How to Become a Data Scientist Without a Degree|A complete Guide 2021
How to Become a Data Scientist Without a Degree: Organizations are using data science to turn data into a competitive advantage by refining products and services.
Indeed, data scientist work is exciting. So it’s not strange if you choose to become a data scientist.
And for those who are wondering if it’s possible to become a data scientist without a degree, this article specifically addressed this case.
I’ll show you everything you need to know about becoming a data scientist, from what to study to essential skills, salary guide, and more!
Stay with me.
Can I Become a Data Scientist Without a Degree?
You probably be wondering if you have a chance of becoming a data scientist without a degree.
The answer is, “Yes, it’s possible.”
And the awesome part is that just you’ve passed the first step by indicating interest in data science.
So, if you’re doubtful about this fact, let me show you that you can become a a Data Scientist without a degree.
I used to be a Project Manager at Ontario. You know what that means; I studied Project Management.
Now, I’m a Data Scientist.
Crazy right? how to become a data scientist from scratch
Mere looking at this case, anyone could tell that I was far from data science.
Well, it took me a lot of effort to get to where I’m now. I had to do a lot of learning online after work.
Then I learnt Machine Learning on my own during my Master’s program to meet up with my peers who are already grounded in this field.
Why am I explaining this? I want you to understand that I was once in a similar position as you!
With the experience I’ve gone through, I can say that a degree in any prerequisite courses for Data Science is helpful, but not necessary.
Since I’ve been on the both sides of getting a degree and learning to become a data scientist online, I can grant you my own perspective to this question.
I say getting a Master’s in Data Science is the easiest way to get into the field. But if you’re not ready to spend $60,000 on tuition, self-discipline should be your next apparel.
That’s the only way you can successfully become a data scientist without a degree.
I hope this will help you in making your decision.
Before we get into the details of what to do to become a data scientist, let’s understand what data science is about.
What is Data Science?
Here is a simple definition of data science:
Data science combines various fields including statistics, scientific methods, and data analysis to extract value from data.
Data science reveals trends and produces information that companies can use to make better decisions and create more innovative products and services.
Most importantly, it enables machine learning (ML) models to learn from the vast amount of data passed to them rather than relying primarily on business analysts to see what they can discover from the data.
Those who practice data science are called data scientists and combine a number of skills to analyse data collected from the web, smartphones, customers, sensors, and other sources.
Data is the foundation of innovation, but its value comes from the information data scientists can obtain from it and then act on it.
What Skills Does a Data Scientists Need if You Don’t Have a Degree (and How to Learn Them)
Here are the basic skills any aspiring data scientist must have especially if you don’t have a degree.
Deep mathematical knowledge is needed to become a good data scientist.
Although some accept this fact, others still think that mathematical knowledge is not required since most statistical analyses are mostly carried out via programming.
Linear algebra, including multivariate calculus. You can learn Linear Algebra for free at Khan Academy.
Regression, including the ability to handle both linear and nonlinear models appropriately. You can learn about Linear Regression at Coursera.
Probability theory, including Bayes’ Law and Central Limit Theorem. You can learn about probability and data at Coursera.
Numerical analysis, including time series analysis and forecasting. You can learn about time series forecasting at Udacity.
Core machine learning methods, including clustering, decision trees, and k-NN. You can learn about machine learning for free via Stanford University’s course on Coursera.
If you’re of the opinion that mathematics is not much an important skill for a data scientist, this might be for the fact you didn’t enjoy math in school.
However, in the world of data scientist, the data represents real world concepts, unlike the driven mathematical formulas.
2. Statistical Methods
A solid understanding of statistics is probably the most important skill for data scientists.
In fact, all the programming, math, and software skills in the world won’t help you if you don’t understand how to accurately and fairly analyse and report statistics.
For example, if you don’t understand when it is appropriate to report the median or mean for a given set of values, you can produce outlier-biased output and as such tell a misleading story.
Also, if you don’t understand confidence interval theory, proper sample size, and statistical significance, you could end up making definitive statements that should be the other way round.
All good data scientists differ in their chosen skills and technologies, but they all share a deep understanding of statistics.
To start, take a basic statistics course that focuses on calculating statistics manually or using statistical software such as IBM’s SPSS.
You must also learn to report statistics fairly, accurately, and clearly.
3. Programming Tools and Techniques
Programming helps data scientists in a number of ways.
They can write scripts to automate one of the most time-consuming tasks in data science: cleaning and preparing data for analysis.
They can also write scripts to transform data from one format to another. For example by transforming the result of an SQL query into a carefully formatted CSV report.
Or, conversely, by keeping the CSV data in a relational database.
In most cases, data analysis is done using specially designed libraries to eliminate most of the repetitive or complex calculations involved, such as pandas.
R is much more difficult to adapt to use cases outside of its core focus on statistics and visualization.
4. Machine Learning
Machine learning is the means by which computers can learn (and improve) tasks without being explicitly programmed.
In the world of data science, machine learning is finding increasing application in the world of data science.
Machine learning techniques can be used to make data-driven decisions and predictions and have many applications in data science.
How to Become a Data Scientist without a Degree – Improving Your Chances
Data science is the most researched field. Not until 2020, Data science has been the most sought field in America.
Since this is a recent field, there is a very low probability that applicants will have an actual data science degree.
So, to make up for that, here are five key steps that will allow you to become a data scientist without a degree.
The steps are:
1. Gain Necessary Prerequisite Knowledge
As earlier stated above, gaining the required skills especially within the prerequisite of data science is one of the easiest way to become a data scientist.
Data science has root in disciplines like Mathematics, Computer Science and Statistics.
So, start by gaining knowledge about mathematical concepts like calculus, linear algebra, probability, discrete math, programming, etc.
Also, you can learn Python and R, the two most popular languages in the field of Data Science.
2. Learn and Understand Data Science
The second most important step to become a data scientist is to learn and understand data science.
Understand what data science is, the role of a data scientist in real life situations.
Understanding this facts, will make your learning easy, because there are various components in data science like data extraction, cleaning, transformation, visualization, and prediction.
It is also important for you to know at least basic algorithms like linear regression, logistic regression, k-means clustering, etc.
3. Explore and Solve Real-Time Case Studies
Once you’ve gotten good knowledge about data science, the next step is to source for different case studies.
For example, explore how big enterprises are using data science to help them improve the organization and its profits.
Exploring and trying out numerous case studies will open up your understanding about data science.
It will help you in finding out problems to solve, and how to approach towards solving a particular problem.
When you’re done exploring, the next is to work on live projects.
You’ll gain hands on experience in solving real ife problems as it will improve your data science skills.
Because data science is a more practical field, you attain true knowledge by actually solving real problems.
4. Get Certified
Once you’ve solved a good number of real world problems, the next step is to get a certificate.
Having an official certification will show the world or your potential employees our skills in Data Science.
This means, a certificate will practically improve your chances of becoming a Data scientist.
Some of the companies that offer certifications in Data Science are Microsoft, Cloudera, SAS, etc.
Here is a list of some of these certifications –
SAS Certified Data Scientist
Cloudera Certified Associate: Spark and Hadoop Developer Certification
Microsoft Certified Azure Data Scientist Associate
5. Build a Portfolio
Creating a portfolio will reflects the work you’ve done in the field of data science.
While exploring real life case studies and solving live projects, you can enrich your portfolio through these various data science projects.
This can increase your presence on websites like Github, Linkedin, Kaggle, Tableau Public, etc., hence grabbing the attention of many job recruiters.
Depending on the type of work you want to do, you can create your portfolio in three different ways.
For a position that requires machine learning, you will need to have a portfolio that reflects projects that involve machine learning algorithms.
If the job role is data analysis, a data analysis portfolio can help you demonstrate your skills in transformation, cleaning, visualization, etc. of data.
Finally, if the job role is storytelling, building a storytelling portfolio, will help translates a business problem into data science?
6. Join a Community
Another way to become a data scientist without a degree is by joining data science community.
A lot of online platforms exist. Some of this platforms allow you participate in data science competitions.
Through these competitions, you can gain real life experience that you can include in your portfolio.
These community will expose you to your right employees.
Learning data science without a degree involves a bit of risk.
From the above mentioned, we went through six important step to take if you want to become a data scientist even without a degree.
Don’t be scared, just make sure you follow the steps above and you’ll be well on your way down a path many have already taken: getting into data science without a data science degree.