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Data scientist is considered a highly coveted job role in the global business landscape, and hence, brings in a plethora of responsibilities alongside it. As a potential data scientist, you need to acquire a number of skills to build a rewarding career in the field of data science & analytics. Although, you must have already acquired many data science skills while doing your college degree course, but there are a few that can be learned only when you are on the job. These might be some modern-day data science concepts that have had emerged recently in the said industry, or some advanced skills that only working professionals know of. As a budding data science professional, you should be a continuous learner all your professional life to grow fast in your career. Skill-honing must be a habit for a present-day data scientist, or someone who is seeking a break in the said industry domain. For aspirants, looking out for, and enrolling in the best data science certifications available online, would certainly help develop and nurture new skills. Here are the top five skills a data scientist must possess to shape a success fuldata science career in the present times. 5 Contemporary Data Science Skills You Must Practice #1. Multicollinearity The word “multicollinear’ consists of two distinct parts – ‘multi’ which means many, and ‘collinear’ meaning linearly associated. Multicollinearity can be defined as a situation wherein two or more variables depict information that is alike, or are closely linked in a regression model. However, there are some reasons that make the said concept raise concerns. Multicollinearity can result in overfitting in some modelling tactics, leading to a decrease in the model performance. There exist two commonly used techniques that data scientists use, when executing on correlation plots and matrices. These are: heatmaps variance inflation factor (VIF) The higher the value of VIF, the lesser deployable the feature becomes for the regression modelling. #2. One-Hot Encoding The feature transformation in the model is known as one-hot encoding. A data science professional, when aims for demonstrating categorical features in numeric form by encoding the same, it comes under one-hot encoding. However, the categorical features possess value themselves, the said process help transpose the information to make each value become a feature. The resulting observation in the rows are either denoted as a ‘1’ or ‘0’. The transformation is highly appreciated when you are dealing with numerical features, and are supposed to demonstrate the numerical depiction with categorical/text features. #3. Sampling When you are facing a heavy dearth of data, oversampling is usually recommended to compensate for the same. Suppose you are executing on a classification problem and you are being provided with a minority class, similar to the below-provided example: Class 1 100 rows Class 2 1000 rows Class 3 1100 rows Class 1 here, has a limited data for its class, that means the dataset provided is not balanced and hence, will be considered as a ‘minority class’. There exist a number of oversampling methods. One among them is known as SMOTE which is an acronym for Synthetic Minority Over-Sampling Technique. The way SMOTE works is by deploying K-neighbour technique to search for the closest neighbour to help develop synthetic samples. There are various other techniques that are similar to SMOTE, and uses the reverse methodology for undersampling. Such tactics are highly effective when you have got outliers in your regression data, or in your class. You would always want to make sure that your sampling is the most-apt demonstration of the data on which your future model will run. #4. Error Metrics There exist a number of error metrics that are deployed in both, regression & classification models that fall under the domain of data science. As per the scikit-learn library, these are a few of the error matrices that are the most apt to regression models: mean_gamma_deviance r2_score explained_variance_score median_absolute_error mean_squared_log_error mean_poisson_deviance mean_squared_error max_error mean_absolute_error However, two of the most popular error metrices listed above, would be RMSE and MSE. #5. Storytelling It falls under the category of most undervalued data science concept, or skill, but actually is, the most powerful skill that you can acquire as a potential data science professional. The meaning of storytelling in the world of data science translates into your ability to communicate problem-solving tactics with the team members and the top management in a corporate setting. What actually happens in the real-life scenarios is that data scientists focus too much on the model accuracy, but fail to identify the specific requirements of the business process as a whole. The business process in total, comprises following elements and variables: What’s the business about? What’s the issue you intend to solve? What’s the need to deploy data science and analytics? By what time, will we get the results? How will the results be leveraged to ensure process improvement, business growth & profitability? What will be the potential impact of the results to be obtained? As we can see clearly, no points stated above result in a significant progress in the model accuracy. The objective here is to understand about using the data in an effective way to resolve the business issues of your company. It’s always advised to form special business relationships with your stakeholders and non-technical colleagues, as someday or the other, you will need to work with them, and will require their services to some degree. You would also be working with the Product Managers who will help identifying issues. Data engineers will also be needed by you at work, for sourcing the relevant data. At a later stage, you will be required to share the reports and presentations with the people from higher management who would eventually assess the model created by you. And hence, being a good communicator will come really handy.

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