In today’s rapidly evolving technological landscape, machine learning has emerged as a game-changing tool that is transforming industries across the globe. But what exactly is machine learning?

At its core, machine learning is a subfield of artificial intelligence that focuses on the development of algorithms that enable computers to learn from and make decisions or predictions based on data. Unlike traditional programming where every possible outcome must be explicitly coded, machine learning systems can analyze vast amounts of data to discover patterns and make decisions without being explicitly programmed to do so.

One of the key aspects of machine learning is its ability to continuously improve and learn from new data. This is achieved through a process called training, where a machine learning algorithm is exposed to large datasets to learn from and make predictions. As more data is fed into the system, the algorithm adjusts its parameters to improve its accuracy and performance.

There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.

Supervised learning involves training a model on a dataset that contains both input and output variables. The model learns to map input data to the correct output based on the patterns it observes in the training data. This type of learning is commonly used for tasks like classification and regression.

Unsupervised learning, on the other hand, involves training a model on a dataset that contains only input data. The model learns to identify patterns and relationships in the data without the need for explicit labels or target variables. This type of learning is often used for tasks like clustering and dimensionality reduction.

Finally, reinforcement learning involves training a model to make decisions based on feedback received from its environment. The model learns to maximize a reward signal by taking actions that lead to positive outcomes and avoiding actions that lead to negative outcomes. This type of learning is commonly used in scenarios where an agent interacts with an environment and learns from its experiences.

In conclusion, machine learning is a powerful tool that has the potential to revolutionize many industries. By understanding the different types of machine learning and how they work, businesses can leverage this technology to improve efficiency, make better decisions, and drive innovation.

Missing values in a dataset can pose challenges to data analysis and can affect the accuracy of results. Therefore, it is essential to implement appropriate methods to handle missing values in order to maintain data integrity and ensure reliable analysis. In this article, we will discuss four methods commonly used to handle missing values in a dataset, namely listwise deletion, average imputation, regression substitution, and multiple imputations.

Listwise deletion is a method where an entire record is excluded from analysis if any single value is missing. This approach is simple and easy to implement, as it removes any incomplete records from the dataset. However, it comes at the cost of losing valuable information, as complete records are discarded regardless of their relevance to the analysis. Listwise deletion is typically used when the proportion of missing values is small and ‘missingness’ is assumed to be completely random.

Another method to handle missing values is average imputation. In this approach, the average value of the other participants' responses is taken and used to fill in the missing value. Although average imputation is straightforward and prevents any loss of data, it may introduce bias to the dataset. This method assumes that the missing values are similar to the observed values, which may not always hold true. Consequently, the imputed values may not accurately reflect the true missing values.

Regression substitution is a more sophisticated method to handle missing values. It involves using multiple-regression analyses to estimate a missing value based on observed values and their relationship with other variables in the dataset. This approach provides a more accurate estimate for the missing value compared to average imputation. However, it requires a strong relationship between the missing variable and other variables in the dataset to be effective. If there is limited or weak correlation, the estimated value may introduce further errors to the analysis.

The last method we will discuss is multiple imputations. This technique constructs plausible values based on the correlations for the missing data and then averages the simulated datasets by incorporating random errors in your predictions. Multiple imputations are advantageous over single imputations as they account for uncertainty and variability in the imputed values. This method allows for estimation of appropriate standard errors and inference without underestimating the uncertainty associated with the missing values. However, multiple imputations require careful implementation and computational resources.

In conclusion, handling missing values in a dataset is essential for accurate data analysis. The choice of method depends on the nature and extent of missingness in the dataset, as well as the assumptions made about the missingness. Listwise deletion, average imputation, regression substitution, and multiple imputations are four commonly employed methods. Each method has its advantages and disadvantages, and researchers must carefully consider the context and limitations of their dataset to choose the most appropriate method. By implementing these methods, researchers can ensure reliable analysis and maintain the integrity of the data.

​In the vast world of statistics, understanding the concept of normal distribution is of utmost importance. It is a continuous probability distribution that is symmetric about the mean, often represented by a bell-shaped curve. Normal distribution is widely applicable in various fields, including finance, social sciences, and engineering, due to its predictable behavior and statistical properties.

One of the key characteristics of normal distribution is that the mean, median, and mode are all equal. This means that the highest point on the curve, or the peak, represents the most common value observed in the dataset. The symmetry of the distribution ensures that the mean, median, and mode are located in the center.

Additionally, normal distribution follows the 68-95-99.7 rule, which helps in understanding the spread of data. According to this rule, approximately 68% of the data falls within one standard deviation of the mean. This implies that the majority of the data is clustered around the mean, indicating a relatively tight distribution.

Moving further, 95% of the data lies between two standard deviations of the mean. This wider range reflects a larger spread and encompasses a greater proportion of the data. Lastly, an astounding 99.7% of the data falls within three standard deviations of the mean. This wider interval suggests a significantly broader spread of data, capturing almost all observations in the dataset.

Normal distribution plays a crucial role in statistical inference and hypothesis testing as it allows researchers to make accurate predictions about the data. It provides a baseline against which other distributions can be compared, aiding in identifying unusual observations or outliers.

In practical applications, normal distribution is often used to model various phenomena. For instance, income distribution of a large population is often assumed to follow a normal distribution, allowing researchers to make predictions and analyze the societal impact of income disparities. Similarly, in finance, normal distribution is often used to understand and predict stock market returns, assuming that they adhere to a bell-shaped curve.

In conclusion, normal distribution is a fundamental concept in statistics with numerous applications across various disciplines. Its symmetric shape not only makes it visually appealing but also provides valuable insights into the behavior of a dataset. From the 68-95-99.7 rule to its significance in statistical inference, understanding normal distribution is essential for any professional working with data analysis and statistical modeling.

In the realm of data analysis and statistics, outliers are often seen as an intriguing and potentially problematic aspect of any dataset. These data points, which are distinctly different from the majority of other data points, can provide valuable insights or introduce biases if not properly addressed and managed.


At its core, an outlier is a data point that deviates significantly from the established pattern exhibited by the rest of the data points. These deviations can be attributed to various factors such as measurement variability, experimental errors, or simply being an extreme case within the dataset. Understanding and correctly interpreting outliers is crucial for accurate data analysis and decision-making.


To illustrate the concept of outliers, let's consider a graphical representation of a dataset. In the graph below, three outliers can be observed, distinctively standing apart from the bulk of the data points.

The presence of outliers can have a significant impact on statistical measures and models that rely on the assumption of normality or require data to follow a certain distribution. Outliers have the potential to skew results, bias calculations, and lead to incorrect conclusions if left untreated. Therefore, it is essential to have effective strategies for handling outliers when analyzing data.


There are several common approaches to deal with outliers, each with its own advantages and limitations. Here are four methods frequently employed:


1. Drop the outlier records:
One straightforward method is simply removing the outlier data points from the dataset. This can be an effective approach if outliers are believed to be the result of measurement errors or other anomalies. However, caution must be exercised when applying this method, as indiscriminately removing outliers without proper justification can lead to incomplete or biased data analysis.


2. Cap your outliers data:
Instead of removing outliers entirely, this method involves capping or limiting the effect of outliers by replacing their values with a predetermined threshold. By setting a maximum or minimum value for outliers, they are still included in the dataset but their impact is controlled. This approach is suitable when outliers are considered to introduce excessive variation or have an undue influence on the analysis.


3. Assign a new value:
In some cases, instead of removing or limiting outliers, it may be appropriate to assign a new value to the outliers. This can be done by replacing the outlier value with a plausible estimate or an aggregated measure such as the average or median of the rest of the data points. However, applying this method requires careful consideration of the nature and context of the data, as well as the potential impact on subsequent analysis and interpretation.


4. Try a new transformation:
If the presence of outliers is significantly affecting the distribution or normality assumptions of the data, applying a transformation can be useful. Transforming the data by using mathematical functions like logarithms or square roots can help to reduce the impact of outliers and make the data more amenable to analysis. However, transformation should be done judiciously, as it can introduce complexities and alter the interpretation of the results.


When faced with outliers in a dataset, it is essential to assess their origins, potential significance, and the impact they may have on data analysis. Outliers can be valuable sources of information, indicating unique patterns, important hidden relationships, or underlying anomalies. Properly addressing outliers ensures accurate statistical analysis, reliable models, and unbiased decision-making.


It is worth noting that the most appropriate method for handling outliers may vary depending on the specific characteristics of the dataset, the goals of the analysis, and the domain knowledge involved. Therefore, it is essential to exercise caution and consult with domain experts and statisticians when dealing with outliers in complex datasets.


In conclusion, outliers in a dataset are data points that deviate substantially from the majority of other data points. They can introduce biases, skew results, and affect statistical measures and models if not properly handled. Strategies such as dropping outlier records, capping outlier data, assigning new values, or applying data transformations can be employed to manage outliers effectively. The key lies in understanding the nature of the outliers and their impact on the analysis while ensuring careful consideration of the context and goals of the analysis. By appropriately addressing outliers, data analysts and researchers can ensure the integrity and accuracy of their findings.

Sampling is an indispensable part of data analysis. It involves selecting a subset of data from a larger population to gain insights and estimate the characteristics of the entire population. Data analysts rely on various sampling techniques to ensure accuracy and representativeness in their findings. In this article, we will explore the different types of sampling techniques employed by data analysts.


The first and perhaps the most straightforward method is simple random sampling. As the name suggests, this technique involves randomly selecting samples from the population, where each member of the population has an equal chance of being chosen. Simple random sampling is ideal when the population is homogenous and no specific characteristics are of particular interest. It helps avoid bias and ensures that each sample has an equal probability of inclusion.


Systematic sampling, on the other hand, is a more structured approach. In this method, the analyst selects samples at regular intervals from an ordered list of the population. For example, if there are 1000 members in the population, and the analyst wants to select a sample size of 100, every 10th member would be chosen. Systematic sampling is preferred when the population has some form of inherent order or structure, ensuring that the sample is representative of the entire population.


Cluster sampling, as the name suggests, involves dividing the population into clusters or groups and randomly selecting entire clusters for analysis. Each cluster should ideally be a microcosm of the entire population, ensuring its representativeness. This method is particularly useful when it is difficult or impractical to sample individual elements from the population, such as when conducting research on schools or neighborhoods.


Stratified sampling is another commonly used technique, especially when the population is diverse and exhibits distinct subgroups. In this method, the population is divided into relevant strata or subgroups based on certain characteristics. The analyst then randomly selects samples from each stratum in proportion to their representation in the population. Stratified sampling ensures that all subgroups are well-represented in the analysis, leading to more accurate results.


Lastly, judgmental or purposive sampling is a technique where the analyst purposefully selects samples based on their knowledge and expertise. This method is subjective and relies on the analyst's judgment to choose samples that are likely to provide the most relevant and valuable information. Judgmental sampling is often used in qualitative research or when specific expertise is required to select the most appropriate samples.


In conclusion, data analysts employ various sampling techniques to ensure accuracy and representativeness in their findings. Simple random sampling, systematic sampling, cluster sampling, stratified sampling, and judgmental or purposive sampling are some of the commonly used methods. Each technique has its advantages and is suitable for different scenarios. The choice of sampling method depends on the nature of the population, the research objectives, and the availability of resources. By carefully selecting appropriate samples, data analysts can extract meaningful insights from large datasets and make valid inferences about the entire population.

​In the field of data analytics, one of the most crucial steps is data wrangling. Data wrangling is the process of cleaning, structuring, and enriching raw data into a usable format for better decision making. Raw data, when extracted from various sources, often comes in a messy and unorganized state. Data wrangling aims to transform this raw data into a more structured format that can be easily analyzed and used in conjunction with other datasets.

The process of data wrangling involves several key steps. The first step is data discovery, where analysts explore and understand the available data sources. This step helps in identifying the relevant data for analysis and determining the data quality. Once the data sources are identified, the next step is data structuring. This involves organizing the data in a consistent and logical manner, such as by columns and rows, ensuring easy access and manipulation of data.

Cleaning the data is an essential part of data wrangling. Raw data often contains errors, missing values, or inconsistencies. Data cleaning involves identifying and correcting these issues, thus ensuring the accuracy and reliability of the data. Techniques such as removing duplicates, filling in missing values, and correcting erroneous entries are applied during this stage.

Enriching the data is another important step in data wrangling. This involves adding additional information to the dataset, such as merging data from different sources or appending new variables to existing ones. By enriching the data, analysts can gain deeper insights and make more informed decisions.

Validation is a crucial step in the data wrangling process. It involves verifying the accuracy and integrity of the data. This typically includes performing quality checks, validating against external sources, and ensuring consistency across datasets. Validating the data helps in identifying any anomalies or discrepancies that may affect the analysis results.

Once the data has been cleaned, structured, enriched, and validated, it is ready to be analyzed. Data analysts use various techniques such as merging, grouping, concatenating, joining, and sorting to extract meaningful insights from the data. These techniques help in uncovering patterns, relationships, and trends within the data, facilitating better decision making.

Data wrangling also plays a crucial role in preparing the data for further analysis with other datasets. By transforming the raw data into a more usable format, it becomes easier to integrate and combine data from different sources. This integration allows for a more comprehensive and holistic analysis, enabling analysts to draw more accurate conclusions and make more informed decisions.

In conclusion, data wrangling is an essential step in the data analytics process. It involves cleaning, structuring, enriching, validating, and analyzing raw data to transform it into a usable format. By performing data wrangling, analysts can ensure the accuracy, reliability, and usefulness of the data, enabling better decision making in data analytics.

In the world of big data and technology, data modeling plays a crucial role. It serves as the foundation upon which databases are built, enabling businesses to organize and structure their data effectively. However, not all data models are created equal. A good data model is one that meets certain criteria and exhibits specific indicators. In this article, we will explore the top three indicators that suggest a data model is good.


The first indicator of a good data model is accuracy and consistency. Accuracy refers to the data model's ability to correctly represent the real-world objects, relationships, and constraints it is designed to capture. A good data model ensures that the information stored in the database is reliable and reflects the actual state of affairs. Consistency, on the other hand, involves maintaining the integrity of the data and avoiding contradictory or duplicate information. A good data model should enforce rules and constraints that prevent data inconsistencies, ensuring the accuracy and reliability of the database.


Scalability is another indicator of a good data model. In today's fast-paced business environment, organizations must be prepared to handle increasing volumes of data. A good data model should be able to accommodate growth and expansion without sacrificing performance or efficiency. It should allow for the addition of new data elements, entities, and relationships without disrupting existing structures or functionality. Scalability ensures that the data model is future-proof and capable of supporting the organization's evolving needs.


Ease of use and understanding is the third indicator of a good data model. A data model should be intuitive and easy to comprehend for both technical and non-technical users. It should use standard naming conventions, clear data definitions, and logical organization to facilitate ease of use. A good data model should also document the relationships between entities and provide documentation that aids in understanding the structure and purpose of the database. Ease of use and understanding ensures that the data model can be effectively leveraged by all stakeholders, enhancing its value and usability.


In conclusion, a good data model exhibits several key indicators. Accuracy and consistency ensure that the model accurately represents the real-world objects it intends to capture while maintaining the integrity of the data. Scalability ensures that the data model can handle increasing volumes of data without sacrificing performance. Ease of use and understanding make the data model accessible and usable by all stakeholders. By considering these top three indicators, businesses can gauge the quality of their data models and make informed decisions about their database management strategies.

In conclusion, a comprehensive data cleaning process is crucial for ensuring the quality and integrity of datasets. By following a systematic approach that involves removing duplicates, and irrelevant data, standardizing capitalization, converting data types, handling outliers, fixing errors, translating languages, and handling missing values, organizations can enhance the reliability and usability of their data. Investing time and effort in data cleaning ultimately leads to more informed decision-making, accurate analysis, and valuable insights.

​Domo has quite a few options for gauge cards, but sometimes it is needed a different type of gauge card to easily portray the situation in the company. In the example below, we have a simple gauge card which we can easily create in Domo. 

If you want to try to create this easy to read card, please keep on reading!

Follow the steps below to create a simple gauge card in Domo. 
You will need three or more images of your gauge. You can use the one I have. ​

You will need to host these images somewhere, because you will need the links to this images. For this example, I’m using Imageshack.com.

Now, you pretty much done. In Domo, you need to create two beast modes.
​
__________________________________
Name: Summary Gauge Summary Number

/*Lilia Taran*/
--  Summary LT Gauge New Colors

CONCAT( '<span style="font-weight:bold;font-size:16px;font-family: Arial, sans-serif;color:#8fc33f;align="justify;padding-left: 0px;">',
       CASE WHEN (AVG(`Retention`)) > 0.799 THEN
      CONCAT( 'Retention - High','</span>','<br>',
'<img src="https://imagizer.imageshack.com/v2/xq70/924/NBznbe.png" width="200" height="170" align="center" style="width:270px;height:210px;padding:0px 0px;"\>','<br>')
       WHEN (AVG(`Retention`)) > 0.499  THEN CONCAT( 'Retention - Medium','</span>','<br>',
'<img src="https://imagizer.imageshack.com/v2/xq70/924/OFkehu.png" width="200" height="170" align="center" style="width:270px;height:210px;padding:2px 2px;"\>','<br>')
       WHEN (AVG(`Retention`)) < 0.49  THEN   CONCAT( 'Retention - Low','</span>','<br>',
'<img src="https://imagizer.imageshack.com/v2/xq70/923/FBAB00.png" width="200" height="170" align="center" style="width:270px;height:210px;padding:2px 2px;"\>','<br>')
       END
,  '<span style="font-weight:bold;font-size:16px;font-family: Verdana, Geneva, sans-serif;color:white;align="justify;padding-left: 8px;">', 'Retention - ',ROUND(AVG(`Retention`)*100,0) ,'%','</span>','<br>'
)

__________________________________

​Second BeastMode
__________________________________
Name: Blank
/*Lilia Taran*/
' '

__________________________________

In Domo, create a textbook type of card, here add your ‘Blank’ BM in place of Text and Count of Blank as an Optional Value.

In the summary line settings, select Summary Gauge Summary Number, remove label and save the card.
That’s it! Now you know how to create custom cards which aren’t available in Domo chart types.