When you start learning data science, there are so many topics and techniques to learn that it can often feel overwhelming to decide what to learn and in what order to learn it. But, the skills that can give you a hike have been much more practical and useful than others.

In this article, I will share with you four skills that can increase your salary by at least 60% in a short period of time.

Automate Small Tasks

The work of a data scientist is much more than just implementing a machine learning model. Data scientists spend about 80% of their time on data preparation and cleaning. All of this is very time-consuming and extraordinarily boring. But much of it can be automated. Don’t write big chunks of code from scratch all the time, try to write code that rather automates your work next time. Understand it like this, write a code keeping in mind that your code will be used by someone else daily, so keep it easy to use for others in the team. For example, if you’re working on text data, and you’ve to perform some preprocessing task, then think of an approach that you can apply to all the textual columns of the dataset, rather than performing it from column to column.

Feature Engineering

Feature engineering allows you to significantly improve the performance of models. Engineering strong features can help give you a better understanding of your data and the business overall. By engineering relevant features with high predictive power, you can get a better understanding of the associations between different variables which can give you a better understanding of what drives the business. This ultimately led to better forecasting and better targeting. Learn this skill if you don’t know it, even an average data scientist spends 80% of the time using this for any Data Science project. Check out my Data Science course, if you want to master this skill.

Data Dives

A deep data dive analysis is an extensive investigation or analysis of a question or topic of interest. It can be investigative or explorative in nature. An investigative dive analysis answers questions like “why did sales drop by 20% last week?” whereas an explorative deep dive might answer a question like “do higher levels of app engagement correlate with more profitable users?”

Data dive analyses allow you to discover golden opportunities. What I mean by “golden opportunities” is things that aren’t immediately evident but have a significant impact on the business. Deep dive analyses give you a better understanding of the products and data that you’re working with. Since you work directly with the data, you often develop the highest level of expertise in the respective domain. Data dives aren’t easy always but once cracked, can give immense results in form of a breakthrough.

Communication Skills

Communication is key to any data science role. As a data scientist, you will be relied upon to communicate your findings and recommendations to non-technical colleagues. As a data scientist, you’ll be pitching your idea to non-tech people who run the company, and before they accept your strategies, they would want to understand and question why they should follow you. That’s why, Effective communication skill is a must-have skill for data scientists, but it’s something that is easily overlooked. Lack of this skill can also lead to inefficiencies in projects and misinterpretation of actionable insights, which overall can be costly for any company. As well as speaking a language that the business understands, data scientists must communicate their findings using data storytelling.

Are you interested in learning about one of the hottest career fields today? Check out my Assignment driven Data Science course, and get Job ready for your first Data Scientist role.

MASTER THE MOST IN-DEMAND SKILL OF 21st CENTURY

Visit the Contact page for any queries.