Learn R and Python programming environments and follow the instructions in R and Python to import, export, and build data frames. Further, sort, combine, aggregate, and add data sets as directed.

Employ measures of central tendency to evaluate the symmetry and variance and skewness in the data, as well as to summarize the data.

Choose the best suitable graph to display the data, for instance use the Box-Plot and Histogram to evaluate the distribution, scatter plots to visualize bivariate relationships, motion charts to display time-series data.

1. Analyze a discrete random variable’s statistical distribution, use R to compute probabilities for the Poisson and Binomial distributions.

2. Fit the observed data to the Binomial and Poisson distributions

3. Analyze the characteristics of the normal and logarithmic distributions.

4. Use R to compute probabilities for normal and lognormal distributions.

5. Fit the observed data to the normal, lognormal, and exponential distributions.

6. Assess the sample distribution idea (t, F, and Chi Square).
7. Create R and Python programs that assess the results of the right hypothesis tests.
8. Use R output to make statistical inferences
9. Convert research issues into statistical predictions
10. Determine which statistical test is best suited for a given hypothesis.
11. For a specific research problem, define the terms variable, factor, and level.
12. Assess the causes of variation, as well as both explained and unexplained variance.
13. Provide an ANOVA/ANCOVA linear model.
14. Verify the assumption’s accuracy using definitions and variation analysis.
15. Use R and Python scripts for analysis to verify the accuracy of the hypotheses.
16. Use the research problem’s statistical analysis to draw conclusions.

1. Analyze the predictors and dependent variables.
2. Create linear models using Python’s.ols function and R’s lm function.
3. Understand the calculated regression coefficients’ signs and values.
4. Using F distributions, interpret the results of the global test.
5. Differentiate between important and irrelevant variables.
6. Identify and fix multicollinearity issues.
7. Update a model after an issue has been fixed.
8. Evaluate the ridge regression model’s performance.
9. Conduct residual analysis, analyzing data graphically and using statistical tests.
10. Find a solution to the heteroscedasticity and non-normality of errors issues.
11. Create models and apply them in accordance with the requirements to testing data.
12. Use k-fold cross validation to assess the models’ stability.
13. Use the hat matrix and Cook’s distance to assess influential findings.

1. Determine the appropriate times to utilize binary linear regression.
2. Create accurate models using Python and R methods.
3. Use linear regression testing to interpret the output of the overall test in order to evaluate the outcomes.
4. Execute an out-of-sample validation that evaluates the model’s ability to forecast.
5. Choose a modeling strategy for categorical variables.
6. Create models in R and Python for dependent variables with nominal and ordinal scales.
7. Analyze the generalized linear model concept.
8. To appropriately count data, use the Poisson regression model and negative binomial regression.
9. Model the “time to event” variable with cox regression.

1. Decomposing time series and evaluating various components are all part of appropriately creating time series objects in R and Python.
2. Determine the stationary nature of a time series.
3. Convert data from a time series that isn’t stationary to a time series that is.
4. Using the autocorrelation function (ACF) and partial auto-correlation function (PACF) to express how closely values are related, determine p, d, and q of the ARIMA model.
5. Use R and Python to create ARIMA models and test if mistakes follow the white noise procedure.
6. Complete the model and forecast n periods in advance to produce precise forecasts.
7. Analyze the panel data regression theory.
8. Analyze the characteristics of panel data.
9. Create panel data regression models for various applications.
10. Compare and contrast models with fixed and random effects.

1. Define Principal Component Analysis (PCA) and its derivations, and evaluate and implement their application.
2. Analyze whether data reduction is necessary.
3. Use R and Python to create scoring models and perform principal component analysis to reduce data loss and enhance data interpretation.
4. Use Principal Component Regression to eliminate multi-collinearity.
5. Use factor scores to interpret the data set after performing data reduction and generating interpretable factors.
6. Compute a multi-dimensional brand impression map.
7. Analyze whether a cluster analysis is necessary.
8. Using appropriate procedures, obtain clusters.
9. Analyze cluster usage for business plans and interpret cluster solutions.

1. Evaluate Naive Bayes and the support vector machine algorithm as classification techniques.
2. Compared to traditional approaches, use decision trees for classification and regression problems.
3. Examine the ideas of bagging and bootstrapping.
4. Use the random forest method in many professional and interpersonal settings.
5. Consider using neural networks to solve classification issues after analyzing market baskets.
6. Create product baskets by looking for potential associations in transaction data.
7. Employ neural networks to a problem of classification in areas including speech recognition, image identification, and document categorization.

1. Evaluate the theories and methods of text mining.
2. Identify the text’s favorable, negative, or neutral tone by doing sentiment analysis on Twitter data and unstructured data. Use the SHINY package to create comprehensible dashboards.
3. Provide the outcomes of data analysis using standalone applications that are hosted on a web page.
4. Assess Hadoop’s foundational ideas.
5. Evaluate the use of big data analytics across different industries.
6. Assess the effectiveness of using the HADOOP platform for big data analytics.
7. Create a straightforward AI model utilizing well-known machine learning techniques to aid in business analysis and decision-making. compared to conventional business theory presumptions.
8. Analyze the performance of core SQL.
9. Use SQL to manipulate and analyze data to find insights in unused data.

1. Examine the technology supporting the digital transformation.
2. Analyze the managerial difficulties in successfully executing digital transformation.
3. Analyze how the use of big data and artificial intelligence has affected business organizations strategically.
4. Examine innovation ideas and identify disruptive and incremental change.
5. Examine the part that ethical codes play in the operation and long-term viability of organizations.
6. Consider the value of reporting and transparency for moral behavior.