Tag: #python

How to Collect Data for Binary Logit Model

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By Kavita Dehalwar

Collecting data for a binary logit model involves several key steps, each crucial to ensuring the accuracy and reliability of your analysis. Here’s a detailed guide on how to gather and prepare your data:

1. Define the Objective

Before collecting data, clearly define what you aim to analyze or predict. This definition will guide your decisions on what kind of data to collect and the variables to include. For a binary logit model, you need a binary outcome variable (e.g., pass/fail, yes/no, buy/not buy) and several predictor variables that you hypothesize might influence the outcome.

2. Identify Your Variables

  • Dependent Variable: This should be a binary variable representing two mutually exclusive outcomes.
  • Independent Variables: Choose factors that you believe might predict or influence the dependent variable. These could include demographic information, behavioral data, economic factors, etc.

3. Data Collection Methods

There are several methods you can use to collect data:

  • Surveys and Questionnaires: Useful for gathering qualitative and quantitative data directly from subjects.
  • Experiments: Design an experiment to manipulate predictor variables under controlled conditions and observe the outcomes.
  • Existing Databases: Use data from existing databases or datasets relevant to your research question.
  • Observational Studies: Collect data from observing subjects in natural settings without interference.
  • Administrative Records: Government or organizational records can be a rich source of data.

4. Sampling

Ensure that your sample is representative of the population you intend to study. This can involve:

  • Random Sampling: Every member of the population has an equal chance of being included.
  • Stratified Sampling: The population is divided into subgroups (strata), and random samples are drawn from each stratum.
  • Cluster Sampling: Randomly selecting entire clusters of individuals, where a cluster forms naturally, like geographic areas or institutions.

5. Data Cleaning

Once collected, data often needs to be cleaned and prepared for analysis:

  • Handling Missing Data: Decide how you’ll handle missing values (e.g., imputation, removal).
  • Outlier Detection: Identify and treat outliers as they can skew analysis results.
  • Variable Transformation: You may need to transform variables (e.g., log transformation, categorization) to fit the model requirements or to better capture the nonlinear relationships.
  • Dummy Coding: Convert categorical independent variables into numerical form through dummy coding, especially if they are nominal without an inherent ordering.

6. Data Splitting

If you are also interested in validating the predictive power of your model, you should split your dataset:

  • Training Set: Used to train the model.
  • Test Set: Used to test the model, unseen during the training phase, to evaluate its performance and generalizability.

7. Ethical Considerations

Ensure ethical guidelines are followed, particularly with respect to participant privacy, informed consent, and data security, especially when handling sensitive information.

8. Data Integration

If data is collected from different sources or at different times, integrate it into a consistent format in a single database or spreadsheet. This unified format will simplify the analysis.

9. Preliminary Analysis

Before running the binary logit model, conduct a preliminary analysis to understand the data’s characteristics, including distributions, correlations among variables, and a preliminary check for potential multicollinearity, which might necessitate adjustments in the model.

By following these steps, you can collect robust data that will form a solid foundation for your binary logit model analysis, providing insights into the factors influencing your outcome of interest.

References

Cramer, J. S. (1999). Predictive performance of the binary logit model in unbalanced samples. Journal of the Royal Statistical Society: Series D (The Statistician), 48(1), 85-94.

Dehalwar, K., & Sharma, S. N. (2023). Fundamentals of Research Writing and Uses of Research Methodologies. Edupedia Publications Pvt Ltd.

Horowitz, J. L., & Savin, N. E. (2001). Binary response models: Logits, probits and semiparametrics. Journal of economic perspectives, 15(4), 43-56.

Singh, D., Das, P., & Ghosh, I. (2024). Driver behavior modeling at uncontrolled intersections under Indian traffic conditions. Innovative Infrastructure Solutions, 9(4), 1-11.

Tranmer, M., & Elliot, M. (2008). Binary logistic regression. Cathie Marsh for census and survey research, paper, 20.

Wilson, J. R., Lorenz, K. A., Wilson, J. R., & Lorenz, K. A. (2015). Standard binary logistic regression model. Modeling binary correlated responses using SAS, SPSS and R, 25-54.

Young, R. K., & Liesman, J. (2007). Estimating the relationship between measured wind speed and overturning truck crashes using a binary logit model. Accident Analysis & Prevention, 39(3), 574-580.

Unlocking Insights: The Binary Logit Model Explained

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By Shashikant Nishant Sharma

The binary logit model is a statistical technique widely used in various fields such as economics, marketing, medicine, and political science to analyze decisions where the outcome is binary—having two possible states, typically “yes” or “no.” Understanding the model provides valuable insights into factors influencing decision-making processes.

Key Elements of the Binary Logit Model:

  1. Outcome Variable:
    • This is the dependent variable and is binary. For instance, it can represent whether an individual purchases a product (1) or not (0), whether a patient recovers from an illness (1) or does not (0), or whether a customer renews their subscription (1) or cancels it (0).
  2. Predictor Variables:
    • The independent variables, or predictors, are those factors that might influence the outcome. Examples include age, income, education level, or marketing exposure.
  3. Logit Function:
    • The model uses a logistic (sigmoid) function to transform the predictors’ linear combination into probabilities that lie between 0 and 1. The logit equation typically looks like this:
    𝑝=11+𝑒−(𝛽0+𝛽1𝑋1+𝛽2𝑋2+…+𝛽𝑛𝑋𝑛)p=1+e−(β0​+β1​X1​+β2​X2​+…+βn​Xn​)1​Here, 𝑝p is the probability of the outcome occurring, and 𝛽𝑖βi​ are the coefficients associated with each predictor variable 𝑋𝑖Xi​.

How It Works:

The graph above illustrates the binary logit model, showing the relationship between the predictor value (horizontal axis) and the predicted probability (vertical axis). This logistic curve, often referred to as an “S-curve,” demonstrates how the logit function transforms a linear combination of predictor variables into probabilities ranging between 0 and 1.

  • The red dashed line represents a probability threshold of 0.5, which is often used to classify the two outcomes: above this threshold, an event is predicted to occur (1), and below it, it’s predicted not to occur (0).
  • The steepest portion of the curve indicates where changes in the predictor value have the most significant impact on the probability.
  • Coefficient Estimation:
    • The coefficients (𝛽β) are estimated using the method of maximum likelihood. The process finds the values that maximize the likelihood of observing the given outcomes in the dataset.
  • Odds and Odds Ratios:
    • The odds represent the ratio of the probability of an event happening to it not happening. The model outputs an odds ratio for each predictor, indicating how a one-unit change in the predictor affects the odds of the outcome.
  • Interpreting Results:
    • Coefficients indicate the direction of the relationship between predictors and outcomes. Positive coefficients suggest that increases in the predictor increase the likelihood of the outcome. Odds ratios greater than one imply higher odds of the event with higher predictor values.

Applications:

  1. Marketing Analysis: Understanding customer responses to a new product or marketing campaign.
  2. Healthcare: Identifying factors influencing recovery or disease progression.
  3. Political Science: Predicting voter behavior or election outcomes.
  4. Economics: Studying consumer behavior in terms of buying decisions or investment choices.

Limitations:

  • Assumptions: The model assumes a linear relationship between the log-odds and predictor variables, which may not always hold.
  • Data Requirements: Requires a sufficient amount of data for meaningful statistical analysis.
  • Model Fit: Goodness-of-fit assessments, such as the Hosmer-Lemeshow test or ROC curves, are crucial for evaluating model accuracy.

Conclusion:

The binary logit model provides a robust framework for analyzing decisions and predicting binary outcomes. By understanding the relationships between predictor variables and outcomes, businesses, researchers, and policymakers can unlock valuable insights to inform strategies and interventions.

References

Cramer, J. S. (1999). Predictive performance of the binary logit model in unbalanced samples. Journal of the Royal Statistical Society: Series D (The Statistician), 48(1), 85-94.

Dehalwar, K., & Sharma, S. N. (2023). Fundamentals of Research Writing and Uses of Research Methodologies. Edupedia Publications Pvt Ltd.

Singh, D., Das, P., & Ghosh, I. (2024). Driver behavior modeling at uncontrolled intersections under Indian traffic conditions. Innovative Infrastructure Solutions, 9(4), 1-11.

Tranmer, M., & Elliot, M. (2008). Binary logistic regression. Cathie Marsh for census and survey research, paper, 20.

Wilson, J. R., Lorenz, K. A., Wilson, J. R., & Lorenz, K. A. (2015). Standard binary logistic regression model. Modeling binary correlated responses using SAS, SPSS and R, 25-54.

Young, R. K., & Liesman, J. (2007). Estimating the relationship between measured wind speed and overturning truck crashes using a binary logit model. Accident Analysis & Prevention, 39(3), 574-580.

Introduction to Structural Equation Modeling

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By Shashikant Nishant Sharma

Structural Equation Modeling (SEM) is a comprehensive statistical approach used widely in the social sciences for testing hypotheses about relationships among observed and latent variables. This article provides an overview of SEM, discussing its methodology, applications, and implications, with references formatted in APA style.

Introduction to Structural Equation Modeling

Structural Equation Modeling combines factor analysis and multiple regression analysis, allowing researchers to explore the structural relationship between measured variables and latent constructs. This technique is unique because it provides a multifaceted view of the relationships, considering multiple regression paths simultaneously and handling unobserved variables.

Methodology of SEM

The methodology of SEM involves several key steps: model specification, identification, estimation, testing, and refinement. The model specification involves defining the model structure, which includes deciding which variables are to be considered endogenous and exogenous. Model identification is the next step and determines whether the specified model is estimable. Then, the model estimation is executed using software like LISREL, AMOS, or Mplus, which provides the path coefficients indicating the relationships among variables.

Estimation methods include Maximum Likelihood, Generalized Least Squares, or Bayesian estimation depending on the distribution of the data and the sample size. Model fit is then tested using indices like Chi-Square, RMSEA (Root Mean Square Error of Approximation), and CFI (Comparative Fit Index). Model refinement may involve re-specification of the model based on the results obtained in the testing phase.

Above is a visual representation of the Structural Equation Modeling (SEM) technique as used in a scholarly context. The image captures a network diagram on a blackboard and a group of researchers discussing the model.

Applications of SEM

SEM is used across various fields such as psychology, education, business, and health sciences. In psychology, SEM helps in understanding the relationship between latent constructs like intelligence, anxiety, and job performance. In education, it can analyze the influence of teaching methods on student learning and outcomes. In business, SEM is applied to study consumer satisfaction and brand loyalty.

Challenges and Considerations

While SEM is a powerful tool, it comes with challenges such as the need for large sample sizes and complex data handling requirements. Mis-specification of the model can lead to incorrect conclusions, making model testing and refinement critical steps in the SEM process.

Conclusion

Structural Equation Modeling is a robust statistical technique that offers detailed insights into complex variable relationships. It is a valuable tool in the researcher’s toolkit, allowing for the precise testing of theoretical models.

References

  • Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). Guilford publications.
  • Schumacker, R. E., & Lomax, R. G. (2016). A beginner’s guide to structural equation modeling (4th ed.). Routledge.
  • Byrne, B. M. (2013). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). Routledge.
  • Hoyle, R. H. (Ed.). (2012). Handbook of structural equation modeling. The Guilford Press.
  • Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). The Guilford Press.

Understanding Thiessen Polygons: Significance and Applications in Spatial Analysis

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By Shashikant Nishant Sharma

Thiessen polygons, also known as Voronoi diagrams, are a fundamental tool in spatial analysis, providing significant insights into geographical and other scientifically relevant data distributions. Named after the American meteorologist Alfred H. Thiessen, who popularized their use in the early 20th century, these polygons help in defining influence zones around a given set of points on a plane. This article explores the concept, creation process, and various applications of Thiessen polygons, emphasizing their utility in multiple scientific and practical fields.

Photo by Kindel Media on Pexels.com

What are Thiessen Polygons?

Thiessen polygons are a geometric representation used to delineate areas of influence for each of several points on a map. Each polygon corresponds to a specific point and consists of all the places that are closer to that point than to any other. These polygons are constructed such that every location within the polygon boundary is nearest to the point generating the polygon, ensuring that any spatial analysis using these zones is precise and relevant to the designated point.

How Are Thiessen Polygons Created?

The process of creating Thiessen polygons involves several mathematical steps:

  1. Point Placement: Begin with a set of points on a plane. These points can represent various data sources like weather stations, cities, or other geographical features.
  2. Perpendicular Bisectors: For each pair of points, draw a line segment connecting them, and then draw the perpendicular bisector of this line segment. The bisector will divide the space into two regions, each closer to one of the two points than to the other.
  3. Intersection of Bisectors: The bisectors from all pairs of points intersect to form the boundaries of the Thiessen polygons. The process continues until the entire plane is divided into contiguous polygons, each surrounding one of the original points.

Applications of Thiessen Polygons

Thiessen polygons have diverse applications across various scientific disciplines and industries:

  1. Meteorology and Climatology:
    • Precipitation Analysis: Thiessen polygons are used to estimate area-averaged rainfall from discrete weather stations. Each station influences a polygonal area, and precipitation data are averaged over these areas to provide a more comprehensive view of rainfall distribution.
  2. Hydrology and Water Resources:
    • Catchment Area Analysis: In hydrology, Thiessen polygons can help determine the catchment areas of rivers or water catchments, aiding in the management of water resources and flood analysis.
  3. Agriculture:
    • Irrigation Planning: Farmers use Thiessen polygons to analyze soil moisture levels and optimize irrigation systems, ensuring that water resources are used efficiently according to the proximity of water sources and field demands.
  4. Urban Planning and Public Health:
    • Service Area Planning: These polygons help in planning public services such as hospitals, schools, and fire stations by defining which areas are closest to each service point, optimizing response times and accessibility.
    • Epidemiology: Health researchers use Thiessen polygons to study the spread of diseases from various epicenters, helping in targeted healthcare interventions.
  5. Telecommunications:
    • Network Coverage Optimization: Thiessen polygons assist in determining areas of coverage and gaps for cellular networks based on the locations of signal towers.
  6. Geography and Ecology:
    • Species Distribution: Ecologists use these polygons to study species distributions and interactions by mapping sightings to understand territorial boundaries.

Challenges and Considerations

While Thiessen polygons are a powerful tool for spatial analysis, they have limitations, particularly in complex terrains and in cases where geographic barriers affect the actual area of influence. Additionally, the accuracy of the polygons depends significantly on the density and distribution of the points used in their creation.

Conclusion

Thiessen polygons are an indispensable tool in geographic information systems (GIS), enabling precise spatial analysis across diverse fields from meteorology to urban planning. By simplifying complex geographical data into manageable zones of influence, they provide valuable insights that guide decision-making and research across the globe. As technology advances, the creation and use of Thiessen polygons are becoming more refined, offering even greater accuracy and utility in spatial analysis.

References

Boots, B. N. (1980). Weighting thiessen polygons. Economic Geography, 56(3), 248-259.

Brassel, K. E., & Reif, D. (1979). A procedure to generate Thiessen polygons. Geographical analysis, 11(3), 289-303.

Croley II, T. E., & Hartmann, H. C. (1985). Resolving thiessen polygons. Journal of Hydrology, 76(3-4), 363-379.

Fiedler, F. R. (2003). Simple, practical method for determining station weights using Thiessen polygons and isohyetal maps. Journal of Hydrologic engineering, 8(4), 219-221.

Rhynsburger, D. (1973). Analytic delineation of Thiessen polygons. Geographical Analysis, 5(2), 133-144.

Understanding Negative Binomial Regression: An Overview

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By Shashikant Nishant Sharma

Negative binomial regression is a type of statistical analysis used for modeling count data, especially in cases where the data exhibits overdispersion relative to a Poisson distribution. Overdispersion occurs when the variance exceeds the mean, which can often be the case in real-world data collections. This article explores the fundamentals of negative binomial regression, its applications, and how it compares to other regression models like Poisson regression.

What is Negative Binomial Regression?

Negative binomial regression is an extension of Poisson regression that adds an extra parameter to model the overdispersion. While Poisson regression assumes that the mean and variance of the distribution are equal, negative binomial regression allows the variance to be greater than the mean, which often provides a better fit for real-world data where the assumption of equal mean and variance does not hold.

Mathematical Foundations

The negative binomial distribution can be understood as a mixture of Poisson distributions, where the mixing distribution is a gamma distribution. The model is typically expressed as:

A random variable X is supposed to follow a negative binomial distribution if its probability mass function is given by:

f(x) = (n + r – 1)C(r – 1) Prqx, where x = 0, 1, 2, ….., and p + q = 1.

Here we consider a binomial sequence of trials with the probability of success as p and the probability of failure as q.

Let f(x) be the probability defining the negative binomial distribution, where (n + r) trials are required to produce r successes. Here in (n + r – 1) trials we get (r – 1) successes, and the next (n + r) is a success.

Then f(x) = (n + r – 1)C(r – 1) Pr-1qn-1.p

f(x) = (n + r – 1)C(r – 1) Prqn

When to Use Negative Binomial Regression?

Negative binomial regression is particularly useful in scenarios where the count data are skewed, and the variance of the data points is significantly different from the mean. Common fields of application include:

  • Healthcare: Modeling the number of hospital visits or disease counts, which can vary significantly among different populations.
  • Insurance: Estimating the number of claims or accidents, where the variance is typically higher than the mean.
  • Public Policy: Analyzing crime rates or accident counts in different regions, which often show greater variability.

Comparing Poisson and Negative Binomial Regression

While both Poisson and negative binomial regression are used for count data, the choice between the two often depends on the nature of the data’s variance:

  • Poisson Regression: Best suited for data where the mean and variance are approximately equal.
  • Negative Binomial Regression: More appropriate when the data exhibits overdispersion.

If a Poisson model is fitted to data that is overdispersed, it may underestimate the variance leading to overly optimistic confidence intervals and p-values. Conversely, a negative binomial model can provide more reliable estimates and inference in such cases.

Implementation and Challenges

Implementing negative binomial regression typically involves statistical software such as R, SAS, or Python, all of which have packages or modules designed to fit these models to data efficiently. One challenge in fitting negative binomial models is the estimation of the dispersion parameter, which can sometimes be sensitive to outliers and extreme values.

Conclusion

Negative binomial regression is a robust method for analyzing count data, especially when that data is overdispersed. By providing a framework that accounts for variability beyond what is expected under a Poisson model, it allows researchers and analysts to make more accurate inferences about their data. As with any statistical method, the key to effective application lies in understanding the underlying assumptions and ensuring that the model appropriately reflects the characteristics of the data.

References

Chang, L. Y. (2005). Analysis of freeway accident frequencies: negative binomial regression versus artificial neural network. Safety science, 43(8), 541-557.

Hilbe, J. M. (2011). Negative binomial regression. Cambridge University Press.

Ver Hoef, J. M., & Boveng, P. L. (2007). Quasi‐Poisson vs. negative binomial regression: how should we model overdispersed count data?. Ecology, 88(11), 2766-2772.

Liu, H., Davidson, R. A., Rosowsky, D. V., & Stedinger, J. R. (2005). Negative binomial regression of electric power outages in hurricanes. Journal of infrastructure systems, 11(4), 258-267.

Yang, S., & Berdine, G. (2015). The negative binomial regression. The Southwest respiratory and critical care chronicles, 3(10), 50-54.

A Comprehensive Guide to Data Analysis Using R Studio

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By Shashikant Nishant Sharma

In today’s data-driven world, the ability to effectively analyze data is becoming increasingly important across various industries. R Studio, a powerful integrated development environment (IDE) for R programming language, provides a comprehensive suite of tools for data analysis, making it a popular choice among data scientists, statisticians, and analysts. In this article, we will explore the fundamentals of data analysis using R Studio, covering essential concepts, techniques, and best practices.

1. Getting Started with R Studio

Before diving into data analysis, it’s essential to set up R Studio on your computer. R Studio is available for Windows, macOS, and Linux operating systems. You can download and install it from the official R Studio website (https://rstudio.com/).

Once installed, launch R Studio, and you’ll be greeted with a user-friendly interface consisting of several panes: the script editor, console, environment, and files. Familiarize yourself with these panes as they are where you will write, execute, and manage your R code and data.

2. Loading Data

Data analysis begins with loading your dataset into R Studio. R supports various data formats, including CSV, Excel, SQL databases, and more. You can use functions like read.csv() for CSV files, read.table() for tab-delimited files, and read_excel() from the readxl package for Excel files.

RCopy code# Example: Loading a CSV file
data <- read.csv("data.csv")

After loading the data, it’s essential to explore its structure, dimensions, and summary statistics using functions like str(), dim(), and summary().

3. Data Cleaning and Preprocessing

Before performing any analysis, it’s crucial to clean and preprocess the data to ensure its quality and consistency. Common tasks include handling missing values, removing duplicates, and transforming variables.

RCopy code# Example: Handling missing values
data <- na.omit(data)

# Example: Removing duplicates
data <- unique(data)

# Example: Transforming variables
data$age <- log(data$age)

Additionally, you may need to convert data types, scale or normalize numeric variables, and encode categorical variables using techniques like one-hot encoding.

4. Exploratory Data Analysis (EDA)

EDA is a critical step in data analysis that involves visually exploring and summarizing the main characteristics of the dataset. R Studio offers a plethora of packages and visualization tools for EDA, including ggplot2, dplyr, tidyr, and ggplotly.

RCopy code# Example: Creating a scatter plot
library(ggplot2)
ggplot(data, aes(x = age, y = income)) + 
  geom_point() + 
  labs(title = "Scatter Plot of Age vs. Income")

During EDA, you can identify patterns, trends, outliers, and relationships between variables, guiding further analysis and modeling decisions.

5. Statistical Analysis

R Studio provides extensive support for statistical analysis, ranging from basic descriptive statistics to advanced inferential and predictive modeling techniques. Common statistical functions and packages include summary(), cor(), t.test(), lm(), and glm().

RCopy code# Example: Conducting a t-test
t_test_result <- t.test(data$income ~ data$gender)
print(t_test_result)

Statistical analysis allows you to test hypotheses, make inferences, and derive insights from the data, enabling evidence-based decision-making.

6. Machine Learning

R Studio is a powerhouse for machine learning with numerous packages for building and evaluating predictive models. Popular machine learning packages include caret, randomForest, glmnet, and xgboost.

RCopy code# Example: Training a random forest model
library(randomForest)
model <- randomForest(target ~ ., data = data)

You can train models for classification, regression, clustering, and more, using techniques such as decision trees, support vector machines, neural networks, and ensemble methods.

7. Reporting and Visualization

R Studio facilitates the creation of professional reports and visualizations to communicate your findings effectively. The knitr package enables dynamic report generation, while ggplot2, plotly, and shiny allow for the creation of interactive and customizable visualizations.

RCopy code# Example: Generating a dynamic report
library(knitr)
knitr::kable(head(data))

Interactive visualizations enhance engagement and understanding, enabling stakeholders to interactively explore the data and insights.

Conclusion

Data analysis using R Studio is a versatile and powerful process that enables individuals and organizations to extract actionable insights from data. By leveraging its extensive ecosystem of packages, tools, and resources, you can tackle diverse data analysis challenges effectively. Whether you’re a beginner or an experienced data scientist, mastering R Studio can significantly enhance your analytical capabilities and decision-making prowess in the data-driven world.

In conclusion, this article has provided a comprehensive overview of data analysis using R Studio, covering essential concepts, techniques, and best practices. Armed with this knowledge, you’re well-equipped to embark on your data analysis journey with R Studio and unlock the full potential of your data.

References

Bhat, W. A., Khan, N. L., Manzoor, A., Dada, Z. A., & Qureshi, R. A. (2023). How to Conduct Bibliometric Analysis Using R-Studio: A Practical Guide. European Economic Letters (EEL), 13(3), 681-700.

Grömping, U. (2015). Using R and RStudio for data management, statistical analysis and graphics. Journal of Statistical Software, 68, 1-7.

Horton, N. J., & Kleinman, K. (2015). Using R and RStudio for data management, statistical analysis, and graphics. CRC Press.

Jaichandran, R., Bagath Basha, C., Shunmuganathan, K. L., Rajaprakash, S., & Kanagasuba Raja, S. (2019). Sentiment analysis of movies on social media using R studio. Int. J. Eng. Adv. Technol, 8, 2171-2175.

Komperda, R. (2017). Likert-type survey data analysis with R and RStudio. In Computer-Aided Data Analysis in Chemical Education Research (CADACER): Advances and Avenues (pp. 91-116). American Chemical Society.

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The Data Industry – A Brief Overview

The data industry is projected to grow by leaps and bounds over the next decade. Massive amounts of data are being generated every day with a quintillion bytes being the safe estimate. Data professionals and statisticians are of paramount requirement in this fast-paced, data-driven world. They perform many tasks ranging from identification of data sources to analysis of data. Additionally, they find trends and patterns in the existing data at hand, however, the real set of duties would depend from organisation to organisation. Since data is relevant in almost every field now, the statistical requirements would also understandably change with the various sectors.

Candidates aspiring to step into this industry would be expected to have a fair knowledge about the statistical software in use, being proficient in one increases the job prospects manifold. It is nevertheless advisable that the potential employees narrow down the types of companies they wish to work for, say, for example, biostatistical organisations, and hone their skills accordingly.

The most popular programming software utilised for statistical analysis is STATA, SAS, R and Python.

STATA

In the words of StataCorp, Stata is “a complete, integrated statistical software package that provides everything you need for data analysis, data management, and graphics”. This software comes in handy while storing and managing large sets of data and is menu-driven. It is available for Windows, Mac and Linux systems. Stata is one of the leading econometric software packages sold in the market today. Such is its importance, that many universities have incorporated this in their coursework to make their students jobs ready. Over 1400 openings posted on Indeed put forward Stata as a precondition for selection. Facebook, Amazon and Mathematica are some of the many companies that require STATA as one of the qualifications for statistical and econometrics related positions.

Python

Being an incredibly versatile programming language, Python is immensely popular. It is accessible for most people as it is easy to learn and write. Organisations ranging from Google to Spotify, all use Python in their development teams. Recently, Python has become synonymous with Data Science. In contrast to other programming languages, such as R, Python excels when it comes to scalability. It is also considerably faster than STATA and is equipped with numerous data science libraries. Python’s growing popularity has in part stemmed from its well-known community. Finding a solution to a challenging problem has never been easier because of its tight-knit community.

SAS

This is a command-driven software package that proves to be useful for statistical analysis as well as data visualization. SAS has been leading the commercial analytics space and provides great technical support. The software is quite expensive, making it beyond reach for many individuals. However, private organisations hold a very large market share of SAS. It is relevant in the corporate world to a large extent.

Educational Qualifications and Online Courses

Employers typically look for statistics, economics, maths, computer science or engineering students for data-related jobs with more preferences given to candidates with post-graduate degree holders. The key skills in demand include proficiency in statistical software, model building and deployment, data preparation, data mining and impeccable analytical skills. People looking to upskill themselves or diversify into a different career path to attain a higher pay bracket should give the data industry a shot. Coursera, Udemy, LinkedIn and various other platforms provide affordable courses in data science, programming and analytics for this purpose. A career in data is a rewarding one, and also ensures maximum job satisfaction. This is a highly recommended profession in today’s time.

How to be a Full Stack Developer?

Before getting into the topic, “how to become a full stack Java developer?” or “how to become a full stack Python developer?”, we learn what is Full Stack development. We are living in a virtual world. We are solving every problem in this virtual world with the help of softwares like some solution. This software contains multiple layers. We have Presentation layer, Business layer and Database layer. The presentation layer is something where the user interacts. For example, if we are going to hariyali.in, we are accessing the front page of the website. That is the presentation layer. We can also say app here instead of website. If we are going to WordPress app, the page that comes first is the presentation layer. Then we are writing an article/a blog and then publishing it or saving it as a draft. All these processing part runs in the server which contains the business logic. As our requirements change, the business logic will also change. And then we have a database layer where we will put our data. The data that we put in should be stored somewhere. That’s why we have a database layer. Now a question arises. If we want to build this application, what are the technologies should we learn?

If we talk about industry, for different layers we have different professionals to work with it. We have experts in presentation layer. We have experts in business layer. As well we have experts in database layer. A Java developer basically works with the business layer. Now a question may arise. Why not presentation layer? Presentation layer can be done by people who are creative. Because he/she must provide the users a good looking UI with a good UX and to build a good UI with good UX, he/she needs creativity. He/she must understand the users; and colour mapping as well. A presentation layer expert must know HTML, CSS, JavaScript. Those are the technologies used in front end. One can also use Angular, React. A business layer expert must know PHP, Java, C sharp, Python. Nowadays JavaScript is also used in business layer. For Database layer, we need experts with knowledge in Oracle, MySql and  NoSql.

MongoDB, ExpressJS, AngularJS and nodeJS are the MEAN stack used. With the help of stack we can build the entire software. What is Stack? Stack simply means one over the other. We have the Presentation layer which communicates with the Business layer which communicates with the Database layer. All these can be done using Stack.

We have seen that there are experts working with every layer. But nowadays there are companies that position a full stack developer. From sta rt to end, everything will be done by him.
The advantages of having a full stack developer:

  • There is no communication gap between teams.
  • The full stack developer is the Jack of all technologies.

But there is also a drawback of having a full stack developer. He is the Jack of all technologies but master of none. But that’s fine. Working on more projects and spending more time with technologies make him trained.

If you want to be a full stack developer, learn front end(Presentation layer) then get into technologies used in business layer and then into database layer. The thing that differs for a full stack Java developer and a full stack Python developer is the business layer. For a full stack Java developer, the business layer will be of Java. For a full stack Python developer, the business layer will be of Python. Just stick to one technology (language) and upgrade on that. You don’t need to learn every language. All the best.