How to Become a Data Scientist from Scratch
Are you looking for a career that’s interesting, challenging, and very much in-demand?
A data scientist career ticks all these boxes and more. Keep reading for the ultimate learning path guide detailing the skills, knowledge, and training you need to become a data scientist.
Last Updated January 2022
Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more! | By Kirill Eremenko, Ligency I Team, Ligency TeamExplore Course
We’re in the middle of the 4th Industrial Revolution (or Industry 4.0), driven by the internet of things and AI. Both are characterized by the collection, analysis, and exchange of data — lots of data.
There’s no doubt that data science skills are in high and growing demand. Companies in all industries need these skill sets, from manufacturers to internet retailers, tech start-ups, and even government agencies. It’s also a well-paid career, with the average data scientist earning $113,436 a year in the US.
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 a role for you in the data science field.
How do you get qualified and establish a career as a data scientist?
This in-depth guide will explain the steps required to become a data scientist, as well as some suggested courses to accelerate your progress.
1. Gain qualifications
First off, you’ll need some technical qualifications. The most common route is to study for a bachelor’s 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 a data science 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 take on people who don’t have formal qualifications. Without a formal degree, you’ll need a fair amount of experience in a relevant role, such as a computer programmer or engineer, or be able to demonstrate strong 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.
2. Develop skills and knowledge
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 you can take, such as e-learning courses, to acquire the relevant knowledge. Depending on the requirements of the role you want, you may need:
- To know how to code with a language such as Python or C#
- To be able to use SQL
- Experience with Hadoop or similar platforms
- Experience in machine learning/AI
- To be able to visualize and present data with software or platforms such as ggplot, d3.js, or tableau
In terms of non-technical skills, the following are usually high on employers’ lists:
- Attention to detail: must be able to ensure the accuracy and integrity of data.
- Organization skills: dealing with large data sets (with potentially millions of data points) requires high-level organizational skills and a logical, methodical approach.
- Problem-solving: a major part of the role includes coming up with novel ways to collate, interpret, and present data. This requires an ability to solve problems and ‘think outside the box.’
- 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.
3. Gain work experience
During your studies and afterward, 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 in industries including finance, retail, manufacturing, and engineering. 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 in these sectors.
Another way to gain valuable experience in the field of data science is to enroll in courses that hold workshops as part of the curriculum. Udemy and SuperDataScience courses offer real-life, hands-on activities that allow you to build your experience level.
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 strong 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 (such as Dash b Plotly) to create data insight visualizations for organizations.
- Exploratory data analysis: This involves interpreting the data, coming up with relevant questions about it that may reveal business insights, then answering those questions using SQL, Python, or other programming languages.
- Machine learning: There are different complexity levels of machine learning projects. As a beginner, stick to linear and logistic regression projects. 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 specialties. This is especially true if you’re not sure which specialty to focus on initially. Following the steps above will empower you for a successful career in data science or business analysis.
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