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[datascience43]

8 Powerful Data Cleaning Techniques for Better Data

We all know that data is messy and difficult to manage. Data cleaning techniques are part of a data-driven approach to improve value for our customers, reduce costs and even increase revenue.  Cleaning up and managing the data in your business is a daily activity that can help boost performance, increase accuracy, and improve results. But how do you know whether your cleaning practices are effective? Where should you start, and which techniques should you use in your particular situation?

In this article, we will discuss some effective data-cleaning techniques that can be used to improve the quality of your business’s performance.

What is Data Cleaning?

Data cleansing is a process to improve the quality of data before it gets to your application and business. In other words, data cleansing is the process of cleaning dirty data. Data cleanup can be applied manually as well as automatically, depending on your purpose.

Importance of data cleaning

You can complete your analysis much more quickly if you start with clean data that are free of erroneous and inconsistent values. By completing this task ahead of time, you would save a lot of time. You could stop many errors by cleaning your data before using it. Your results won't be accurate if you use data with false values. Data purification and cleaning take up a lot more time for a data scientist than data analysis. 

Efficiency

Identifying data Quality

Accuracy

 Error Margin 

Consistency

Uniformity 

Effective Data Cleaning Techniques

The first step to cleaning your data is to understand what types of data you have. Once you know this, you can determine what types of tools are best suited for the job. 

You can also select specific values from within each row or column and then export them as text files to create a list of all the data elements that need cleaning up. This is useful when working with large amounts of information because it allows you to see exactly what needs fixing before moving forward with any further steps in order

1. Remove Duplicate Data

Duplicate data is a problem that can be solved easily. In fact, it can be done manually or automatically and the results are almost the same. The first step in removing duplicate data is to identify all duplicate records in your database. Next, you need to merge these records into one record. There are two different methods that you can use for merging records: 

Merge by Right-Clicking/Ctrl+Click

Merge by Using an Excel Add-In

A detailed explanation of data cleaning techniques can be found via the best data science course in Chennai, designed in collaboration with IBM.

 2. Remove irrelevant data

Any analysis you attempt to conduct will be complicated and slowed down by irrelevant data. Deciding what information is relevant and what is not is therefore necessary before you start your data cleaning. You don't have to include their email addresses, for example, if you are analyzing the age range of your customers.

You should also eliminate the following components because they don't add anything to your data:

URLs

HTML tags

Tracking codes

Personal Identifiable (PI)

Blank space between text

3. Remove Nulls

Nulls should be eliminated as well, as they can cause problems when they are used in arithmetic operations or comparisons. You can do this by using a unique index on the column containing the null values and using a WHERE clause to remove them from your data set.

4. Convert data types

The most typical type of data that needs to be converted when cleaning your data is a number. However, they must appear as numerals to be processed. Numbers are frequently imputed as text.

They are classified as a string if they appear as text, which prevents your analysis algorithms from solving mathematical equations on them.

Likewise, dates that are saved as text are accurate. Make them all numerals, please. For instance, you must change entries to read 09/24/2022, if they currently say September 24th, 2022.

5. Clear Formatting

Your information cannot be processed by machine learning models if it is heavily formatted. Different document formats are probably present if you are using data from a variety of sources. Your data may become muddled and inaccurate as a result.

To start over, you should eliminate any formatting that has been applied to your documents. This is typically not a challenging process; for instance, standardization functions are available in both Google Sheets and Excel.

6. Handle missing values

There would always be some information lacking. It's unavoidable. In order to keep your data accurate and clean, you should be aware of how to handle them. Your dataset might contain too many missing values in one particular column.

In that case, since there isn't enough information to work with, it would be prudent to remove the entire column.

Thus, you should never ignore missing values. 

If the missing value is completely removed, your data can now no longer contain insightful information. After all, there was a reason why you initially wanted to gather this information.

Hence, it might be preferable to fill in the missing data by doing the necessary research. You could use the word missing in its place if you have no idea what it is. You can enter a zero in the blank field if it is numerical.

However, you should remove the entire section if there are so many missing values that there isn't enough data to use.

7 Fix the errors 

You should obviously take care to rectify any errors in your data before using it. You might miss out on important data findings if you make mistakes as simple as typos. With something as simple as a quick spell check, some of these can be avoided.

You might miss out on communicating with your customers because of misspellings or extra punctuation in data like an email address. It might also cause you to send unsolicited emails to recipients who have not requested them.

Inconsistency in formatting is another type of error. To maintain a consistent standard currency, for instance, if you have a column of US dollar amounts, you must convert any other currency type into US dollars.

8. Language Translation 

You need everything to be in the same language to have reliable data.

Software used to analyze data typically uses monolingual Natural Language Processing (NLP) models, which are unable to process multiple languages. Therefore, you must translate everything into a single language.

Summary

To sum up, the best way to go about cleaning data is always dependent on the problem you are trying to solve. The time required for data cleaning will always depend on the data itself, and if any anomalies need to be resolved.

This article is written based on the knowledge of data cleaning techniques applied by experienced professionals. However, you can apply these tips at home to clean your own data, or to help you get a better feel for how much cleaning is needed in your own data before processing or loading it. In the end, invest a little time applying these tips and you'll be rewarded with higher-quality records. For detailed information, you can check Learnbay’s data analytics course in chennai , and get ready for a better and efficient data for your next projects. 

[datascience43]

Developing a Better Recruitment Process - Applications of HR Analytics

Data science has gained popularity in making organizations flourish and deliver value. 

It has been in the limelight over the past few years and has risen to prominence as a key technology within the HR industry. Among all the sectors, HR analytics can find consistent application in solving problems faced by companies across the globe. This is based on the understanding that many reasons for employee attrition affect companies every year. The basic idea behind data collection and analysis centers around identifying the best practices for HR analytics to combat this challenge.

Data analytics can offer opportunities to improve workforce management by creating personalized and effective training strategies, leveraging onboarding programs to optimize employee recruiting efforts and better managing employee retention metrics. Data science can also be used to provide more precise information about current and past employee engagements, including when and why an employee leaves the company for specialties or areas of expertise that can be addressed in future training initiatives or recruitment decisions.

Overview of Data Science 

Data science is a set of techniques and tools that are used to collect, analyze, and interpret data. This data can be used to gain insight into problems or opportunities to answer questions or make decisions. Data science is also used to predict future outcomes of certain events based on past events.

HR analytics uses data science to help companies with their HR practices. These practices include hiring, training, performance management, compensation and benefits, etc. Data scientists use their skills in programming languages like Python, R and SQL and machine learning techniques such as neural networks and decision trees which can be learnt from India’s best Data Science course in Chennai, developed in partnership with IBM.

What Is HR Analytics?

HR analytics uses data to improve your company's operations and increase its bottom line. It gives you insights into everything from employee satisfaction to turnover rates to productivity trends, allowing you to make informed decisions about how best to run your business.

Application of HR analytics

Employee training is another topic where data science can help improve existing evaluations. For example, data analysis can determine which courses have previously been shown to be most beneficial to employees in later performance reviews. This crucial workflow can also be made better through the use of technology and analytics. Recruitment is another field that makes use of data science to empower hiring managers to define their ideal candidate through applicant tracking systems, social networking sites, market analysis, and applicant review assessments. This will result in a more appreciable recruitment process for both the hiring manager and applicants.

People analytics, workforce analytics, and talent analytics are all covered in HR analytics. These analytics components serve different human resource activities by automating and making them more cost-effective over time.

1. Attendance

2. Employee surveys

3. Salary and remuneration

4. Appraisal and Promotion 

5. Work history of the employee

6. Past database of employees

This information is gathered and simplified for improved strategic decision-making and human resource planning. Data may also aid in greater alignment and coordination among the organization's various departments. Furthermore, the HR software may be upgraded to deal with employee and manager issues.

The top applications of data science in HR are as follows;

1. Workforce analytics  

Data science, by thoroughly analyzing the corporate workforce, enables HR management professionals to grasp the major demands of their firm better and properly monitor critical parameters. HR professionals might locate and hire suitable professionals quicker and directly influence a company's overall performance by properly knowing which candidate's traits are the most beneficial to the company's objectives.

2. Talent analytics 

According to Deloitte's 2017 Global Human Capital Trends Report, 90% of HR professionals desire to overhaul their whole organizational paradigm. This comprises leadership, diverse management methods, and increasing possibilities for applicants to establish successful careers and jobs. 

That's where data science can be beneficial. It facilitates the smart structuring of convenient talents, improving current training programs, evaluating attrition, and perfecting recruitment methods to ensure a high level of staff retention. Data science can drastically revolutionize the whole HR sector by eliminating outdated methods of assessing HR metrics and providing firms with insights they would never have obtained from traditional surveys or candidate interviews.

3. Employee Performance  

Analyzing and measuring employee performance is critical for obtaining a more accurate employee assessment report. Greater analytics may help organizations retain talented and experienced personnel while also providing better employee growth. Analytics may assist in identifying the organization's best and underperforming performers, determining the average length of employment, motivating elements for employees, and so on. This will improve career advancement decisions, enhance employee happiness, identify leadership skills, and motivate them to improve overall performance. As a result, analyzing employee performance will enable the firm to enhance its overall ROI and identify prospective leaders.

4. Training and development 

Many organizations confront the challenge of a skills mismatch. Thus, most employees lack the necessary skills to perform various tasks. In-house training is also in high demand because there is always a shortage of adequate skills in entry-level professions. HR analytics can assist in more efficiently bridging that gap. It can aid in collecting data about employees and their level of expertise to determine how they can be taught. Analytics may also assist in directing resources to the appropriate locations for staff training and in reviewing the overall development process. This will help companies in making their personnel more qualified and competent, which will not only improve corporate performance but also provide a competitive advantage.

5. Employee Retention 

One significant benefit of adopting HR Analytics and having a data scientist on the HR team is the potential to identify why people leave and remain. HR can essentially forecast (and hence avoid) employee attrition by evaluating data from techniques like employee satisfaction surveys, team evaluations, social media, and leave and stay interviews, among others.

Data science specialists could also assist the HR team in identifying issues that contribute to low employee engagement and chances to increase engagement, resulting in a more successful workforce. 

For example, suppose an organization has been experiencing high turnover rates among salespeople. In that case, they could use predictive analytics tools like machine learning algorithms to find out why this is happening—and design strategies to prevent it from happening again.

Summary 

As you can see, the world is rapidly moving towards digitalization, which has revolutionized several industries in many ways. HR Analytics is one such industry that has undergone a lot of changes, especially with the wide range of advanced data science techniques present to help businesses take important, data-driven decisions regarding their staff requirements.

Data science could be a game changer for HR to manage their workforce, monitor key performance indicators and analyze relevant data that are more insightful compared to previously used traditional tools. The HR analytics market is growing rapidly, and the need for data scientists will only increase. So given the rate at which this industry is growing, we expect more data scientists to join HR analytics teams in the future. If you’re already working in the HR field, you can easily become a data scientist with an IBM-accredited Data analytics course in Chennai. Master the analytics skills and get ready to improve your organization.