Your journey into Data Science with Python - A simple guide

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. (Wikipedia).

 
Step 1: Find a mentor in the Data Science field or someone you can always get answers to questions from.

 
Step 2: Do not rush yourself but be disciplined and get into a data science network or community.

 
Step 3: Enroll for a Python course which has graded exercises. This will help to get familiar with hands-on coding practice. I recommend this course: course link. You may learn up to Classes/Object-Oriented Programming since you are learning towards data science.

 
Step 4: Enroll for an online Data Science or Machine Learning with Python course by getting guidance from your mentor.

 
Step 5: Alternatively or in addition, you can explore free resources like this: free resources.

 
Step 6: Seek to understand the Mathematics and Statistics behind each concept.

 
Step 7: Start a project in a field of interest (soccer, finance, sales, ...) to you. My first project use soccer data. The benefits of this cannot be over-stated.

 
Step 8: Create a Kaggle account to get exposed to wealth of data and project ideas: link.

 
Step 9: Create a GitHub account to save your codes for others to see and you may learn Git: here.

 
Step 10: Explore dataset from sites like this: awesome dataset.

 
Step 11: Get acquainted with latest research by reading. This is a useful resource: resource.

 
Step 12: Learn, Unlearn and Relearn.

 
Check my blog on CRISP-DM: Data Science Life Cycle to know what to focus on as a data scientist. You explore data to build Machine Learning models not to do Exploratory Data Analysis alone. You are working towards being a Predictive Analyst not a Descriptive Analyst.

 
Thanks for reading. Feel free to like, share, leave comments with suggestions on more insights to the content or how you find it helpful.