Top 8 Data Science Expertise that Every Employee Needs

At present, data has become one of the most essential features of any organization. The data holds the importance of the growth of products throughout the sectors and groups because it helps them to move ahead inspite of strong competition. It also helps to develop companies and taking them to the next stage of growth. Therefore, from the past few years Data Science, Big Data, data analytics is gaining more popularity. Well, data analytics is all about making your team understand the data that it requires to create better products and make the correct decisions related to your organization.

But if a team in an organization does not know about data, then this all becomes waste. Using software’s the organization’s data can be made simple, however, each team member should have basic data skills. With the help of data skills, any team of an organization can interpret the data doesn’t matter whether it is marketing or a sales team. It also provides a team with the skills to work with data scientists to suggest new concepts and gives the confidence to work together with them to enhance the business. In order to perform this, each employee of the company should possess some important skills related to data. Let’s explore them:

1. Understanding of correlation: Correlations plays a significant role in Data Science. They are the backbone of data science. We always have a concern about how various variables changes with each other. Let’s take an example, consider two variables, one is the number of people finishing the on-boarding process, and the other one is the number of people retained after a month. If the on-boarding process is valuable and supports new users to achieve success, then the two numbers are correlated positively. When the first variable increases, i.e. people finishing the on-boarding process, the second variable also increases, i.e. people retention after one month. Correlation varies between -1 and +1. A positive value indicates that both variables are moving in the same direction while a negative value indicates that the two variables are affected in the opposite direction. When the correlation is precisely zero it shows that there is no association exists between the two variables.

2. Discover the best sample size for your tests: An employee should be able to find the best sample from the analyses. Let’s take an example, the theory states that the font of the Signup page footer is holding back your changes. Your designer selects Roboto, while the latest enhancement tells you Comic Sans is a conversion winner. You begin you’re A/B test, and nothing happens. This means that you will not get any results. Your sample size will be too small. If a page gets a lot of views in a month, only a few people will go to the signup page, and just a few people will move to the next page. On dividing the small portion partially for control and testing pages, the final sample size will be too small to represent any important modification. However, to work for A/B tests, you should have a large number of people in both A and B situations. These change experiments can work for Facebook and Google. If any employee who wants to set up an A/B test requires understanding the limitations which sample size will put on.

3. Identify why PPV (positive predictive value) matters: Positive predictive value, i.e. PPV is the measure of the accuracy of your tests. It enables you and your team to identify whether the activities which you are measuring are predictive of the metrics that you are interested, for example, retention. PPV is calculated by considering the accurate positive samples and dividing it by both true and false positive samples.

4. Bayesian Thinking: Bayesian statistics are different from more conventional “frequentist” statistics by handling the world as probabilistic. This implies that in place of sharp decision boundaries, the probabilities can be made on hypothesis, i.e. true or false. Another basic difference is that with the Bayesian thinking you can use your knowledge of the world, i.e. recall your previous experience to create the primary model. These probabilities later can be updated when more data enter; this implies that while running your experiments, you can modernize your thinking relies on the proof.

5. Limitations of Machine Learning: Machine learning is important for various algorithms and statistical approaches. Well, it is essential for each employee to understand what machine learning is and how it is capable of. This can help you in the following ways:

• If you know some data, you will be in a better position to know whether machine learning can help you to comprehend that data better.
• If an alternative company comes and states that their algorithms are the solution to all the problems, then you will know better whether they are correct or not.

Machine learning works on an iterative procedure. With the help of this iterative training, these algorithms can study to define dataset features. They train a model, which can be simple like linear regression or as tricky as convolutional neural network on identified data. Later that model can be utilized to categories unidentified data. Thus, Machine learning is not the solution for all the issues; however; it is able to discover relationships. It cannot provide you all the solutions that are not present in your data. It is dependent on the excellent quality of data.

Machine learning approaches such as k-nearest neighbors, random forest, etc. will help you to work for a big organization which has a huge amount of data. It also supports organizations to find a new aspect of data management.

6. Write SQL: It would be an added advantage if the team is aware of SQL. SQL stands for Structured Query Language, and it is utilized in almost all databases. If you know SQL, it means that you know your data and database perfectly. You will have all of your data at your fingertips. Occasionally, you may be interested in testing a theory before presenting it to your Data Scientist. If you already know SQL, then you can smoothly run a few queries to see if your assumptions are correct.

7. Data cleaning: Cleaning of data is an important activity. Data scientist wants every team to learn this activity. If you provide Data Scientist with a clean data set for analysis, it will help them to work faster and find the solution quickly.

8. Convey a good Story: This is yet data science proficiency that every team should possess. The other skills are useless if none of your data scientist or you hold this skill. A data scientist should create a convincing story with your data. They require having storytelling skills to influence the audience regarding what data represents. With the help of these skills and working as an analytical translator, the data scientists can easily explain your data to the company in a more convincing manner.

Three important skills that every Data Scientist should have: As it is essential for an employee to hold data science skills, similarly data scientists should also have some skills from other areas of the business:

• A data scientist should have business acumen quality as they do not know about the essential data for the success of the company.
• A data scientist should be more creative, as without this skill they cannot think of the best questions and the potentials with the data.
• A data Scientist should have reasoning quality, without they won’t reach out at the correct answer for all their queries and data.

Conclusion

Data science is the future of all types of organization. Thus, using the above skills, any team of an organization can interpret the data. Also, they can work with Data Scientists to suggest new ideas. If your team knows a few fundamental concepts of data science which supports for Data scientist’s role, then they can organize their data and have a detailed discussion with them.