Skill Analyses
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      Standard Assessments

      Learn more about the standard assessments we offer.

      Table of Contents

      Data Science Assessment

      Data Engineering Assessment

      Data Analyst Assessment

      Spreadsheet Assessment

      Data Science Assessment

      13 skills, 57 minutes

      Modeling

      Skill Topics Covered
      Advanced Modeling Least Squares, Lasso, Ridge, Polynomial regression models, model, Survival Analysis, k-Nearest Neighbors, Naive Bayes algorithms, and related topics.
      Applying classification and clustering ROC charts, Logistic, loss functions, distance metrics, k-means and Agglomerative, density based, evaluation metrics and related topics.
      Applications of deep learning CNN, GAN YOLO, Markov Decision Process, Q-learning, actor-critic method, epsilon-greedy algorithm and related topics.
      Executing deep learning Gradient descent, SGD, batch GD, learning rate, optimizers, GRU, RNN, ReLU, Sigmoid function, weights, and related topics.
      Executing machine learning Gradient descent, SGD, batch GD, learning rate, optimizers, GRU, RNN, ReLU, Sigmoid function, weights, and related topics.
      Machine learning data Supervised, Unsupervised, Reinforcement and Classified Learning, PCA, feature elimination methods, feature scaling and related topics.
      Natural language processing Text tokenization, transformer, retrieval/generation models, tagging, evaluation and related topics.

      SQL

      Skill Topics Covered

      Exploring data with SQL

        		 Least Squares, Lasso, Ridge, Polynomial regression models, model, Survival Analysis, k-Nearest Neighbors, Naive Bayes algorithms, and related topics.

        Working with SQL

          		 Data extraction, date formatting, NULL values, JOINS, INDEX, PostgreSQL, MySQL, T-SQL, SQLite, Primary Key, subqueries and related topics

          Wrangling data with SQL

            		 Logical operators, ORDER BY, GROUP BY, HAVING, filtering, aggregate, and related topics.

            Python

            Skill Topics Covered
            Exploratory data analysis with Python Query writing to replace missing values, remove records, calculate metrics, find values, produce specific output, identifying ERRORS and related topics.

            Modeling with Python

              		 Creating functions to merge matrices, apply up/down sampling, create models (Random forest, linear, etc.), fix code, and related topics.

              Wrangling data with Python

                		 Complete functions to sort an array, process training/test sets, create binary flags, remove observations, fix code, and related topics.

                 

                Data Engineering Assessment

                12 skills, 54 minutes

                Data Analyzing – (Exploration, Storing, Wrangling)

                Skill Topics Covered

                Applying OLAP/OLTP and implementing databases

                  		 Database schema designs, operations like pivots, slice, roll-up, etc., cubes, types of facts, dimension types and related topics.

                  Data structure and formatting

                    		 Missing-value imputation, data types, text processing, data conversions, concatenation, joins, and related topics.

                    Exploring and analyzing data

                      		 Distributions, measures of central tendency, Kolmogorov-Smirnov test, correlations, multivariate analysis, coefficients and related topics.

                      Exploring data quality and structure

                        		 Bootstrapping, missing value types, data quality standards, Consistency, Precision, Accuracy, Relevancy, completeness and related topics.

                        Feature engineering

                          		 Time series, power distributions, transformations, types of categorical encoding, PCA, date/time formats, one-hot encoding and related topics.

                          Selecting and working with databases and warehouses

                            		 MongoDB, RDBMA, CAP properties, storage arch., in-memory DB, scaling up design, JSON, Redshift, DMQL, schemas (e.g., Snowflake) and related topics.

                            SQL

                            Skill Topics Covered

                            Exploring data with SQL

                              Extracting data, query writing, identifying ERRORS, sorting, pattern recognition, tables, and related topics.
                                Working with SQL Data extraction, date formatting, NULL values, JOINS, INDEX, PostgreSQL, MySQL, T-SQL, SQLite, Primary Key, subqueries and related topics.

                                Wrangling data with SQL

                                  		 Logical operators, ORDER BY, GROUP BY, HAVING, filtering, aggregate, and related topics.

                                  Python

                                  Skill Topics Covered

                                  Exploratory data analysis with Python

                                    		 Query writing to replace missing values, remove records, calculate metrics, find values, produce specific output, identifying ERRORS and related topics.

                                    Working with Python

                                      		 Writing queries to extract data, aggregate, sort, join, match records, and deal with errors, fix code and related topics.

                                      Wrangling data with Python

                                        		 Complete functions to sort an array, process training/test sets, create binary flags, remove observations, fix code, and related topics.

                                         

                                        Data Analyst Assessment

                                        13 skills, 48 minutes

                                        Data Analyzing – (Exploration, Storing, Wrangling)

                                        Skill Topics Covered

                                        Applying OLAP/OLTP and implementing databases

                                          		 Database schema designs, operations like pivots, slice, roll-up, etc., cubes, types of facts, dimension types and related topics.

                                          Data structure and formatting

                                            		 Missing-value imputation, data types, text processing, data conversions, concatenation, joins, and related topics.

                                            Exploring and analyzing data

                                              		 Distributions, measures of central tendency, Kolmogorov-Smirnov test, correlations, multivariate analysis, coefficients and related topics.

                                              Exploring data quality and structure

                                                		 Bootstrapping, missing value types, data quality standards, Consistency, Precision, Accuracy, Relevancy, completeness and related topics.

                                                Feature engineering

                                                  		 Time series, power distributions, transformations, types of categorical encoding, PCA, date/time formats, one-hot encoding and related topics.

                                                  Selecting and working with databases and warehouses

                                                    		 MongoDB, RDBMA, CAP properties, storage arch., in-memory DB, scaling up design, JSON, Redshift, DMQL, schemas (e.g. Snowflake) and related topics.

                                                    Statistics

                                                    Skill Topics Covered

                                                    Data exploration and description

                                                      		 PCA, PDF, distributions, outliers, measures of central t. and related topics.

                                                      Hypothesis testing and inferential statistics

                                                        		 MANOVA, t-tests, Chi-Square, Pearson’s, CI, SD, Gaussian d., and related topics.

                                                        Regression and predictive modeling

                                                          		 Confusion matrix, OLS, assumptions, Poisson, and related topics.

                                                          Sampling techniques

                                                            		 Stratified, bootstrap, cluster, random, experimental d., and related topics.

                                                            Python

                                                            Skill Topics Covered

                                                            Exploratory data analysis with Python

                                                              		 Query writing to replace missing values, remove records, calculate metrics, find values, produce specific output, identifying ERRORS and related topics.

                                                              Working with Python

                                                                		 Writing queries to extract data, aggregate, sort, join, match records, and deal with errors, fix code and related topics.

                                                                Wrangling data with Python

                                                                  		 Complete functions to sort an array, process training/test sets, create binary flags, remove observations, fix code, and related topics.

                                                                   

                                                                  Spreadsheet Assessment

                                                                  2 skills, ~40 minutes

                                                                  The spreadsheet assessment is designed to allow for multiple correct approaches to answering each question. For example, a question might be answered using a Pivot Table, Filtering, or an Equation. The candidate can use whichever approach with which they are most comfortable.

                                                                  Exploring data with spreadsheets

                                                                  Topics Approaches
                                                                  Dealing with outliers, joins, categorization, logical operators, correlations, frequency, searching, creating new variables and similar topics. Pivot tables, macros, writing equations, sub dividing the worksheet, sorting, filtering and many others.

                                                                  Wrangling data with spreadsheets

                                                                  Topics Approaches
                                                                  Measures of central tendency, aggregation, filtering, ranking, sorting, merging, creating charts, creating features and similar topics. Pivot tables, macros, writing equations, sub dividing the worksheet, sorting, filtering and many others.

                                                                   

                                                                  If you have any questions or for custom assessments, please reach out to support@quanthub.com .