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What Is Factor Analysis in Data Science?

Factor analysis is a part of the general linear model (GLM). It is a method in which large amounts of data are collected and reduced in size to a smaller dataset. This reduction in the size of the dataset ensures that the data is manageable and easily understood by people.  In addition to manageability and interpretability, it helps extract patterns in data as well as show the characteristics that are commonly seen in the different patterns (that are extracted). It helps create a  variable set for data points in the datasets that are similar. This similar set of data is also known as dimensions.  AssumptionAn assumption while dealing with factor analysis is that, in a collection of the variables observed, there is a set of underlying variables, which is known as ‘factor’. This factor helps explain the inter-relationship between these variables.  There should be a linear relationship between the variables in the data.  There should be no multicollinearity between variables in the data.  There should be true correlation between the variables and factors in the data.  There are multiple methods to extract factors from data, but principal component analysis is one of the most frequently used methods. In Principal component analysis (PCA), maximum variance is extracted and placed in the first factor. Once this is done, the variance explained by the first set of factors is eliminated and then maximum variance is again extracted for the second factor. This goes on until the last factor in the variable set.  Types of factor analysisThe word ‘factor’ in factor analysis refers to the variable set which has similar patterns. They are sometimes associated with a hidden variable, which is also known as confounding variable. This hidden variable is not measured directly. The ‘factors’ talk about the variation in data which can be explained.  There are two types of factors:  Exploratory;Confirmatory Exploratory factor analysisThis deals with data that is unstructured or when the person/s dealing with the data are clueless about the structure of the data and the dimensions of the variable associated with the data. Exploratory factor analysis gives information about the optimum number of factors which may be required to represent the data. If a researcher wishes to explore patterns, it is suggested to use exploratory factor analysis.Confirmatory factor analysisThis kind of analysis is used to verify the structure of the data, given the condition that the people dealing with the data are aware of its structure and dimensions of the variable associated with the data. This kind of analysis helps specify the number of factors required to perform the analysis.Factor analysis is a multivariate method- this means it deals with multiple variables associated with data. This is a data reduction technique wherein the basic idea is to use a smaller set of variables, which is known as ‘factors’, that is a representation of a bigger set of variables.It helps the researcher in understanding whether the relationship between the observed variables (aka manifest variables) and their underlying construct exists or not.If a researcher wishes to perform hypothesis testing, it is suggested to use exploratory factor analysis.What are factors?Factors can be understood as a construct which can’t be measured with the help of a single variable. Factor analysis is generally used with interval data, but it can be used for ordinal data as well.  What is ordinal data?Ordinal data is statistical data in which variables exist in naturally occurring categories that are in a particular order. The distance between categories in ordinal data can’t be found using ordinal data itself.For a dataset to be ordinal data, it needs to fulfil a few conditions.  Multiple terms in the dataset are in an ordered fashion.  The difference between variables in the dataset is not homogeneous/uniform.  A group of ordinal numbers indicates ordinal data, and a group of ordinal data can be represented using an ordinal scale.Likert Scale is one type of ordinal data. Let us understand Likert scale with the help of an example:Suppose we have a question that says “Please indicate how satisfied you are with this product purchase”. A Likert scale may have numbers between 0/1 to 5 or 0/1 to 10. On this scale, 0/1 indicates a lesser value and 5 or 10 indicates a higher value.Let us understand ordinal data with the help of another example. If we have variables stored in a specific order, say “low, medium, high” or “not happy, slightly happy, happy, very happy, extremely happy”, it is considered as ordinal data.Conditions for variables in factor analysisThese variables (in factor analysis) need to be linearly associated with each other. Linear relationship or association describes a relationship that forms a straight line when two variables are plotted on a graph. It can also be represented as a mathematical equation in the form ‘y = mx + b’.This linear associativity can be checked by plotting scatterplots of the pairs of variables. This indicates that the variables need to be moderately correlated to each other.If the variables are not correlated, the number of factors will be the same as the number of original variables. This means that performing factor analysis on this kind of variables would be useless.How can factor analysis be performed?Factor analysis is a complex mathematical procedure. It can be performed with the help of software applications. Before performing the analysis, it is essential to check if the data is relevant. This can be done with the help of Kaiser-Meyer-Olkin test.Kaiser-Meyer-Olkin testThis is also known as the KMO test, which is used to see how well the data is suited to perform factor analysis. It measures the sampling adequacy for every variable in the model.This statistic measures the proportion of variance among all the variables in the data. The lower the proportion, more suited the data is to perform factor analysis.KMO returns values between 0 and 1.If KMO value lies between 0.8 and 1, it means that the sampling is adequate.If KMO value is less than 0.6 or lies between 0.5 and 0.6, it means that the sampling is not adequate. This means proper actions need to be taken.If KMO value is closer to 0, this indicates that the data contains large number of partial correlations in comparison to the sum of correlations. This is not suited for factor analysis. Values between 0 and 0.49 are considered unacceptable. Values between 0.50 and 0.59 are considered not good. Values between 0.60 and 0.69 are considered mediocre.  Values between 0.70 and 0.79 are considered to be good.  Values between 0.80 and 0.89 are considered to be great. Values between 0.90 and 1.00 are considered to be absolutely fantastic. The formula to perform KMO test is:Here, R =  which is the correlation matrix; and U =  which is the partial covariance matrix.Once the relevant data has been collected, factor analysis can be performed in a variety of ways.Using StataIt can be performed in Stata with the help of postestimation command- ‘estat kmo’.Using RIt can be performed in R using the command ‘KMO(r)’ where ‘r’ refers to the correlation matrix that needs to be analysed.Using SPSSSPSS is a statistical platform that can be used to run factor analysis. First go to Analyze -> Dimension Reduction -> Factor, and check the “KMO and Bartlett’s test of sphericity” box.If the measure of sampling adequacy (MSA) for single variable is needed, the ‘”anti-image” box needs to be checked. An ‘anti-image’ box shows the MSAs listed in diagonals of matrix.The test can also be executed by specifying KMO in the Factor Analysis command. The KMO statistic is found in the “KMO and Bartlett’s Test” table in the Factor output.ConclusionIn short, Factor Analysis brings in simplicity after reducing variables. Factor Analysis, including Principal Component Analysis, is also often used along with segmentation studies. In this post, we understood about the factor analysis method, and the assumptions made before working on the method. We also saw different kinds of factor analysis, and how they can be performed on different platforms.
What Is Factor Analysis in Data Science?
Dipayan
Dipayan

Dipayan Ghatak

Project Manager

Leading Projects across geographies in Microsoft Consultant Services.

Posts by Dipayan Ghatak

What Is Factor Analysis in Data Science?

Factor analysis is a part of the general linear model (GLM). It is a method in which large amounts of data are collected and reduced in size to a smaller dataset. This reduction in the size of the dataset ensures that the data is manageable and easily understood by people.  In addition to manageability and interpretability, it helps extract patterns in data as well as show the characteristics that are commonly seen in the different patterns (that are extracted). It helps create a  variable set for data points in the datasets that are similar. This similar set of data is also known as dimensions.  AssumptionAn assumption while dealing with factor analysis is that, in a collection of the variables observed, there is a set of underlying variables, which is known as ‘factor’. This factor helps explain the inter-relationship between these variables.  There should be a linear relationship between the variables in the data.  There should be no multicollinearity between variables in the data.  There should be true correlation between the variables and factors in the data.  There are multiple methods to extract factors from data, but principal component analysis is one of the most frequently used methods. In Principal component analysis (PCA), maximum variance is extracted and placed in the first factor. Once this is done, the variance explained by the first set of factors is eliminated and then maximum variance is again extracted for the second factor. This goes on until the last factor in the variable set.  Types of factor analysisThe word ‘factor’ in factor analysis refers to the variable set which has similar patterns. They are sometimes associated with a hidden variable, which is also known as confounding variable. This hidden variable is not measured directly. The ‘factors’ talk about the variation in data which can be explained.  There are two types of factors:  Exploratory;Confirmatory Exploratory factor analysisThis deals with data that is unstructured or when the person/s dealing with the data are clueless about the structure of the data and the dimensions of the variable associated with the data. Exploratory factor analysis gives information about the optimum number of factors which may be required to represent the data. If a researcher wishes to explore patterns, it is suggested to use exploratory factor analysis.Confirmatory factor analysisThis kind of analysis is used to verify the structure of the data, given the condition that the people dealing with the data are aware of its structure and dimensions of the variable associated with the data. This kind of analysis helps specify the number of factors required to perform the analysis.Factor analysis is a multivariate method- this means it deals with multiple variables associated with data. This is a data reduction technique wherein the basic idea is to use a smaller set of variables, which is known as ‘factors’, that is a representation of a bigger set of variables.It helps the researcher in understanding whether the relationship between the observed variables (aka manifest variables) and their underlying construct exists or not.If a researcher wishes to perform hypothesis testing, it is suggested to use exploratory factor analysis.What are factors?Factors can be understood as a construct which can’t be measured with the help of a single variable. Factor analysis is generally used with interval data, but it can be used for ordinal data as well.  What is ordinal data?Ordinal data is statistical data in which variables exist in naturally occurring categories that are in a particular order. The distance between categories in ordinal data can’t be found using ordinal data itself.For a dataset to be ordinal data, it needs to fulfil a few conditions.  Multiple terms in the dataset are in an ordered fashion.  The difference between variables in the dataset is not homogeneous/uniform.  A group of ordinal numbers indicates ordinal data, and a group of ordinal data can be represented using an ordinal scale.Likert Scale is one type of ordinal data. Let us understand Likert scale with the help of an example:Suppose we have a question that says “Please indicate how satisfied you are with this product purchase”. A Likert scale may have numbers between 0/1 to 5 or 0/1 to 10. On this scale, 0/1 indicates a lesser value and 5 or 10 indicates a higher value.Let us understand ordinal data with the help of another example. If we have variables stored in a specific order, say “low, medium, high” or “not happy, slightly happy, happy, very happy, extremely happy”, it is considered as ordinal data.Conditions for variables in factor analysisThese variables (in factor analysis) need to be linearly associated with each other. Linear relationship or association describes a relationship that forms a straight line when two variables are plotted on a graph. It can also be represented as a mathematical equation in the form ‘y = mx + b’.This linear associativity can be checked by plotting scatterplots of the pairs of variables. This indicates that the variables need to be moderately correlated to each other.If the variables are not correlated, the number of factors will be the same as the number of original variables. This means that performing factor analysis on this kind of variables would be useless.How can factor analysis be performed?Factor analysis is a complex mathematical procedure. It can be performed with the help of software applications. Before performing the analysis, it is essential to check if the data is relevant. This can be done with the help of Kaiser-Meyer-Olkin test.Kaiser-Meyer-Olkin testThis is also known as the KMO test, which is used to see how well the data is suited to perform factor analysis. It measures the sampling adequacy for every variable in the model.This statistic measures the proportion of variance among all the variables in the data. The lower the proportion, more suited the data is to perform factor analysis.KMO returns values between 0 and 1.If KMO value lies between 0.8 and 1, it means that the sampling is adequate.If KMO value is less than 0.6 or lies between 0.5 and 0.6, it means that the sampling is not adequate. This means proper actions need to be taken.If KMO value is closer to 0, this indicates that the data contains large number of partial correlations in comparison to the sum of correlations. This is not suited for factor analysis. Values between 0 and 0.49 are considered unacceptable. Values between 0.50 and 0.59 are considered not good. Values between 0.60 and 0.69 are considered mediocre.  Values between 0.70 and 0.79 are considered to be good.  Values between 0.80 and 0.89 are considered to be great. Values between 0.90 and 1.00 are considered to be absolutely fantastic. The formula to perform KMO test is:Here, R =  which is the correlation matrix; and U =  which is the partial covariance matrix.Once the relevant data has been collected, factor analysis can be performed in a variety of ways.Using StataIt can be performed in Stata with the help of postestimation command- ‘estat kmo’.Using RIt can be performed in R using the command ‘KMO(r)’ where ‘r’ refers to the correlation matrix that needs to be analysed.Using SPSSSPSS is a statistical platform that can be used to run factor analysis. First go to Analyze -> Dimension Reduction -> Factor, and check the “KMO and Bartlett’s test of sphericity” box.If the measure of sampling adequacy (MSA) for single variable is needed, the ‘”anti-image” box needs to be checked. An ‘anti-image’ box shows the MSAs listed in diagonals of matrix.The test can also be executed by specifying KMO in the Factor Analysis command. The KMO statistic is found in the “KMO and Bartlett’s Test” table in the Factor output.ConclusionIn short, Factor Analysis brings in simplicity after reducing variables. Factor Analysis, including Principal Component Analysis, is also often used along with segmentation studies. In this post, we understood about the factor analysis method, and the assumptions made before working on the method. We also saw different kinds of factor analysis, and how they can be performed on different platforms.
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What Is Factor Analysis in Data Science?

Factor analysis is a part of the general linear m... Read More

How To Become A Data Aanalyst In 2021?

In 2020, Data Analysis has become one of the core functions in any organization. This is a highly sought-after role that has evolved immensely in the past few years. But what is Data Analysis?  What do Data Analysts do? How to become a Data Analyst in 2020? What are the skills one needs to have to be a Data Analyst? There are many such questions which strike our mind when we talk about this profession.Let's walk through the answers to all the questions to ensure we have a clear picture in our mind.What is Data analytics?Information collected from different sources is used to make informed decisions for the organization, and is analyzed for some specific goals through Data Analysis. Data Analysis is not only used for research and analysis; but it helps organizations learn more about their customers, develop marketing strategies and optimize product development, just to name a few areas where it makes an impact.To be precise, there are four types of Data Analytics:Descriptive Analytics: - In this type of analytics, analysts examine the past data like monthly sales, monthly revenue, website traffic and more to find out the trend. They then draft a description or a summary of the performance of the firm or website. This type of analytics uses arithmetic operations, mean, median, max, percentage and other statistical summaries.Diagnostic Analytics:- As the name suggests, here we diagnose the data and find out the reasons behind any particular trend, issue or scenario.  If a company is faced with any negative data then this type of analysis will help them to find out the main reasons/causes for the decline in the performance, against which decisions and actions can be taken.Predictive Analytics:-  This type of analytics helps in predicting the future outcome by analyzing the past data and trends. It will help companies to take proactive actions for the better outcomes. Not just this, but predictive analysis also helps us forecast the sales, demand, fraud, failures and set our budgets and other resources accordingly.  Prescriptive Analytics:- This type of analytics helps in determining what action the company should take next in response to the situation, to keep the business going and growing.Why do we need Data Analysts?Organizations across different fields or sectors rely on data analysis to take important decisions for the development of a new product, to forecast sales for the near future, or find out about entry into new markets or new customers to target. Data analysis is also used to analyze the business performance based on the present data and find out various inefficiencies in the organizations. Not only industries or businesses use data analysis, but it is also used by different political parties and other groups to find out about opportunities as well as challenges.What does a data analyst do?There are several functions which an analyst performs, but some of the functions may also depends on the type of business and organization. Generally, a data analyst performs the following responsibilities:Data collection from various sources like primary sources and secondary sources and arranging the data in a proper sequence.Cleaning and processing the data as per requirement. A data analyst may be required to treat missing values, clean invalid or wrong data and remove unwanted information.Using different kind of statistical tools like R, Python, SPSS or SAS, to interpret the data collected.Adjusting the data according to the upcoming trends or changes like seasonal trends and then making interpretations.Preparing a data analysis report.Identifying opportunities and threats from the analyzed data and apprising the same to the organization.Now that you know what areas a Data Analyst works on, let us move to the skills and knowledge you would require to get started in this field.What are the skills necessary to be a Data Analyst?Broadly, a data analyst needs to have two type of skills at a broader level:Technical skills - Knowledge of different technical languages and tools like R, SQL, Microsoft Excel, Tableau, Mathematical skills, statistical skills and data visualization skills. These technical skills would help an analyst actually use the data and visualize the final outcome in a form that could be beneficial for the firm. This may include tables, graphs, charts, and more.   Decision making – This is extremely necessary to present the outcome and take the executives through the various changes, trends, demand, and downfall. Deep analysis is required to be able to take logical, factual and beneficial decisions for the firm. Data analysts must have the ability to think strategically and get a 360 degree view of the situation, before suggesting the way forward.After acquiring the above mentioned skills, it is very much required to keep yourself updated with the latest trends in the industry, so a mindset of continuous learning is a must.How to become a data analyst in 2021?The year 2020 changed all the definitions of a business and its processes. COVID-19 put companies across the world in a tailspin, forcing them to rethink their business strategies in order to cope with the evolving challenges thrown up by the pandemic. Some companies that were market leaders in their domain were unable to cope, and many had to even close down. The question therefore arises, in such an uncertain scenario, with challenges around every corner, is it even prudent to consider stepping into the role of a Data Analyst at this juncture?   The answer to this is, “YES”. This is the best time to be a data analyst because organizations everywhere are looking for expert Analysts who can guide them in making the right decisions, helping the organization to survive through the pandemic and beyond. Data analysts can perform detailed sales forecasting, or carry out a complete market analysis to make the right predictions for future growth. Companies need to prepare smart strategies for sales and marketing, to survive and thrive in the long run.If you want to shape your career in data analytics then You must have a degree in Mathematics, Economics, Engineering, Statistics or in a field which emphasizes on statistical and analytical skills. You must know some data analytics tools or skills which are mentioned above like R, SQL, Tableau, Data Warehousing, Data visualization, Data mining and advanced Microsoft Excel. You must consider some good certifications in the above-mentioned skills.   You may also consider a master’s degree in the field of data analytics.Let us now take you through the scope of Data Analysis in 2021.What is the scope of data analytics in 2021?The world is witnessing a surge in demand for data analytics services. According to a report, it is expected that there will be 250,000 new openings in the Data Analytics field in 2021, which is almost 60% higher than the demand in 2019-20. To stay ahead of the competition, organizations are employing Data Analysts and the demand for experts in the field is only set to rise. According to another report published in 2019, there were 150000 jobs which were vacant in the Data Analytics sector because of lack of available talent. This is a lucrative field, and those professionals who have expertise and experience can easily climb to the top in a short time. A report by IBM predicts that by 2021, Data Science and Analytics jobs would grow to nearly 350,000.What are the sectors in which Data Science jobs are expected to grow in India in 2021?Though the need for data analytics is growing across every sector, there are a few sectors that are more in-demand than others. These include:Aviation sector: uses data analysis for pricing and route optimization. Agriculture sector: analyses data to forecast the output and pricing. Cyber security: Global companies are adopting data engineering and data analysis for anomaly and intrusion detection. Genomics: Data analytics is used to study the sequence of genomes. It is heavily used to diagnose abnormalities and identify diseases.Conclusion If you would like to enter the field of Data Analytics, there’s no time like now! Data is useless without the right professional to analyze it. Leading companies leverage the power of analytics to improve their decision making and fuel business growth, and are always looking to employ bright and talented professionals with the capabilities they need.  Opportunities are plentiful and the rewards are immense, so take the first step and start honing all the skills that can make you fulfil your dream!
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How To Become A Data Aanalyst In 2021?

In 2020, Data Analysis has become one of the core ... Read More

How To Switch To Data Science From Your Current Career Path?

WHAT DO DATA SCIENTISTS DO?A data scientist needs to be well-versed with all aspects of a project and needs to have an in-depth knowledge of what’s happening. A data scientist’s job needs loads of exploratory data research and analysis on a daily basis with the help of various tools like Python, SQL, R, and Matlab. The life of a data scientist involves getting neck-deep into huge datasets, analysing them, processing them, learning new aspects and making novel discoveries from a business perspective.This role is an amalgamation of art and science that requires a good amount of prototyping, programming and mocking up of data to obtain novel outcomes. Once they get desired outcomes, data scientists move forward for production deployment where the customers can actually experience them. Every day, a data scientist is required to come up with new ideas, iterate them on already built products and develop something better.WHY SHOULD YOU GET INTO DATA SCIENCE?One of the most in-demand industries of the modern world is Data Science. Year on year, the increase in the total data generated by customers is huge, and has now almost touched 2.5 quintillion bytes per day. You can imagine how large that is! For any organization, customer data is of the utmost priority as with its help, they can sell their customer the products they want, by creating the advertisements they would be attracted to, providing the offers they won't reject, and in short delighting their customers every step of the way.The money factor has already been mentioned by me earlier. A Data Scientist earns about 25% more than a computer programmer. A person with a die-hard passion to work on large datasets and to draw meaningful insights can definitely begin their journey in becoming a great data scientist. WHAT ALL DO YOU NEED TO KNOW AND UNDERSTAND TO BECOME A DATA SCIENTIST?Data science skill sets are in a continuous state of fluctuation. Many people are confused with the thought that if they can gain expertise in 2 - 3 software technologies, they are well equipped to begin a career in data science and some also think that if they just learn machine learning, they can become a good data scientist. It is an undeniable fact that all these things together can make you a good data scientist but having only these skills will definitely not make you one. A good data scientist is a big data wrangler, who has the ability to apply quantitative analysis, statistics, programming and business acumen to help an enterprise grow. Solving just a data analysis problem or creating a machine learning algorithm will not make you a great enterprise data scientist. An expert in programming and machine learning who is not able to glean valuable insights to help the growth of an organization cannot be called a real Data Scientist. Data scientists work very closely with different business stakeholders to analyse where and what kind of data can actually add value to the real-world business applications. Data scientists should be able to discern the impacts of solving a data analysis problem such as what is the criticality of the problem, identifying the logical flaws in the analysis outcomes and must always ponder on the question- Does the outcome of the analysis make any sense to the business?Now the next question that arises is HOW TO GET INTO DATA SCIENCE FROM YOUR CURRENT CAREER PATH? The first and the foremost step is to understand the urgent need to change your path to Data Science, because if you have doubts in your mind then it would be hard to succeed. This does not mean that you need to quit your job, sit at home and wait for some company to hire you as a data scientist. It means that you need to understand your priority and have to work and develop the required skills to hone your knowledge in that field, so as to excel in the career path you tend to follow next.A data scientist must be able to navigate through multifaceted data issues and various statistical models, keeping the business perspective in mind. Translation of the business requirements into datasets and machine learning algorithms to obtain value from the data, are the core responsibilities of a Data Scientist. Moreover, communication plays a pivotal role in data science as well because through the entire data science process, a data scientist must be able to closely communicate with the business partners. Data scientists should work in collaboration with top level executives in the organization like marketing managers, product development managers, etc. to figure out how to support each of the departments in the company to grow with their respective data driven analysis. Data Science requires three main skills :-Statistics: To enter the field of data science, a solid foundation in statistics is a must. Professionals must be well-equipped with statistical techniques, and should know when and how to apply them to a data-driven decision-making problem.     Data Visualisation: Data visualization is the heart of the data science ecosystem as it assists to present the solution and outcome to a data driven decision making problem in a better format to the clients who do not belong to data analytics background. Data visualization in data science is challenging as it requires finding answers to complex questions. Before stepping into this field, a lot of preparation in visualization needs to be done. Programming: People often ask themselves “Do I need to be a BIG time coder or an expert programmer to pursue a lucrative career in Data Science?” The answer to this is probably no. Expertise in programming skills can be an added advantage in Data Science, but it is not compulsory. Programming skills are not needed in big data applications but are rather needed to solve a data equation that is time consuming when solved manually. If a data scientist can figure out what needs to be done with the dataset, that would be enough.WHAT IS DATA IN DATA SCIENCE?Data is the essence of Data Science. Data Science revolves around big datasets but many a times, data is not of the quality that is required to take decisions. Before being ready for processing, data goes through pre-processing which is a necessary group of operations that translate raw data into a more understandable format and thus, useful for further processing. Common processes are:Collect raw data and store it on a server. This is untouched data that scientists cannot analyze straight away. This data may come from surveys, or through popular automatic data collection methods, like using cookies on a website.Class-label the observationsThis consists of arranging the data by categorizing or labelling data points to the appropriate data type such as numerical, or categorical data.Data cleansing / Data scrubbingDealing with incongruous data, like misspelled categories or missing values.Data balancingIf the data is unbalanced, for instance if the categories contain unequal numbers of observations and are not representative, applying certain data balancing methods, like extracting equal numbers of observations for the individual categories, and then processing it, fixes the issue.Data shufflingRe-arranging the data points to remove the unwanted patterns and improve predictive performance is the major task here. An example would be, if the first 1000 observations in the dataset are from the first 1000 people who have used a website; the data is not randomized due to different sampling methods used.The gist of the requirements for a Data Scientist are:Hands on with SQL is a must. It is a big challenge to understand the dicing and slicing of data without expert knowledge of various SQL concepts.Revisit Algebra and MatricesDevelop expertise in statistical learning and implement them in R or Python based on the kind of dataset.Ability to understand and implement Big Data, as the better the data, the more is the accuracy of a machine learning algorithm.Data visualization is the key to master data science as it provides the summary of the solution.WHERE SHOULD YOU LEARN DATA SCIENCE FROM?There are many institutions which offer in-depth courses on data science. You can also undertake various online courses to equip yourself with Data Science skills. As the Data Science market is growing exponentially, more professionals are leaning toward a career in this rewarding space.  To explore some course options in data science, you can visit.
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How To Switch To Data Science From Your Current Ca...

WHAT DO DATA SCIENTISTS DO?A data scientist needs ... Read More

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.
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Data Science Foundations & Learning Path

In the age of big data processing, how to store th... Read More

Top industries for a data science professionals

The quantity of data collected every day, every minute and every second is simply astounding. According to this 2019 report, Americans alone consume 4,416,720 GB of data every minute of the day. And this is just one part of the world. Imagine how much data is being generated by 4.39 billion people who are connected to the internet all around the world? It’s beyond imaginable. But what is unimaginable to us is a virtual goldmine to many especially when they have data scientists who are able to mine the tons of data created by human activity and turn it into business opportunities. While data is often in a raw or unstructured form, data scientists are able to apply tools, technologies and platforms to convert the unstructured data into a structured form that can be used in virtually every industry for gains. So not just is it invaluable in the field of IT; but data science has applications in medicine, communication, retail, transport, government, security and a lot more areas. Data Science will soon become an indispensable part of our lives and in this article we attempt to give you an insight into the industries where it would be used most.So, what exactly is Data Science?According to Josh Wills, Director of Data Engineering at Slack, “Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician”. In other words, Data Science is the science or making data useful with the use of machine learning principles, algorithms, and various tools that are used to identify, represent, and extract data.The top 9 industries which makes use of Data ScienceHealthcareTelecommunicationsTelecom service providers collect data from their users phones. This data is then studied by data scientists to study the behavior of customers  This allows telecom providers to give personalized services to each and every customer depending on what they need rather than open the same service to the whole country. The industry being accessed by millions of users around the world is also highly susceptible to fraud and data theft. Data science can help prevent fraud by monitoring data activity.Internet IndustryThe demands of consumers are growing; and they're growing in a specific and sophisticated way. Software service providers are using real time data to mirror content usage patterns of customers. With data science being perfected, data scientists use the data generated by consumers and put it to good use. Google for example collects & stores each individual user's search history across a host of its services like Google Search, YouTube, Google AdWords, Gmail etc. Online marketers or advertisers use data about a customer’s search history and display real time ads specific to the individual customer’s preferences. All this is possible through the advances in data science.A very common example: If I search for ‘ flight from Pune to Delhi’ on Google and then I hop on to some other website, immediately I get an ad from MakeMyTrip saying Pune to Delhi flight starting at ₹1999 only. Or if I search Amazon for a Samsung mobile phone, I immediately get an ad about Samsung phones, on the next page I visit, including the price and other specs. All this is possible only through data analysis.  Entertainment IndustryThe entertainment industry has made very good use of data science. Netflix, for example, has been using data from its customers ever since its inception; putting analytical tools to task to generate insights about its customers and their viewing habits. Netflix also uses data science to find faults within its own server systems. Netflix uses Density-Based Spatial Clustering of Applications with Noise (DBSCAN),  which is basically a clustering algorithm that uses data over a period of time (or as it's called in the industry time series data) to find out which servers are healthy and which are causing latency in providing content to the customers.AI written movie scriptsAI is being increasingly used for activities that require imagination and creativity. This includes writing movie scripts and songs. A good example of this is the 2016 movie Sunspring that has been completely scripted by an AI bot.AI written songsOpenAI has an open-source AI system called ‘Jukebox’. It creates full songs complete with music lyrics and vocals in any genre that a person likes, and it can emulate singers like Justin Bieber to Elvis and Sinatra. AIWA is a program that is made to compose classical & symphonic music.GamingMajor video and computer gaming companies currently in the market have adopted machine learning models to design new games, which improve and update themselves gradually as each individual player uses the game. For example, PETER games learn from their players and upgrade themselves accordingly, which gives advanced players the thrill they look for while playing games and it also gives a feeling of satisfaction to the not so good players who still want to play a game and feel the rush of victory.VR gaming setups use ML to analyze the opponent moves while playing against computers to generate strategies to defeat the human player. Energy SectorEnergy is the quintessential resource that mankind is dependent on. Data science has only recently been put to use in the energy sector, but the growth has been exponential. From failure modelling, outage detection and prediction to grid security & theft detection; data science is being used everywhere in the energy sector.Real time data solutions are being used to analyze and predict when an outage might occur owing to weather conditions or for predicting the effect of an asset that has neared the completion of its life cycle. Electricity is a vulnerable resource. Billions of dollars’ worth of energy is stolen or illegally captured from open electrical lines. To prevent this the companies, turn to advanced metering infrastructure which use data science and big data principles in real-time to monitor suspicious usage of electricity over any particular grid or any particular description line.  Companies use data science to improve operational efficiency by real-time monitoring of the data such as activity rate power to prevent outages.Automotive IndustryData science has made huge leaps in the automotive sector especially in the fields of self-driving and energy efficient cars. Tesla is already up and about with this new change in the industry. It has also launched intelligent & interactive assistants for its cars so that a car can learn depending on its owner's uses; how fast to charge or how slow to charge and  what speeds & routes should be recommended for the least time taken to get to a destination and also to ensure optimum performance of its battery.  It has also launched the autopilot for use on the freeways which uses computer vision and deep learning neural networks on the move to analyze every object within 200 yards and make intelligent split second decisions.  Also, data science can be used in developing a new car model. Companies have access to all the data that the consumers generate and the feedback from consumers regarding a product design, which the company can then put in a machine learning model to generate a design that would be perfect for every customer. Car manufacturers need to know exactly how many units to produce so that they produce according to demand and not in surplus, that may result in loss of precious resources. To optimize production, companies use machine learning models to generate sales forecasts in real time after analyzing years & years of data. Automotive giants like Mercedes Benz and Audi use AI models to forecast the marketing spend for each of their car models depending upon the demographics of a certain city. Isn’t it amazing!Conclusion All the amazing things that are listed above are possible only because of dedicated and intelligent Data Scientists, Machine Learning Engineers, Text Engineers, Data Analysts & many more people, who work with data to solve problems, create innovations and in general help us lead better lives.
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Top industries for a data science professionals

The quantity of data collected every day, every mi... Read More

What Is Data Splitting in Learn and Test Data?

Data is the fuel of every machine learning algorithm, on which statistical inferences are made and predictions are done. Consequently, it is important to collect the data, clean it and use it with maximum efficacy. A decent data sampling can guarantee accurate predictions and drive the whole ML project forward whereas a bad data sampling can lead to incorrect predictions. Before diving into the sampling techniques, let us understand what the population is and how does it differ from a sample? The population is the assortment or the collection of the components which shares a few of the other characteristics for all intents and purposes. The total number of observations is said to be the size of the populationImage SourceThe sample is a subset of the population. The process of  choosing a sample from a given set of the population is known as sampling. The number of components in the example is the sample size. Data sampling refers to statistical approaches for picking observations from the domain to estimate a population parameter. Whereas data resampling refers to the drawing of repeated samples from the main or original source of data. It is the non-parametric procedure of statistical extrapolation. It produces unique sample distributions based on the original data and is used to improve the accuracy and intuitively measure the uncertainty of the population parameter. Sampling methods can be divided into two parts: Probability sampling procedure  Non-probability sampling procedure  The distinction between the two is that the example of determination depends on randomization. With randomization, each component persuades equivalent opportunity and is important for test for study. Probability Sampling – It is a method in which each element of a given population has an equivalent chance of being selected. Simple random sampling –For instance, a classroom has 100 students and each student has an equal chance of getting selected as the class representative Systematic sampling- It is a sampling technique in which the first element is selected at random and others get selected based on a fixed sampling interval. For instance, consider a population size of 20 (1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19.20) Suppose we want the element with the number 3 and a sample size of 5. The next selection will be made at an interval of 20/5 i.e. 4 so 3 + 4 = 7 so 3,7,11 and so on.  Stratified sampling – In this sampling process, the total group is subdivided into smaller groups, known as the strata, to obtain a sampling process. Assume that we need to identify the average number of votes in three different cities to elect a representative. City x has 1 million citizens, city y has 2 million citizens, city z has 3 million citizens. We can randomly choose a sample size of 60 for the entire population. But if you notice, the random samples are not balanced with respect to the different cities. Hence there could be an estimation error. To overcome this, we may choose a random sample of 10,20,30 from city x, y, z respectively. We can therefore minimize the total estimated error. Reservoir sampling is a randomized algorithm. It is used to select k out of n samples. The n is generally very large or unknown. For instance, reservoir sampling can be used to obtain k out of the number of fish in a lake. Cluster sampling - samples are taken as subgroup /clusters of the population. These subgroups are selected at random. Image SourceNon-probability sampling – In a non-probability sampling method, each instance of a population does not have an equivalent chance of being selected. There is an element of risk of ending up with a non-representative sample which might not bring out a comprehensive outcome. Convenience sampling - This sampling technique includes people or samples that are easy to reach. Though it is the easiest methodology to collect a sample it runs a high risk of not being representative of a population. For instance, consider a population size of 20 (1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19.20) The surveyor wants the person 4,7,11,18 to participate, hence it can create selection bias. Quota sampling – In Quota sampling methods the sample or the instances are chosen based on their traits or characteristics which matches with the population For instance, consider a population size of 20 (1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19.20) Consider a quota in multiple of 4 - (4,8,12,16,20) Judgement sampling - Also known as selective sampling. Here individuals are asked to participate.  Snowball sampling - In this sampling technique, an individual element/person can nominate further elements/people known to them. It is only applicable when the sampling frame is difficult to identify.  A nominates P, P nominates G, G nominates M A > P > G > M The non-probability sampling technique may lead to selection bias and population misrepresentation.  Image SourceWe often come across the case of an imbalanced dataset.  Resampling is a technique used to overcome or to deal with  imbalanced datasets It includes removing samples/elements from the majority class i.e. undersampling  Adding more instances from the minority class i.e. Oversampling  There is a dedicated library to tackle imbalanced datasets in Python - known as imblearn. Imblearn has multiple methods to handle undersampling and oversampling    Image SourceTomek Links for under-sampling - pairs of examples from opposite classes in close instancesMajority elements are eliminated from the Tomek Links which intuitively provides a better understanding and decision boundary for ML classifier  SMOTE for oversampling - Synthetic Minority Oversampling Technique - works by increasing new examples from the minority cases. It is a statistical technique of increasing or generating the number of instances in the dataset in a more balanced manner.  Image SourcePick a minority class as the input vector  Discover its k closest neighbors (k_neighbors is indicated as a contention in the SMOTE()) Pick one of these neighbors and spot a synthetic point anyplace on the line joining the point viable and its picked neighbor  Rehash the above steps until it is adjusted or balanced Other must-read sampling methods - Near miss, cluster centroids for under sampling, ADASYN and bSMOTE for oversampling  Train-Test split  Python is bundled with overpowered ML library. The train_test_Split() module from Scikit-Learn library is one of the major python modules that provides a function to split the datasets into multiple subsets in different ways or let us say randomly into training and validation datasets. The parameter train_size takes a fraction between zero and one for specifying the training size. The remaining samples in the original data set are for testing purposes. The record which is selected for training and test sets are randomly sampled. The simplest method train_test_split() or the split_train_test() are more or less the same. train set – the subset of the dataset to train a model test set - the subset of the dataset to test the trained model The train-test method is used to measure the performance of ML algorithms  It is appropriate to use this procedure when the dataset is very large For any supervised Machine learning algorithms, train-test split can be implemented.  Involves taking the data set as a whole and further subdividing it into two subsets The training dataset is used to fit the model  The test dataset serves as an input to the model The model predictions are made on the test data  The output (prediction) is compared to the expected values  The ultimate objective is to evaluate the performance of the said ML model against the new or unseen data. A visual representation of training or test data:  Image SourceIt is important to note that the test data adheres to the following conditions:   Be large enough to fetch statistically significant results Is a representation of the whole dataset. One must not pick the test set with different traits/characteristics of the training set. Never train on test data - don’t get fooled by good results and high accuracy. It might be the case that one has accidentally trained the model on the test data. The train_test_split() is coupled with additional features: a random seed generator as random_state parameter – this ensures which samples go to training and which go to the test set It takes multiple data sets with the matching number of rows and splits them on similar indices. The train_test_split returns four variables  train_X  - which covers X features of the training set. train_y – which contains the value of a response variable from the training set test_X – which includes X features of the test set test_y – which consists of values of the response variable for the test set. There is no exact rule to split the data by 80:20 or 70:30; it depends on the data and the target variable. Some of the data scientists use a range of 60% to 80% for training and the rest for testing the model. To find the length or the number of records we use len function of python > len(X_train), len (X_test) The model is built by using the training set and is tested using the test set X_train and y_train contain the independent features or variables and response variable values for training datasets respectively. On the other hand, X_test and y_test include the independent features and response variables values for the test dataset respectively. Conclusion:Sampling is an ongoing process of accumulating the information or the observations on an estimate of the population variable. We learnt about sampling types - probability sampling procedure and non-probability sampling procedure. Resampling is a repeated process to draw samples from the main data source. And finally, we learnt about training, testing and splitting the data which are used to measure the performance of the model. The training and testing of the model are done to understand the data discrepancies and develop a better understanding of the machine learning model. 
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What Is Data Splitting in Learn and Test Data?

Data is the fuel of every machine learning algo... Read More