How to Become a Data Scientist from Scratch

How to Become a Data Scientist from Scratch

03-Jun-2021 17:38:48 pm

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.

1. Gain qualifications

Firstly, you will 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 have a minimum master's degree, and 46% have a Ph.D.

To get the most of the skills and knowledge needed for a data science job, you should study for a degree in math and statistics, computer science, or engineering. Other qualifications may be sufficient, but these are the most common.

Alternatively, since there is a shortage of data scientists, more and more companies compete with people who do not 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 math and computing skills. You will 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 acquire specialist skills at an affordable cost, 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 competency, you must be able to demonstrate specific skills and specialist knowledge.

Many people earn a master's degree in data science, but there are other routes you can take to gain relevant knowledge, such as e-learning courses. 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 and after your studies, it's a good idea to get some work experience.

You might be lucky enough to find paid work for any business that needs data scientists in industries including finance, retail, manufacturing, and engineering. Nonprofits and charitable organizations are a good place to look if you are struggling to find work experience, although you may have to settle for unpaid work in these areas.

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, practical activities that allow you to build your experience level.

The variety of specialist projects is too many to list in full detail, but here are some 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 helpful to build a professional portfolio that includes a few different types of successful projects, so don't be afraid to try a few different specialties. This is especially true if you're not sure which feature to focus on initially. Following the above steps will empower you for a successful career in data science or business analysis.