I. Introduction

Most academic training programs in data science are focused mostly on teaching hard skills. Time and time again, industry data, market trends, and insights from top business leaders highlight soft skills as a key component to success in the workplace. This article will discuss the essential hard and soft skills for success in data science practice.

II. Hard Skills

1. Mathematics and Statistics Skills

Math skills are essential in data science and machine learning. For more about the basic math skills needed for data science and machine learning, please see this article: How Much Math do I need in Data Science?

2. Essential Programming Skills

Programming skills are essential in data science. Since Python and R are considered the 2 most popular programming languages in data science, essential knowledge in both languages is crucial. For more information on essential programming skills needed for data science, please see this article: How Much Programming do I need in Data Science?

3. Data Wrangling and Preprocessing Skills

Data is key for any analysis in data science, be it inferential analysis, predictive analysis, or prescriptive analysis. The predictive power of a model depends on the quality of the data that was used in building the model. Data comes in different forms such as text, table, image, voice, or video. Most often, data that is used for analysis has to be mined, processed, and transformed to render it to a form suitable for further analysis.

i)** Data Wrangling**: The process of data wrangling is a critical step for any data scientist. Very rarely is data easily accessible in a data science project for analysis. It’s more likely for the data to be in a file, a database, or extracted from documents such as web pages, tweets, or PDFs. Knowing how to wrangle and clean data will enable you to derive critical insights from your data that would otherwise be hidden.

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