Data collection is crucial in determining the validity, reliability, and quality of conclusions drawn. The primary data collection designs include quantitative data collection, which focuses on gathering numerical data that can be quantified and subjected to statistical analysis. Key designs include surveys and questionnaires, experiments, observational studies, and mixed methods designs. Qualitative data collection focuses on understanding human behaviour and social phenomena by collecting rich, descriptive data that can provide deeper insights into participants’ experiences and perspectives. Key qualitative designs include interviews, focus groups, case studies, and mixed methods designs. Interviews allow researchers to engage with participants in dialogue and can be structured, semi-structured, or unstructured. Focus groups involve guided discussions among a small group of participants, typically led by a moderator, and are effective for exploring collective views, attitudes, and experiences. Case studies provide an in-depth examination of a particular individual, group, event, or phenomenon within its real-life context, allowing researchers to explore complex issues and gain a comprehensive understanding of the subject matter. Mixed methods research combines both quantitative and qualitative data collection designs to provide a more comprehensive understanding of the research problem. Researchers may use qualitative data to explore a phenomenon and then follow up with quantitative measures to generalize the findings across a larger population. In conclusion, choosing the appropriate data collection design is crucial for early academic researchers as it directly influences the validity, reliability, and overall quality of their conclusions. Understanding these designs is essential for effective research practice and ensuring the validity of their findings.
There are differenttypes of data in Statistics, that are collected, analysed, interpreted and presented. The data are the individual pieces of factual information recorded, and it is used for the purpose of the analysis process. The two processes of data analysis are interpretation and presentation. Statistics are the result of data analysis. Data classification anddata handlingare important processes as it involves a multitude of tags and labels to define the data, its integrity and confidentiality. In this article, we are going to discuss the different types of data in statistics in detail.
What are Types of Data in Statistics?
The data is classified into majorly four categories:
Nominal data
Ordinal data
Discrete data
Continuous data
Further, we can classify these data as follows:
Let us discuss the different types of data in Statistics herewith examples.
Qualitative or Categorical Data
Qualitative data, also known as the categorical data, describes the data that fits into the categories. Qualitative data are not numerical. The categorical information involves categorical variables that describe the features such as a person’s gender, home town etc. Categorical measures are defined in terms of natural language specifications, but not in terms of numbers.
Sometimes categorical data can hold numerical values (quantitative value), but those values do not have a mathematical sense. Examples of the categorical data are birthdate, favourite sport, school postcode. Here, the birthdate and school postcode hold the quantitative value, but it does not give numerical meaning.
Nominal Data
Nominal data is one of the types of qualitative information which helps to label the variables without providing the numerical value. Nominal data is also called the nominal scale. It cannot be ordered and measured. But sometimes, the data can be qualitative and quantitative. Examples of nominal data are letters, symbols, words, gender etc.
The nominal data are examined using the grouping method. In this method, the data are grouped into categories, and then the frequency or the percentage of the data can be calculated. These data are visually represented using the pie charts.
Ordinal Data
Ordinal data/variable is a type of data that follows a natural order. The significant feature of the nominal data is that the difference between the data values is not determined. This variable is mostly found in surveys, finance, economics, questionnaires, and so on.
The ordinal data is commonly represented using a bar chart. These data are investigated and interpreted through many visualisation tools. The information may be expressed using tables in which each row in the table shows the distinct category.
Quantitative or Numerical Data
Quantitative data is also known as numerical data which represents the numerical value (i.e., how much, how often, how many). Numerical data gives information about the quantities of a specific thing. Some examples of numerical data are height, length, size, weight, and so on. The quantitative data can be classified into two different types based on the data sets. The two different classifications of numerical data are discrete data and continuous data.
Discrete Data
Discrete data can take only discrete values. Discrete information contains only a finite number of possible values. Those values cannot be subdivided meaningfully. Here, things can be counted in whole numbers.
Example: Number of students in the class
Continuous Data
Continuous data is data that can be calculated. It has an infinite number of probable values that can be selected within a given specific range.
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Today we are living in an era in which there is constant surveillance on us through various sources. Among these, there are satellites and various other electronic equipment that we use daily. Some of those apps collect our data in the background.
The Internet has oven us many things but along with that, there are also some scary consequences. Among those consequences, there is also a breach of our privacy. Especially, smart devices are the main culprits that help in such breaches of trust. We are using them daily. We are also giving consent to various apps to access our microphones as well as our contact list as a whole. This data is then sold to advertising companies, who then show advertisements relevant to us on our devices.
These findings have been proved recently by some recent research. During the research, the users were asked whether they see advertisements based on their conversations or not. In return, several users admitted to getting ads based on their conversations, during phone calls. Several users even admitted to getting emails with the relevant products of their choice. On the other hand, some users did not see any such advertisements. Then some users had no opinion on such matters and some did get such ads some and some very times.
The above problem now needs some strict rules or regulations. This can only happen when the government will pass the Personal Data Protection Bill 2019. Once this will gets passed then people will have control over their privacy. The bill will also ensure that the apps need to specify the data which they are accessing. There also needs to be some clarification about the collection of data and their transactions with third parties.
These changes will make sure that the companies whose apps we are using are alert to the way our data is being used by them. They will also need to be careful with the way they handle our data. Then our data will get some protection om the wrong hands. These changes will also bring about some much-needed changes in the data field. The companies will focus more on the protection of the privacy of data. The data will also need some serious protection from outer elements. It is because various attacks are happening nowadays which are affecting various big organizations. In recent times, AIIMS servers were also hacked and there is a demand for 200 crore rupees in cryptocurrencies from hackers to give back control of servers to the major medical organization.
The recent attacks on the data of such major organizations are also asking for some data-related laws in the country. The changes will pave way for the implementation of some better rules for future generations so that their privacy remains in their own hands. These data-related issues will also safeguard the future of the country. Nowadays, data is the real gold. Data now paves the way for development shortly as technology is getting more dependent on the data being collected from users like us.
Data Science is nothing but finding patterns within the data. It uses various techniques to draw insights from the data. The goal of a data scientist is to derive useful information from huge amounts of data. In this era of Artificial Intelligence and Big Data, almost 2.5 exabytes of data is transferred daily. The need for data has begun to rise tremendously since the last decade. Many companies have started to do business on data. New sectors have been created in the IT due to data.
What is data?
In computing language, data is nothing but useful information which has been translated into a for which is efficient for movement or processing. Data which is in its beginning stage is referred to as raw data. That is the basic form of any kind of data. In the modern era, almost everything is stored digitally in computers or smart phones in the form of data. Data in computers is represented as binary values, either 0 or 1. Data is usually measured in Bytes, Kilo bytes, mega bytes, etc.
What makes data so precious?
Nowadays, data is stored digitally rather than in physical format. This usually takes less space but requires a bit of money to store. Data stored digitally is considered safe from any kind of damage and attacks except for hackers. The only way data can be stolen is through hacking. As the population is increasing, the amount of data transferred and stored is also increasing.
There needs to be a way to extract necessary, important data from this huge ocean. Normally it will be a very tedious and time consuming task to search the data we need from such huge amounts of data. But data science provides us with a solution to search, extract and organize our data. Data science churns raw data into meaningful insights.
Why is Data Science Important?
Data is the new magic. A data scientist is a wizard who is able to create magic from the data. A professional and skilled data scientist can extract meaningful data from any chunk of raw data he is given. He helps the company in the right direction. The Data Scientist is an expert in various underlying fields of Statistics and Computer Science. He uses his analytical aptitude to solve business problems.
Data Scientist is well versed with problem-solving and is assigned to find patterns in data. His goal is to recognize redundant samples and draw insights from it. Data Science requires a variety of tools to extract information from the data. A Data Scientist is responsible for collecting, storing and maintaining any form of data.
Companies are using Data to analyze their marketing strategies and create better advertisements. Many times, businesses spend an astronomical amount on marketing their products but at times this may also not get them a solution. Therefore, by studying and analyzing customer feedback, companies are able to create better advertisements. The companies do so by carefully analyzing customer behavior online. Also, monitoring customer trends helps the company to get better market insights.
Therefore, businesses need Data Scientists to assist them in making strong decisions with regards to marketing campaigns and advertisements. Thus, data science has taken over the world and is ruling it currently.
This year, Twitter is under a lot of pressure in India. However, even before the country imposed stringent new regulations on tech firms, the corporation was bombarded with requests from the government to delete material or disclose user data.
Indian authorities requested more account information from Twitter in the final six months of 2020 than any other nation, according to the Silicon Valley-based company’s newest transparency report released on Wednesday. The number of requests for content removal submitted by India increased by 152% to over 7,000.
This journalist explains why one of her tweets vanished.
India’s demands for user information amounted to 25% of the total it received during the reporting period, which runs from July 1 to December 31, 2020, according to Twitter (TWTR). Over 99 percent of the requests were ignored by Twitter.
“Notably, this is the first time since we began releasing our transparency report in 2012 that the US is not the top worldwide requester,” the firm stated, adding that the US came in second in terms of global volume.
According to the company, the information requests comprised normal legal demands as well as emergency requests from government agencies and law enforcement authorities.
“Where appropriate, Twitter will push back on requests for account information that are incomplete or inappropriate,” according to the firm, such as those that are “invalid or overbroad in scope.” In the event of an emergency involving the risk of death or serious harm, the firm may reveal account information if sufficient proof is presented that such information will prevent the hazards.
Just over 150 information requests from India were classed as emergencies by Twitter. According to the firm, the United States sent the most such requests in the globe, with 822.
Meanwhile, legal requests from India to delete or withhold information increased by 152% in the latter six months of 2020 compared to the previous reporting period. Just over 9% of the 6,971 demands were met, according to Twitter.
After Japan, which made over 16,000 requests mostly connected to narcotics, obscenity, or money lending, India became the world’s second-highest submitter of such requests. The number of requests from Japan decreased by 16 percent from the previous quarter, but it still accounted for 43 percent of all worldwide requests.
According to the firm, 361 requests for information removal were made on 199 verified journalist and news outlet accounts throughout the world. It went on to say that India accounted for 128 of the requests.
The research excludes 2021 when Twitter is embroiled in a heated standoff with the Indian government over new information technology regulations.
During a series of farmer demonstrations in February, the business fought with the Ministry of Electronics and Information Technology over accounts that the agency wanted to be taken down. Some of the requests were granted, but Twitter declined to take action against accounts belonging to journalists, activists, or politicians.
Weeks after the feud, India issued new rules requiring social media companies to establish three roles in the country: a “compliance officer” who will ensure that their platforms comply with local laws, a “grievance officer” who will handle complaints from Indian users about their platforms, and a “contact person” who will be available to Indian law enforcement 24 hours a day, seven days a week. They are all required to live in India. If authorities demand it, companies must track out the “first originator” of messages.
In May, the firm raised worries about “fundamental components of the new IT Rules” as well as the country’s “potential threat to freedom of speech.” It promised to fulfill the new standards a few days later.
A Delhi court recently chastised Twitter for failing to comply with the new guidelines promptly. In a court filing last week, the business stated that it had appointed an interim compliance officer. It also stated in the filing that it will “attempt in good faith to make an offer of employment to a qualified candidate” for all of the positions within eight weeks.
The company’s website featured a complaints officer and a Bangalore location where Twitter could be reached as of last weekend.
Privacy in general terms is the right to be left alone or freedom from interference or intrusion. In terms of the internet, privacy is the right to have some control over how your personal information is collected and used.
More technical innovation gives way to more efficient and advanced technologies. In recent years the information has become the most important component to cultivate this innovation. As many new people are coming on the internet and the amount of information being shared is increased manifold. Every organization or individual is entering the realm of the digital world and data is the entity of this world. Data is also very essential to understand a user or a customer or a client but sometimes there is no limit to the amount of data that an internet corporation is willing to extract. Somewhere there has to be a line that needs to be drawn. In recent years there have been many issues regarding the privacy policy of many tech corporations. Facebook has been in controversy much time. Recently Whatsapp’s privacy policy also raised many questions, then in an ironic move Whatsapp raised the question of privacy by suing the government regarding the new Information technology rules. But this is just the tip of the iceberg and numerous other aspects get overlooked. First, we will need to understand what this privacy means for us as individuals. Only then we can clearly determine the relevance of these rapidly changing developments in this subject.
“Arguing that you don’t care about the right to privacy because you have nothing to hide is no different than saying you don’t care about free speech because you have nothing to say.”
-Edward Snowden
It’s not that privacy means that there is something to hide; rather privacy is having things you don’t want to show. For instance, people would not like to post their Bank account online or Bank statements. People would also not like to have a public camera inside their homes. Similarly, there are some things that an individual would not like to share online. Privacy can provide secrecy, but there is more to it. Privacy also provides autonomy and therefore freedom to an individual. Well, there is even more to privacy than the freedom that many people do not realize which is that when we think we’re being watched, we make behavior choices that we believe other people want us to make. Humans intrinsically like to avoid societal condemnation and perception of whether or not we are in private changes the way we behave. This indicates the benefit that a state can have with surveillance and can lead to a conformist population.
As it is stated by many that data is the oil and for many tech organizations it truly is. We can witness this with the various technological corporations that rely upon user data including Facebook, Google, Amazon etc. But Even Smartphone manufacturers like Xiaomi have realized the lucrative benefits of collecting and selling user data. For these companies, our data is money and they earn billions of dollars with this data. Both the private tech giants have clauses in their privacy policy that allows government agencies and third parties to access the data. The data that gets uploaded on the internet never gets deleted and stays there forever. What we have to realize is that even if the information seems futile today, it may have an importance tomorrow. In conclusion, privacy is not a trivial issue and people will have to understand its importance until it’s too late.
The use of digital technology is growing, and people are becoming more aware of it, as well as the numerous benefits that technology provides. They are always connected, can contact almost anyone from anywhere, and carry the world’s most powerful information source in their pockets. People, on the other hand, appear to overlook one fact: how much data technology generates. And that’s where the issue of privacy comes into the picture and it is no secret that the modern use of the Internet and social media has a significant impact on people’s privacy. And our need for privacy is one of the characteristics that define us as human beings. Nobody like having their privacy invaded, whether it’s by someone looking over our shoulder or by a data breach. Every day, we come across headlines concerning privacy breaches that make us worry. The Internet is commonly regarded as a tool that has enabled people to write their own stories and share their experiences with a global network of people. As we continue to digitize every aspect of our lives, from gyms to vacation destinations, security and privacy protection will become increasingly more important in the coming years. As a result, it is growing easier to unintentionally give out sensitive information via email, social media, and other means these days. From internet service providers collecting user browsing history to software vendors gathering broad data about our personal life, there is a lot of data being collected about us. As a result, simple laws and regulations ensuring consumers’ ability to opt out of personal data collecting are urgently needed. In the digital era, this would go a long way toward safeguarding privacy.
Until 2021, the total sum of our personal information on the internet will be around 1.2 Million TBs, and it will undoubtedly rise in the future. Have we ever thought about how to keep it safe? If we continue to take this lightly, the day will come when digital privacy issues will attract an increase in cyber attacks, resulting in a loss of reputation, theft of sensitive documents, and a lack of confidence among users.
To emphasize the importance of data privacy in our lives, we should all take a look at the approaches or methods that can help us add value to data privacy.
Using technologies to effectively manage compliance and vulnerability in security.
Auditing security setups on a regular basis.
Staffs should be well-trained and educated on the ongoing threat of ransom ware.
Make the Privacy Procedures sound more effective by updating them.
Creating and restoring backup files with caution.
For reaching the unreachable updates, technology should be used.
Obtaining illegible H/W Channels from the attackers.
Also education has the duty of transferring awareness, attitudes, skills, and conduct from actual life domains such as home, school, and friends to young people.
From beginning till the end, it can be concluded that data privacy should not be overlooked. And what we should be doing right now is actively participating in the fight to make our lives more secure in this era of digitization.
Before starting I would like to thank you for all your support and help please see my other articles about coding and how to learn it .Well, starting with computing becomes really tough, especially because nobody knows where to. As mentioned earlier, C++ may be a good start. it’s an honest language with various applications, and is accepted in most programming contests. Python is another good language for beginners, but its easy and possesses its demerits as a primary language(you will find it tough to maneuver to C++ if you recognize Python as your maternal language , whereas learning Python after C++ is trivial). 1. Find a course on C++.Start with Bucky’s C++ tutorials. they’re easy to know and to the purpose . If you watch and practice 2-3 videos each day , then you’ll easily finish it in 1 month. confirm you implement everything taught either on Ideone or on your Computer’s IDE. the primary video during this series explains the way to install and use it. Follow the instructions well, and there wouldn’t be any issues. Don’t move to subsequent video without understanding and practicing the previous one on your own. Another great website is www.learncpp.com. 2. Have a revision toolAfter completing the above tutorial, you ought to have an honest revision tool. It are often a book, a website, an app, etc. Books have both theory and questions in them, hence they’re more useful. you’ll start with this very famous book called allow us to C++. it’ll prove very beneficial after you’ve got done Bucky’s tutorials. You can also use this app Sololearn, which is beneficial if you would like to catch up the syntax of any language. 3. Get a practice sourceThere are tonnes of internet sites out there which contain questions for practice. an honest one is SPOJ. Solve them within the order of decreasing number of users(click on the users tab to rearrange in decreasing number of users order). this will be done while reading the book(step 2). After you’re thorough with it, you’ll move to the classical section and again arrange and solve. These are very nice problems which they’re very useful for developing good coding skills. There are many other sites like CodeChef, HackerRank, CodeForces, HackerEarth, etc. SPOJ may be a excellent spot to start out because it requires brooding about the matter from scratch with none precoded stuff. 4. determine the way to use the online to unravel your problemsThere tons of resources online. Learn to google well and use few keywords. once you see a compilation error, paste the exception type on search bar. you’ll definitely find an answer on StackOverflow. Similarly, if you can’t solve say problem ID 3458, then google SPOJ 3458. most likely you’ll get a solution . this is often a really important tip for aspiring programmers.Like this text and provides your feedback it’ll be highly appreciated and share the article together with your friends. Also check the previous article on the way to start with coding. And my other article on data science
In this post, we’ll be discussing about two fundamental questions which can assist you in shaping your career in data science. 1 . What is data science and what are its components? 2. What are the prerequisite skills needed to get a job in data science? 1 . Data science is actually the techniques which are used for extracting meaningful insights from a huge dataset. With the presence of many people on the web , companies like Facebook, Instagram, Google collected plenty of knowledge about its users. This led to Big Data. It comprises of unstructured datasets. Hence, several methods were developed to work on this data and are available up with wide scale applications. An example is that Netflix collects data from its users to return with the choices like where to put subtitles, the way to place the top credits and the way to make transition between episodes of an internet series. So the first step is to collect data and store it. It involves collecting differing types of knowledge like user generated data, external data and storing them. To ensure the reliable flow of knowledge , data pipelines are built on the idea of a standard structure, ETL which is Extract, Transform and cargo . Through this, the transformation of raw data is done so that it can be suitably analysed. This task is handled by data engineers. Only after this, the “analysis” on data can be performed. Often this is often the sole part which is concentrated ignoring the essential foundations, hence people have the misunderstanding that only data analytics comprise data science. On top of this, metrics are built on which data is tracked, categorisation of users is completed and data also can be trained with the assistance of labels. Before deploying ML models, an experimentation framework is put for getting an estimate of the changes before it’s implemented on the whole dataset. 2. Now to be able to implement it, you need the following skills for getting started in the domain of data science. Programming LanguageIrrespective of the role, you would like to understand a programing language suitable for manipulating statistical data like Python or R. Besides this, you furthermore may got to know a database command language like SQL. With the help of Python libraries, the application of machine learning models get simpler hence it is not required to know how exactly the algorithms work initially.
Applied Mathematics You should have a solid understanding of statistics, as it will be needed for making decisions for evaluation of experiments. Knowledge of calculus and linear algebra helps in using the results of a machine learning or statistical implementation in a different case independently. Data cleaning and visualisationDo not think that data will be readily available to you for processing. Often an excellent deal of your time is spent in cleaning the info , adjusting missed values, correcting formatting. Without this, data can’t be processed to further stages. You should skills to use visualisation techniques to draw meaningful insights from the info . Matplotlib, ggplot can help in visualisation. Tableau has also been a well-liked tool for rendering data visually. Software engineeringA strong software engineering background is that the most essential requirement. You should have a clear understanding of algorithms, data structures, memory management which will be always tested in the first rounds for the data science roles.
With the resources present on the web , it’s very easy to urge started with programming. If you’ve got a laptop/desktop and a reliable Internet connection, you’ll start your coding journey immediately . Follow this roadmap to begin without any confusion.
Object Oriented Programming Language( OOP ) Start your coding journey with an OOP Language like C++. Learn the nitty gritties of it and master the important libraries such as STL. For a detailed strategy to learn C++, check this article. It is equally important to solve different programming problems. This tests if you’ll actually convert an algorithm into working code. Hackerrank is a good source for covering the basics and solving challenging problems.
Mastering Data Structures and Algorithms You should develop the ability to tackle different programming questions by implementing the correct algorithm. This is also crucial for software developer roles, as every company tests the knowledge of algorithms and data structures. Competitive programming websites such as Codechef, Codeforces are some of the recommended sources to begin with.
Building Software Start developing your own applications. Simple projects like web scrapers, document searchers are an honest start line . Learn Python which may be used for developing several web applications. You can also start learning android and web development. You can also build your own portfolio after this.
Delve Deeper Software engineering is a huge field. Increase the complexity of projects gradually such as using the web scraper to fetch data from a website and adding classification by using ML. You can try to build applications powered by databases( apps such as Quora or a small social network application that can group users by their common interests and engage them accordingly. )
Specialized Skills Try to gain skills which can be leveraged massively. Start with Machine Learning from Andrew Ng’s Coursera course. Join Kaggle and participate within the ir contests in order that you’ll understand the implementation of ML in the world . Often the practical use of different ML algorithms is more essential than understanding the theory fully. You can also explore areas such as Cryptography, Network Security depending on your interests.
IMPORTANT TIPS TO LEARN CODING FASTER When learning how to code, it is important to know the right way to learn. Otherwise even after spending a huge amount of your time , it can happen that you simply aren’t actually good in coding and there are gaps in your learning.
Practice coding Only seeing tutorials for coding would not help. Often students are satisfied in watching videos and that they feel that the training is over. This is not how it works in real world and is certainly not coding. Unless you are stuck at problems, you do not know your weaknesses. Writing code is the deal. And you have to get things executed. Practice problems from sites like Hackerrank to urge started. There are elaborate editorials for problems and you’ll understand from there if you’re stuck. For running Python programs, you’ll also use Google Colaboratory. You have the power of Google data centres at your disposition and there is no additional need to install anything else. The notebooks are present in the Google Drive connected to your account. Try Google Colaboratory.
Googling Google things on how to do it. Stackoverflow has many solutions to very frequently occurring errors. There are blog posts available for it. Use the main keywords and not unnecessary grammar. Googling in itself is a great skill. You need not remember each and each syntax of each programing language . Be smart to take the help of the resources.
Patience Writing code by yourself by following the logic requires patience and it’s okay to struggle initially . You can understand the essential syntax of a language at one go but that’s not enough. For understanding the subtleties within the various problems, you would like to spend longer time with the precise questions. If you’re stuck in writing a program, follow the 2nd step, read the code, the relevant material and execute step 1. Once you recognize the way to do these stuff, you’ll be learning things quickly.
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