Analysis

Top 5 Online Courses for Learning Data Science

Over the last decade or so Data Science has exploded. Reasons for this are the amount of data being collected and stored by companies. As our lives become increasingly intertwined with technology, increases in data storage are inevitable. The data collected is known as ‘Big Data’ and estimates are that 2.5 quintillion bytes (2.5 million terabytes) of data collected daily and data scientists are tasked with making sense of the data, or manipulating to the needs of the company. With all this data available, no wonder why data science is one of the most promising industries of the near future. If you’re looking to learn a new skill or at a career change, data science is a great option and we’ve compiled 5 of the best online courses for you.

Criteria for Courses in the List

It’s always hard to filter a list such as this. The main factor being ‘what works for one doesn’t work for another’, but we’ve tried to add the most well-rounded courses using the list below:

  • Course is easy to follow
  • Course has a good amount of detail
  • Course has a mixture of theory and ‘do it yourself’ projects
  • Course is well structured
  • Course has a good overall rating

It must be noted that the list is geared towards beginner and intermediate levels of learning and are in no particular order.

1. Professional Certificate in Data Science from Harvard University

Level: Beginner to Intermediate

Pace: Self-paced

Duration: 100 – 180 hours of work

Cost: $441 USD

Certificate Issued: Yes

Rating: 4.8 / 5

Syllabus:

  • R Basics
  • Visualisation
  • Probability
  • Inference and Modeling
  • Productivity Tools
  • Wrangling
  • Liner Regression
  • Machine Learning
  • Capstone Project

2. Data Science Specialisation by John Hopkins University (Coursera)

Level: Beginner

Pace: Self-paced

Duration: 160 hours

Cost: $49.99 USD per month (Financial Aid is offered for those who cannot afford the fees)

Certificate Issued: Yes

Rating: 4.5 / 5

Syllabus:

  • The Data Scientists Toolbox
  • R Programming
  • Getting and Cleaning Data
  • Exploratory Data Analysis
  • Reproducible Research
  • Statistical Inference
  • Regression Models
  • Practical Machine Learning
  • Developing Data Products
  • Capstone Project

3. IBM Data Science Professional Certificate (Coursera)

Level: Beginner

Pace: Self-paced

Duration: 96 hours

Cost: 0$ – $49.99 USD per month

Certificate Issued: No

Rating: 4.6 / 5

Syllabus:

  • What is Data Science?
  • Open Source tools for Data Science
  • Data Science Methodology
  • Python for Data Science and AI
  • Databases and SQL for Data Science
  • Data Analysis with Python
  • Data Visualisation with Python
  • Machine Learning with Python
  • Applied Data Science Capstone Project

4. Dataquest: Data Scientist in Python Path

Level: Intermediate

Pace: Self-paced

Duration: 96 hours

Cost: $49.99 USD per month (Financial Aid is offered for those who cannot afford the fees)

Certificate Issued: Yes

Rating: 4.6 / 5

Syllabus:

  • Python for Data Fundamentals, Intermediate, Advanced
  • Pandas & NumPy Fundamentals
  • Exploratory Data Visualisation
  • Storytelling Through Data Visualisation
  • Data Cleaning and Analysis
  • Data Cleaning in Python: Advanced
  • Command Line Beginner, Intermediate
  • Git & Version Control
  • SQL & Databases Fundamentals, Intermediate, Advanced
  • APIs & Web Scraping
  • Statistics Fundamentals, Intermediate
  • Machine Learning in Python Fundamentals, Intermediate
  • Decision Trees
  • Deep Learning Fundamentals
  • Machine Learning Project
  • Data Structures & Algorithms
  • Kaggle Fundamentals
  • Natural Language Processing
  • Spark & Map-Reduce

5. Microsoft Professional Program in Data Science

Level: Intermediate

Pace: Self-paced

Duration: 180 – 250 hours + Final Project

Cost: $1089 USD

Certificate Issued: Yes

Rating: 4.5 / 5

Syllabus:

  • Introduction to Data Science
  • Analysing and Visualising Data
  • Analytics
  • Ethics and Law in Data and Analytics
  • Querying Data with Transact-SQL
  • Essential Math
  • Essential Statistics for Data Analysis using Excel
  • Data Science Research Methods
  • Principles of Machine Learning
  • Developing Big Data Solutions
  • Capstone Project

Learning to Learn

It may seem straightforward but learning how to learn effectively can save hours of hair-pulling and frustration. The idea is to maximise results and information retention in the least time possible. Now,  this doesn’t mean rushing through things or learning things by heart, in fact, it’s a mixture of methods. When learning to code, I remember watching hours of YouTube videos, understanding the concepts but when it came to actual coding, my mind would draw a blank. What I was doing wrong was just watching videos and not practically coding to help my muscle memory and problem-solving skills. We also know that everyone has their own way of learning, but following the guideline below, your data science learning journey should be a little bit more productive and hopefully less frustrating.

  • Find a quiet and comfortable place to learn
  • Apply your learning in a real world example as soon as possible
  • Try and find accountability buddies / go to meet ups
  • Look into joining hackathons to boost your skills

Caveats in Learning Data Science

Whenever learning something new, it’s difficult to ascertain what you need to learn, as normally one would have very little information about the subject. Despite courses offering structured syllabi, there will always remain areas/subjects that are related to the main subject but not covered in the syllabus. It will help your learning if you review the list below and brush up on any skills that require it.

Mathematics

  • Algebra
  • Statistics
  • Probability
  • Basic Calculus
  • Optimisation

Programming Skills

  • Python or R
  • SQL
  • Extracting and cleaning data from SQL databases, JSON and CSV files
  • Visualisation of Data
  • Basic Machine Learning Concepts

Soft Skills

  • Curiosity
  • Determination
  • Problem Solving
  • Communication

Other

It will be quite easy to feel overwhelmed as there is a lot of information to digest but with time it all falls into place. It is important to note that you will not learn everything from one resource, so it’s always worth looking around for alternative resources which include:

  • Books
  • Specialist sites / forums that cover Data Science
  • Videos

Due to modern technology and interest in data science, there are countless courses to choose from. Those listed above are some of the best available, and though not all may be affordable for many, there are cost effective options available. Those with financial aid are worth looking into too. Before starting the learning path, it’s important that a plan is made and daily / weekly slots are allocated in your schedule. There will be tough times, but facing issues and researching answers is all part of the learning process. Be sure to pace yourself rather than go all in at the beginning and face burnout later.

Click to comment

Leave a Reply

Your email address will not be published.

This site uses Akismet to reduce spam. Learn how your comment data is processed.

To Top