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Almost every business depends on data to grow; in the near future1, leveraging the power of data might become a necessity for their survival. As the volume of data, we generate grows2, we need to learn better ways to extract value from it because not all data can be used in the raw form that it is generated in.

Before any type of data can be used by organizations, the data must go through a process to make it ready for use. If this process is not done right, it can degrade the dataset’s quality, triggering a chain of issues in various parts of the business.

To remedy this, we have curated this article to:

Explain what data preprocessing is.

Why is it important?

And list the top 5 steps for preprocessing any data.

What is data preprocessing?

Since raw data or unstructured data (Text, image, audio, video, documents, etc.) can not be directly fed into machine learning models, data preprocessing is used to make it usable.

Usually, this is the first step of starting a machine learning project to ensure that the data used for the project is well-formatted and clean. However, data preprocessing is not limited to developing and training AI or ML models. Organizations can use this process to prepare any data in their business. They can process:

Customer data

Department-specific data (Sales data, financial data, etc.)

Product data

Research data

Why is it important?

While working with datasets, you must have heard the term “garbage in, garbage out.” This simply means the performance of your AI/ML model (or any other data-hungry project) will be as good as the data that you train it with. Even the most sophisticated algorithms can produce garbage results or be harmful and biased if trained with dirty or unprocessed data.

In the current business environment, a successful business is considered one where data-driven decision-making is practiced. However, if the data is flawed, then the decisions driven from it will also be impaired. Preprocessing data before using it in any data-hungry project can remove data issues such as:

Data duplication

Image/video data with hidden objects

Spelling errors in document/text data

Audio data with too much noise, etc.

Sponsored

What are the steps of preprocessing data?

The following steps can be followed to preprocess unstructured data:

1. Data completion

One of the first steps of preprocessing a dataset is adding missing data. Feeding an AI/ML model with a dataset with missing fields can take time and effort. The following actions can be taken to manage missing fields:

Consider the impact of the missing data: The data scientists must decide whether to manually fill, discard or ignore missing fields in a dataset. Their decision must consider the impact of the missing data, the size of the dataset, and the amount of the missing data.

Use the average (mean) method: In some cases, average or estimated values can be added in the missing fields based on other values. For instance, missing fields can be filled with the average (mean) of the previous and next temperature values for a temperature-measuring application.

2. Data noise reduction

Noisy data is useless for machine learning models since they can not read it. Noisy data can make its way into the dataset due to faulty data collection, humanoid errors, glitches in the system, etc. It can be cleared in the following ways:

Binning method: This method involves arranging the data in different segments and binning it. Then the data in the bins can be replaced by their average, medium, or minimum and maximum values.

Regression method: A linear or multiple variable regression function is used to smooth the data.

Clustering: method Clustering helps smoothen the data by identifying similar data groups in a dataset and adding them to separate clusters (groups).

3. Data transformation

Raw or unstructured data is collected from multiple sources in different formats. While some of these formats are acceptable by a machine learning model, others are not. The data transformation step involves changing the unacceptable forms of certain data types into acceptable ones. This is done in the following ways:

Normalization: When the data involves large values, they are converted into ranges to bring uniformity to the values

Attribute Selection: In this method, only the relevant attributes according to the project’s features are selected. For instance, if a computer vision system only needs to scan objects in daylight, data with dark images will be removed. 

Aggregation: This stage involves making a summary of the total dataset. For instance, purchasing data can be summarized to be shown as per month. It’s basically a description of the dataset.

Concept hierarchy generation: Lower-level data is converted into higher-level data to make the data more general and organized. For instance, in data related to addresses, cities can be converted into countries.

4. Data Reduction

We have discussed before that the bigger the dataset, the more accurate the AI/ML model will be. However, this is only applicable when the quality is maintained. 1 clear image is better than 10 blurry ones. Sometimes datasets can involve redundant items that can make the dataset unnecessarily complicated.

In such cases, data reduction can help eliminate redundant data and bring down the dataset size to just right. However, if the dataset is too small, it can make the model underfitted or biased. Therefore it is important to ensure that the necessary data are not eliminated during the reduction process. Data can be reduced in the following ways:

Creating data combinations: In this method, data is fitted into smaller pools. So, for instance, if the data tags are male, female, or doctor, they can be combined as male/doctor or female/doctor.

Dimensionality reduction: This method involves eliminating unnecessary data points. For example, if a computer vision-enabled quality control system is not required to scan the products from different angles, then image data with angle variations can be removed. This can be done by using Algorithms such as K-nearest neighbors3.

Data compression: This involves compressing large machine-learning data files. This can be done in a non-lossy way, by saving the original data, or a lossy way, by deleting the original data. 

5. Data validation

This step consists in assessing the dataset for quality assurance. Validation involves feeding the data into a machine-learning model to test its performance. If the data scientists are unsatisfied, the data goes through the cleaning process again. This cycle is repeated till the optimum results are achieved.

Further reading

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References

Shehmir Javaid

Shehmir Javaid is an industry analyst at AIMultiple. He has a background in logistics and supply chain management research and loves learning about innovative technology and sustainability. He completed his MSc in logistics and operations management from Cardiff University UK and Bachelor’s in international business administration From Cardiff Metropolitan University UK.

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Data Preprocessing Using Pyspark – Filter Operations

If you are already following my PySpark series then you will easily know what steps I’m gonna perform now. Let’s first discuss them in the nutshell:

First, we imported the SparkSession to start the PySpark Session.

Then with the help of the getOrCreate() function, we created our session of Apache Spark.

At the last, we saw what our spark object holds in a graphical format.

Note: I have discussed these steps in detail in my first article, Getting Started with PySpark Using Python.

from chúng tôi import SparkSession spark = SparkSession.builder.appName('filter_operations').getOrCreate() spark

Output:

Reading the Dataset

In this section, we will be reading and storing the instance of our dummy dataset with header and Schema as True which will give us the exact information about the table and its column types.

df_filter_pyspark = spark.read.csv('/content/part2.2.csv', header = True, inferSchema=True) df_filter_pyspark.show()

Output:

Relational Filtering

Here comes the section where we will be doing hands-on filtering techniques and in relational filtration, we can use different operators like less than, less than equal to, greater than, greater than equal to, and equal to.

df_filter_pyspark.filter("EmpSalary<=25000").show()

Output:

Inference: Here we can see that the records are filtered out where employees have a salary less than or equal to 25000.

Selecting the relevant columns instead of showing all the columns

This is one of the best cost-effective techniques in terms of execution time as when working with a large dataset if we will retrieve all the records (all columns) then it will take more execution time but if we know what records we want to see then we can easily choose selected columns as mentioned below:

df_filter_pyspark.filter("EmpSalary<=25000").select(['EmpName','EmpAge']).show()

Output:

Code Breakdown

So the above code can be broken down into three simple steps for achieving the goal:

This particular filter operation can also come into the category of multiple filtering as in the first condition we are filtering out the employees based on the salary i.e. when the employee’s salary is less than 25000.

Then comes the main condition where we are selecting the two columns “Emp-Name” and “Emp-Age” using the select function.

At the last show the filtered DataFrame using the show function.

Note: Similarly we can use other operators of the relational type according to the problem statement we just need to replace the operator and we are good to go.

Another approach to selecting the columns

Here we will be looking at one more way where we can select our desired columns and get the same result as in the previous output

Tip: By looking at this line of code one will get reminded about how Pandas used to filter the columns.

df_filter_pyspark.filter(df_filter_pyspark['EmpSalary']<=25000).select(['EmpName','EmpAge']).show()

Output:

Inference: In the output, we can see that we got the same result as we got in the previous filter operation. The only change we can see here is the way how we selected the records based on the salary – df_filter_pyspark[‘EmpSalary’]<=25000 here we have first taken the object and entered the name of the column then at the last simply we added the filter condition just like we used to do in Pandas.

Logical Filtering

In this section, we will be using different cases to filter out the records based on multiple conditions, and for that, we will be having three different cases

AND condition

OR condition

NOT condition

“AND” condition: The one familiar with SQL or any programming language in which they have to deal with the manipulation of data are well aware of the fact that when we will be using AND operation then it means all the conditions need to be TRUE i.e. if any of the condition will be false then there would not be any output shown.

Note: In PySpark we use the “&” symbol to denote the AND operation.

df_filter_pyspark.filter((df_filter_pyspark['EmpSalary']<=30000)

Output:

Code breakdown: Here we can see that we used two conditions one where the salary of the employee is less than equal to 30000 & (AND) greater than equal to 18000 i.e. the records which fall into this bracket will be shown in the results other records will be skipped.

Condtion 1: df_filter_pyspark[‘EmpSalary’]<=30000 where salary is greater than 30000

Condtion 2: df_filter_pyspark[‘EmpSalary’]<=18000 where salary is less than 18000

Then we used the “&” operation to filter out the records and at the last show() function to give the results.

“OR” condition: This condition is used when we don’t want to get very stiff with filtration i.e. when we want to access the records if any of the condition is True unlike AND condition where all the condition needs to be True. So be careful to use this OR condition only when you know either of the condition can be picked.

df_filter_pyspark.filter((df_filter_pyspark['EmpSalary']<=30000)

Output:

Code breakdown: If one will compare the results of AND and OR then they would get the difference between using both of them and the right time to use it according to the problem statement. Let’s look at how we have used OR operation:

Condition 1: df_filter_pyspark[‘EmpSalary’]<=30000 here we were plucking out the person who has a salary less than equal to 30000.

“NOT” condition: This is the condition where we have to counter the condition i.e. we have to do everything else the condition which we have specified itself if we try to simplify more then we can say that if the condition is False then only NOT operation will work.

Note: In PySpark we use the “~” symbol to denote the NOT operation.

Output:

Inference: Here we can see how the employee who has an age greater than equal to 30 doesn’t even appear in the list of records so it is clear that if the condition is False then only there is the credibility of NOT operation

In this section we will summarize everything on we did previously like as we started by setting up the environment for the Python’s distribution of PySpark then we had head toward performing both relational and logical filtering on our dummy dataset.

Firstly, we completed our mandatory steps of setting up the Spark Session and reading the dataset as these are the pillars of further analysis.

Then we got to know about Relation filtering techniques which include hands-on operations using PySpark DataFrame here we discussed a single operator and learned how to implement another basis on the same approach.

At last, we move to the second type of filtering, i.e. logical filtering, where we discussed all three types of it: AND, OR, and Not a condition.

Connect with me on LinkedIn for further discussion.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

Importance Of Statistics: How Is Statistics Related To Data Science?

How is Statistics Important for Data Science?

One of the important aspects of the data science approach is the process of extracting and interpreting data. When data is extracted we develop perceptions or best to say cultivating possibilities out of that extracted data. In data science, these possibilities are interpreted with the help of statistics and the term is known as statistical analysis. With

Classification and Organization

Data is classified into accurate, observable, and mining fields with the help of statistical methods. For all companies’ classification and organization are important to make predictions and create business plans. Some data are operational and some are unusable, statistics help in classifying and filtering out unusable data for further process.  

Machine Learning and Data Analytics

These statistical methods are pathways to grasp the basics of

Detect Anomalies in Data

Almost every company deals with huge stacks of data received from various sources. Statistics in this process helps in detecting oddities and structures in the data. This enables researchers to reject inapplicable data at the early stage and thus reduces wasting of time, effort, and resources.  

Data Visualization

Visualization in data is the depiction and elucidation of structures, models, and perceptions found in interactive, comprehensible, and effective formats. These formats must be easy to process. For data visualization and representation statistical formats like graphs, pie charts, and histograms are used. It makes data understandable and also helps in intensifying them when required.  

Identification of Structures in Data

Detailing on values and networks without statistical methods of distribution can lead to evaluations that are not accurate and reliable. Statistics helps in identifying distinguishing structures and clusters in data that are dependent on variable factors like space, time, etc.  

Logical Representation of Data

Data is a series of complex interactions between factors and variables. To represent these or display them logically and accurately, statistical methods using graphs and networks are the only key.   Also read:

One of the important aspects of the data science approach is the process of extracting and interpreting data. When data is extracted we develop perceptions or best to say cultivating possibilities out of that extracted data. In data science, these possibilities are interpreted with the help of statistics and the term is known as statistical analysis. With digital transformation and the growing importance of decisions and functions based on data, statistics holds a critical position in the process of analyzing, controlling, and presenting data. Statistics is the only key to control, manage, and learn from data and also determine problems that often lead to incorrect solutions. Data has become an important part of everybody’s life. Without data we are nothing. Data mining for digging insights has marked the demand to be able to use data for business strategies. Therefore, the field of data science is growing with increasing demand. Data science is not limited to only consumer goods or tech or healthcare. There is a high demand to optimize business processes using data science from banking and transport to manufacturing. We often think about how data which are in the form of images, text, videos, or any unstructured form gets processed and interpreted by machine learning models. What is the process? How does it work? How is it so easy to process data? The process includes the conversion of data into a numerical form which is not exactly data but its numerical equivalent. So, this brings us to the importance of statistics in data science. When the data is converted into a numerical form, it provides us with interminable possibilities to interpret the information out of it. Statistics is the key to extract and process data and bring successful results. Detecting structure in data, large or small and making predictions are critical stages in data science that can either make or break research. Statistics provides measures and methods to evaluate insights out of data by getting the right mathematical approach for data. How is Statistics Important for Data Science?Data is classified into accurate, observable, and mining fields with the help of statistical methods. For all companies’ classification and organization are important to make predictions and create business plans. Some data are operational and some are unusable, statistics help in classifying and filtering out unusable data for further process.These statistical methods are pathways to grasp the basics of machine learning and algorithms like logistic regressions. Cross-validation and LOOCV methods of statistics have been brought into the field of Machine Learning and Data Analytics for reasoning-based research, and hypothesis testing.Almost every company deals with huge stacks of data received from various sources. Statistics in this process helps in detecting oddities and structures in the data. This enables researchers to reject inapplicable data at the early stage and thus reduces wasting of time, effort, and resources.Visualization in data is the depiction and elucidation of structures, models, and perceptions found in interactive, comprehensible, and effective formats. These formats must be easy to process. For data visualization and representation statistical formats like graphs, pie charts, and histograms are used. It makes data understandable and also helps in intensifying them when required.Detailing on values and networks without statistical methods of distribution can lead to evaluations that are not accurate and reliable. Statistics helps in identifying distinguishing structures and clusters in data that are dependent on variable factors like space, time, chúng tôi is a series of complex interactions between factors and variables. To represent these or display them logically and accurately, statistical methods using graphs and networks are the only key.

Top 12 Data Observability Tools In 2023

Data accumulation is accelerating, with ~330 million terabytes of data created every day. To put this into perspective, a single terabyte can contain approximately 250,000 hours of music.

In this article, we have examined the top 12 data observability tools, based on their capabilities and features to help businesses in their vendor selection to find the best platform that suits their needs.

Data observability vs. data monitoring

Source: Hayden James

Figure 1. Data monitoring vs. data observability

Before delving into the data observability tools capabilities, it’s critical to distinguish between data observability and data monitoring. While both aims to ensure data reliability and quality, their scope and approach differ.

Data monitoring is largely concerned with measuring certain metrics such as data pipeline performance, resource use, and processing times. It frequently takes a reactive strategy, with data teams responding to challenges as they arise.

Data observability, on the other hand, is a more comprehensive and proactive approach to analyzing and controlling data quality. It includes data monitoring but goes above and beyond by offering in-depth insights into the data itself, its lineage, and transformations. Data observability solutions allow data owners to identify and rectify issues before they have an influence on downstream processes and consumers, promoting data quality.

Data observability tools help data engineers to monitor, manage, and analyze their data pipelines, ensuring that data is accurate, timely, and consistent. Some key capabilities of data observability tools include:

1- Data lineage tracking

These tools can trace the origin and transformations of data as it moves through various stages in the data pipeline. This helps data analysts:

Identify dependencies

Understand the impact of changes,

Troubleshoot data quality issues

Save debugging time.

2- Automated monitoring

Data observability tools can continuously monitor and assess the quality of data based on predefined rules and metrics. This can include anomaly detection, data drift, and identifying data inconsistencies.

3- Real-time & customized alerts

Data observability tools can be integrated with communication platforms (e.g., Slack) and can send instant alerts and notifications to inform data scientists of potential issues.

4- Central data cataloging

These tools can automatically create and maintain a data catalog that documents all available data sources, their schemas, and metadata. This provides a central location for data teams to search and discover relevant data assets.

5- Data profiling

Data observability tools can analyze and summarize datasets, providing insights into the distribution of values, unique values, missing values, and other key statistics. This helps data teams understand the characteristics of their data and identify potential issues.

6- Data validation

These tools can run tests and validations against the data to ensure that it adheres to predefined business rules and data quality standards. This helps increase data health by catching errors and inconsistencies early in the data pipeline.

7- Data versioning

Data observability tools can track changes to data over time, allowing data teams to compare different versions of datasets and understand the impact of changes.

8- Data pipeline monitoring

These tools can monitor the performance and health of data pipelines, providing insights into processing times, resource usage, and potential bottlenecks. This helps data engineers to find and fix bad data to optimize their data pipelines for efficiency and scalability.

9- Collaboration and documentation 10- Integration with external data sources

Data observability tools can typically integrate with a wide range of data sources, processing platforms, and data storage systems, allowing data scientists to monitor and manage their data pipelines from a single unified interface.

11- Analytics & reporting

Data observability technologies can provide a variety of reports and visualizations to assist data teams in understanding the health of their data pipelines and the quality of their data. These findings can help guide decisions and enhance overall data management practices.

12- Instant customer support

Many data observability tools provide extensive customer service via different methods such as chat, email, and phone. Dedicated solutions engineers make sure that data teams have access to expert assistance anytime they encounter difficulties or require instruction on how to use the tool efficiently.

Vendor selection criteria

After identifying whether the vendors provide the capabilities presented above, we narrowed our vendor list based on some criteria. We used the number of B2B reviews and employees of a company to estimate its market presence because these criteria are public and verifiable.

Therefore, we set certain limits to focus our work on top companies in terms of market presence, selecting firms with

15+ employees

20+ reviews on review platforms including G2, Trustradius, Capterra

The following companies fit these criteria:

Databand

Metaplane

Monte Carlo

Mozart Data

Integrate.io

Anomalo

Datafold

Telmai

decube

Unravel Data

AccelData

Bigeye

As all vendors offer data cataloging, profiling, validation, versioning, and reporting, we did not include these capabilities in the table. Below you can see our analysis of data capability tools in terms of the capabilities and features mentioned above. You can sort Table 1, for example, by real-time alerting capabilities.

VendorsReviewsEmployee sizeStarting price/yearWarehouse integrationLineage trackingMonitored pipelinesReal-time alertingCustomer supportQuality of support* (out of 10) DataBand3539Not provided20+ data sourcesColumn-level100-1,000sEmail, Slack, Pagerduty, Opsgenie24 hour issue response and mitigation with a dedicated support channel9.2 Metaplane3715Pro: $9,900/year with monthly commitment options20+ data sourcesColumn-level lineage to BIUnlimitedEmail, Slack, PagerDuty, MS Teams, API, WebhooksShared Slack channel, CSM9.9 Monte Carlo71257Not provided30+ data sourcesField-levelNot providedN/ANot provided9.6 Mozart Data6932Starts from $12,000/year with monthly commitment options300+ data sourcesField-levelNot providedN/ANot provided9.5 Integrate.io18537Starts from $15,000/year150+ data sourcesField-levelNot providedN/AEmail, Chat, Phone, Zoom support9.2 Anomalo3349Not provided20+ data sourcesAutomated warehouse-to-BIUnlimited with unsupervised learningEmail, Slack, Microsoft TeamsNot provided9 Datafold2436Not provided12+ data sourcesColumn-levelNot providedEmail, SlackEmail, Intercom, dedicated Slack channel9.1 Telmai1513Not provided18+ data sourcesField-levelUnlimitedEmail, Slack, PagerDutyEmail9.2 decube1215Starts from $499 / year13+ data sourcesAutomatedNot providedEmail, SlackEmail, Chat8.3 Unravel Data23171Starts from $1 / per feature50+ data sourcesCode-levelNot providedEmailEmail8.6 AccelData12214Not provided30+ data sourcesColumn-levelNot providedAutomatedEmail8.6 Bigeye1569Not provided20+ data sourcesColumn-levelNot providedEmail, Slack, PagerDuty, MS Teams, WebhooksEmail7.9

*Based on G2 reviews.

Disclaimer:

The data is gathered from the websites of vendors. If you believe we have missed any material, please contact us so that we can consider adding it to our article.

Contact us if you need help in data observability tool selection:

Begüm Yılmaz

Begüm is an Industry Analyst at AIMultiple. She holds a bachelor’s degree from Bogazici University and specializes in sentiment analysis, survey research, and content writing services.

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Importance Of Proper Exception Handling In Java

Java is a widely-used programming language, & as such, it is necessary to understand the importance of proper exception handling to develop robust and efficient code. Exception handling is a mechanism used to handle errors and unexpected conditions in a program, and it is essential for maintaining the integrity and stability of a Java application.

Proper exception handling is a critical component of Java programming that helps developers to anticipate and recover from errors and unexpected conditions in a program. It is an essential aspect of maintaining the integrity & stability of a Java application. This is why it is crucial to understand the importance of proper exception handling and to follow best practices for exception handling in Java.

Importance of Exception Handling

Below refers to the points why exception handling is important. Let’s see one by one.

Ensures the Continuity of the Program

One of the key benefits of exception handling is that it ensures the continuity of the program. Without proper exception handling, an unhandled exception would cause the program to terminate abruptly, which can lead to data loss & other issues. With proper exception handling, the program can continue to execute and provide a more stable user experience.

Enhances the Robustness of the Program

Exception handling allows for the program to anticipate and recover from errors, thus making the program more robust and resistant to unexpected conditions. By catching and handling exceptions, the program can continue to execute and provide a more stable user experience.

Improves the Readability & Maintainability of the Code

Proper exception handling also improves the readability & maintainability of the code. By catching and handling exceptions, the program can provide clear error messages that accurately describe the error and provide information on how to resolve the issue. This makes it easier for developers to understand and modify the code in the future. Additionally, by providing detailed error messages, proper exception handling allows for more accurate error reporting, which is essential for debugging and troubleshooting purposes.

Allows for more Accurate Error Reporting

Exception handling allows the program to catch & report errors in a more accurate & detailed manner, providing valuable information to developers for debugging and troubleshooting purposes.

Facilitates Debugging and Troubleshooting

Exception handling allows the program to catch & report errors in a more accurate and detailed manner, which facilitates debugging and troubleshooting. By providing detailed error messages and stack traces, exception handling allows developers to quickly identify and resolve issues, reducing the amount of time and resources required for debugging.

Improves the Security of the Program

Exception handling can also improve the security of a program by preventing sensitive information from being exposed in the event of an error. By catching and handling exceptions, the program can prevent sensitive information, such as passwords and personal data, from being displayed to the user or logged-in error messages.

Provides a Better user Experience

Proper exception handling allows the program to anticipate and recover from errors, providing a more stable user experience. It is particularly important for user-facing applications, as it ensures that the program continues to function even in the event of an error, reducing the likelihood of user frustration and abandonment.

Enables the use of error-recovery Mechanisms

Exception handling enables the use of error-recovery mechanisms, such as retries or fallbacks, which can improve the reliability and availability of the program. For example, if a program encounters a network error, it can retry the operation or fall back to a different network connection, ensuring that the program continues to function even in the event of an error.

Improves the Scalability and Performance of the Program

Proper exception handling can also improve the scalability and performance of a program by reducing the amount of unnecessary processing and resource consumption. By catching and handling exceptions, the program can avoid performing unnecessary operations and releasing resources that are no longer needed, reducing the overall load on the system and improving performance.

Best Practices

To ensure proper exception handling in Java, developers should follow best practices such as using specific exception types, providing meaningful error messages, using try-catch-finally blocks, and avoiding empty catch blocks.

Use specific exception types − Use specific exception types, such as FileNotFoundException and SQLException, rather than the general exception type. It allows for more accurate error handling and reporting.

Provide meaningful error messages − Provide meaningful error messages that accurately describe the error and provide information on how to resolve the issue.

Use try-catch-finally blocks − Use try-catch-finally blocks to handle exceptions & ensure that resources are properly released & cleaned up.

Avoid using empty catch blocks − Avoid using empty catch blocks, as they can conceal important information about the error and make it more difficult to troubleshoot & resolve the issue.

Conclusion

Proper exception handling is a critical aspect of Java programming and is essential for developing robust and efficient code. It ensures the continuity of the program, enhances the robustness of the program, improves the readability and maintainability of the code, and allows for more accurate error reporting. By following best practices and utilizing specific exception types, meaningful error messages, try-catch-finally blocks, and avoiding empty catch blocks, developers can ensure that their code is well-equipped to handle errors and unexpected conditions. It is crucial for maintaining the integrity and stability of a Java application & for providing a stable user experience.

Top Data Science Funding And Investment In August 2023

Companies to expedite data science projects raising more funds amidst COVID-19.    

The explosion of data, significantly generated by sensor-driven devices, is making data science a crucial

chúng tôi

Amount Funded: US$100 million Transaction Type: Fund-II Lead Investor(s): Vulcan Capital, Adams Street Partners

Mode Analytics

Amount Funded: US$33 million Transaction Type: Series D Lead Investor(s): H.I.G. Growth Partners

Data Sutram

Amount Funded: US$20 million Transaction Type: Seed Round Lead Investor(s): Indian Angel Network

Pachyderm

Amount Funded: US$16 million Transaction Type: Series B Lead Investor(s): M12

Narrative

Amount Funded: US$8.5 million Transaction Type: Series A Lead Investor(s): G20 Ventures

Climax Foods

Amount Funded: US$7.5 million Transaction Type: Seed Round Lead Investor(s): At One Ventures, Manta Ray Ventures, S2G Ventures,

The explosion of data, significantly generated by sensor-driven devices, is making data science a crucial business analytics solution . The field of data science draws a variety of scientific tools, processes, algorithms and knowledge to extract data from structured and unstructured datasets that help identify meaningful patterns in it. Currently, the domain is increasingly utilized in almost all organizations, thus increasing the demand of data scientists capable of deriving actionable insight from a cluster of data. This will eventually lead to data-driven decisions and increase profitability, improve operational efficiency, business performance and workflows. Today, more and more organizations are looking to invest in data science thanks to its power of innovation, with data-driven tools and techniques. Here are the top data science funding that companies/startups have raised in August 2023.Amount Funded: US$100 million Transaction Type: Fund-II Lead Investor(s): Vulcan Capital, Adams Street Partners chúng tôi , an early-stage venture capital firm that invests in companies using models built through data science, closed US$100 million for its second fund. The fund was backed by Vulcan Capital, US private markets investment manager Adams Street Partners, and the family office of Marc Andreessen, and Chris Dixon. Founded by a team of data scientists and entrepreneurs, Fund II will enable chúng tôi to continue investing globally across different sectors and company stages, and amplify the company’s number of follow-on investments. According to the company, the first fund closed in 2024 was US$40 million in size.Amount Funded: US$33 million Transaction Type: Series D Lead Investor(s): H.I.G. Growth Partners Mode Analytics , which provides an online service for collaboratively analyzing data, raised US$33 million in Series D funding round led by HIG Growth Partners, with additional participation from Valor Equity Partners, Foundation Capital, REV Venture Partners and Switch Ventures. Reportedly, the company will use the funds to invest in its analytics platform, which combines analytics and business intelligence, data science and machine learning. Recently, Mode Analytics has started to introduce tools, including SQL and Python tutorials, for less technical users, especially those in product teams, so that they can structure queries that data scientists can subsequently execute faster and with more complete responses.Amount Funded: US$20 million Transaction Type: Seed Round Lead Investor(s): Indian Angel Network Data Sutram , an AI-based Location Intelligence Enterprise with a Cloud-based B2B product that works on a Data as a Service business model to help businesses, secured US$20 million in a Seed funding round from Indian Angel Network (IAN) angels, Uday Sodhi, Mitesh Shah and Nitin Jain. Founded in 2023 by three Jadavpur University engineering graduates and operated by Extrapolate Advisors Pvt Ltd, the startup helps companies by pinpointing new locations for them to expand, improve the performance of existing assets both physical and digital, and micro-target the right audience for their products.Amount Funded: US$16 million Transaction Type: Series B Lead Investor(s): M12 Pachyderm , an enterprise-grade, open-source data science platform, closed US$16 million in Series B funding round from Microsoft’s venture fund. This latest funding round comes as the company launches the general availability of Pachyderm Hub, a fully-managed service solution that has been operating in public beta since November, which is available today. According to Pachyderm, the fund will be used toward hiring which has become necessary as the coronavirus-spurred remote work shift has led to a major uptick in sales.Amount Funded: US$8.5 million Transaction Type: Series A Lead Investor(s): G20 Ventures Narrative , which empowers participants in the data economy, received US$8.5 million in Series A funding round to launch a new product designed to further simplify the process of buying and selling data. The round was led by G20 Ventures with existing backers Glasswing Ventures, MathCapital, Revel Partners, Tuhaye Venture Partners, and XSeed Capital. According to the company, this round of funding supports the launch of a new category: Data Streaming, which effectively replaces the broken data broker industry model with a transformative solution.Amount Funded: US$7.5 million Transaction Type: Seed Round Lead Investor(s): At One Ventures, Manta Ray Ventures, S2G Ventures, Climax Foods , a data science company, raised US$7.5 million in a Seed funding round to stimulate AI research into how plants can be converted into products. The round was led by At One Ventures, founded by GoogleX co-founder Tom Chi, along with Manta Ray Ventures, S2G Ventures, Valor Siren Ventures, Prelude Ventures, ARTIS Ventures, Index Ventures, Luminous Ventures, Canaccord Genuity Group, Carrot Capital and Global Founders Capital. Climax Foods aims to create a smart way to make food by converting plants, with less processing, into products with the same taste as animal-based products, at a price point accessible to everyone.

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