These Data Science Skills will be your Superpower

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.

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?

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?

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.

ii) Data Preprocessing: Knowledge about data preprocessing is very important and include topics such as:

e) Techniques of feature transformation and dimensionality reduction such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).

Understand the essential components of good data visualization (see figure below). Be able to use several data visualization packages such as matplotlib, seaborn, and ggplot2.

Machine Learning is a very important branch of data science. It is important to understand the machine learning framework: Problem Framing; Data Analysis; Model Building, Testing & Evaluation; and Model Application.

Find out more about the machine learning framework from here: Machine Learning Process Tutorial.

Skills acquired from course work alone will not make you are a data scientist.