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40 Free Resources to Help You Learn Machine Learning on Your Own

13 minute read | June 5, 2019
CJ Haughey

Written by:
CJ Haughey

Ready to launch your career?

When you decide that you want to learn machine learning (ML), the first challenge is simply figuring out where to start. This form of artificial intelligence (AI) technology is a deep and complex field that continues to develop at a rapid pace. This can make it daunting for those who aspire to study machine learning.

Fortunately, we’ve discovered a treasure trove of fantastic resources. From machine learning basics to complex AI techniques, blogs to ebooks, videos to real-life case studies, the internet is rich with opportunities for new students and professionals looking for a refresh.

And the best part?

These machine learning resources are free. So, newcomers can progress and pros can stay sharp all without spending a cent.

It’s time to show you how to learn about machine learning.

4 Reasons to Study Machine Learning

Before diving in, let’s consider just why you should learn machine learning. This isn’t an easy ride, so what makes it worth the effort?

Here are a few good reasons:

ML Is Future-Proof

AI is not just a passing fad—this is the future. The revolutionary technology in these fields is already changing many industries, including healthcare, energy, and marketing.  

According to Forbes, by 2022 there will be 58 million new jobs in AI and machine learning. So, if job security is important to you, there’s no safer bet.

The World Is Data-Driven

From one-man startups to Fortune 500 companies, the world can’t get enough data. It has the power to transform a business, letting you learn more about your market, your competitors, and your customers. However, that’s only if you have the right person for the job. The increasing value of a data scientist or machine learning engineer is undeniable now.

Big Data, Big Money

Data from Indeed on average salary rates found that machine learning engineers typically reel in around $145,000 each year. That’s among the highest salaries in tech, and those rates only increase with experience.

RelatedMachine Learning Engineer Salary Guide

Exciting Career Path

While there is much more to machine learning jobs than building futuristic robots and clever programs that will reduce repetitive work, there’s no doubt that exploring the most revolutionary technology on our planet is a pretty cool way to make a living. It’s full of possibilities, so you’re sure to have fun.

Why Learn Machine Learning on Your Own?

learn machine learning on your own

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Since the technology has gone mainstream, more people have been attracted to machine learning and AI, even transferring from unrelated fields. However, many others are deterred by the idea of expensive and lengthy study routes.

If you opt for the traditional academic path, you could quickly get bogged down by prolonged periods of tackling high-level mathematics theories and textbook learning.

We believe that the best way to learn machine learning is to do it. There are three key advantages to adopting a self-starter attitude:

  1. You’ll have more fun – With greater freedom to switch between theory and practical work, you’ll enjoy the learning process more. Better yet, you’ll see faster results, which is great for morale and motivation.
  2. You’ll develop more practical skills – Nowadays, employers care less about your qualifications and more about what you can actually do for them—can you transform data into profits? By learning machine learning your own way, you can focus on the skills the industry wants.
  3. You’ll build up your portfolio – Through continuous projects, your professional portfolio will grow throughout your studying, which makes you a more attractive prospect for a machine learning internship or entry-level role.

To sum it up, studying machine learning on your own is a quicker, more effective approach—at least to pick up the foundational skills needed to succeed.

Now that you understand that, it’s time to start your journey.

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40 Resources to Learn Machine Learning

The most logical way to learn machine learning is by starting with the basics, then building up your knowledge, one level at a time. By taking a linear approach, this will help you reinforce new knowledge, and you should see a steady progression in your skills.

With that in mind, we’ve divided our list of free machine learning resources into three core stages:

  1. Learning
  2. Practice
  3. Deployment

Without further ado, let’s dive in.

Stage 1 – Learning

In the first stage, you must get to grips with the three fundamental aspects of machine learning:

  • Python
  • Statistics
  • Math

All of these are directly related to data science, and anyone who wants to enter the field should build a strong foundation in these areas before anything else.

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Prerequisites

Machine Learning in Python

A lot of machine learning engineers use R, but Python is still the best programming language to learn if you want a career in machine learning or AI. You can study with Springboard for free, as our well-structured learning path offers students an introduction to Python in a flexible way that is full of concise yet rigorous hands-on tutorials.

Introduction to Statistics for Data Science

When you begin to study machine learning, you’ll soon realize the importance of KDnuggets. This tutorial is an invaluable resource for delving into data science statistics, as it covers core topics like sampling distribution and the central limit theorem.

Data Science Math Skills by Coursera

If math is not your jam, this may not be the career for you. Math is an intrinsic component of every machine learning job, so you need to get acquainted with complex topics like pre-calculus and matrices. Thankfully, there are plenty of courses that build from basic skills all the way to the tougher stuff. This course is one of the best.

Machine Learning Basics

Probability Theory

Probability concepts and conditional probability are vital machine learning basics. If you don’t know these topics, you’ll struggle to understand algorithms. We recommend you take Khan Academy’s free course on statistics and probability so you can build up a sound knowledge of descriptive and inferential statistics.

Statistical Error

Understanding statistical error is essential in order to comprehend machine learning algorithms, and also to be able to determine their accuracy. Here’s a visual prompt to help you remember the difference between Type I (false positive) errors and Type II (false negative) errors.

Linear Algebra

When you want to manipulate large data sets, you’ll need to be good at linear algebra. In this post, you’ll find a list of great resources that explain linear algebra in relation to machine learning concepts and problems.

Bayesian Inference

If you’ve only started to learn machine learning, you may not have encountered Bayesian principles yet. Rest assured, you’ll soon get familiar with themthey are a vital part of understanding machine learning. This online textbook will kickstart your knowledge of Bayesian statistical principles.

Data Pipelines

You’ll need to know the fundamentals of data pipelines, and how they process streaming data. This is essential to understanding how machine learning works with dynamic data sets. Machine Learning Mastery has created a useful tutorial that explains the basics of data pipelines.

Skills

Probabilistic Programming

One of the best ways to learn statistical concepts is by actually sitting down to write code and play around with functions. This online textbook on “Probabilistic Programming & Bayesian Methods for Hackers” puts math in the back seat, making programming the priority in an interactive iPython Notebook style that is enjoyable to learn with.

Calculus

A lot of machine learning revolves around function loss and optimizing for these losses. Understanding the error rate in machine learning systems is only possible if you have a solid knowledge of calculus—specifically multivariate calculus. Check out this simplified rundown to get to grips with essential calculus principles.

Matrix Factorization

When you’re studying math for machine learning, there are several core aspects that really require their own dedicated study program. Matrix factorization is one of those aspects. This is a critical facet of machine learning implementation, so you must work hard to develop your knowledge in this area.

Algorithms

Every algorithm has its own intricate features and functions. You must understand how to use algorithms properly if you’re going to solve any machine learning problems. This extensive exploration of machine learning algorithms is a great primer for machine learning beginners.

Wrangling Data

Quite often, you’ll find data sets that have not been cleaned. Therefore, it’s not easy to dive in and start working with them right away. It’s a worthwhile venture to learn how to format and collapse data in any format. This article will help you clean and process data sets to use in whatever machine learning project you need it for.

Stage 2 – Practice

After you have solidified your knowledge and basic abilities in machine learning, the next stage is to put your skills to the test.

The internet has a ton of great machine learning resources for learners to practice with, no matter what stage of education you are at.

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Case Studies

Towards Data Science

This collection of insightful case studies from Towards Data Science explores a diverse range of real-world problems, showing learners just how we may encounter machine learning in everyday life. From Netflix to hotel bookings, football predictions to Spotify selections, you can see how machine learning is impacting the world all around us.

eConsultancy

Email marketing may seem like Stone Age tech in comparison to machine learning. However, the latter has the power to turbocharge the former, much to the delight of modern marketers. This round-up by eConsultancy explores how machine learning has helped major brands like Dell, Sky, and Harley Davidson boost their bottom line through smarter, data-driven email marketing.

Springboard

In case you’re thinking AI and machine learning were only for business and profits, here is a list of real-world examples where the technology has been used for social good and sustainability. From tackling climate change and ocean pollution to improving agriculture and healthcare, machine learning and AI have the power to change our world for the better.

Data Sets

Machine Learning Frameworks

Data sets are essential as they allow your algorithms to learn how to perform text classification, mining, and categorization tasks. By accessing data sets and creating projects with them, you can develop a solid understanding of software engineering. When you implement machine learning at scale, you’ll need to be adept at using distributed data tools like Spark and Hadoop. Check out this introduction for frameworks you can use.

Kaggle

All who aspire to a career in machine learning should be familiar with Kaggle. In addition to a wealth of resources, Kaggle offers an unrivaled community of people studying machine learning. Each of the data sets here is the centerpiece of the ongoing discussion. It’s easy to jump in and use these resources to create your own projects, and many other data scientists have uploaded notebooks dedicated to each data set, helping learners with algorithms and specific problems.

GitHub

Another stronghold of machine learning education is the GitHub community. Here, you’ll find a deep reserve of data sets that have been organized by topics. Looking for something to do with education, economics, or biology? You name it, and there’s a good chance GitHub has it.

Machine Learning Repository

The University of California has done the machine learning community a great service with this repository. The School of Information and Computer Science at UCI currently has over 470 data sets, all of which have been classified by the type of problem. Many of these data sets are cleaned, making them easier to use.

Amazon

That’s right, Amazon has data sets too. You’ll find them in the Amazon Web Services (AWS) resources section. This makes it easy for anyone who is using AWS to experiment with machine learning. Similar to GitHub, the data sets here have been categorized by various niche topics including ecological resources and public transport.

Gengo.AI

The resources here may not be anywhere near as vast as Kaggle, but Gengo.AI has some good links to offer for specialist interests. This includes a lot on sentiment analysis, natural language processing (NLP), and data sets for autonomous vehicles.

Tools

Scikit-learn

Over time, you’ll probably need to develop skills in C++ and Java. However, for as long as you’re coding in Python, you’ll struggle to find a better tool than scikit-learn. This is among the most popular Python libraries for people studying machine learning, boasting a vast array of algorithms for classification, preprocessing, regression, clustering, and model selection. It’s also user-friendly and full of good tutorials and working examples.

Microsoft Azure Machine Learning

This cloud platform is a great training ground for developers. You can build and train your own AI models here. Microsoft continues to update the platform with new tools and improved features. You can sign up for 12 months free.

IBM Watson

IBM’s cloud service is designed for users who want to put their machine learning models into production. You can use the tool to conduct fundamental operations including training and scoring. IBM Watson works best when you are building your machine learning applications through an API connection. The “Lite” version is free.

TensorFlow

Google uses TensorFlow for research and production purposes. This open-source library is full of useful software for dataflow projects and programming. This makes it an excellent machine learning framework, especially because of its easy visualization of neural networks.

Stage 3 – Deployment

While we think the best way to learn machine learning (or any discipline) is through a self-starter approach, you don’t need to go it alone all the way. In fact, it’s much better if you actively work with others.

So, once you’ve built your skills and confidence, it’s time to get involved with the wider machine learning community.

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Online Communities

By networking with other people who are studying and working in machine learning, you’ll learn from others and may get the chance to collaborate on projects. Over time, you’ll develop a professional network that can present new job opportunities.

r/MachineLearning

This is the biggest subreddit for machine learning. With 647,000 members, you’re sure to find all the help you need, whatever your problem may be. One of the best parts about this group is their active Slack channel that encourages collaboration and brainstorming sessions.

r/MachinesLearn

At just over 8,600 members, this group is much smaller. However, it is rich in resources, offering members all sorts of tutorials, projects, tools, papers, and videos about machine learning. This is a very active, engaging community that is great for those who want access to practical AI problems and solutions.

Machine Learning Facebook Group

Much of the focus here is on machine learning, but you can find plenty of discussions about related topics like statistics, artificial intelligence, and computer programming.

Artificial Intelligence and Machine Learning Facebook Group

This group is specifically for beginners, with members sharing knowledge and experience about machine learning basics. You’ll get a lot of engaging discussion about core concepts, as well as useful machine learning resources for beginner projects.

Data Science With R Facebook Group

Are you still trying to wrap your head around Python? This group may be your savior. The members are always chatting about Python and R in relation to machine learning and deep learning. Most of the people here are experienced data scientists with a lot of astute knowledge to share with the group.

Job Hunting

Ultimately, the time will come when you want to get a machine learning job. You can only learn machine learning for so long before you really want to test your mettle and make some money. Aside from broader job boards and networks like LinkedIn, here are a few places to look for machine learning jobs.

ML Jobs List

This is one of the best places to look for a machine learning job. It has over 200 new positions listed each month, covering many pathways, including research and development, data science, and machine learning engineering. You can upload your resume and share your profile with top recruiters and companies.

iCrunchData

This website is regularly updated with new jobs, many of which are senior positions. You can search by keyword or narrow your scope to specific skills like Python, Java, or data analysis.

ML Conf

ML Conf curates jobs from other sites and focuses solely on machine learning jobs. All jobs are sorted by date, making it easy to find the latest postings.

AngelList

If you want to get in on the ground floor with an exciting new startup, AngelList is the place to be. New enterprises list job openings here, quite often offering long-term positions with solid pay and sometimes equity in the company.

Competitions

You don’t have to be truly elite to enter competitions. Yes, you will find some incredible programmers and engineers here, but these competitions are open to everyone. It’s a great way to continue learning and building your network.

Kaggle

Kaggle isn’t just a place to learn machine learning—it’s a battleground for you to pit your wits against others. This platform hosts regular competitions in AI, data science, and machine learning.

CrowdANALYTIX

This community enables experts to come together to collaborate on machine learning and AI projects. You can join and find open contests. If you win, you’ll gain kudos on the site from your peers, and could also claim a cash prize.

Bonus Resources

Arxiv Insights

Xander Steenbrugge is a renowned machine learning professional, currently working as a researcher at ML6. He has a fantastic YouTube channel where he covers key points about machine learning and AI. He definitely looks at things from a technical perspective, however, his unique delivery and insights make the topics accessible to a wider audience.

Talking Machines

This podcast has been going strong since 2015, building an audience around its discussion on machine learning topics and robotics. The show features regular industry experts, who come on to give their insights and expert opinions on news and trends in the industry, making it one of the best machine learning podcasts for newcomers to learn from.

Learning Machines 101

This podcast aims to pull back the curtain on AI and machine learning, effectively demystifying the field to explain important concepts in an entertaining way that is easy to digest and understand. Things do get technical now and then, but these short 20-30 minute episodes are great for listeners at all levels.

The Best Way to Learn Machine Learning: Never Stop Studying

If you really want to learn machine learning and actually forge a successful career in the field, you must be prepared to pursue the subject for the long term. It’s a fast-moving field and there is no room for complacency.

By taking a self-starter approach, you can progress faster and learn more practical uses and skills than any textbook will show you.

From forums and videos to competitions and collaborative projects, there is no end of great machine learning resources for you to learn from.

It all comes down to your will and passion. With a proactive attitude, you’re sure to find a path forward.

Companies are no longer just collecting data. They’re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists – the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.

In this article, we’ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you’re more drawn out to the creative side or interested in the strategy planning part of data architecture, there’s a niche for you. 

Is Data Science A Good Career?

Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It’s an interdisciplinary field that keeps the work varied and interesting.

10 Data Science Careers To Consider

Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.

Data Scientist

Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research – all depending on the field they operate in.

They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies. 

General Requirements

A data scientist’s career starts with a solid mathematical foundation, whether it’s interpreting the results of an A/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills. 

Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses and certifications that demonstrate their expertise and willingness to learn.

Average Salary

The average salary of a data scientist in the US is $156,363 per year.

Data Analyst

A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.

A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.

General Requirements

To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps to acquire these skills. 

For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.

Average Salary

The average base salary of a data analyst is $76,892 per year.

Business Analyst

Business analysts often have an essential role in an organization, driving change and improvement. That’s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation. 

A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.

General Requirements

Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master’s degree, especially in more complex industries or corporate environments.

Employers also value a business analyst’s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.

Average Salary

A business analyst can earn an average of $84,435 per year.

Database Administrator

The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools. 

A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.

General Requirements

Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it’s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server. 

Average Salary

Database administrators earn an average salary of $77,391 annually.

Data Engineer

Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).

General Requirements

Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently. 

Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.

Average Salary

Data engineers earn a whooping average salary of $125,180 per year.

Database Architect

A database architect’s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure. 

Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.

General Requirements

If you’re aiming to excel as a database architect but don’t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don’t forget programming languages – SQL, Python, or Java. 

If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard.

Average Salary

Data architecture is a very lucrative career. A database architect can earn an average of $165,383 per year.

Machine Learning Engineer

A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions. 

General Requirements

As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.

Average Salary

Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K per year, one of the highest-paying AI careers.

Quantitative Analyst

Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions. 

They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.

General Requirements

This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background. 

Average Salary

A quantitative analyst earns an average of $173,307 per year.

Data Mining Specialist

A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.

General Requirements

If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field. 

Average Salary

Data mining specialists earn $109,023 per year.

Data Visualisation Engineer

Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data’s context. 

General Requirements

Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.

Average Salary

The average annual pay of a data visualization engineer is $103,031.

Resources To Find Data Science Jobs

The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.

Job Boards

When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity. 

Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.

Online Communities

Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.

Network And LinkedIn

Don’t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the “Easy Apply” options, as you’ll be competing with thousands of applicants who bring nothing unique to the table.

FAQs about Data Science Careers

We answer your most frequently asked questions.

Do I Need A Degree For Data Science?

A degree is not a set-in-stone requirement to become a data scientist. It’s true many data scientists hold a BA’s or MA’s degree, but these just provide foundational knowledge. It’s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.

Does Data Science Need Coding?

Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.

Is Data Science A Lot Of Math?

It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.

What Skills Do You Need To Land an Entry-Level Data Science Position?

To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you’re acing problem-solving, critical thinking, and effective communication.

Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!

About CJ Haughey

CJ is a journalist, creative writer, and self-described digital marketing nerd who is currently studying data analytics.