Begin your journey in applied data science and machine learning with this blueprint — Part II
Are you interested in pursuing a career in applied data science and machine learning? After reading Part I, continue your learning journey with this article.
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Previously, in Part I of this series, we have discussed about the essential pre-requisites that you need to go through for practicing applied data science. Mostly we have covered about whether to choose Python or R (my preference being Python), if you are proceeding with Python, what exactly do you need to know in Python, learning SQL, Statistics for Data Science, and Data collection from different data sources. In this article, we will focus particularly on learning and applying various Machine Learning and Deep Learning techniques. Let’s dive in!
Exploratory Data Analysis and Data Processing
Data science is all about gaining knowledge from your data and generating valuable insights for data driven decision making process.
I would always argue and say that Exploratory Data Analysis (EDA) is one of the most important steps of an ML workflow. So, as a data scientist, make sure to spend sufficient time in EDA to explore and learn interesting observations from the data. It is indeed a crucial step before building ML models as with EDA you can get valuable information for doing Feature Engineering and Data Processing for building efficient ML models.
What do I need to know about EDA to get started?
In the EDA process, you might need to apply your knowledge about Data Manipulation (maybe using Python), Statistics and Data Visualization. This is where you will apply the knowledge gained from most of the topics discussed in Part I of this series. The following are some additional links which you can use to gain more knowledge on the EDA process:
- Data Visualization: https://towardsdatascience.com/10-viz-every-ds-should-know-4e4118f26fc3
