Hand Made

Advertising Agency

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About Hand Made
Full service advertising agency - from a thoughtful strategy for creating all the most important media, equally offline, as well as online. Twice as long as the advertising agency.
NA
50 - 249
Poland
Hand Made
Advertising Agency
0.00/5 (0 Reviews)
3 Questions
Missing data is a huge problem because it makes the findings inaccurate. It becomes difficult to completely rely on the findings when some entries have missing values. There are three types of missing data. One is Missing Completely at Random (MCAR) - here the data is randomly missing across the dataset without any pattern. Then there is Missing at Random (MAR) - when the missing data is not random but is missing only within sub-samples of data. Last is Not Missing at Random (NMAR) - there is a noticeable trend in the manner of missing values. The best techniques to handle missing data are: Use Deletion Methods to Eliminate Missing Data The deletion methods will work only for some datasets where the participants have missing fields. The two common deleting methods are Listwise Deletion and Pairwise Deletion. These methods are useful where there is a large volume of data as the values can be deleted without having a significant impact on the final readings. An alternative is that data scientists can contact the participants to fill out the missing values. However, this will not be practical for large data sets. Use Regression Analysis to Systematically Eliminate Data Regression is helpful to handle missing data as it can be used in predicting the null value using other information from the data. Regression methods can prove to be successful in finding the missing values but it depends on how well the remaining data is connected. A major drawback with regression analysis is that it will need substantial computing power which can be a problem in the case of a large dataset. Use Data Imputation Techniques Generally, data scientists use two data imputation techniques to manage missing data - average imputation and common point imputation. The first technique will use the average value of responses from other data entries. Keep in mind that this technique can artificially reduce the data set’s variability. On the other hand, common point imputation is when the middle point or the most commonly chosen value is used. For this technique, keep in mind that there are three types of middle value - mean, median, and mode. For numerical data, a mode is valid whereas, for non-numerical data, mean and median are relevant. Conclusion The missing data situation cannot be completely avoided because there are many correctional steps that data scientists need to take so that it doesn’t negatively affect the analytics process. These methods are definitely helpful but not completely reliable as their various aspects depend greatly on the circumstances. 
Missing data is a huge problem because it makes the findings inaccurate. It becomes difficult to completely rely on the findings when some entries have missing values. There are three types of missing data. One is Missing Completely at Random (MCAR) - here the data is randomly missing across the dataset without any pattern. Then there is Missing at Random (MAR) - when the missing data is not random but is missing only within sub-samples of data. Last is Not Missing at Random (NMAR) - there is a noticeable trend in the manner of missing values. The best techniques to handle missing data are: Use Deletion Methods to Eliminate Missing Data The deletion methods will work only for some datasets where the participants have missing fields. The two common deleting methods are Listwise Deletion and Pairwise Deletion. These methods are useful where there is a large volume of data as the values can be deleted without having a significant impact on the final readings. An alternative is that data scientists can contact the participants to fill out the missing values. However, this will not be practical for large data sets. Use Regression Analysis to Systematically Eliminate Data Regression is helpful to handle missing data as it can be used in predicting the null value using other information from the data. Regression methods can prove to be successful in finding the missing values but it depends on how well the remaining data is connected. A major drawback with regression analysis is that it will need substantial computing power which can be a problem in the case of a large dataset. Use Data Imputation Techniques Generally, data scientists use two data imputation techniques to manage missing data - average imputation and common point imputation. The first technique will use the average value of responses from other data entries. Keep in mind that this technique can artificially reduce the data set’s variability. On the other hand, common point imputation is when the middle point or the most commonly chosen value is used. For this technique, keep in mind that there are three types of middle value - mean, median, and mode. For numerical data, a mode is valid whereas, for non-numerical data, mean and median are relevant. Conclusion The missing data situation cannot be completely avoided because there are many correctional steps that data scientists need to take so that it doesn’t negatively affect the analytics process. These methods are definitely helpful but not completely reliable as their various aspects depend greatly on the circumstances. 

Missing data is a huge problem because it makes the findings inaccurate. It becomes difficult to completely rely on the findings when some entries have missing values. There are three types of missing data. One is Missing Completely at Random (MCAR) - here the data is randomly missing across the dataset without any pattern. Then there is Missing at Random (MAR) - when the missing data is not random but is missing only within sub-samples of data. Last is Not Missing at Random (NMAR) - there is a noticeable trend in the manner of missing values. 

The best techniques to handle missing data are: 

Use Deletion Methods to Eliminate Missing Data 

The deletion methods will work only for some datasets where the participants have missing fields. The two common deleting methods are Listwise Deletion and Pairwise Deletion. These methods are useful where there is a large volume of data as the values can be deleted without having a significant impact on the final readings. An alternative is that data scientists can contact the participants to fill out the missing values. However, this will not be practical for large data sets. 

Use Regression Analysis to Systematically Eliminate Data 

Regression is helpful to handle missing data as it can be used in predicting the null value using other information from the data. Regression methods can prove to be successful in finding the missing values but it depends on how well the remaining data is connected. A major drawback with regression analysis is that it will need substantial computing power which can be a problem in the case of a large dataset. 

Use Data Imputation Techniques 

Generally, data scientists use two data imputation techniques to manage missing data - average imputation and common point imputation. The first technique will use the average value of responses from other data entries. Keep in mind that this technique can artificially reduce the data set’s variability. On the other hand, common point imputation is when the middle point or the most commonly chosen value is used. For this technique, keep in mind that there are three types of middle value - mean, median, and mode. For numerical data, a mode is valid whereas, for non-numerical data, mean and median are relevant. 

Conclusion 

The missing data situation cannot be completely avoided because there are many correctional steps that data scientists need to take so that it doesn’t negatively affect the analytics process. These methods are definitely helpful but not completely reliable as their various aspects depend greatly on the circumstances. 

Source Control Management is unavoidable for the developers. Like Git, Subversion, and Mercurial, the SCM collaboration tool is a set of best practices related to a change lifecycle and a change request system. SCM is actually related to core project files comprising of the source code and the way, these shared files are managed. This vital system enables the developers to work together they are working either in the same room or on distant continents. SCM helps you when your code gets messy and make the quickest solution to the problem.Here I am describing in detail how you can efficiently go with source control management and succeed well in your development projects. #1. First of all, select a source control system.#2. Keep the source code in source control (excluding the files generated or compiled from it). #3. Ensure the working file is taken from the source file’s latest version.#4. Check-out the only file that is being worked upon.#5. As the alterations are completed, check-in immediately. #6. Review all the changes before committing. Use the diff function.#7. Commit often, you always get a rollback position with every commit.#8. Make detailed notes in the comment section about why you have made the changes.#9. Developers should commit their own changes only.#10. Use the ignore button for the uncommitted files. Always add pre-commit filters for preventing the entry of the wrong kind of files to the source control. For example, the accidental checking-in of the personal user setting documents.#11. Always ensure the addition of the personal dependencies to the source control. Everything quite often works on the system of the contributing developer, but not anywhere else as they forgot to add dependent files to the system. If you will not miss these basic steps, you will hardly face any error or committed files that are actually useless in the repository. 
Source Control Management is unavoidable for the developers. Like Git, Subversion, and Mercurial, the SCM collaboration tool is a set of best practices related to a change lifecycle and a change request system. SCM is actually related to core project files comprising of the source code and the way, these shared files are managed. This vital system enables the developers to work together they are working either in the same room or on distant continents. SCM helps you when your code gets messy and make the quickest solution to the problem.Here I am describing in detail how you can efficiently go with source control management and succeed well in your development projects. #1. First of all, select a source control system.#2. Keep the source code in source control (excluding the files generated or compiled from it). #3. Ensure the working file is taken from the source file’s latest version.#4. Check-out the only file that is being worked upon.#5. As the alterations are completed, check-in immediately. #6. Review all the changes before committing. Use the diff function.#7. Commit often, you always get a rollback position with every commit.#8. Make detailed notes in the comment section about why you have made the changes.#9. Developers should commit their own changes only.#10. Use the ignore button for the uncommitted files. Always add pre-commit filters for preventing the entry of the wrong kind of files to the source control. For example, the accidental checking-in of the personal user setting documents.#11. Always ensure the addition of the personal dependencies to the source control. Everything quite often works on the system of the contributing developer, but not anywhere else as they forgot to add dependent files to the system. If you will not miss these basic steps, you will hardly face any error or committed files that are actually useless in the repository. 

Source Control Management is unavoidable for the developers. Like Git, Subversion, and Mercurial, the SCM collaboration tool is a set of best practices related to a change lifecycle and a change request system. 

SCM is actually related to core project files comprising of the source code and the way, these shared files are managed. This vital system enables the developers to work together they are working either in the same room or on distant continents. SCM helps you when your code gets messy and make the quickest solution to the problem.

Here I am describing in detail how you can efficiently go with source control management and succeed well in your development projects. 

#1. First of all, select a source control system.

#2. Keep the source code in source control (excluding the files generated or compiled from it). 

#3. Ensure the working file is taken from the source file’s latest version.

#4. Check-out the only file that is being worked upon.

#5. As the alterations are completed, check-in immediately. 

#6. Review all the changes before committing. Use the diff function.

#7. Commit often, you always get a rollback position with every commit.

#8. Make detailed notes in the comment section about why you have made the changes.

#9. Developers should commit their own changes only.

#10. Use the ignore button for the uncommitted files. Always add pre-commit filters for preventing the entry of the wrong kind of files to the source control. For example, the accidental checking-in of the personal user setting documents.

#11. Always ensure the addition of the personal dependencies to the source control. Everything quite often works on the system of the contributing developer, but not anywhere else as they forgot to add dependent files to the system. 

If you will not miss these basic steps, you will hardly face any error or committed files that are actually useless in the repository. 

A CRM software will offer insights into your customers that will make the overall marketing efforts more meaningful and productive. CRM can also be used to strengthen pre-existing customer relationships. This applies to both small and large businesses. It is no surprise that CRM often integrates with other aspects of marketing. Following are some ways in which CRM can be helpful in handling email from customers: More Effective Segmentation Segmenting of the target audience is a critical aspect of any marketing campaign. This is how a business finds out members of the audience that will be more responsive to what they are selling. Using CRM and marketing automation features will make segmenting the audience easier. Not only is sorting the audience easier, but it is also possible to hyper-segment the audience. As there is more demographic info filtering in the CRM features, businesses can divide the audience on the basis of location, buying history, age, gender, or even personal interests. Email Personalization Personalizing the emails is essential in the current market scenario. A CRM software will allow businesses to do some basic personalization like adding the customer's name in the email subject line as well as the opening line. Moreover, the data gathered using segmentation will help target the emails as per their exact interests. Not to forget a customer’s past shopping behavior can also be used to craft future emails. Alerts for More Useful Follow-Up A CRM software will let the marketers know whenever someone opens an email, clicks on the link in the email, or watches a video in the email. Due to these alerts, businesses will never miss an important moment in the customer journey. These alerts can help in planning the follow-up emails. If the follow-up emails are sent at the right time and the messages are personalized too then it is like hitting a home run. Customers may not always be responsive to such follow-up emails but most of them will be. Conclusion CRM software is known for its detailed analytics that is both - historical and predictive. The majority of the analytics data is highly visual too. These are some of the ways in which CRM can help in handling emails from customers but not all of them. So, businesses should keep exploring the possibilities that come with using CRM. 
A CRM software will offer insights into your customers that will make the overall marketing efforts more meaningful and productive. CRM can also be used to strengthen pre-existing customer relationships. This applies to both small and large businesses. It is no surprise that CRM often integrates with other aspects of marketing. Following are some ways in which CRM can be helpful in handling email from customers: More Effective Segmentation Segmenting of the target audience is a critical aspect of any marketing campaign. This is how a business finds out members of the audience that will be more responsive to what they are selling. Using CRM and marketing automation features will make segmenting the audience easier. Not only is sorting the audience easier, but it is also possible to hyper-segment the audience. As there is more demographic info filtering in the CRM features, businesses can divide the audience on the basis of location, buying history, age, gender, or even personal interests. Email Personalization Personalizing the emails is essential in the current market scenario. A CRM software will allow businesses to do some basic personalization like adding the customer's name in the email subject line as well as the opening line. Moreover, the data gathered using segmentation will help target the emails as per their exact interests. Not to forget a customer’s past shopping behavior can also be used to craft future emails. Alerts for More Useful Follow-Up A CRM software will let the marketers know whenever someone opens an email, clicks on the link in the email, or watches a video in the email. Due to these alerts, businesses will never miss an important moment in the customer journey. These alerts can help in planning the follow-up emails. If the follow-up emails are sent at the right time and the messages are personalized too then it is like hitting a home run. Customers may not always be responsive to such follow-up emails but most of them will be. Conclusion CRM software is known for its detailed analytics that is both - historical and predictive. The majority of the analytics data is highly visual too. These are some of the ways in which CRM can help in handling emails from customers but not all of them. So, businesses should keep exploring the possibilities that come with using CRM. 

A CRM software will offer insights into your customers that will make the overall marketing efforts more meaningful and productive. CRM can also be used to strengthen pre-existing customer relationships. This applies to both small and large businesses. It is no surprise that CRM often integrates with other aspects of marketing. Following are some ways in which CRM can be helpful in handling email from customers: 

More Effective Segmentation 

Segmenting of the target audience is a critical aspect of any marketing campaign. This is how a business finds out members of the audience that will be more responsive to what they are selling. Using CRM and marketing automation features will make segmenting the audience easier. Not only is sorting the audience easier, but it is also possible to hyper-segment the audience. As there is more demographic info filtering in the CRM features, businesses can divide the audience on the basis of location, buying history, age, gender, or even personal interests. 

Email Personalization 

Personalizing the emails is essential in the current market scenario. A CRM software will allow businesses to do some basic personalization like adding the customer's name in the email subject line as well as the opening line. Moreover, the data gathered using segmentation will help target the emails as per their exact interests. Not to forget a customer’s past shopping behavior can also be used to craft future emails. 

Alerts for More Useful Follow-Up 

A CRM software will let the marketers know whenever someone opens an email, clicks on the link in the email, or watches a video in the email. Due to these alerts, businesses will never miss an important moment in the customer journey. These alerts can help in planning the follow-up emails. If the follow-up emails are sent at the right time and the messages are personalized too then it is like hitting a home run. Customers may not always be responsive to such follow-up emails but most of them will be. 

Conclusion 

CRM software is known for its detailed analytics that is both - historical and predictive. The majority of the analytics data is highly visual too. These are some of the ways in which CRM can help in handling emails from customers but not all of them. So, businesses should keep exploring the possibilities that come with using CRM. 

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Hand Made
ul. Czarnowiejska 55, Zabierzow, Krakow 30049
Poland