Handpicked learning resources to break into the data industry

Become job ready by enhancing your skillset

Handpicked learning resources to break into the data industry
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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:

  • Classification
  • Regression
  • Unsupervised learning (K-Means/Hierarchical clustering)
  • Exploratory Data Analysis
  • NLP

Here are some code-along tutorials I've compiled in each of these areas. Some of these were created by me:

  1. A Beginner's Guide to Machine Learning in Python

In this article, I walk you through four supervised machine learning techniques, and provide code implementation for the Kaggle Pima Indian Diabetes Dataset.

2. A Beginner's Guide to Data Visualization in Python

In this article, I provide you with a step-by-step guide to performing exploratory data analysis on the Kaggle Titanic Dataset.

3. Python Sentiment Analysis Tutorial (Datacamp)

This is a free sentiment analysis code-along tutorial created by Datacamp.

4. K-Means Clustering Tutorial

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.

Here is an example of an end-to-end data analytics project, and here is a tutorial you might find useful.

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:

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:

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:

That's it!

I hope you find these resources useful.

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