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According to the Bureau of Labor Statistics (BLS), the employment of research analysts, including data analysts, is projected to increase by 23% from 2023 to 2031. This significant growth in data analysis careers presents promising prospects for aspiring candidates. It profoundly impacts the services and products provided to the public. As a data analyst, you must possess problem-solving and analytical skills and technical knowledge of computer science, statistics, and mathematics. This field offers ample opportunities for personal and professional growth, allowing you to work with cutting-edge technologies. But what exactly does this exciting career path entail? Let’s explore the expectations placed upon an ideal candidate providing data analysis services to a company.What Does a Data Analyst Do?
Data analysis refers to gaining information from data or analyzing it to use it for business benefit. It provides quality insights with crucial points that direct the company’s decision-making process. The job’s roles and responsibilities include:
Gathering data for analysis. It will involve discovering or collecting different types of data through various modes. Examples include surveys, polls, questionnaires, and tracking the visitor characteristics on the website. Alternatively, the datasets can be purchased depending on the requirement and availability.
Programming languages perform the cleaning process on the data generated after the previous step, called raw data. The name implies the presence of unwanted information, including outliers, errors, and duplicates, that require processing. The cleaning process aims to enhance the quality of the data and make it usable.
The data needs to be modeled now by providing it with a structure and representation in an organized manner. It will also involve categorizing data and other relevant processes to make it presentable.
The data thus formed will serve multiple purposes. The usage will depend on the problem statement, which will also determine the method of interpretation. Data interpretation mainly involves finding trends or patterns in data.
Presentation of data is an equally important task where the prime requirement is to let the information reach the viewers and involved parties in the same way as intended. It requires presentation and communication skills. Often data analysts take the aid of charts and graphs, followed by report writing and information presentation.
Source: ForageReasons to Become a Data Analyst
Multiple reasons can encourage one to become a data analyst. The five most important ones are:
High demand: The rise in data generation has led to tons of unprocessed data. It holds numerous secrets that the companies can use. The requirement for individuals who can carry out the task is growing exponentially, with the standard requirement of 3000 positions annually.
High pay: The pay scale for a data analyst position is high and worth pursuing the career. The pay rise varies according to the industry and promises higher incomes with bonuses in some fields.
Lead the career choice: The skillful data analyst is set to bring value to the position and company. The possibility of growth, promotions, and additional benefits remains open everywhere. It positions you to get a change, lead the groups, teach them, become competitive, or shape the workforce culture.Demand and Future Job Trends
The current demand for data analysts is high at a good pay scale. The requirement in the future is also expected to grow based on the current speed of data generation. With the generation of new technologies and ease of data collection, the future will surely provide new opportunities to the talents. Some of the expected new job roles for data analysts in the future include:
Explain the functionality and suitability of AI. Quality analysis of the newly developed functions.
Working on a combination of real-time analytics in business operations and data processing. It will guide toward planning based on logic and strategy.
Generated data interpretation reports need to be self-explainable and easy to interpret. Data visualization is crucial, and the field holds good career scope.
Expect the introduction of augmented analytics, where complex datasets can be handled via ML algorithms and NLP algorithms. It will be engaging and universally accessible.
Development of Machine Learning and the Internet of Things to ensure the possibility of currently impossible things is also expected to occur.
Source: College VidyaSpecializations in Data Analyst Field
The data analyst position offers specific fields to work in. The different specializations to look forward to include:
Source: Online ManipalRisk Analyst
It includes working for money-based companies such as financial institutions and insurance companies. Their work is mainly focused on predictions based on the data. The risk analyst must go through economic conditions, financial documents, and other things as per the requirement.Budget Analyst
They are generally found to work in businesses or industries to assist in analyzing downward and upward trends. Typical examples of this sector include private businesses, educational institutions, and government-based institutions. The work here also is dependent on financial situations and documents.Operation Analyst
These job profiles are concerned with finding solutions to the problems in business. The specific focus here is on operations which can include different projects, manufacturing, or any other operation problem in the company.Research Analyst
They are concerned with research and deep insight into the available data. The research analyst has to deal with market information and extract information for investment, selling, and designing future strategies. The financial, investment, and equity expertise are of focus here.Marketing Analyst
It involves a sole focus on market conditions with an emphasis on trends, requirements, and needs of customers. It includes products and services, the target audience, and the ideal price for the company’s offerings.Business Intelligence Analyst
Business Intelligence Analyst analyzes complex data sets to provide insights and make data-driven recommendations for business improvement. They develop and maintain dashboards, reports, and data models, ensuring accurate and timely information. Their role involves translating data into actionable insights to support strategic decision-making and drive business success.Healthcare Analyst
The field is basically for a healthcare system that includes hospitals and pharmaceutical companies. The work here broadly focuses on public health, clinical information, pharmaceuticals, claims, costs, patient behavior, and satisfaction. The ultimate aim is to improve the process.8 Must Have Skills to Become a Data Analyst
The candidates looking for data analyst roles must have technical and professional skills.
Source: Rocket RecruitingTechnical Skills 1. Knowledge of Database Tools Such as Microsoft
Microsoft Excel helps summarize and simplify the data through pivot tables and provides fascinating representation methods, commands, and add-ins or additional features to handle the data. SQL helps manage data in relational databases, interpretation, reading, and manipulation.2. Ability to Work with Programming Languages Such as R or Python
Knowledge of programming languages is required for statistical analysis, machine learning, web development, data manipulation, and integration with web applications to simplify complex mathematical problems and data processing.3. Good Presentation Skills to Present the Interpreted Data
It involves using software like Jupyter Notebook and Tableau. They aid by providing interactive and dynamic visualization and helping curate dashboards and reports. Helping with data exploration, analysis, and iterative development allows independent running of code cells and debugging.4. Information and Application Ability of Statistics and Mathematics
Statistics serve to summarize and describe the data through variability, correlation, central tendency, and identification of patterns. Mathematics contributes to algorithm development and data modeling, providing concepts of linear regression, probability, and multiple significant theories.Professional Skills 6. Ability to Have a Passion for Solving the Problem and Overcoming Challenges
Dealing with a wide variety and high amount of data will pose a challenge. The demands and problems will vary with no solution. The analytical and problem-solving mindset will help you overcome them without looking for existing answers. It will help you create the solution.7. Communicate Clearly and Precisely
Data visualization is crucial in data analysis. However, it must be communicated effectively to the team members, seniors, management, and other involved authorities for easier and clearer interpretation. The inability to clarify them will decrease the value of your work leading to your efforts in vain.8. Core Knowledge of the Industry
Data analysis is applicable in a wide array of industries. Working in a specific industry, for instance, healthcare will make you come across medical terms. Familiarity with them will ease the workflow and increase the efficiency of results and work.
Data analysts have to work online. The different involved responsibilities require a variety of tools and proficiency in them. The important ones are:SAS
SAS or Statistical Analysis Software is used for statistical modeling. It allows data processing and manipulation by providing comprehensive procedures, functions, and data programming technology. SAS allows description, inferential statistics and regression, time series, and survival analysis which serves in the analysis procedure. It also contributes to visualization and data mining through information charts, plots, and graphs with customization options. Data analysts also use SAS to handle large-scale datasheets by efficiently subsetting, sorting, and merging the data.
Source: SAS InstituteMicrosoft Excel
Source: Learning computerSQL
The software is primarily used by Data Analysts while dealing with relational databases. They need it to retrieve, manipulate and filter the data for aggregations, joining multiple tables and calculations. The SQL allows data transformation into different data types and the creation of new derived columns. It also provides basic functions similar to Excel. One of the characteristic features here is the WHAT clause to find the data based on search criteria like inclusion or exclusion of specific ranges or values and logical conditions. Besides, data analysts use SQL for defining indexes, modification of database structures, security and permission management, and optimizing query performance.
Source: Microsoft LearnJupyter Notebooks
It is a web-based application highly beneficial and efficient for data analysts in their tasks like creating and sharing documents comprising live code and narrative texts. Jupyter Notebook provides an interactive environment through the possibility of writing and executing code in different programming languages. It is integrated with significant analytical libraries, such as NumPy, pandas, and scikit-learn in Python useful for statistical and machine learning. Some visualization-based libraries include Seaborn, Matolotlib, and Plotly. It has the characteristic feature of reproducibility, a flexible learning environment, and integration services such as APIs and cloud platforms.
Source: JupyterGoogle Sheets
Source: GoogleR or Python
The programming languages R and Python serve different purposes: R aids in statistical computing and analysis, while Python is widely employed for general-purpose programming. Python is preferable for integrating external sources and its vast library, resources, and tools specifically designed for data analysis. Both machine learning and data analysts use R and Python through their packages like xgboost, caret, scikit-learn, and TensorFlow. Data analysts utilize them for data manipulation, visualization, analysis, and transformation.
Source: TableauMicrosoft Power BI
It is a business intelligence tool that helps in processing the raw data. Microsoft Power BI provides a wide variety of features. It allows data exploration and analysis. Advanced analytics options such as Azure Machine Learning and Cognitive Services help with data analysts in sentiment analysis and incorporation of machine learning models. It also aids natural language processing, interactive dashboards creation, and custom visuals. The tools come coupled with mobile-friendly access to the program. The responsive designs are flexible and adjustable according to different devices and screen sizes, allowing non-stop working on the go. It also supports row-level security and role-based access control.
No career is made in a day. It requires discipline and consistency to set your foot strong in any field. The driving factor or purpose of going into the career is the key to remain consistent and continue working hard. Be it any phase of life, gaining knowledge is easy now. Regardless of background, you can choose the right career path to reach the goal of becoming a data analyst. You might consider being a data analyst a good job. The answer to this lies in your passion and will to make a career out of it. If you love different aspects of this job, you will never have to ask yourself if being a data analyst is good. Head on to learn, through Certified AI and ML Blackbelt program offered by Analytics Vidhya to know if data analytics is a good career.Frequently Asked Questions
Q1. How can I become a data analyst?
Ans. If you are at the school level, select math and statistics subjects. Further, pursue a bachelor’s degree in statistics, computer science, math, or others. Gain experience through internships and polish your skills with professional certificates. People wishing to switch fields can also gain professional certifications and enter their careers.
Q2. What is the salary of a data analyst in India?
Ans. The average data analyst salary in India is around 6 lakhs per year. The additional average compensation is around INR 75,000.
Q3. Does a Data analyst come into an IT job?
Ans. Yes, data analysis is part of the IT job. It requires mathematical and computer science skills, which are technical.
Q4. Are data analysts and data scientists the same?
Ans. No, though the positions hold similarities, the job profiles and responsibilities for both are different. Data analysts interpret the data to derive meaningful information, but data scientists focus on complex statistical models and algorithms for pattern identification and predictions.
Q5. Is data analysis a good job?
Ans. Data analysis is a good job with numerous opportunities. It is a demanding career profile with a good pay scale.
Q6. What are the types of data analysis in business?
Ans. Four types of analysis are done on the data to serve the business. It includes descriptive, predictive, diagnostic, and prescriptive analysis.
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Over last 6 years, I have come across more than hundreds of analysts and have conducted almost equal number of interviews. Over this time, I have developed a knack of differentiating best analysts from good and good analysts from bad. If you face this challenge regularly, this post might help you.
So how do you differentiate between good and bad analysts?
Thankfully, it’s not that difficult. I have put a framework around how to judge an analyst. You can use the same to make your life simpler. This framework has it’s genesis in hiring guidelines at Capital One. I have modified it to adopt it in Indian Analytics industry.
This is the most important attribute that distinguishes a good analyst from bad. This attribute is not only required to be a successful analyst, it becomes absolutely critical for a person managing Business Analysts.
So what is Structured thinking?
Structured thinking is a process of putting a framework to an unstructured problem. Having a structure not only helps an analyst understand the problem at a macro level, it also helps by identifying areas which require deeper understanding.
How do I test for structured thinking?
Typically I test this by throwing a open business problem at an analyst and then observing closely how he / she is solving it.
An example is asking a question like: “You have been appointed as CEO of a loss making restaurant at Delhi / London Airport and you are expected to join the company in a week. What would you want to do as a CEO of the company as soon as you join them?”
If the person lays out a nice structure about where the problems could be, he has already ticked one box. If he starts giving you answers out of his hat (e.g. I would be looking at what marketing are we doing?), you should consider it as a red flag. He will not be able to sail through the world of Analytics.
Business understanding and problem solving
There is a reason why Business Analysts are called so and not just Analysts. Until a person understands what he is trying to solve and the business owners are confident that he can solve problems in meaningful manner, he is a dead analyst.
So, how do you test for business understanding?
For an experienced analyst, I typically start judging this by asking about business context for the projects he might have worked on. If he can explain that clearly, it’s a good start. If he can’t, you can almost make your hiring decision here. Next, you can look at the answers a person gives in response to question asked for judging structured thinking. If he gives answers based on numbers only, you need to probe him further. He needs to put a business thinking hat and provide some out of box suggestions.
Attention to details
If a person is not detail oriented, he can never be a good analyst. Every analyst should have the ability to understand business at high level, but he should be able to get down to nuts and bolts of all the levers you might have.
So how do you judge for attention to details?
Start by looking at the CV of a person, has he spent time choosing words carefully? Has he mentioned impact of the projects he might have worked on?
For an experienced analyst, probe on the projects he might have worked on before. Did he consider all the aspects and possibilities? How much time does take to explain his previous projects?
Another way to judge it is by asking the candidate to a guess estimate, something like “Estimate the number of smartphones used in India” and looking at how the candidate answers them. How many factors does he consider to come up with answers? How many segments does he consider to arrive at sizing? These aspects should give you a good read on how detail oriented a person is.
Ability to triangulate numbers & do back of the envelope calculations
While the first three characteristics help you identify a better than average analyst, this characteristic and the next differentiates best of analysts from good analyst. This is an activity I love to do and something I know every good analyst loves. This is the ability to set up equations on page and then do back of the envelope calculations to answer 80% of questions without touching any excel / calculator or laptop. It is also the ability to arrive at a number through various sources and then validating them.
How to judge ability to triangulate numbers and do back of the envelope calculations?
Guess estimate comes to your rescue here. Just ask the candidate to perform the guess estimate on a paper and then ask him to validate the number through an alternate approach.
Communication skills – Ability to tell stories based on numbers
Any analyst is only as good as he can communicate. If a person can not take the complex world of numbers and create a meaningful story out of it, he will always be looked upon as a nerd. He can be a good analyst, but not the best one. Ability to create a story and present it almost has an equal, if not higher influence on your customers and hence increases the chances of success of any analytics project.
How do you judge communication skills?
You can get a sense on this through the entire interview. If this is very critical to the role you are evaluating for, you can provide datasets in excel and ask the person to present some open ended questions
Hopefully, this framework will help you for any analyst hiring in future. In case you have some suggestions, do let me know.
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Cybersecurity and data science have emerged as powerhouses in today’s quickly changing digital landscape, bringing exciting career prospects and the ability to have a substantial effect. Professionals with expertise in these fields are in high demand as businesses need help to preserve their sensitive data and capitalize on big data’s promise. But the crucial query still stands: Which route should you take? Which is better, cybersecurity or data science? Join us as we examine the fields of cyber security vs. data science, dissecting their nuances, contrasting their skill sets, examining their career paths, and eventually determining a better career path.
So buckle up and prepare to cross the exciting intersection of cyber security vs. data science, where technology, creativity, and opportunity meet.What is Cyber Security?
Protecting computer systems, networks, and data from unauthorized access, theft, or damage is the goal of cyber security. Cyber security experts are in charge of spotting weaknesses, putting preventive measures in place, spotting and handling security incidents, and developing risk-mitigation plans. They cover topics including network security, data security, application security, and incident response in their job.
Checkout – Future of AI and Machine Learning in CybersecurityWhat is Data Science?
In contrast, data science aims to glean insights, patterns, and essential information from massive amounts of structured and unstructured data. To solve complicated problems, make data-driven decisions, and create predictive models, data scientists use statistical analysis, and machine learning algorithms. They use programming languages, statistical tools, and visualization techniques to extract useful information and generate practical insights.Cyber Security vs Data Science
Cyber SecurityData ScienceFocuses on protecting computer systems, networks, and data from unauthorized access, attacks, and breaches.Focuses on extracting insights and knowledge from data through statistical analysis, machine learning, and other techniques.Involves measures such as encryption, firewalls, intrusion detection systems, and vulnerability assessments to safeguard information and mitigate cyber threats.Involves data collection, cleaning, analysis, and interpretation to uncover patterns, trends, and insights for informed decision-making.Addresses concerns related to confidentiality, integrity, and availability of data, as well as managing risks associated with cyber attacks and breaches.Deals with data acquisition, preparation, modeling, and evaluation to generate meaningful information and make data-driven decisions.Involves roles such as security analysts, penetration testers, incident responders, and cybersecurity architects.Involves roles such as data scientists, data analysts, machine learning engineers, and data chúng tôi goals include preventing unauthorized access, identifying vulnerabilities, detecting and responding to security incidents, and implementing effective security chúng tôi goals include extracting knowledge from data, making predictions, optimizing processes, and providing insights for business improvement.Cyber Security vs. Data Science: Skill Sets
To evaluate the suitability of data science vs. cyber security as career paths, it is essential to understand the requisite skill sets for each field:
While cyber security vs. data science has distinct technical skill requirements, there are areas of overlap and transferable knowledge. Both fields benefit from a solid foundation in mathematics, problem-solving, critical thinking, and analytical skills.
Additionally, proficiency in programming and working with large datasets are valuable in both domains.Educational Background and Training
Both data science and cyber security offer training programs, online courses, and resources to upskill and acquire new knowledge. Many universities and online platforms provide specialized classes and boot camps tailored to the specific requirements of each field. Continuous learning and staying updated with the latest developments are essential for professionals in both domains.Career Trajectory and Salary Potential Career Paths in Cyber Security
Source: CyberseekCareer Paths in Data Science
Data scientists can operate as data analysts, machine learning engineers, data engineers, data consultants, or research scientists, among other positions. They can specialize in predictive analytics, recommendation systems, computer vision, or natural language processing. Collaboration with cross-functional teams, including business analysts and software developers, is familiar with data science roles.
Source: Analytics India MagazineData Science vs Cyber Security: Salary
Data science vs. cyber security presents promising career prospects and attractive compensation potential. Salaries can change according to characteristics, including education, experience, location, and industry. Industry statistics indicate a considerable need for experts in both disciplines, which results in attractive remuneration plans. Salary ranges can differ significantly depending on the market and an individual’s unique situation.
Cybersecurity salary ranges can vary depending on criteria like experience, competence, industry, and location. Cyber security experts should anticipate competitive pay on average, nevertheless.
Entry-level positions: Entry-level analysts or engineers in the field of cyber security can generally expect to make between $60,000 and $90,000 a year.
Mid-level positions: Cybersecurity specialists might make between $90,000 and $120,000 annually after a few years of expertise.
Senior-level positions: Managers, consultants, and cyber security architects can make over $150,000 a year.
All industries have a significant demand for data scientists. Data scientists may earn between a specific range and a certain amount of money depending on their expertise, education, industry, and location.
Entry-level jobs: Entry-level data scientists can anticipate yearly incomes between $70,000 and $100,000.
Mid-level positions: Data scientists with a few years of experience can expect to make between $100,000 and $150,000 annually.
Senior-level jobs: Senior data scientists, data science managers, or data science directors can make more than $150,000 a year.Future Outlook
Current and Future Trends in Cyber SecurityCurrent and Future Trends in Data ScienceBecause cyber threats are coming at us more frequently and with greater sophistication, the field of cyber security is expanding quickly. Strong cyber security measures are now essential due to the rise of linked devices, the Internet of Things (IoT), and cloud computing. To safeguard their assets and sensitive data, sectors like finance, healthcare, government, and technology urgently need cybersecurity chúng tôi businesses increasingly value data-driven insights for decision-making, data science is in high demand. The development of big data, AI, and machine learning has accelerated the field’s expansion. Numerous industries use data science, including finance, healthcare, e-commerce, marketing, and manufacturing. Combining data science with cutting-edge technologies like automation and IoT presents exciting opportunities for future development.
Although there are many job prospects in data science and cyber security, each discipline has unique difficulties. Cyber security personnel must constantly learn new skills and adapt to new attack vectors due to the always-changing world of cyber threats. On the other hand, data scientists must balance gaining valuable insights with addressing data integrity, privacy, and ethics issues. However, it is anticipated that shortly, there will still be a high demand for qualified workers in both disciplines.Cyber Security vs. Data Science – Salary
When considering career options, salary is an essential factor to evaluate. It’s crucial to note that cyber security vs. data science salary can vary widely based on factors such as experience, location, industry, and company size.Choosing the Right Career Path
Choosing between a cyber security or data science career requires carefully considering personal skills, interests, and goals.Self-Assessment of Skills, Interests, and Goals
Evaluate your strengths and interests to identify which field aligns better with your aptitude and passion. Consider whether you enjoy problem-solving, data analysis, programming, or working in a highly technical and rapidly evolving environment.Factors to Consider When Deciding Between Cyber Security and Data Science Personal Preferences and Aptitude for Each Field
Ultimately, choosing a job route is heavily influenced by personal tastes. Others could be more drawn to the analytical parts of data science and concluding data. At the same time, some people might be drawn to the difficulties and excitement of protecting networks and systems against cyber threats. To make the most excellent decision for yourself, consider your personality, work preferences, and long-term professional aspirations.Data Science vs Cyber Security – Final Verdict!
So, data science or cyber security, which is better?
Both cyber security and data science offer promising career paths with unique opportunities and challenges. The two decisions ultimately depend on individual preferences, skills, and career aspirations. It’s important to assess personal strengths carefully, evaluate the industry demand, consider salary potential, and stay informed about the latest trends to make an informed career choice. Whether you pursue a career in cyber security or data science, both fields have significant potential for growth and can provide a fulfilling and rewarding professional journey.
For experts in cyber security vs. data analytics, Analytics Vidhya is an online platform that acts as a comprehensive reference center. Although it primarily focuses on data science, it discusses several cybersecurity-related topics. Analytics Vidhya is an invaluable resource for those looking for information, educational materials, and opportunities for community interaction in data science and Cybersecurity. Its extensive scope and various content aid in the professional development of experts in multiple fields.Frequently Asked Questions
Q1: What are the primary differences between cyber security and data science?
A. Cyber security protects computer systems and networks from unauthorized access, while data science involves extracting insights and patterns from data to make data-driven decisions.
Q2: Which field offers better salary prospects: cyber security or data science?
A. Both fields offer competitive salary potential, but salaries vary based on experience, location, industry, and company size.
Q3: What are the critical technical skills required for a career in cyber security?
A. Technical skills in networking protocols, operating systems, vulnerability assessment, penetration testing, and incident response are crucial in cyber security.
Q4: What academic qualifications are required for a career in data science?
A. A strong background in mathematics, statistics, computer science, or a related field is typically required for a career in data science, along with a bachelor’s or master’s degree in data science, computer science, or statistics.
Microsoft has announced that on April 8, 2014, it will stop supporting Windows XP. This means that the 12-year-old OS will no longer receive security updates to fix vulnerabilities which have been reported to Microsoft. The result is that hackers could increase their attacks on Windows XP users, especially in the case of any zero-day vulnerabilities that Microsoft subsequently fixes in other versions of Windows but which remain in XP. If you do stick with XP, you should read our XP end of support guide.
Microsoft’s own website tells users to not let their PCs “go unprotected.” And, of course, they want you to upgrade to another version of Windows which costs money. If you are looking for a free alternative to Windows XP, Zorin OS could be the one for you.
Zorin OS is a Linux distribution which tries to bridge the gap to Windows. It has been designed specifically for Windows users who want to move away from XP. It is based on Ubuntu and can be installed alongside XP. It also provides a way to run Microsoft Windows programs with the help of WINE and PlayOnLinux. Programs like Adobe Photoshop CS3 (10.0) or Yahoo! Messenger are reported to work without any problems. Also games like Final Fantasy XI Online and StarCraft should run out-of-the-box.
Installing Zorin OS is quite simple, especially if you want to replace XP with Linux. Since Zorin OS is based on Ubuntu, creating a dual-boot setup is simple enough. You can find details in our guide to dual-booting Windows and Ubuntu. The first step to installing Zorin OS is to boot the Live CD and then run the “Install Zorin OS” program. Follow the steps, but make sure that you don’t delete your existing Windows installation by mistake. When the installation has finished reboot your PC.
Zorin OS has been designed to be familiar to XP users, however it doesn’t try to blatantly copy the Windows look and feel. In the bottom left is the Z icon which serves as the “Start” button and gives you access to the installed programs. Along the bottom is the task bar, and at the bottom right is the clock and other tray icons.
Overall Zorin OS manages to make the transition from Windows to Linux a little bit easier. The UI is designed to be familiar to Windows users, and the inclusion of WINE helps with software that is only available for Windows. However, Zorin OS is still Linux and it can’t be considered as a slot in replacement for XP. The differences between the two operating systems, although in no way insurmountable, mean that only those with a reasonable level of technical competence will find Zorin OS a viable alternative. However, if you can’t upgrade to a newer version of Windows and you are stuck with XP, then there is no harm in giving Zorin OS a try! Being able to dual boot also helps as you can always return to Windows XP if you don’t like Zorin OS.
Gary has been a technical writer, author and blogger since 2003. He is an expert in open source systems (including Linux), system administration, system security and networking protocols. He also knows several programming languages, as he was previously a software engineer for 10 years. He has a Bachelor of Science in business information systems from a UK University.
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Recently Mark Zuckerberg made the news when a picture showed him with his laptop, and the camera and microphone were covered up. This set everyone discussing it as to whether it was necessary or whether it was leading too much into paranoia. We decided to pose this question to our writers and asked them “Is covering up your laptop’s microphone and camera a good practice?”Our Opinion
For the most part, we found that many of our writers believe covering up the camera and microphone is a good idea. While Derrik doesn’t have a laptop anymore, he does believe it’s a good practice to not have the camera and microphone available. When he did have a laptop he uninstalled the Linux module for cameras and just used a USB camera when he needed to.
Mahesh also believes it’s a good idea so that you’re not sharing anything with “spying eyes.” However, he wants to make sure people are doing it because they feel they should, not just because Zuckerberg did it. After that picture made headlines, he saw websites teaching everyone how to do it, informing everyone they should do it as well.
Trevor figures there is always someone smarter than him who might want to use his information to steal his identity or mine it for someone else. He sees it as a good idea even if you’re not a person of interest like Zuckerberg. He compares it to locking the door on your house or not writing your PIN number on your ATM card: preventative.
Charnita sees it as a good practice if it makes you feel comfortable but not just because it’s a new trend. She rarely uses a computer that has a camera or microphone so doesn’t worry about it. She doesn’t see it as important in her case, whereas she can see how it would be important for Zuckerberg. She jokes, “If someone wants to watch me stare at my screen all night long, they must be really bored.”
I very much agree with Charnita. I don’t see anything wrong with it, but I don’t necessarily see the point for someone like me. To me, it’s a little too “foil hat.” I’m usually on my iPad and not a laptop anymore, but I still wouldn’t be worried about it if I was. They wouldn’t see or hear anything interesting other than me talking to my family. Maybe they’d heard some good gossip, but that would be about it. Maybe it’s ignorance on my part, but as they say, “Ignorance is bliss.”Your Opinion
Laura has spent nearly 20 years writing news, reviews, and op-eds, with more than 10 of those years as an editor as well. She has exclusively used Apple products for the past three decades. In addition to writing and editing at MTE, she also runs the site’s sponsored review program.
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In other words, I felt a little edgy. Part of it was nerves, but I was also feeling resentment. I used to be a staff writer for newspapers and magazines, but through some seriously bad timing, I found myself out of a job and freelancing in 2008, just as the financial crisis rocked the publishing world to its core.
Ugh. Pizza face wasn’t the effect I was hoping for. But the mirror didn’t lie: I was 40 years old and needed a tube of Clearasil. Plus, I had developed a chronic stomachache. And my left eye kept twitching.
“That’s great,” my wife said, when she noticed the red bumps on my chin and nose. “You’ll fit right in with the kids.”
It was an awful time (and still is). Magazines closed; newspapers went bankrupt; there were massive layoffs. Two companies instituted hiring freezes within a month after I interviewed with them. Like many other Americans, I couldn’t find a job in my chosen profession. And meanwhile, my wife and I had just had a baby. I was in trouble, and I had to think about a career change.
Teaching was my first and last idea. I come from a family of educators, and people have always told me I’d be good in this field. I found an internship program through a nearby university that offers hands-on classroom experience — a one-year gig as an in-house substitute teacher at a high school. In the evening, I would take classes. At the end of a year and a half, I’d emerge with a master’s degree and certification. I decided to go for it.
But as my start date approached, I got more and more nervous. It didn’t help that I kept reading so many negative news stories about education. They all seemed to be about low pay, metal detectors, standardized testing, and tenured but ineffective teachers. No wonder my skin was breaking out. Still, I registered for classes at the university, and on a cold winter Monday, I walked through the front doors into the cinder-block hallways of the high school where I was to intern.
If my life were a movie, this is the part when the violins would crescendo, the action would switch to slow-mo, and I would experience an epiphany: “This is exactly where I belong.”
The reality wasn’t quite so dramatic. But you know what? It wasn’t that far off, either.
I became a writer because I love stories. I love to talk about people. I love to find out what excites them. What terrifies them. What’s hard for them. What makes them proud. As a journalist, I covered a lot of artists and actors, and I thought they had pretty interesting stories. But they don’t hold a candle to the stories I can tell after just a brief time at that high school.
My best story so far is about watching Barack Obama’s inauguration with the students. The day of the swearing in, the school set up a screen in the gymnasium and hosted an assembly. At first, the students were squirmy. But when the new president stepped up to take the oath, an amazing thing happened: Many of them spontaneously rose for a standing ovation.
“What is required of us now is a new era of responsibility,” I heard Obama say, “a recognition, on the part of every American, that we have duties to ourselves, our nation, and the world, duties that we do not grudgingly accept, but rather seize gladly, firm in the knowledge that there is nothing so satisfying to the spirit, so defining of our character, than giving our all to a difficult task.”
Credit: Wesley Bedrosian
Credit: Wesley Bedrosian
Yes, teaching might be a difficult task. But as I gazed around the gym at those students, I started to feel something that went beyond grudging acceptance for my new career. I realized that it just might work. And ever since then, my complexion has been much improved.Russell Scott Smith lives in Connecticut and teaches at Norwalk High School.
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