Explanation
Data visualization is the visual depiction of data through the use of graphs, plots, and informational graphics.
Its practitioners use statistics and data science to conveythe meaning behind data in ethical and accurate ways.

Explanation
As a consumer, you can do the following:
Discover and test third-party data sources.
Receive frictionless access to raw data products from vendors.
Combine new datasets with your existing data in Snowflake to derive new business insights.
Have datasets available instantly and updated continually for users.
Eliminate the costs of building and maintaining various APIs and data pipelines to load and up-date data.
Use the business intelligence (BI) tools of your choice.

Explanation
Computes length of column A and Sum of Column B values of each group

Explanation
Data processing and analysis cannot happen without data profiling-reviewing source data for con-tent and quality. As data gets bigger and infrastructure moves to the cloud, data profiling is increasingly important.
What is data profiling?
Data profiling is the process of reviewing source data, understanding structure, content and interrelationships, and identifying potential for data projects.
Data profiling is a crucial part of:
Data warehouse and business intelligence (DW/BI) projects-dataprofiling can uncover data quality issues in data sources, and what needs to be corrected in ETL.
Data conversion and migration projects-data profiling can identify data quality issues, which you can handle in scripts and data integration tools copying data from source to target. It can also un-cover new requirements for the target system.
Source system data quality projects-data profiling can highlight data which suffers from serious or numerous quality issues, and the source of the issues (e.g. user inputs, errors in interfaces, data corruption).
Data profiling involves:
Collecting descriptive statistics like min, max, count and sum.
Collecting data types, length and recurring patterns.
Tagging data with keywords, descriptions or categories.
Performing data quality assessment, risk of performing joins on the data.
Discovering metadata and assessing its accuracy.
Identifying distributions, key candidates, foreign-key candidates, functional dependencies, embedded value dependencies, and performing inter-table analysis.

ExplanationFeature Scaling
Feature Scaling is the process of transforming the features so that they have a similar scale. This is important in machine learning because the scale of the features can affect the performance of the model.
Types of Feature Scaling:
Min-Max Scaling: Rescaling the features to a specific range, such as between 0 and 1, by subtracting the minimum value and dividing by the range.
Standard Scaling: Rescaling the features to have a mean of 0 and a standard deviation of 1 by subtracting the mean and dividing by the standard deviation.
Robust Scaling: Rescaling the features to be robust to outliers by dividing them by the interquartile range.
Benefits of Feature Scaling:
Improves Model Performance: By transforming the features to have a similar scale, the model can learn from all features equally and avoid being dominated by a few large features.
Increases Model Robustness: By transforming the features to be robust to outliers, the model can become more robust to anomalies.
Improves Computational Efficiency: Many machine learning algorithms, such as k-nearest neighbors, are sensitive to the scale of the features and perform better with scaled features.
Improves Model Interpretability: By transforming the features to have a similar scale, it can be easier to understand the model’s predictions.

Explanation
Hex – collaborative data science and analytics platform
Denodo – data virtualization and federation platform
DvSum – data catalog and data intelligence platform
Diyotta – data integration and migration

Explanation
Leave-one-out cross-validation (LOO cross-validation) is not suitable for very large datasets due to the fact that this validation technique requires one model for every sample in the training set to be created and evaluated.
Cross validation
It is a technique to evaluate a machine learning model and it is the basis for whole class of model evaluation methods. The goal of cross-validation is to test the model’s ability to predict new data that was not used in estimating it. It works by the idea of splitting dataset into number of subsets, keep a subset aside, train the model, and test the model on the holdout subset.
Leave-one-out cross validation
Leave-one-out cross validation is K-fold cross validation taken to its logical extreme, with K equal to N, the number of data points in the set. That means that N separate times, the function approximator is trained on all the data except for one point and a prediction is made for that point. As be-fore the average error is computed and used to evaluate the model. The evaluation given by leave-one-out cross validation is very expensive to compute at first pass.

Explanation
Feature engineering is the pre-processing step of machine learning, which is used to transform raw data into features that can be used for creating a predictive model using Machine learning or statistical Modelling.
What is a feature?
Generally, all machine learning algorithms take input data to generate the output. The input data re-mains in a tabular form consisting of rows (instances or observations) and columns (variable or at-tributes), and these attributes are often known as features. For example, an image is an instance in computer vision, but a line in the image could be the feature. Similarly, in NLP, a document can be an observation, and the word count could be the feature. So, we can say a feature is an attribute that impacts a problem or is useful for the problem.
What is Feature Engineering?
Feature engineering is the pre-processing step of machine learning, which extracts features from raw data. It helps to represent an underlying problem to predictive models in a better way, which as a result, improve the accuracy of the model for unseen data. The predictive model contains predictor variables and an outcome variable, and while the feature engineering process selects the most useful predictor variables for the model.
Some of the popular feature engineering techniques include:
1. Imputation
Feature engineering deals with inappropriate data, missing values,human interruption, general errors, insufficient data sources, etc. Missing values within the dataset highly affect the performance of the algorithm, and to deal with them “Imputation” technique is used. Imputation is responsible for handling irregularities within the dataset.
For example, removing the missing values from the complete row or complete column by a huge percentage of missing values. But at the same time, to maintain the data size, it is required to impute the missing data, which can be done as:
For numerical data imputation, a default value can be imputed in a column, and missing values can be filled with means or medians of the columns.
For categorical data imputation, missing values can be interchanged with the maximum occurred value in a column.
2. Handling Outliers
Outliers are the deviated values or data points that are observed too away from other data points in such a way that they badly affect the performance of the model. Outliers can be handled with this feature engineering technique. This technique first identifies the outliers and then remove them out.
Standard deviation can be used to identify the outliers. For example, each value within a space has a definite to an average distance, but if a value is greater distant than acertain value, it can be considered as an outlier.
Z-score can also be used to detect outliers.
3. Log transform
Logarithm transformation or log transform is one of the commonly used mathematical techniques in machine learning. Log transform helps in handling the skewed data, and it makes the distribution more approximate to normal after transformation. It also reduces the effects of outliers on the data, as because of the normalization of magnitude differences, a model becomes much robust.
4. Binning
In machine learning, overfitting is one of the main issues that degrade the performance of the model and which occurs due to a greater number of parameters and noisydata. However, one of the popular techniques of feature engineering, “binning”, can be used to normalize the noisy data. This process involves segmenting different features into bins.
5. Feature Split
As the name suggests, feature split is the process of splitting features intimately into two or more parts and performing to make new features. This technique helps the algorithms to better understand and learn the patterns in the dataset.
The feature splitting process enables the new features to be clustered and binned, which results in extracting useful information and improving the performance of the data models.
6. One hot encoding
One hot encoding is the popular encoding technique in machine learning. It is a technique that converts the categorical data in a form so that they can be easily understood by machine learning algorithms and hence can make a good prediction. It enables group theof categorical data without losing any information.

Explanation
Normalization is a scaling technique in Machine Learning applied during data preparation to change the values of numeric columns in the dataset to use a common scale.It is not necessary for all datasets in a model. It is required only when features of machine learning models have different ranges.
Scikit-Learn provides a transformer called MinMaxScaler for Normalization.
This technique uses minimum and max values for scaling of model.Itis useful when feature distribution is unknown.It got affected by outliers.

Explanation
Binary vs. Multi-Class Classification
Classification problems are common in machine learning. In most cases, developers prefer using a supervised machine-learning approach to predict class tables for a given dataset. Unlike regression, classification involves designing the classifier model and training it to input and categorize the test dataset. For that, you can divide the dataset into either binary or multi-class modules.
As the name suggests, binary classification involves solving a problem with only two class labels. This makes it easy to filter the data, apply classification algorithms, and train the model to predict outcomes. On the other hand, multi-class classification is applicable when there are more than two class labels in the input train data.
The technique enables developers to categorize the test data into multiple binary class labels.
That said, while binary classification requires only one classifier model, the one used in the multi-class approach depends on the classification technique. Below are the two models of the multi-class classification algorithm.
One-Vs-Rest Classification Model for Multi-Class Classification
Also known as one-vs-all, the one-vs-rest model is a defined heuristic method that leverages a binary classification algorithm for multi-class classifications. The technique involves splitting a multi-class dataset into multiple sets of binary problems. Following this, a binary classifier is trained to handle each binary classification model with the most confident one making predictions.
For instance, with a multi-class classification problem with red, green, and blue datasets, binary classification can be categorized as follows:
Problem one: red vs. green/blue
Problem two: blue vs. green/red
Problem three: green vs. blue/red
The only challenge of using this model is that you should create a model for every class. The three classes require three models from the above datasets, which can be challenging for large sets of data with million rows, slow models, such as neural networks and datasets with a significant number of classes.
The one-vs-rest approach requires individual models to prognosticate the probability-like score. The class index with the largest score is then used to predict a class. As such, it is commonly used forclassification algorithms that can naturally predict scores or numerical class membership such as perceptron and logistic regression.

Explanation
Jupyter Notebook is a web-based interactive computational environment.
The command used to install Jupyter Notebook is pip install jupyter.
The command used to start Jupyter Notebook is jupyter notebook.

Explanation
By using ethnicity as a sensitive field, and comparing disparity between selection rates and performance metrics for each ethnicity value, you can evaluate the fairness of the model.

Explanation
Model versioning in a way involves tracking the changes made toan ML model that has been previously built.
Put differently, it is the process of making changes to the configurations of an ML Model. From another perspective, we can see model versioning as a feature that helps Machine Learning Engineers, Data Scientists, and related personnel create and keep multiple versions of the same model.
Think of it as a way of taking notes of the changes you make to the model through tweaking hyperparameters, retraining the model with more data, and so on.
In model versioning, a number of things need to be versioned, to help us keep track of important changes. I’ll list and explain them below:
Implementation code: From the early days of model building to optimization stages, code or in this case source code of the model plays an important role. This code experiences significant changes during optimization stages which can easily be lost if not tracked properly. Because of this, code is one of the things that are taken into consideration during the model versioning process.
Data: In some cases, training data does improve significantly from its initial state during model op-timization phases. This can be as a result of engineering new features from existing ones to train our model on. Also there is metadata (data about your training data and model) to consider versioning. Metadata can change different times over without the training data actually changing. We need to be able to track these changes through versioning Model: The model is a product of the two previous entities and as stated in their explanations, an ML model changes at different points of the optimization phases through hyperparameter setting, model artifacts and learning coefficients. Versioning helps take record of the different versions of a Machine Learning model.
MLFlow & Pachyderm are the tools used to manage ML lifecycle & Model versioning.

Explanation
The EXECUTE TASK command manually triggers a single run of a scheduled task (either a standalone task or the root task in a DAG) independent of the schedule defined for the task. A successful run of a roottask triggers a cascading run of child tasks in the DAG as their precedent task completes, as though the root task had run on its defined schedule.
This SQL command is useful for testing new or modified standalone tasks and DAGs before you enable them to execute SQL code in production.
Call this SQL command directly in scripts or in stored procedures. In addition, this command sup-ports integrating tasks in external data pipelines. Any third-party services that can authenticate into your Snowflake account and authorize SQL actions can execute the EXECUTE TASK command to run tasks.

Explanation
Options for Sharing in Snowflake
You can share data in Snowflake using one of the following options:
a Listing, in which you offer a share and additional metadata as a data product to one or more ac-counts,
a Direct Share, in which you directly share specific database objects (a share) to another account in your region,
a Data Exchange, in which you set up and manage a group of accounts and offer a share to that group.

Explanation
To return the contents of a DataFrame as a Pandas DataFrame, use the to_pandas method.
For example:
1.>>> python_df = session.create_dataframe([“a”, “b”, “c”])
2.>>> pandas_df = python_df.to_pandas()

Explanation
Type of visualization used for exploration:
Correlation heatmap
Class distributions by feature
Two-Dimensional density plots.
All the visualizations are interactive, as is standard for Plotly.
For More details, please refer the below link:
https://towardsdatascience.com/data-exploration-understanding-and-visualization-72657f5eac41

Explanation
Secure Data Sharing lets you share selected objects in a database in your account with other Snow-flake accounts. You can share the following Snowflake database objects:
Tables
External tables
Secure views
Secure materialized views
Secure UDFs
Snowflake enables the sharing of databases through shares, which are created by data providers and
“imported” by data consumers.

Explanation
Since the normal curve is symmetric about its mean, its skewness is zero. This is a theoretical expla-nation for mathematical proofs, you can refer to books or websites that speak on the same in detail.