How to Become a Data Scientist from Scratch

Photo by Shahadat Shemul on Unsplash

Looking for a career that’s interesting, challenging and very much in-demand?

A Data Scientist career ticks all those boxes, and more.

Whether you’re aware of it or not, we’re in the middle of the 4th Industrial Revolution (or Industry 4.0) which is being driven by the Internet of Things (IoT) and AI. Both are characterised by the collection, analysis and exchange of data. Lots of data.

Pursuing a career in data science is a wise move these days. Data science-related jobs feature heavily in LinkedIn’s emerging jobs report. Data Science Specialist and Data Science Manager are both listed in the top 15.

There’s no doubt that data science skills are in high and growing demand. All sorts of companies need them, from manufacturers to internet retailers, from tech start-ups to government agencies.

It’s also a well-paid career, with the average data scientist earning a salary of $113,436 in the USA.

So, whether you’re interested in helping businesses plan their marketing by interpreting vast amounts of data, or helping governments focus their resources in the right areas by studying data correlations or patterns, there’s plenty of variety out there.

But how do you get qualified and establish a career as a data scientist?

This in-depth guide will explain the steps required, as well as some suggested courses to accelerate your progress.

Steps to Becoming a Data Scientist

First off, you’ll need some technical qualifications.

The most common route is to study for a bachelors or master’s degree. In fact, 88% of data scientists hold a minimum of a master’s degree, and 46% have a PhD.

To gain most of the skills and knowledge needed for the job you should study for a degree in Mathematics and Statistics, Computer Science or Engineering. Other qualifications may suffice, but these are the most common.

Alternatively, as there is a shortage of data scientists more and more companies are taking on people that don’t have formal qualifications. Instead, you’ll need to have a good amount of experience in a relevant role (computer programmer, engineer) or be able to demonstrate good mathematics and computing skills. You’ll also need to complete some specialist courses.

These days you can find fully certified courses online that are taught by experts in the field of data science. E-learning platforms have become the best way to obtain specialist skills at an affordable price, and are overtaking formal educational institutions as the number one way to gain in-depth knowledge and skills.

As well as qualifications, you’ll need to be able to demonstrate specific skills and specialist knowledge.

Many people pursue a master’s degree in data science, but there are other routes such as e-learning courses to acquire the relevant knowledge. Depending on the requirements of the role, you may need to know:

  • how to code with a language such as Python or C#
  • be able to use SQL
  • experience with Hadoop or similar platforms
  • experience of machine learning/AI
  • visualizing and presenting data with software or platforms such as ggplot, d3.js or tableau.

In terms non-technical skills, the following are usually high on employer’s lists:

  • Attention to detail — must be able to ensure accuracy and integrity of data
  • Organization skills — dealing with large data sets, with potentially millions of data points, takes high level organization skills and a logical, methodical approach
  • Problem-solving — a major part of the role is coming up with novel ways to collate, interpret and present data. This requires an ability to solve problems and ‘think outside the box’ at times.
  • Desire to learn — our technological world is constantly changing, including methods of gathering data and the demands placed on using this data. Data scientists must be prepared to continually study and practice new technology and techniques.
  • Resilience and focus — these character traits are essential for data scientists as they will often spend a long time on one problem, trying different ways to solve it.
  • Communication and Teamwork — most data science jobs will require you to work with others, often from different departments and disciplines.

During your studies and afterwards, it’s a good idea to get some work experience.

You may be lucky enough to find paid work for any number of businesses that need data scientists. These businesses operate in all areas of the economy, including finance, retail, manufacturing, engineering, etc. Non-profit and charity organizations are a good place to look if you’re struggling to find work experience, although you may have to settle for unpaid work.

Another way to gain valuable experience in the field of data science is to enrol on courses that hold workshops as part of the curriculum.

The variety of specialist projects are too numerous to list in full detail, but here are a few examples to whet your appetite:

  • Cleaning Data — Big, complex database systems will need frequent cleaning, reshaping and archiving of datasets. Data cleaning projects require a good knowledge of Python or R.
  • Creating Interactive Data Visualizations — If you enjoy presenting data in unique and interesting formats, this kind of project will suit you. You will use dashboard software of some kind, e.g. Dash b Plotly, to create data insight visualizations for organizations.
  • Exploratory Data Analysis (EDA) — This involves interpreting the data, coming up with relevant questions about it that may reveal business insights, then answering the questions using SQL, Python or other programming language.
  • Machine Learning — There are different complexity levels of machine learning projects. As a beginner, stick to linear and logistic regression projects as they are ideal. These types of projects are often used to create models to interpret data and communicate insights to managers.

It’s useful to build a professional portfolio that includes a few different types of successful projects, so don’t be afraid to try out a few different specialisms to begin with. This is especially true if you’re not sure which specialism to focus on initially.

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ML Engineer, Lifelong Learner