If you are a fresh graduate or someone looking to transition into the data industry, you're probably spending a lot of time and energy searching for resources to help you get started.
To make your life easier, I've handpicked a list of resources to help kickstart your journey in data science.
Most of these resources are free or can be audited for a period of time, and there are NO affiliate links added.
I hope you find this list useful :)
Step 1: Python and ML
Take an introductory Python course. Then, take an introductory machine learning course. I recommend taking this Python bootcamp by Jose Portilla, followed by his machine learning bootcamp.
Udemy has frequent sales from time to time, and their course prices can go down as low as $9. Make sure to look out for their sales, you'll get to save a lot!
After learning Python and machine learning basics, start working on simple Kaggle projects in these areas:
- Unsupervised learning (K-Means/Hierarchical clustering)
- Exploratory Data Analysis
Here are some code-along tutorials I've compiled in each of these areas. Some of these were created by me:
In this article, I walk you through four supervised machine learning techniques, and provide code implementation for the Kaggle Pima Indian Diabetes Dataset.
In this article, I provide you with a step-by-step guide to performing exploratory data analysis on the Kaggle Titanic Dataset.
This is a free sentiment analysis code-along tutorial created by Datacamp.
In this tutorial, I created a customer segmentation model with Kaggle's Mall Customer Segmentation Dataset. I used two unsupervised algorithms to create this project - Principal Component Analysis and K-Means clustering.
Step 2: Data Collection
Data collection is an important skill to have in the data science industry. Companies usually can't just rely on internal data. They need to collect data from a variety of external sources to build models and come up with insights.
There are paid tools available to do this, but they aren't always reliable as they can't always scrape every site.
Learning to build your own web scraper is extremely useful, as you will be able to customize it according to your project's requirements.
The most popular tools to build a web scraper in Python are BeautifulSoup and Selenium.
Here is a tutorial to help you get started.
Step 3: Data Analytics Projects
Data analytics is a separate field on it's own, and is very broad. As a data scientist, however, you will be expected to have some skills in the field of analytics.
A data analyst specializes in collecting data from a variety of sources and deriving useful business insights from it. These insights aid company stakeholders make decisions in driving the company forward.
If you want to dive deeper into the field of data analytics, I suggest gaining some domain specific knowledge.
Here are some courses you can take:
- Marketing Analytics with Python (Datacamp, paid course)
- Marketing Analytics (Coursera, free to audit)
- Google Data Analytics Professional Certificate (Coursera, free to audit)
Step 4: Model Deployment and Cloud Platforms
When you start working as a data scientist, your company would most likely be working with a cloud service provider like AWS, Microsoft Azure, or GCP.
You will build machine learning solutions on one of these platforms, and need to understand how to navigate the environment.
Here are some learning paths you can follow to learn how to work with these platforms:
- Google Cloud Fundamentals (Coursera)
- AWS Fundamentals (Coursera)
- Microsoft Azure Fundamentals (Coursera)
In many of these companies, you might need to deploy and monitor the machine learning models you build. Here are some resources to help you understand the importance of model deployment:
- A Gentle Introduction to MLOps (Article)
- Build a ML web app with Python - You can follow along to this tutorial to build your first Flask ML application, and deploy it with Google Cloud.
- Deploy your ML web app to Heroku
I hope you find these resources useful.
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