces Issues Related Status & Privacy Settings

#WhatsApp Went Down in India and Faces Issues Related Status & #Privacy_Settings #Data_Privacy

WhatsApp went down on Friday evening for millions of users in India and elsewhere who reported issues with privacy settings as well as last seen online status not working.

According to outage monitor portal Down Detector, there was a 66 per cent spike in WhatsApp down reports, with last seen online status not working for them as well as 28 per cent reporting connection issues.

Both Android and…

[hub-pub-bub]

Following an investigation into Santa Cruz Public Libraries’ (SCPL) use of Gale Analytics on Demand, a California grand jury reported on June 24 that the use of data analytics tools by libraries “is a potential threat to patron privacy and trust.” The report’s broadly negative view regarding the use of big data and analytics software raises several questions about library privacy policies and how they should apply to the use of any data collected about patrons by third parties, when patrons have not explicitly given libraries permission to use that data.

This finding wasn’t the result of a lawsuit. California’s Superior Court convenes 58 separate civil grand juries each year—one for each of the state’s counties. These carry out several functions, including “investigating and reporting on the operations of local government.” In this watchdog role, a grand jury acts as a representative for county residents, generating recommendations for improving operations and enhancing local government accountability. Any local government entity subject to an investigation is required to respond to the recommendations within 90 days. In this case, the investigation was launched in early 2019, in response to concerns raised by SCPL staff.

These recommendations are not legally binding, and the report explains that SCPL’s use of Analytics on Demand does not appear to have violated any state laws. In addition, SCPL Director Susan Nemitz told LJ that the combination of staff concerns about utilizing commercial big data software to analyze patron habits, and the sense that it would require a major initiative to integrate Analytics on Demand into the library’s marketing efforts, had already led SCPL leadership to discontinue use of the tool prior to the investigation.

“Even though it’s a relatively simple product” to use, she explained, library management ultimately decided that “it really would take a major staff effort to make it part of our institutional research processes. So I don’t think our experiments [with Analytics on Demand] really went very far.”

Analytics on Demand is built on Experian Mosaic, a demographic analysis and classification tool used by many businesses for neighborhood-level analysis of customers and potential customers. Mosaic classifies households into 19 groups and 71 unique types such as “middle-class melting pot” or “young, city solos.” Since it is driven by the vast trove of consumer data collected and aggregated by multinational credit-reporting agency Experian, the tool can generate a lot of information, reporting demographic composition and predicting consumer habits, product preferences, and the prevailing attitudes of neighborhoods—or even individual households.

SCPL officials had used an Analytics on Demand license provided by the Pacific Library Partnership (PLP) consortium for a handful of projects beginning in 2017, Nemitz said.

“We aren’t a large library system—we don’t have a huge marketing team—so we had a couple of staff…go to a [PLP] training at Oakland Public,” she explained. “For us, the interest was, we collect no demographic data on our users. Could we [use Analytics on Demand to] provide our funding bodies with some reports about demographic use? Proving that we are serving low-income patrons? Another thing that we looked at when temporarily closing a branch, was…where to put temporary services. We did do one marketing thing to try to figure out where history programs geared toward older adults might be best presented.”

These uses are typical for Analytics on Demand, and indicative of pressures common throughout the library field, including limited outreach budgets and a demand for specific information about a library’s usage and local impact from government and other funding bodies. Yet SCPL’s staff concerns are also reflective of the tension between the implicit promise of privacy for library users and the competition of library services with commercial entities, such as Amazon, that have expansive data collection and analysis policies built into their terms of service agreements.

According to the report, a key sticking point for concerned SCPL staff was that by inputting address information into Analytics on Demand, the library was downloading significant household-level data that patrons had never consented to give the library.

“This gets into the question of combining data sets,” explained Becky Yoose, Library Data Privacy Consultant for LDH Consulting Services. “You have patron data in your integrated library system. You have patron data collected by individual electronic systems, like your catalog, your web analytics software, your electronic resources, [and] authentication systems like EZproxy. The issue comes when you start combining this information in one central place—especially when you’re combining this information with other external datasets that might have other sensitive or ‘high-risk’ data,” including information that could personally identify a user.

In addition, SCPL staff expressed concern about how Gale might be using patron data generated by the platform. Noting that the grand jury report did not include any specific recommendations for Gale, company representatives declined to comment for this article. However, the report cited prior SCPL communication with Gale, in which the company stated that “Gale does not personally handle the library data. There is no need for someone outside the library to manually review, handle, or receive files, like there is with other services. All data is submitted to [Analytics on Demand] directly by the library. In other words, there is no data being ‘exchanged with third parties’…. When the tool generates reports, the library can delete the report at their discretion. There is nothing maintained by us or [any additional third] party. The only information [Analytics on Demand] requires to function is an address. We do not require a name or any other identifiable information that is not public record.”

These statements imply that libraries using Analytics on Demand are pulling data directly from Experian Mosaic via patron address ranges, and Gale is not storing or exchanging any resulting reports with other third parties. Still, the grand jury report found that the library’s use of Analytics on Demand was inconsistent with its policy on Confidentiality of Library Records and companion document, “Information We Keep About You,” which was most recently revised in 2010. Among its many recommendations, the report states that the use of any data analytics tools should be clearly addressed in privacy policies. Patrons should be informed about their use, and all vendor contracts should be thoroughly vetted to ensure that vendors protect the interests of patrons and libraries.

Carol Frost, CEO of PLP and executive director, Peninsula Library System, noted that the grand jury process is not yet complete (SCPL’s reply to the report is due September 23), and PLP wished to honor that process in comments to LJ. But she added that “the section of the report which applies to PLP has some points which all libraries should consider when signing contracts. PLP has an NDA (Non-Disclosure Agreement) which covers patron privacy as well as the non-sharing of data, and addresses most of the items listed in the recommendations. We think it is a best practice for all libraries to use an NDA as a supplement to an agreement when patron privacy is involved, as well as having patron privacy policies. Gale Cengage also has several documents which were not referenced in the Grand Jury report which outline the protection of data when using Analytics on Demand.”

PLP member libraries are located in communities throughout Silicon Valley, and the consortium is “acutely aware of data privacy,” Frost added. “The Facebook sharing of data last year, along with the California Consumer Protection Act (which goes into effect in January 2020) made our libraries start to think about their own data privacy policies. In January we decided to apply for a [Library Services and Technology Act] grant to explore that nexus between library policies and the Consumer Protection Act.”

The grant was awarded, and PLP has used the funding to develop California-specific training workshops, as well as “a resource toolkit for libraries on privacy-related topics surrounding library data privacy and digital safety, including privacy policy and procedure best practices, tips for library staff for working with vendors in sharing patron data, and an overview of the data privacy lifecycle in libraries,” according to an announcement regarding the funding.

SCPL will be one of the library systems taking advantage of these new classes and other resources this fall, Nemitz said. SCPL also has established a page on its website with a list of every third party vendor the library uses, along with links to the privacy policies of those vendors, login methods, data retained by each vendor, and other information at santacruzpl.org/data_privacy.

“I want to own that, clearly, we did not address staff concerns well enough” with the library’s use of Analytics on Demand, Nemitz said. Going forward, SCPL is facing a challenge that is becoming increasingly common within the field—meeting the expectations of patrons who have become accustomed to the seamless conveniences enabled by big data, while adhering to policies that promise privacy.

The grand jury report “keeps us talking about really important issues in our field,” Nemitz said. “And I don’t think there are perfect answers right now…. But we as professionals need to care, and we need to help our patrons understand a lot more about data privacy.”

[selfhowcom]

역추적이 가능한 익명화된 데이터의 개인정보

[ankaapmo]

Apple Transfers Chinese Users' iCloud Data to State-Controlled Data Centers - #Ankaa

Apple Transfers Chinese Users' iCloud Data to State-Controlled Data Centers There’s terrible news for Apple users in China. Apple’s Chinese data center partner has transferred iCloud data, belonging to 130 million China-based users, to a cloud storage service managed by a state-owned mobile telecom provider—raising concerns about privacy. Back in February t... https://ankaa-pmo.com/apple-transfers-chinese-users-icloud-data-to-state-controlled-data-centers/ #Apple #Apple_ICloud #Apple_Icloud_Account #Censorship #China #Cyber_Security #Data_Localization_Law #Data_Privacy #Hacking_Apple_ICloud #Icloud_Download #Privacy

[edujournalblogs]

Navigating the Data Science Job Market. Tips and Strategies.

The field of Data Science is growing rapidly like never before and ‘data’ has become the main driving force behind it. Companies around the world are competing with each other to leverage the power of data to derive insights out of it.  They require skilled professionals who can collect and pre-process data, analyze the data, and provide insights to the management for decision making.  The professionals working in Data Science are basically Data Analytics, Data Scientist, Data Engineers, machine learning specialist, AI Engineers,  Deep Learning specialists etc., who are professionally skilled to transform raw data into meaningful insights. 

There is an escalating demand for adopting data science strategies in various domains (industries) like finance, Ecommerce, Banking, Insurance, healthcare, technology, Logistics, Cyber Security etc to optimize their productivity and efficiency.

Let us navigate into the Data Science Job markets and see what are the critical skill sets you require for data science jobs, and the tips and strategies.

Technical Skills:

- Processing Raw Data and Data Wrangling (ie., converting raw data into usable or workable form)

- Database Management System like Oracle, Sql server and MySQL,  and NoSQL, BigData, Data Warehousing, Data Mining etc.,

- Mathematics and Statistics

- Programming Languages like Python and R

- Data Visualization with PowerBI or Tableau

- Machine Learning, Deep Learning , Neural Networks and Parallel Computing

- Natural Language Processing (NLP)

- Cloud Computing

- Tools like Hadoop and Spark

- SAS (Statistical Analytical Software for Statistical Analysis and  Computing)

Non-Technical Skills:

- Strong Business Acumen

- Good Communication Skills (both verbal and written)

- Critical Thinking

- Good Analytical Skills

- Good Decision Making Ability

Tips to become a Data Science Professional :

The Data Science jobs require domain specific skills and knowledge to perform the task.  The skill sets vary from industry to industry, ranging from  healthcare, banking, Ecommerce, insurance, logistics etc , The jobs in data science that are likely to boom by the year 2025 are data analyst, data scientist, data engineers, machine learning specialists, deep learning specialists, and  AI engineers.  A blend of both technical and non-technical skills are required to make your career path a smooth going affair.  Here are a some tips that can help you out in your career journey.

1 Understanding the basics:  You need to know the basics of core subjects in  mathematics, statistics and basic programming.  With this basic foundation grows your building ie., Data Science.

2. Cleaning and Manipulation of data: Cleansing, preprocessing and manipulate data  (ie., remove redundant and irrelevant data), before doing analysis.

3. Develop your skills : on BigData, Programming languages such as Python or R,  libraries, tools, Data Visualization Techniques,  statistical model to make  inference.

4. Build a Strong Portfolio : to showcase your skills to potential employers, which reflects on your exposure to the projects you have done.

5. Networking with seniors and other stake holders: Connect with other data scientists, seniors, managers. professionals from other departments, and other stakeholders, who are  people with shared goals and interest, and  makes you stay updated and connected with latest trends.

6. Develop your Analytical Skills and Critical Thinking : You can develop this skills by Testing a few Hypotheses and making  some challenging Assumptions to it.

7. Develop your communication skills (both verbal and written):  Communicate findings clearly to stakeholders and  other non-technical persons. 

8. Ensure Data Privacy : As you are dealing with sensitive data, you will need to ensure privacy of data.

Data Science Strategies:

The Data Science field is constantly evolving day by day with new technologies emerging on a regular basis.  With that comes a greater scope for optimization.  There are few strategies to deal with this trend.

1. Problem Definition: Define your problem clearly, understand the goals and objectives and understanding the problem etc.,

2. Analyze the data : After Cleaning and Preprocessing of data, data is transformed and formatted to make  suitable analysis to draw insight.    In the case of large and  complex data sets of varied forms, to discover trends and patterns and draw insights from data, machine learning algorithms are used. You can use the various Python library for that like Keras for developing neural networks in machine learning models, PyTorch for NLP and computer vision for ML model.  Matplotlib and Seaborn for data visualization, Theano for numerical computations in machine learning, TensorFlow is used to visualize machine learning models on desktop and mobile and many other libraries.

3. Exploring the data : Data is aggregated with Data Visualizations techniques, correlation analysis etc  to understand the characteristics of data.

4. Choose a model :  Based on the exploration of data, you can select an appropriate statistical model like regression, classification, clustering etc to understand the pattern.

5. Test the model : Based on the above model, you need to test it on the various subsets of data, to evaluate the performance of each subset, and ascertain  the best among them.

6. Optimizing the model: if the model performance is unsatisfactory, you should explore ways to optimize it further. 

7.  Communicate your findings to the stakeholder :   if the model is performing satisfactorily, you should communicate the findings to the stakeholders.

8. Collaborate with others : Collaborating with others can give you new insight and perspective on the problem. 

9.  Keep yourself updated with the latest trends : You should stay relevant by catching up with the latest trends and technologies.

Conclusion:

The above tips and strategies can help you navigate the job markets and boost your motivation and interest in breaking into the  Data Science career. For that, honing the right skills set is important along with developing skills like  business acumen and communication skills  to work your way competently.  Choosing for a career in data science today would mean treading a promising career path.  I hope you will  find these tips and strategies  helpful.  Contact edujournal (www.edujournal.com) for further queries.

[biedexcom]

As Europe faces 2nd wave of virus, tracing apps lack impact#stockmarkets#2019-2020_coronavirus_pandemic #2nd #adoption #application_software #apps #business #child_welfare #communication_technology #computing_and_information_technology #consumer_electronics #coronavirus #data_privacy #disease_outbreaks #diseases_and_conditions #epidemics #europe #faces #family_issues #general_news #government_and_politics #health #human_welfare #humanitarian_crises #impact #infectious_diseases #lack #lung_disease #media #mobile_communication_technology #mobile_media #mobile_phones #mobile_software #pandemics #public_health #smartphones #social_affairs #social_issues #software #technology #technology_issues #tracing #virus #wave

[smestreet]

Data Privacy Compromised As Hackers Are Selling 73 Million Records on Dark Web for Around Rs. 13.6 Lakhs

#Data_Privacy Compromised As #Hackers Are Selling 73 Million Records on #Dark_Web for Around Rs. 13.6 Lakhs #CyberSecurity #Cybercriminals

Information is kept at a premium specially in the modern world. And Internet is the major source of information gathering. However, some cybercriminals are opting the wrong ways of acquiring valuable data of common Internet Users and selling it in their market. in a latest cybersecurityreport, a hacker group is found of selling data of 10 companies including online dating app Zoosk, US newspaper…

[hub-pub-bub]

Following an investigation into Santa Cruz Public Libraries’ (SCPL) use of Gale Analytics on Demand, a California grand jury reported on June 24 that the use of data analytics tools by libraries “is a potential threat to patron privacy and trust.” The report’s broadly negative view regarding the use of big data and analytics software raises several questions about library privacy policies and how they should apply to the use of any data collected about patrons by third parties, when patrons have not explicitly given libraries permission to use that data.

This finding wasn’t the result of a lawsuit. California’s Superior Court convenes 58 separate civil grand juries each year—one for each of the state’s counties. These carry out several functions, including “investigating and reporting on the operations of local government.” In this watchdog role, a grand jury acts as a representative for county residents, generating recommendations for improving operations and enhancing local government accountability. Any local government entity subject to an investigation is required to respond to the recommendations within 90 days. In this case, the investigation was launched in early 2019, in response to concerns raised by SCPL staff.

These recommendations are not legally binding, and the report explains that SCPL’s use of Analytics on Demand does not appear to have violated any state laws. In addition, SCPL Director Susan Nemitz told LJ that the combination of staff concerns about utilizing commercial big data software to analyze patron habits, and the sense that it would require a major initiative to integrate Analytics on Demand into the library’s marketing efforts, had already led SCPL leadership to discontinue use of the tool prior to the investigation.

“Even though it’s a relatively simple product” to use, she explained, library management ultimately decided that “it really would take a major staff effort to make it part of our institutional research processes. So I don’t think our experiments [with Analytics on Demand] really went very far.”

Analytics on Demand is built on Experian Mosaic, a demographic analysis and classification tool used by many businesses for neighborhood-level analysis of customers and potential customers. Mosaic classifies households into 19 groups and 71 unique types such as “middle-class melting pot” or “young, city solos.” Since it is driven by the vast trove of consumer data collected and aggregated by multinational credit-reporting agency Experian, the tool can generate a lot of information, reporting demographic composition and predicting consumer habits, product preferences, and the prevailing attitudes of neighborhoods—or even individual households.

SCPL officials had used an Analytics on Demand license provided by the Pacific Library Partnership (PLP) consortium for a handful of projects beginning in 2017, Nemitz said.

“We aren’t a large library system—we don’t have a huge marketing team—so we had a couple of staff…go to a [PLP] training at Oakland Public,” she explained. “For us, the interest was, we collect no demographic data on our users. Could we [use Analytics on Demand to] provide our funding bodies with some reports about demographic use? Proving that we are serving low-income patrons? Another thing that we looked at when temporarily closing a branch, was…where to put temporary services. We did do one marketing thing to try to figure out where history programs geared toward older adults might be best presented.”

These uses are typical for Analytics on Demand, and indicative of pressures common throughout the library field, including limited outreach budgets and a demand for specific information about a library’s usage and local impact from government and other funding bodies. Yet SCPL’s staff concerns are also reflective of the tension between the implicit promise of privacy for library users and the competition of library services with commercial entities, such as Amazon, that have expansive data collection and analysis policies built into their terms of service agreements.

According to the report, a key sticking point for concerned SCPL staff was that by inputting address information into Analytics on Demand, the library was downloading significant household-level data that patrons had never consented to give the library.

“This gets into the question of combining data sets,” explained Becky Yoose, Library Data Privacy Consultant for LDH Consulting Services. “You have patron data in your integrated library system. You have patron data collected by individual electronic systems, like your catalog, your web analytics software, your electronic resources, [and] authentication systems like EZproxy. The issue comes when you start combining this information in one central place—especially when you’re combining this information with other external datasets that might have other sensitive or ‘high-risk’ data,” including information that could personally identify a user.

In addition, SCPL staff expressed concern about how Gale might be using patron data generated by the platform. Noting that the grand jury report did not include any specific recommendations for Gale, company representatives declined to comment for this article. However, the report cited prior SCPL communication with Gale, in which the company stated that “Gale does not personally handle the library data. There is no need for someone outside the library to manually review, handle, or receive files, like there is with other services. All data is submitted to [Analytics on Demand] directly by the library. In other words, there is no data being ‘exchanged with third parties’…. When the tool generates reports, the library can delete the report at their discretion. There is nothing maintained by us or [any additional third] party. The only information [Analytics on Demand] requires to function is an address. We do not require a name or any other identifiable information that is not public record.”

These statements imply that libraries using Analytics on Demand are pulling data directly from Experian Mosaic via patron address ranges, and Gale is not storing or exchanging any resulting reports with other third parties. Still, the grand jury report found that the library’s use of Analytics on Demand was inconsistent with its policy on Confidentiality of Library Records and companion document, “Information We Keep About You,” which was most recently revised in 2010. Among its many recommendations, the report states that the use of any data analytics tools should be clearly addressed in privacy policies. Patrons should be informed about their use, and all vendor contracts should be thoroughly vetted to ensure that vendors protect the interests of patrons and libraries.

Carol Frost, CEO of PLP and executive director, Peninsula Library System, noted that the grand jury process is not yet complete (SCPL’s reply to the report is due September 23), and PLP wished to honor that process in comments to LJ. But she added that “the section of the report which applies to PLP has some points which all libraries should consider when signing contracts. PLP has an NDA (Non-Disclosure Agreement) which covers patron privacy as well as the non-sharing of data, and addresses most of the items listed in the recommendations. We think it is a best practice for all libraries to use an NDA as a supplement to an agreement when patron privacy is involved, as well as having patron privacy policies. Gale Cengage also has several documents which were not referenced in the Grand Jury report which outline the protection of data when using Analytics on Demand.”

PLP member libraries are located in communities throughout Silicon Valley, and the consortium is “acutely aware of data privacy,” Frost added. “The Facebook sharing of data last year, along with the California Consumer Protection Act (which goes into effect in January 2020) made our libraries start to think about their own data privacy policies. In January we decided to apply for a [Library Services and Technology Act] grant to explore that nexus between library policies and the Consumer Protection Act.”

The grant was awarded, and PLP has used the funding to develop California-specific training workshops, as well as “a resource toolkit for libraries on privacy-related topics surrounding library data privacy and digital safety, including privacy policy and procedure best practices, tips for library staff for working with vendors in sharing patron data, and an overview of the data privacy lifecycle in libraries,” according to an announcement regarding the funding.

SCPL will be one of the library systems taking advantage of these new classes and other resources this fall, Nemitz said. SCPL also has established a page on its website with a list of every third party vendor the library uses, along with links to the privacy policies of those vendors, login methods, data retained by each vendor, and other information at santacruzpl.org/data_privacy.

“I want to own that, clearly, we did not address staff concerns well enough” with the library’s use of Analytics on Demand, Nemitz said. Going forward, SCPL is facing a challenge that is becoming increasingly common within the field—meeting the expectations of patrons who have become accustomed to the seamless conveniences enabled by big data, while adhering to policies that promise privacy.

The grand jury report “keeps us talking about really important issues in our field,” Nemitz said. “And I don’t think there are perfect answers right now…. But we as professionals need to care, and we need to help our patrons understand a lot more about data privacy.”

Matt Enis

Matt Enis (menis@mediasourceinc.com, @MatthewEnis on Twitter, matthewenis.com) is Senior Editor, Technology for Library Journal.

[edujournalblogs]

Why choose data science for your career

Looking to get a career in the field of Data Science? YES. it is a lucrative, in-demand, progressive, futuristic and growth-oriented technology. So what is it that makes data science such a scorching hot field to get into?

Being a Data Scientist involves having a basic skill set viz., knowledge of basic mathematical, basic statistics and probability, basic computational and business analytical skills. In other words, as a data scientist, you need to consistently have one foot on the IT sector, and the other planted firmly in the business world. You need to have expertise in all the domains.

Data Science is mainly focused on exploration of data , making an inference from the data, and deriving an insight or prediction from the inference with the help of various statistical and mathematical models, programming languages like python or R language, algorithms in machine learning with python, visualization tools like Tableau or Power BI etc., Data Science requires the usage of both structured and unstructured data.

The machine learning requires two inputs for it to operate viz.,

a) Algorithms and

b) Data.

You should always provide clean data, otherwise the models that you develop will be all junk. You can derive insights and trends from data using any of the models and tools mentioned above. The choice is yours and the decision is taken after taking in account the complexity and scale of the problem. Thus, it helps business in taking the right decisions at the right time and also facilitates better strategic planning.

Let’s take an example in the healthcare sector where to detect or identify cancer in a person early, various medical reports (data) of the patient are provided to the system. The algorithms in machine learning makes learning by using the algorithms and comparing your data with previous available records of patients, comparing the various parameters with the existing normal values, making an analysis and derives at a final conclusion (result). The more data (historical data) of patients you have, the more accurate your result will be. Also, if you provide some arbitrary historical data, your result will not reflect the correct picture.

If you wish to make a career in data science, you have two options viz.,

Research Field like PhD and Post Doctoral: If you intend to go into the research field, you need to be qualified in that area of study, and have a thorough knowledge and understanding of mathematical, statistical and computational concepts related to the study.

Product Analytics and Visualization for industries and Service Sectors: To choosing this program, you need to have a basic knowledge of the mathematical and statistical concepts, basic probability, basic knowledge of python and a good knowledge of python libraries like pandas, matplotlib and numpy and various tools associated with it viz., visualization tools like Tableau or Power BI. Also, be well versed with SQL databases (MySQL, SQL Server or Oracle), and Business Analytics along with machine learning and Deep Learning (including neural networks), Predictive Modeling,  and NLP.  Our master program in Data Science is basically dealing with developing Product Analytics and Visualization for companies and our training program covers all of the above.

Our Data Science master program at eduJournal (www.edujournal.com), is a comprehensive program, designed to help learners of all skill levels, master this technology. Our syllabus is designed in such a way that it covers everything from the basic to advanced concepts which include expert instructions, coding exercise, quizzes, case studies and real world projects. It provides learners with the skill and knowledge to analyze, visualize and derive insights, trends or predictions from the data and hone their skills by learning concepts by providing case studies associated with it and working on real world projects. We also hone your skills with Data Science Interview Questions widely asked in interviews like scenario based interview questions, where you will be given a scenario and asked questions based on that scenario. To get through this round you will need a good working practical knowledge, which can be achieved by doing some real world projects. We will guide you to prepare for that round. Also, we have Data Science quizzes to measure your Data Science skills.

Roles and Responsibilities of a Data Scientist:

1. Understanding the Clients requirements.

2. Gather and Extract the dataset associated with the requirement.

3. Clean and pre-process the data.

4. Explore, Analyze and visualize data using various analytical tools and various statistical or mathematical models and computational libraries and algorithms.

5. Derive insights and make predictions.

6. Evaluate the performance of these models and make improvements if required.

7. Communicate the results and findings to stakeholders (client).

8. Monitor and maintain the performance over time.

How to become a Data Scientist:

1.Learn the basics of python (viz., libraries like pandas, matplotlib, numpy, scikit-learn, TensorFlow etc., and developing algorithms for machine learning using python) or R Language (if you are developing statistical and mathematical models).

2. Familiarize yourself with the tools used for data analysis like the Power BI, Seaborn and Tableau and the various libraries mentioned above.

3. Understand the basic mathematical concepts (linear algebra, decision trees), statistical concepts (Linear regression) and probability, neural networks (Deep Learning & AI), which are required for developing algorithms which are the core to data science.

4. Familiarize yourself with working with different types of data such as structured and unstructured data and various file formats like json, csv, xls, sql dump .

5. Understand the importance of data ethics and how to handle sensitive data carefully.

Advantages of Data Science:

1. Abundance of opportunities: Data Science is greatly in demand today, and there is lots of opportunities for job seekers with excellent remuneration packages. It is estimated to generate 11.5 million jobs by the year 2026. As the data becomes increasingly important to aid in decision making process, the demand for data scientists continues to grow, making it a highly demanding skill in the job market.

2. Used in multiple domains: it is a versatile field used in multiple domains such as finance, healthcare, marketing, banking, insurance, telecommunication, automobile, consultancy services etc., giving you the flexibility in career path.

3. Empowering managements to make better decisions: Enables companies to make smart business decisions thus, improving the overall performance of the company. The ability to work with large quantity of data and generate insights or predictions, creating new patterns, analyze data and generate reports etc., can help in the overall development and increase the productivity of the company.

4. Provide personalized insights: Enables computers to understand and predict human behavior and make data-driven decisions based on historical data. For eg., Ecommerce sites providing personal insights to users based on past historical purchases.

5. Handling complex problems: Facilitates breaking a larger complex problem into smaller manageable units and deriving at a solution.

6. Technological Advancement: With the improvements in technology, the ability to collect and store data, make analysis from data, deriving insights and make predictions etc., has made data science a popular field with greater potential for innovation.

7. Personal growth: It is a rewarding career for professionals who wish to use their problem solving skills and creativity to find solutions to problems.

Disadvantages of Data Science:

1. Mastering Data Science is close to impossible: Data Science is a vast subject. The role of a data scientist depends on domain in which the company is specialized in. For example, in a healthcare sector, a data scientist working on the analysis of genome sequencing will require some knowledge of genetics and molecular biology to create an algorithm for machine learning.

2. Arbitrary data may yield unexpected results: Many times, the data provided is arbitrary and does not yield desired results.

3. Data Privacy issues: While data scientists help clients make data-driven decisions, the ethical concern of individuals regarding the preservation of data privacy and its usage have been a cause for concern.

Some common Python libraries used in Data Science for data analysis:

a) pandas

b) numpy

c) matplotlib

d) TensorFlow

e) Scipy

f) keras

g) scikit-learn

Data Science has become an inevitable part of any industry today. The role of a data Scientist is to assist the management to make better decisions. Data Science is a trending field today, helps you develop valuable skills, opening up newer career opportunities and has a great impact on society at large by offering both personal and professional growth. Our program provides students with real world projects which strength their portfolios to get their dream Data Science job by implementing these real-world Data Science projects.

Dive into the world of endless possibilities as you learn to harness the power of data to uncover hidden insights, from predicting trends to uncovering patterns, Data Science has the power to transform the way we live and work. Whether you are an absolute beginner or an experiences professional hoping to switch over to a Data Science career, our master program will take care of your journey to explore the world of data analytics and visualization. Get ready to uncover the future with Data Science!!!

URL : https://www.edujournal.com/why-choose-data-science-for-your-career/

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