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Data Science Foundations & Learning Path

In the age of big data processing, how to store these terabytes of data surfed over the internet was the key concern of companies until 2010. Now that the issue of storage of big data has been solved successfully by Hadoop and various other frameworks, the concern has shifted to processing these data. From website visits to online shopping, transitions from cell phones to browsing computers, every little thing we search online forms an enormous source of business industry data.The pandemic has led to an increase in data science demand as the world has shifted in pursuit of the "new normal" from offline to online. But what is Data Science? What are its salient characteristics? Where are we going to learn more about this? Let's take a look at all the fuss about data science, its courses, and the path to the future.What is Data Science?In order to discover insights and then analyse multiple structured and unstructured data, Data Science requires the use of different instruments, algorithms and principles. This is achieved using different methods and languages that we will eventually address in the alternative portion.Predictive causal analytics, prescriptive analytics and machine learning are some tools used to make decisions and predictions in data science.Predictive causal analytics: When lending your friends money, do you ever wonder if they're going to give it back to you or not? Or are you making predictions that are the same? If so, then this is exactly what casual predictive analysis does. In the future, it estimates the possibilities of a real occurrence that may or may not happen. This tool helps businesses measure the probability of such events, such as whether or not purchases made by a customer will be on time.Prescriptive analytics: Back in the 2000s, people admired flying vehicles. Today, when self-driven vehicles are already on the market, we have entered a point where we do not even need to drive a vehicle. How was this possible? If you want a model that has the intelligence to make its own choices and the ability to change it with dynamic parameters, what is needed is prescriptive analytics. This helps to make a decision based on the predictions of a computer programme. The best thing is that, the best course of action to take is advised for a certain situation.Machine learning for making predictions: Machine Learning (ML) is a computer programme framework that allows algorithms and is capable without human intervention of taking decisions and generating outputs. Known to be one of the most powerful and important technological advances in recent times, machine learning has already enabled us to conduct real-world calculations and analytics, something that would have taken years to solve through traditional computing. For example, it is possible to plan and train a fraud detection model, using the past records of fraudulent transactions. Machine learning for discovery of pattern: If you don't have the parameters you can forecast, you need to figure out the secret trends in the dataset in order to be able to make any predictions that are meaningful. Clustering, a technique in which data points are grouped together according to the similarity of their characteristics and patterns, is the most used algorithm for pattern discovery.Suppose you work in a telephone company, for instance, and you are expected to set up a network by building towers in an area. In this case, to locate the tower positions, you can use the clustering technique to ensure that all users obtain the maximum signal power.The Base For Data Science Though data scientists come from different backgrounds, have different skills and work experience, most of them should either be strong in it or have a good grip on the four main areas:  Business and ManagementStatistics and Probability.B.Tech(Computer Science) Or Data Architecture.  Verbal and Written Communications.  Based on these foundations, we can conclude that a data scientist is a person who has the expertise to extract some useful knowledge and actionable insights from data,by managing complicated data sources and the above areas. The knowledge we receive can be used to make strategic business decisions and to make improvements necessary to achieve business objectives.  This is done by the use of experience in the business domain, efficient communication and analysis of findings and the use of some or all of the related statistical techniques and methods, databases, programming languages, software packages, data infrastructure, etc.Data Science Goals and DeliverablesLet's look at the paradigms that data science has proven to succeed in. There are different fields in which data science has been extremely beneficial. Data scientists set certain targets and results to be accomplished by the data science process. Let's discuss them in brief:Prediction  Classification  Recommendations  Pattern detection and classification  Anomaly detection  Recognition  Actionable insights  Automated processes and decision-making  Scoring and ranking  SegmentationOptimization  Forecast SalesAll of these are intended to address specific problems and solve it.Many managers are highly intelligent people, but they may still not be well versed in all the instruments or techniques and algorithms available (e.g., statistical analysis, machine learning, artificial intelligence, etc.). Therefore, they might not be able to tell a data scientist what they want as a final deliverable, or recommend the sources, features and the right direction to get there from the data sources.Therefore an ideal data scientist must have a reasonably detailed understanding of how organisations function in general and how data from an organisation can be used to achieve top-level business objectives. With exceptional experience in the business domain, a data scientist should be able to constantly discover and recommend new data projects to help the organisation accomplish its objectives and optimise its KPIs.Data Scientists vs. Data Analysts vs. Data EngineersLike several other related positions, the role of data scientist is most frequently misunderstood. Data Analysts and Data Engineers are the two key ones, both very distinct from each other as well as from Data Science.Let us look into how they are different from one another so that we may have a clear understanding of all these different job roles and profiles.Data AnalystData analysts have many skills and responsibilities similar to a data scientist, and sometimes even have a similar educational background as well. Some of these similar skills include the ability to:Access and query (e.g., SQL) different data sources Process and clean data Summarize data Understand and use statistics and mathematical techniques Prepare data visualizations and reports Some of the distinctions, however are that computer programmers are not data analysts, nor are they accountable for mathematical modelling or machine learning, and several other measures explained above in the data science process.The various instruments used are often typically different. Data analysts typically use analytical and business intelligence software such as MS Excel, Tableau, PowerBI, QlikView, SAS, and may also use a few SAP modules. Analysts also do data mining and modelling tasks occasionally, but typically prefer to use visual tools for data science activities, such as IBM SPSS Modeler, Rapid Miner, SAS, and KNIME.Data scientists, on the other hand, usually perform the same tasks with software such as R or Python, together with some relevant libraries for the language used. Data scientists are also more accountable for teaching linear, non-linear algorithms in mathematical models.Data EngineerData scientists also use data from different sources, which are then collected, transformed, combined, and ultimately processed in a manner that is optimised for analytics, business intelligence, and modelling. On the other hand, computer engineers are responsible for the design of data and the setting up of the necessary infrastructure. They need to be competent programmers with some skills that are very similar to those necessary in a DevOps job, and with good and powerful writing skills for data query. Another main aspect of this position is database design (RDBMS, NoSQL, and NewSQL), data warehousing, and setting up a data lake. This means they need to be very familiar with many database technology and management systems available, including those associated with big data (For example, Hadoop, Redshift, Snowflake and Cassandra).The Data Scientist’s ToolboxData scientists should be proficient with such programming languages such as Python, R, SQL, Java, Julia, Apache Spark and Scala, as computer programming is a huge part. Usually, in all of these, it's not important to be an expert programmer, but Python or R, and SQL are certainly the main languages they should be familiar with.Some useful and famous data science courses which you can definitely avail to strengthen your knowledge and concepts are as follows :Data Science Specialization from JHU (Coursera)   Introduction to Data Science from Metis   Applied Data Science along with Python Specialization from University of Michigan (Coursera)   Dataquest   Statistics and Data Science Micro Masters from MIT (edX)   CS109 Data Science from Harvard   Python for Data Science and Machine Learning Bootcamp from UdemySome of the many courses available online related to Data Science are the courses listed above. At the end of the course, all the courses provide you with a certificate of completion. These courses will, above all other advantages, help you develop a database on data science and eventually move you to a level where you will be fully prepared to deal with some real data!ConclusionData science has become an important part of today's generation. Even the tiniest move we take on the internet leaves our digital footprint and extracts information from it. Having expertise in the processing of data science can help you go a long way. Perhaps it's not unfair to suggest that Data Science would control a large portion of our future. Data scientists have a huge positive effect and impact on the performance of a company, but sometimes they may also cause financial losses, which is one of the many reasons why it is important to employ a top-notch data scientist. However it can bring prosperity, effectiveness and sustainability to any organisation if implemented in a perfect manner.

Data Science Foundations & Learning Path

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Data Science Foundations & Learning Path

In the age of big data processing, how to store these terabytes of data surfed over the internet was the key concern of companies until 2010. Now that the issue of storage of big data has been solved successfully by Hadoop and various other frameworks, the concern has shifted to processing these data. From website visits to online shopping, transitions from cell phones to browsing computers, every little thing we search online forms an enormous source of business industry data.

The pandemic has led to an increase in data science demand as the world has shifted in pursuit of the "new normal" from offline to online. But what is Data Science? What are its salient characteristics? Where are we going to learn more about this? Let's take a look at all the fuss about data science, its courses, and the path to the future.

What is Data Science?

In order to discover insights and then analyse multiple structured and unstructured data, Data Science requires the use of different instruments, algorithms and principles. This is achieved using different methods and languages that we will eventually address in the alternative portion.

Predictive causal analytics, prescriptive analytics and machine learning are some tools used to make decisions and predictions in data science.

  • Predictive causal analytics: When lending your friends money, do you ever wonder if they're going to give it back to you or not? Or are you making predictions that are the same? If so, then this is exactly what casual predictive analysis does. In the future, it estimates the possibilities of a real occurrence that may or may not happen. This tool helps businesses measure the probability of such events, such as whether or not purchases made by a customer will be on time.
  • Prescriptive analytics: Back in the 2000s, people admired flying vehicles. Today, when self-driven vehicles are already on the market, we have entered a point where we do not even need to drive a vehicle. How was this possible? If you want a model that has the intelligence to make its own choices and the ability to change it with dynamic parameters, what is needed is prescriptive analytics. This helps to make a decision based on the predictions of a computer programme. The best thing is that, the best course of action to take is advised for a certain situation.
  • Machine learning for making predictions: Machine Learning (ML) is a computer programme framework that allows algorithms and is capable without human intervention of taking decisions and generating outputs. Known to be one of the most powerful and important technological advances in recent times, machine learning has already enabled us to conduct real-world calculations and analytics, something that would have taken years to solve through traditional computing. For example, it is possible to plan and train a fraud detection model, using the past records of fraudulent transactions. 
  • Machine learning for discovery of pattern: If you don't have the parameters you can forecast, you need to figure out the secret trends in the dataset in order to be able to make any predictions that are meaningful. Clustering, a technique in which data points are grouped together according to the similarity of their characteristics and patterns, is the most used algorithm for pattern discovery.

Suppose you work in a telephone company, for instance, and you are expected to set up a network by building towers in an area. In this case, to locate the tower positions, you can use the clustering technique to ensure that all users obtain the maximum signal power.

The Base For Data Science 

Though data scientists come from different backgrounds, have different skills and work experience, most of them should either be strong in it or have a good grip on the four main areas:  

  • Business and Management
  • Statistics and Probability.
  • B.Tech(Computer Science) Or Data Architecture.  
  • Verbal and Written Communications.  

Based on these foundations, we can conclude that a data scientist is a person who has the expertise to extract some useful knowledge and actionable insights from data,by managing complicated data sources and the above areas. The knowledge we receive can be used to make strategic business decisions and to make improvements necessary to achieve business objectives.  

This is done by the use of experience in the business domain, efficient communication and analysis of findings and the use of some or all of the related statistical techniques and methods, databases, programming languages, software packages, data infrastructure, etc.

Data Science Goals and Deliverables

Let's look at the paradigms that data science has proven to succeed in. There are different fields in which data science has been extremely beneficial. Data scientists set certain targets and results to be accomplished by the data science process. Let's discuss them in brief:

  • Prediction  
  • Classification  
  • Recommendations  
  • Pattern detection and classification  
  • Anomaly detection  
  • Recognition  
  • Actionable insights  
  • Automated processes and decision-making  
  • Scoring and ranking  
  • Segmentation
  • Optimization  
  • Forecast Sales

All of these are intended to address specific problems and solve it.

Many managers are highly intelligent people, but they may still not be well versed in all the instruments or techniques and algorithms available (e.g., statistical analysis, machine learning, artificial intelligence, etc.). Therefore, they might not be able to tell a data scientist what they want as a final deliverable, or recommend the sources, features and the right direction to get there from the data sources.

Therefore an ideal data scientist must have a reasonably detailed understanding of how organisations function in general and how data from an organisation can be used to achieve top-level business objectives. With exceptional experience in the business domain, a data scientist should be able to constantly discover and recommend new data projects to help the organisation accomplish its objectives and optimise its KPIs.

Data Scientists vs. Data Analysts vs. Data Engineers

Like several other related positions, the role of data scientist is most frequently misunderstood. Data Analysts and Data Engineers are the two key ones, both very distinct from each other as well as from Data Science.

Let us look into how they are different from one another so that we may have a clear understanding of all these different job roles and profiles.

Data Analyst

Data analysts have many skills and responsibilities similar to a data scientist, and sometimes even have a similar educational background as well. Some of these similar skills include the ability to:

  • Access and query (e.g., SQL) different data sources 
  • Process and clean data 
  • Summarize data 
  • Understand and use statistics and mathematical techniques 
  • Prepare data visualizations and reports 

Some of the distinctions, however are that computer programmers are not data analysts, nor are they accountable for mathematical modelling or machine learning, and several other measures explained above in the data science process.

The various instruments used are often typically different. Data analysts typically use analytical and business intelligence software such as MS Excel, Tableau, PowerBI, QlikView, SAS, and may also use a few SAP modules. Analysts also do data mining and modelling tasks occasionally, but typically prefer to use visual tools for data science activities, such as IBM SPSS Modeler, Rapid Miner, SAS, and KNIME.

Data scientists, on the other hand, usually perform the same tasks with software such as R or Python, together with some relevant libraries for the language used. Data scientists are also more accountable for teaching linear, non-linear algorithms in mathematical models.

Data Engineer

Data scientists also use data from different sources, which are then collected, transformed, combined, and ultimately processed in a manner that is optimised for analytics, business intelligence, and modelling. 

On the other hand, computer engineers are responsible for the design of data and the setting up of the necessary infrastructure. They need to be competent programmers with some skills that are very similar to those necessary in a DevOps job, and with good and powerful writing skills for data query. 

Another main aspect of this position is database design (RDBMS, NoSQL, and NewSQL), data warehousing, and setting up a data lake. This means they need to be very familiar with many database technology and management systems available, including those associated with big data (For example, Hadoop, Redshift, Snowflake and Cassandra).

The Data Scientist’s Toolbox

Data scientists should be proficient with such programming languages such as Python, R, SQL, Java, Julia, Apache Spark and Scala, as computer programming is a huge part. Usually, in all of these, it's not important to be an expert programmer, but Python or R, and SQL are certainly the main languages they should be familiar with.

Some useful and famous data science courses which you can definitely avail to strengthen your knowledge and concepts are as follows :

  • Data Science Specialization from JHU (Coursera)  
  • Introduction to Data Science from Metis  
  • Applied Data Science along with Python Specialization from University of Michigan (Coursera)  
  • Dataquest  
  • Statistics and Data Science Micro Masters from MIT (edX)  
  • CS109 Data Science from Harvard  
  • Python for Data Science and Machine Learning Bootcamp from Udemy

Some of the many courses available online related to Data Science are the courses listed above. At the end of the course, all the courses provide you with a certificate of completion. These courses will, above all other advantages, help you develop a database on data science and eventually move you to a level where you will be fully prepared to deal with some real data!

Conclusion

Data science has become an important part of today's generation. Even the tiniest move we take on the internet leaves our digital footprint and extracts information from it. Having expertise in the processing of data science can help you go a long way. Perhaps it's not unfair to suggest that Data Science would control a large portion of our future. 

Data scientists have a huge positive effect and impact on the performance of a company, but sometimes they may also cause financial losses, which is one of the many reasons why it is important to employ a top-notch data scientist. However it can bring prosperity, effectiveness and sustainability to any organisation if implemented in a perfect manner.

Dipayan

Dipayan Ghatak

Project Manager

Leading Projects across geographies in Microsoft Consultant Services.

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Role of Unstructured Data in Data Science

Data has become the new game changer for businesses. Typically, data scientists categorize data into three broad divisions - structured, semi-structured, and unstructured data. In this article, you will get to know about unstructured data, sources of unstructured data, unstructured data vs. structured data, the use of structured and unstructured data in machine learning, and the difference between structured and unstructured data. Let us first understand what is unstructured data with examples. What is unstructured data? Unstructured data is a kind of data format where there is no organized form or type of data. Videos, texts, images, document files, audio materials, email contents and more are considered to be unstructured data. It is the most copious form of business data, and cannot be stored in a structured database or relational database. Some examples of unstructured data are the photos we post on social media platforms, the tagging we do, the multimedia files we upload, and the documents we share. Seagate predicts that the global data-sphere will expand to 163 zettabytes by 2025, where most of the data will be in the unstructured format. Characteristics of Unstructured DataUnstructured data cannot be organized in a predefined fashion, and is not a homogenous data model. This makes it difficult to manage. Apart from that, these are the other characteristics of unstructured data. You cannot store unstructured data in the form of rows and columns as we do in a database table. Unstructured data is heterogeneous in structure and does not have any specific data model. The creation of such data does not follow any semantics or habits. Due to the lack of any particular sequence or format, it is difficult to manage. Such data does not have an identifiable structure. Sources of Unstructured Data There are various sources of unstructured data. Some of them are: Content websites Social networking sites Online images Memos Reports and research papers Documents, spreadsheets, and presentations Audio mining, chatbots Surveys Feedback systems Advantages of Unstructured Data Unstructured data has become exceptionally easy to store because of MongoDB, Cassandra, or even using JSON. Modern NoSQL databases and software allows data engineers to collect and extract data from various sources. There are numerous benefits that enterprises and businesses can gain from unstructured data. These are: With the advent of unstructured data, we can store data that lacks a proper format or structure. There is no fixed schema or data structure for storing such data, which gives flexibility in storing data of different genres. Unstructured data is much more portable by nature. Unstructured data is scalable and flexible to store. Database systems like MongoDB, Cassandra, etc., can easily handle the heterogeneous properties of unstructured data. Different applications and platforms produce unstructured data that becomes useful in business intelligence, unstructured data analytics, and various other fields. Unstructured data analysis allows finding comprehensive data stories from data like email contents, website information, social media posts, mobile data, cache files and more. Unstructured data, along with data analytics, helps companies improve customer experience. Detection of the taste of consumers and their choices becomes easy because of unstructured data analysis. Disadvantages of Unstructured data Storing and managing unstructured data is difficult because there is no proper structure or schema. Data indexing is also a substantial challenge and hence becomes unclear due to its disorganized nature. Search results from an unstructured dataset are also not accurate because it does not have predefined attributes. Data security is also a challenge due to the heterogeneous form of data. Problems faced and solutions for storing unstructured data. Until recently, it was challenging to store, evaluate, and manage unstructured data. But with the advent of modern data analysis tools, algorithms, CAS (content addressable storage system), and big data technologies, storage and evaluation became easy. Let us first take a look at the various challenges used for storing unstructured data. Storing unstructured data requires a large amount of space. Indexing of unstructured data is a hectic task. Database operations such as deleting and updating become difficult because of the disorganized nature of the data. Storing and managing video, audio, image file, emails, social media data is also challenging. Unstructured data increases the storage cost. For solving such issues, there are some particular approaches. These are: CAS system helps in storing unstructured data efficiently. We can preserve unstructured data in XML format. Developers can store unstructured data in an RDBMS system supporting BLOB. We can convert unstructured data into flexible formats so that evaluating and storage becomes easy. Let us now understand the differences between unstructured data vs. structured data. Unstructured Data Vs. Structured Data In this section, we will understand the difference between structured and unstructured data with examples. STRUCTUREDUNSTRUCTUREDStructured data resides in an organized format in a typical database.Unstructured data cannot reside in an organized format, and hence we cannot store it in a typical database.We can store structured data in SQL database tables having rows and columns.Storing and managing unstructured data requires specialized databases, along with a variety of business intelligence and analytics applications.It is tough to scale a database schema.It is highly scalable.Structured data gets generated in colleges, universities, banks, companies where people have to deal with names, date of birth, salary, marks and so on.We generate or find unstructured data in social media platforms, emails, analyzed data for business intelligence, call centers, chatbots and so on.Queries in structured data allow complex joining.Unstructured data allows only textual queries.The schema of a structured dataset is less flexible and dependent.An unstructured dataset is flexible but does not have any particular schema.It has various concurrency techniques.It has no concurrency techniques.We can use SQL, MySQL, SQLite, Oracle DB, Teradata to store structured data.We can use NoSQL (Not Only SQL) to store unstructured data.Types of Unstructured Data Do you have any idea just how much of unstructured data we produce and from what sources? Unstructured data includes all those forms of data that we cannot actively manage in an RDBMS system that is a transactional system. We can store structured data in the form of records. But this is not the case with unstructured data. Before the advent of object-based storage, most of the unstructured data was stored in file-based systems. Here are some of the types of unstructured data. Rich media content: Entertainment files, surveillance data, multimedia email attachments, geospatial data, audio files (call center and other recorded audio), weather reports (graphical), etc., comes under this genre. Document data: Invoices, text-file records, email contents, productivity applications, etc., are included under this genre. Internet of Things (IoT) data: Ticker data, sensor data, data from other IoT devices come under this genre. Apart from all these, data from business intelligence and analysis, machine learning datasets, and artificial intelligence data training datasets are also a separate genre of unstructured data. Examples of Unstructured Data There are various sources from where we can obtain unstructured data. The prominent use of this data is in unstructured data analytics. Let us now understand what are some examples of unstructured data and their sources – Healthcare industries generate a massive volume of human as well as machine-generated unstructured data. Human-generated unstructured data could be in the form of patient-doctor or patient-nurse conversations, which are usually recorded in audio or text formats. Unstructured data generated by machines includes emergency video camera footage, surgical robots, data accumulated from medical imaging devices like endoscopes, laparoscopes and more.  Social Media is an intrinsic entity of our daily life. Billions of people come together to join channels, share different thoughts, and exchange information with their loved ones. They create and share such data over social media platforms in the form of images, video clips, audio messages, tagging people (this helps companies to map relations between two or more people), entertainment data, educational data, geolocations, texts, etc. Other spectra of data generated from social media platforms are behavior patterns, perceptions, influencers, trends, news, and events. Business and corporate documents generate a multitude of unstructured data such as emails, presentations, reports containing texts, images, presentation reports, video contents, feedback and much more. These documents help to create knowledge repositories within an organization to make better implicit operations. Live chat, video conferencing, web meeting, chatbot-customer messages, surveillance data are other prominent examples of unstructured data that companies can cultivate to get more insights into the details of a person. Some prominent examples of unstructured data used in enterprises and organizations are: Reports and documents, like Word files or PDF files Multimedia files, such as audio, images, designed texts, themes, and videos System logs Medical images Flat files Scanned documents (which are images that hold numbers and text – for example, OCR) Biometric data Unstructured Data Analytics Tools  You might be wondering what tools can come into use to gather and analyze information that does not have a predefined structure or model. Various tools and programming languages use structured and unstructured data for machine learning and data analysis. These are: Tableau MonkeyLearn Apache Spark SAS Python MS. Excel RapidMiner KNIME QlikView Python programming R programming Many cloud services (like Amazon AWS, Microsoft Azure, IBM Cloud, Google Cloud) also offer unstructured data analysis solutions bundled with their services. How to analyze unstructured data? In the past, the process of storage and analysis of unstructured data was not well defined. Enterprises used to carry out this kind of analysis manually. But with the advent of modern tools and programming languages, most of the unstructured data analysis methods became highly advanced. AI-powered tools use algorithms designed precisely to help to break down unstructured data for analysis. Unstructured data analytics tools, along with Natural language processing (NLP) and machine learning algorithms, help advanced software tools analyze and extract analytical data from the unstructured datasets. Before using these tools for analyzing unstructured data, you must properly go through a few steps and keep these points in mind. Set a clear goal for analyzing the data: It is essential to clear your intention about what insights you want to extract from your unstructured data. Knowing this will help you distinguish what type of data you are planning to accumulate. Collect relevant data: Unstructured data is available everywhere, whether it's a social media platform, online feedback or reviews, or a survey form. Depending on the previous point, that is your goal - you have to be precise about what data you want to collect in real-time. Also, keep in mind whether your collected details are relevant or not. Clean your data: Data cleaning or data cleansing is a significant process to detect corrupt or irrelevant data from the dataset, followed by modifying or deleting the coarse and sloppy data. This phase is also known as the data-preprocessing phase, where you have to reduce the noise, carry out data slicing for meaningful representation, and remove unnecessary data. Use Technology and tools: Once you perform the data cleaning, it is time to utilize unstructured data analysis tools to prepare and cultivate the insights from your data. Technologies used for unstructured data storage (NoSQL) can help in managing your flow of data. Other tools and programming libraries like Tableau, Matplotlib, Pandas, and Google Data Studio allows us to extract and visualize unstructured data. Data can be visualized and presented in the form of compelling graphs, plots, and charts. How to Extract information from Unstructured Data? With the growth in digitization during the information era, repetitious transactions in data cause data flooding. The exponential accretion in the speed of digital data creation has brought a whole new domain of understanding user interaction with the online world. According to Gartner, 80% of the data created by an organization or its application is unstructured. While extracting exact information through appropriate analysis of organized data is not yet possible, even obtaining a decent sense of this unstructured data is quite tough. Until now, there are no perfect tools to analyze unstructured data. But algorithms and tools designed using machine learning, Natural language processing, Deep learning, and Graph Analysis (a mathematical method for estimating graph structures) help us to get the upper hand in extracting information from unstructured data. Other neural network models like modern linguistic models follow unsupervised learning techniques to gain a good 'knowledge' about the unstructured dataset before going into a specific supervised learning step. AI-based algorithms and technologies are capable enough to extract keywords, locations, phone numbers, analyze image meaning (through digital image processing). We can then understand what to evaluate and identify information that is essential to your business. ConclusionUnstructured data is found abundantly from sources like documents, records, emails, social media posts, feedbacks, call-records, log-in session data, video, audio, and images. Manually analyzing unstructured data is very time-consuming and can be very boring at the same time. With the growth of data science and machine learning algorithms and models, it has become easy to gather and analyze insights from unstructured information.  According to some research, data analytics tools like MonkeyLearn Studio, Tableau, RapidMiner help analyze unstructured data 1200x faster than the manual approach. Analyzing such data will help you learn more about your customers as well as competitors. Text analysis software, along with machine learning models, will help you dig deep into such datasets and make you gain an in-depth understanding of the overall scenario with fine-grained analyses.
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Role of Unstructured Data in Data Science

Data has become the new game changer for busines... Read More

What Is Statistical Analysis and Its Business Applications?

Statistics is a science concerned with collection, analysis, interpretation, and presentation of data. In Statistics, we generally want to study a population. You may consider a population as a collection of things, persons, or objects under experiment or study. It is usually not possible to gain access to all of the information from the entire population due to logistical reasons. So, when we want to study a population, we generally select a sample. In sampling, we select a portion (or subset) of the larger population and then study the portion (or the sample) to learn about the population. Data is the result of sampling from a population.Major ClassificationThere are two basic branches of Statistics – Descriptive and Inferential statistics. Let us understand the two branches in brief. Descriptive statistics Descriptive statistics involves organizing and summarizing the data for better and easier understanding. Unlike Inferential statistics, Descriptive statistics seeks to describe the data, however, it does not attempt to draw inferences from the sample to the whole population. We simply describe the data in a sample. It is not developed on the basis of probability unlike Inferential statistics. Descriptive statistics is further broken into two categories – Measure of Central Tendency and Measures of Variability. Inferential statisticsInferential statistics is the method of estimating the population parameter based on the sample information. It applies dimensions from sample groups in an experiment to contrast the conduct group and make overviews on the large population sample. Please note that the inferential statistics are effective and valuable only when examining each member of the group is difficult. Let us understand Descriptive and Inferential statistics with the help of an example. Task – Suppose, you need to calculate the score of the players who scored a century in a cricket tournament.  Solution: Using Descriptive statistics you can get the desired results.   Task – Now, you need the overall score of the players who scored a century in the cricket tournament.  Solution: Applying the knowledge of Inferential statistics will help you in getting your desired results.  Top Five Considerations for Statistical Data AnalysisData can be messy. Even a small blunder may cost you a fortune. Therefore, special care when working with statistical data is of utmost importance. Here are a few key takeaways you must consider to minimize errors and improve accuracy. Define the purpose and determine the location where the publication will take place.  Understand the assets to undertake the investigation. Understand the individual capability of appropriately managing and understanding the analysis.  Determine whether there is a need to repeat the process.  Know the expectation of the individuals evaluating reviewing, committee, and supervision. Statistics and ParametersDetermining the sample size requires understanding statistics and parameters. The two being very closely related are often confused and sometimes hard to distinguish.  StatisticsA statistic is merely a portion of a target sample. It refers to the measure of the values calculated from the population.  A parameter is a fixed and unknown numerical value used for describing the entire population. The most commonly used parameters are: Mean Median Mode Mean :  The mean is the average or the most common value in a data sample or a population. It is also referred to as the expected value. Formula: Sum of the total number of observations/the number of observations. Experimental data set: 2, 4, 6, 8, 10, 12, 14, 16, 18, 20  Calculating mean:   (2 + 4 + 6 + 8 + 10 + 12 + 14 + 16 + 18 + 20)/10  = 110/10   = 11 Median:  In statistics, the median is the value separating the higher half from the lower half of a data sample, a population, or a probability distribution. It’s the mid-value obtained by arranging the data in increasing order or descending order. Formula:  Let n be the data set (increasing order) When data set is odd: Median = n+1/2th term Case-I: (n is odd)  Experimental data set = 1, 2, 3, 4, 5  Median (n = 5) = [(5 +1)/2]th term      = 6/2 term       = 3rd term   Therefore, the median is 3 When data set is even: Median = [n/2th + (n/2 + 1)th] /2 Case-II: (n is even)  Experimental data set = 1, 2, 3, 4, 5, 6   Median (n = 6) = [n/2th + (n/2 + 1)th]/2     = ( 6/2th + (6/2 +1)th]/2     = (3rd + 4th)/2      = (3 + 4)/2      = 7/2      = 3.5  Therefore, the median is 3.5 Mode: The mode is the value that appears most often in a set of data or a population. Experimental data set= 1, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4,4,5, 6  Mode = 3 (Since 3 is the most repeated element in the sequence.) Terms Used to Describe DataWhen working with data, you will need to search, inspect, and characterize them. To understand the data in a tech-savvy and straightforward way, we use a few statistical terms to denote them individually or in groups.  The most frequently used terms used to describe data include data point, quantitative variables, indicator, statistic, time-series data, variable, data aggregation, time series, dataset, and database. Let us define each one of them in brief: Data points: These are the numerical files formed and organized for interpretations. Quantitative variables: These variables present the information in digit form.  Indicator: An indicator explains the action of a community's social-economic surroundings.  Time-series data: The time-series defines the sequential data.  Data aggregation: A group of data points and data set. Database: A group of arranged information for examination and recovery.  Time-series: A set of measures of a variable documented over a specified time. Step-by-Step Statistical Analysis ProcessThe statistical analysis process involves five steps followed one after another. Step 1: Design the study and find the population of the study. Step 2: Collect data as samples. Step 3: Describe the data in the sample. Step 4: Make inferences with the help of samples and calculations Step 5: Take action Data distributionData distribution is an entry that displays entire imaginable readings of data. It shows how frequently a value occurs. Distributed data is always in ascending order, charts, and graphs enabling visibility of measurements and frequencies. The distribution function displaying the density of values of reading is known as the probability density function. Percentiles in data distributionA percentile is the reading in a distribution with a specified percentage of clarifications under it.  Let us understand percentiles with the help of an example.  Suppose you have scored 90th percentile on a math test. A basic interpretation is that merely 4-5% of the scores were higher than your scores. Right? The median is 50th percentile because the assumed 50% of the values are higher than the median. Dispersion Dispersion explains the magnitude of distribution readings anticipated for a specific variable and multiple unique statistics like range, variance, and standard deviation. For instance, high values of a data set are widely scattered while small values of data are firmly clustered. Histogram The histogram is a pictorial display that arranges a group of data facts into user detailed ranges. A histogram summarizes a data series into a simple interpreted graphic by obtaining many data facts and combining them into reasonable ranges. It contains a variety of results into columns on the x-axis. The y axis displays percentages of data for each column and is applied to picture data distributions. Bell Curve distribution Bell curve distribution is a pictorial representation of a probability distribution whose fundamental standard deviation obtained from the mean makes the bell, shaped curving. The peak point on the curve symbolizes the maximum likely occasion in a pattern of data. The other possible outcomes are symmetrically dispersed around the mean, making a descending sloping curve on both sides of the peak. The curve breadth is therefore known as the standard deviation. Hypothesis testingHypothesis testing is a process where experts experiment with a theory of a population parameter. It aims to evaluate the credibility of a hypothesis using sample data. The five steps involved in hypothesis testing are:  Identify the no outcome hypothesis.  (A worthless or a no-output hypothesis has no outcome, connection, or dissimilarities amongst many factors.) Identify the alternative hypothesis.  Establish the importance level of the hypothesis.  Estimate the experiment statistic and equivalent P-value. P-value explains the possibility of getting a sample statistic.  Sketch a conclusion to interpret into a report about the alternate hypothesis. Types of variablesA variable is any digit, amount, or feature that is countable or measurable. Simply put, it is a variable characteristic that varies. The six types of variables include the following: Dependent variableA dependent variable has values that vary according to the value of another variable known as the independent variable.  Independent variableAn independent variable on the other side is controllable by experts. Its reports are recorded and equated.  Intervening variableAn intervening variable explicates fundamental relations between variables. Moderator variableA moderator variable upsets the power of the connection between dependent and independent variables.  Control variableA control variable is anything restricted to a research study. The values are constant throughout the experiment. Extraneous variableExtraneous variable refers to the entire variables that are dependent but can upset experimental outcomes. Chi-square testChi-square test records the contrast of a model to actual experimental data. Data is unsystematic, underdone, equally limited, obtained from independent variables, and a sufficient sample. It relates the size of any inconsistencies among the expected outcomes and the actual outcomes, provided with the sample size and the number of variables in the connection. Types of FrequenciesFrequency refers to the number of repetitions of reading in an experiment in a given time. Three types of frequency distribution include the following: Grouped, ungrouped Cumulative, relative Relative cumulative frequency distribution. Features of FrequenciesThe calculation of central tendency and position (median, mean, and mode). The measure of dispersion (range, variance, and standard deviation). Degree of symmetry (skewness). Peakedness (kurtosis). Correlation MatrixThe correlation matrix is a table that shows the correlation coefficients of unique variables. It is a powerful tool that summarises datasets points and picture sequences in the provided data. A correlation matrix includes rows and columns that display variables. Additionally, the correlation matrix exploits in aggregation with other varieties of statistical analysis. Inferential StatisticsInferential statistics use random data samples for demonstration and to create inferences. They are measured when analysis of each individual of a whole group is not likely to happen. Applications of Inferential StatisticsInferential statistics in educational research is not likely to sample the entire population that has summaries. For instance, the aim of an investigation study may be to obtain whether a new method of learning mathematics develops mathematical accomplishment for all students in a class. Marketing organizations: Marketing organizations use inferential statistics to dispute a survey and request inquiries. It is because carrying out surveys for all the individuals about merchandise is not likely. Finance departments: Financial departments apply inferential statistics for expected financial plan and resources expenses, especially when there are several indefinite aspects. However, economists cannot estimate all that use possibility. Economic planning: In economic planning, there are potent methods like index figures, time series investigation, and estimation. Inferential statistics measures national income and its components. It gathers info about revenue, investment, saving, and spending to establish links among them. Key TakeawaysStatistical analysis is the gathering and explanation of data to expose sequences and tendencies.   Two divisions of statistical analysis are statistical and non-statistical analyses.  Descriptive and Inferential statistics are the two main categories of statistical analysis. Descriptive statistics describe data, whereas Inferential statistics equate dissimilarities between the sample groups.  Statistics aims to teach individuals how to use restricted samples to generate intellectual and precise results for a large group.   Mean, median, and mode are the statistical analysis parameters used to measure central tendency.   Conclusion Statistical analysis is the procedure of gathering and examining data to recognize sequences and trends. It uses random samples of data obtained from a population to demonstrate and create inferences on a group. Inferential statistics applies economic planning with potent methods like index figures, time series investigation, and estimation.  Statistical analysis finds its applications in all the major sectors – marketing, finance, economic, operations, and data mining. Statistical analysis aids marketing organizations in disputing a survey and requesting inquiries concerning their merchandise. 
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What Is Statistical Analysis and Its Business Appl...

Statistics is a science concerned with collection,... Read More

Measures of Dispersion: All You Need to Know

What is Dispersion in StatisticsDispersion in statistics is a way of describing how spread out a set of data is. Dispersion is the state of data getting dispersed, stretched, or spread out in different categories. It involves finding the size of distribution values that are expected from the set of data for the specific variable. The statistical meaning of dispersion is “numeric data that is likely to vary at any instance of average value assumption”.Dispersion of data in Statistics helps one to easily understand the dataset by classifying them into their own specific dispersion criteria like variance, standard deviation, and ranging.Dispersion is a set of measures that helps one to determine the quality of data in an objectively quantifiable manner.The measure of dispersion contains almost the same unit as the quantity being measured. There are many Measures of Dispersion found which help us to get more insights into the data: Range Variance Standard Deviation Skewness IQR  Image SourceTypes of Measure of DispersionThe Measure of Dispersion is divided into two main categories and offer ways of measuring the diverse nature of data. It is mainly used in biological statistics. We can easily classify them by checking whether they contain units or not. So as per the above, we can divide the data into two categories which are: Absolute Measure of Dispersion Relative Measure of DispersionAbsolute Measure of DispersionAbsolute Measure of Dispersion is one with units; it has the same unit as the initial dataset. Absolute Measure of Dispersion is expressed in terms of the average of the dispersion quantities like Standard or Mean deviation. The Absolute Measure of Dispersion can be expressed  in units such as Rupees, Centimetre, Marks, kilograms, and other quantities that are measured depending on the situation. Types of Absolute Measure of Dispersion: Range: Range is the measure of the difference between the largest and smallest value of the data variability. The range is the simplest form of Measure of Dispersion. Example: 1,2,3,4,5,6,7 Range = Highest value – Lowest value   = ( 7 – 1 ) = 6 Mean (μ): Mean is calculated as the average of the numbers. To calculate the Mean, add all the outcomes and then divide it with the total number of terms. Example: 1,2,3,4,5,6,7,8 Mean = (sum of all the terms / total number of terms)                = (1 + 2 + 3 + 4 + 5 + 6 + 7 + 8) / 8                = 36 / 8                = 4.5 Variance (σ2): In simple terms, the variance can be calculated by obtaining the sum of the squared distance of each term in the distribution from the Mean, and then dividing this by the total number of the terms in the distribution.  It basically shows how far a number, for example, a student’s mark in an exam, is from the Mean of the entire class. Formula: (σ2) = ∑ ( X − μ)2 / N Standard Deviation: Standard Deviation can be represented as the square root of Variance. To find the standard deviation of any data, you need to find the variance first. Formula: Standard Deviation = √σ Quartile: Quartiles divide the list of numbers or data into quarters. Quartile Deviation: Quartile Deviation is the measure of the difference between the upper and lower quartile. This measure of deviation is also known as interquartile range. Formula: Interquartile Range: Q3 – Q1. Mean deviation: Mean Deviation is also known as an average deviation; it can be computed using the Mean or Median of the data. Mean deviation is represented as the arithmetic deviation of a different item that follows the central tendency. Formula: As mentioned, the Mean Deviation can be calculated using Mean and Median. Mean Deviation using Mean: ∑ | X – M | / N Mean Deviation using Median: ∑ | X – X1 | / N Relative Measure of DispersionRelative Measures of dispersion are the values without units. A relative measure of dispersion is used to compare the distribution of two or more datasets.  The definition of the Relative Measure of Dispersion is the same as the Absolute Measure of Dispersion; the only difference is the measuring quantity.  Types of Relative Measure of Dispersion: Relative Measure of Dispersion is the calculation of the co-efficient of Dispersion, where 2 series are compared, which differ widely in their average.  The main use of the co-efficient of Dispersion is when 2 series with different measurement units are compared.  1. Co-efficient of Range: it is calculated as the ratio of the difference between the largest and smallest terms of the distribution, to the sum of the largest and smallest terms of the distribution.  Formula: L – S / L + S  where L = largest value S= smallest value 2. Co-efficient of Variation: The coefficient of variation is used to compare the 2 data with respect to homogeneity or consistency.  Formula: C.V = (σ / X) 100 X = standard deviation  σ = mean 3. Co-efficient of Standard Deviation: The co-efficient of Standard Deviation is the ratio of standard deviation with the mean of the distribution of terms.  Formula: σ = ( √( X – X1)) / (N - 1) Deviation = ( X – X1)  σ = standard deviation  N= total number  4. Co-efficient of Quartile Deviation: The co-efficient of Quartile Deviation is the ratio of the difference between the upper quartile and the lower quartile to the sum of the upper quartile and lower quartile.  Formula: ( Q3 – Q3) / ( Q3 + Q1) Q3 = Upper Quartile  Q1 = Lower Quartile 5. Co-efficient of Mean Deviation: The co-efficient of Mean Deviation can be computed using the mean or median of the data. Mean Deviation using Mean: ∑ | X – M | / N Mean Deviation using Mean: ∑ | X – X1 | / N Why dispersion is important in a statisticThe knowledge of dispersion is vital in the understanding of statistics. It helps to understand concepts like the diversification of the data, how the data is spread, how it is maintained, and maintaining the data over the central value or central tendency. Moreover, dispersion in statistics provides us with a way to get better insights into data distribution. For example,  3 distinct samples can have the same Mean, Median, or Range but completely different levels of variability. How to Calculate DispersionDispersion can be easily calculated using various dispersion measures, which are already mentioned in the types of Measure of Dispersion described above. Before measuring the data, it is important to understand the diversion of the terms and variation. One can use the following method to calculate the dispersion: Mean Standard deviation Variance Quartile deviation For example, let us consider two datasets: Data A:97,98,99,100,101,102,103  Data B: 70,80,90,100,110,120,130 On calculating the mean and median of the two datasets, both have the same value, which is 100. However, the rest of the dispersion measures are totally different as measured by the above methods.  The range of B is 10 times higher, for instance. How to represent Dispersion in Statistics Dispersion in Statistics can be represented in the form of graphs and pie-charts. Some of the different ways used include: Dot Plots Box Plots Stems Leaf Plots Example: What is the variance of the values 3,8,6,10,12,9,11,10,12,7?  Variation of the values can be calculated using the following formula: (σ2) = ∑ ( X − μ)2 / N (σ2) = 7.36 What is an example of dispersion? One of the examples of dispersion outside the world of statistics is the rainbow- where white light is split into 7 different colours separated via wavelengths.  Some statistical ways of measuring it are- Standard deviation Range Mean absolute difference Median absolute deviation Interquartile change Average deviation Conclusion: Dispersion in statistics refers to the measure of variability of data or terms. Such variability may give random measurement errors where some of the instrumental measurements are found to be imprecise. It is a statistical way of describing how the terms are spread out in different data sets. The more sets of values, the more scattered data is found, and it is always directly proportional. This range of values can vary from 5 - 10 values to 1000 - 10,000 values. This spread of data is described by the range of descriptive range of statistics. The dispersion in statistics can be represented using a Dot Plot, Box Plot, and other different ways. 
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Measures of Dispersion: All You Need to Know

What is Dispersion in StatisticsDispersion in stat... Read More