8 Pillars of Data Science: Volume 1 – Data Analysis

Data Analysis is a crucial step in the field of Data Science. It involves extracting insights and knowledge from raw data, with the goal of making informed decisions. The process of data analysis typically includes three main steps: feature engineering, data wrangling, and exploratory data analysis (EDA). In this article, we will discuss each of these steps in detail, and provide some useful tools and techniques for each, as well as resources for further learning.

Todays contributor's

Donnie Maclary

Associate Director - Data Science

Feature Engineering

Feature engineering is the process of transforming raw data into features that can be used in machine learning models. This step is important because it can greatly affect the performance of the model. Features can be created by combining multiple variables, creating new variables, or transforming existing variables. Some popular tools and techniques used in feature engineering include:

Data Wrangling

Data wrangling is the process of cleaning, transforming and manipulating data, in order to make it suitable for analysis. This step is important because dirty data can lead to inaccurate or unreliable results. Some popular tools and techniques used in data wrangling include:

Exploratory Data Analysis (EDA)

Exploratory Data Analysis (EDA) is the process of analyzing and summarizing the main characteristics of a data set. It is an important step in data analysis because it helps to identify patterns, outliers, and relationships in the data. Some popular tools and techniques used in EDA include:

To upskill yourself in data analysis, you can take online courses and tutorials or read books on the topics mentioned above. Some popular resources for learning data analysis include:

In Summary

Data analysis is just one aspect of the larger field of Data Science. Data Science is a rapidly growing field with many career opportunities, ranging from data analyst to data scientist to machine learning engineer. If you’re interested in pursuing a career in data science, it’s important to have a strong understanding of the fundamentals of data analysis, as well as the skills to work with various tools and techniques. This article has provided an overview of some of the important concepts and tools in data analysis, as well as resources for further learning.

If you are interested in learning more about data science, or exploring career opportunities in the field, feel free to get in touch with us. We would be more than happy to provide guidance and support as you navigate this exciting field.

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