To get started on your journey towards becoming a data science professional, there are some things that you need to take into account first. These steps outlined below will have you covered:
1. Understand The Basics Of Python’s Syntax:
While learning a programming language, it is advisable to start with the fundamentals which include gaining an understanding of how loops, functions, and variables work and what their purpose is. Furthermore, make sure to look up files, data structures such as lists and dictionaries and familiarize yourself with them as well.
Source Practice: Python.org, Codecademy, and freeCodeCamp.
2. Expand Your Knowledge On Data Science Libraries:
Pandas: This software allows a person to retrieve data and perform tasks of altering it, while also assisting with filtering the data.
NumPy: Provides a wide variety of tools such as numerical computation on matrices and arrays.
Matplotlib/Seaborn: Get educated on how to visually transmit data in the form of various graphs and charts.
Resources: The appropriate library documentation as well as DataCamp and Kaggle.
3. Engage In Data Sets Of Larger Volumes:
Kaggle is a platform that allows you to look for large data sets of various tasks that are to be worked on so it is advised to utilize it effectively to broaden your educational yardsticks. Try to progress slowly, by cleaning smaller sets and gradually increasing the volume.
Project Ideas: Data cleansing, EDA, and a little data visualization such as graphs and charts.
4. Get Acquainted With Activities Set For Data Scientists:
In the beginning you should do some EDA, covering how to do statistical analysis or start with a few abbreviated machine learning tasks.
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