This article will show you How To Become A Data Scientist Without A College Degree. With an average annual income of $122,657, data science is the most appealing career in the world today. Many data scientists are self-taught because most computer science degrees do not offer data science as a major.
So, if you believe it’s difficult to work as a data scientist without a bachelor’s degree, think again. I’ve been there, and I can assure you that becoming a data scientist without a formal degree is feasible.
Are you ready to start a Data Science career with UDEMY? You can get started TODAY for only $9.99 (a 95% discount).
We’ll look at how to become a data scientist without a college diploma in this post.
Along the way, we’ll look at what it’s like to work as a data scientist, the skills needed, and how to get them. There’s this wonderful article on How To Learn Artificial Intelligence for Free [7 Steps] I previously wrote.
So let’s get this party started. Let’s take a look at what data scientists do first.
What Does a Data Scientist Do?
You must be ready to address issues as a data scientist.
But, most importantly, you must know how to ask the appropriate questions and respond to them with facts.
You will also get the opportunity to work on a variety of challenges, including:
- Organizing consumers into useful groups
- Detecting data irregularities
- Making projections for revenue
- Predicting the likelihood of a client cancelling a subscription
- Using data to identify risk categories for kids with poor socioeconomic status
- Making judgments about how to market items more successfully using data, models, and analytics
As a data scientist, you will be faced with a variety of duties.
Apart from that, data scientists employ mathematics, computer tools, and statistical approaches to extract information from data.
Are you still up for the challenge?
Let’s look at the 7 stages to becoming a data scientist without a degree today.
1. Acquiring the Required Knowledge
We must all acknowledge that data science is a broad area that encompasses various disciplines such as statistics, mathematics, and computer science.
But, if you’re wondering where you may discover these criteria, go no further.
Breaking news! Some of the most significant statistics for data science may be found in DataFlair’s comprehensive reference on statistics for data scientists.
Khan Academy also has resources for learning multivariable calculus, differential equations, and linear algebra.
Furthermore, you can study Python or R programming, which are the most popular languages in the field of data science, to gain a fundamental understanding of computer science. You may also read through the 10 Best Reasons to Learn Python Programming Language Today.
If you really want to learn Python quickly, you should also read my other article on the best Python courses for mastering Python.
2. Learning Data Science
You can’t be a data scientist if you don’t know what data science is. As a result, this step is critical.
It may also prompt the enquiry, “What is data science?”
Data science is the process of extracting information and insights from large amounts of unstructured and organised data.
With that out of the way, let’s look at some of the components of data science:
- Extraction of data
- Transformation of data
- Prediction
- Cleaning
- Visualization
It’s important to notice that each of these elements necessitates a distinct level of skill.
To gain these abilities, you must be well-versed in a variety of tools.
For example, to visualise data, you’ll need to be familiar with programmes like seaborn, ggplot2, and others.
However, you may learn data science through a variety of online sites that provide data science courses.
If you’re interested in learning more about data science, check out my previous article where I examined some of Udemy and Pluralsight’s finest data science courses.
3. Exploring Real-time Case Studies
So, now that you have a basic understanding of data science and the numerous tools utilised in the data science process, it’s time to look into and learn about various case studies.
These case studies will show you how major businesses are utilising data science to enhance their operations while also making money.
However, by looking at additional case studies, you may improve your ability to identify problems to solve and how to approach addressing them.
I’m not sure where I’m going to acquire these case studies.
Nobody likes to tell you this, but DataFlair contains a tonne of data science case studies for you.
Now read them and have a thorough understanding of the subject.
4. Working on live projects
The fascinating part about data science is that it is more of a practical area in which you must address actual problems by working on real projects if you want to get meaningful expertise.
Because you’re working on real-world challenges, working on live projects gives you hands-on experience that will help you develop your data science abilities.
As a fresher, you should constantly remember that improving your abilities is the KEY to landing a solid data science job.
I’m sure you’re wondering where you may learn how to practise these live projects. Right? You only need to go to DataFlair. That’s right, you read that correctly.
Review this similar post: 10 Free Java Projects For Beginners with Video Tutorials on Coding.
5. Getting A Certified
Okay, I’ll say it: I’m a snob.
Obtaining a certification is an optional step, but it will enhance your opportunities of becoming a data scientist.
Consider it this way: a formal certification demonstrates the data science abilities you’ve acquired.
Microsoft, Cloudera, SAS, and more firms now provide data science credentials.
Here’s a rundown of some of the certifications you might earn:
- SAS Data Scientist Certification
- Certification as a Cloudera Certified Associate: Spark and Hadoop Developer
- Associate in Azure Data Science Certification from Microsoft
These credentials will also improve your chances of landing a good job soon.
TIP: Best Coursera Certifications for Cloud Computing using AWS, GCP, and Azure.
6. Building A Portfolio
As you may be aware, putting up a portfolio represents the work you’ve done in the field of data science.
You will stand out as an excellent data scientist if you have a strong portfolio.
You may now diversify your portfolio with a variety of data science projects by establishing online profiles on sites such as:
- Github
- Tableau Public
- Kaggle
This is the truth: by doing so, you’re really attracting the attention of a lot of employment recruiters.
So, in essence, you may construct a portfolio based on the employment function.
A position that requires data analysis, for example, will require you to have a portfolio that includes data analysis tasks.
7. Participating In Hackathons
Because doing is the best way to learn data science, there are a variety of online platforms, such as Kaggle, that allow active involvement in data science contests.
Furthermore, you will earn experiences that will be added to your CV and portfolio as a result of participating in these events.
As a result of the extensive data:
- Cleaning
- Transformation
- Analysis
- Visualization
You can have a better understanding of how to apply data science in real-world settings.
It’s also worth noting that you may improve your data science skills by acquiring experience by addressing a variety of data science challenges.
As a result, you should always take part in hackathons. If you’re wondering where you may find these platforms, look no further. Don’t worry about it.
On Analytics Vidhya, you may participate in some of these hackathons.
Let’s look at what you need to know as a data scientist now that we’ve looked at the stages to become a data scientist without a degree.
The Skills Data Scientist Need
1. Mathematics
To begin with, the degree of mathematical expertise necessary to be a successful data scientist is a fiercely disputed topic, with some claiming that profound mathematical understanding is essential.
Others say that math expertise is less necessary than you may think because most statistical studies are performed using computer tools like Numpy.
So, for the most basic mathematical principles, go to DataScienceWeekly.
2. Programming Tools And Techniques
Data scientists benefit from programming skills in a number of ways.
For starters, having a programming language allows you to build scripts to automate one of data science’s most time-consuming activities.
In addition, you may use scripts to convert data from one format to another. You could, for example, convert data from a SQL query into a nicely organised CSV file and store it in a relational database.
3. Machine learning
You would evaluate the available data regarding previous fraudulent transactions and seek for trends as a Data Scientist working without machine learning.
For example, you may group the data and discover that transactions coming from a given geographic region, purchasing items from a specific category, and/or using a specific payment method are all extremely likely to be fraudulent.
Your Software Engineering team would next most likely create a mechanism that flags transactions like these for manual inspection.
However, by combining records of both fraudulent and non-fraudulent transactions as training data, you might apply machine learning to solve the same problem.
So, which one do you think you’d prefer? Learn more.
Is it better to do things the easy way with machine learning or the hard way without it?
4. SQL
The majority of data is kept in relational databases, as you may well know. Read this article; Generating Windows Server and SQL Server on Amazon EC2 estimates.
To deal with data, you must be able to query the database and retrieve the information you want. As a data scientist, this implies that knowing the foundations of SQL is critical. Lastly, these are the 15 Best SQL Courses and Certifications this year.
5. Statistical Methods
The most crucial ability for a data scientist is a solid knowledge of statistics.
To put it another way, you can have all the programming, math, and software abilities you want. It won’t help you if you don’t know how to correctly and fairly evaluate and report statistics.
For example, if you don’t know when to report the median or mean for a particular collection of numbers, you could end up with an output that is imbalanced by outliers.
So, now that you know how to become a data scientist and have the necessary abilities, let’s get started.
How do you land your first data scientist job?
Getting Your First Job As Data Scientist
According to my study, there are a few criteria that determine whether or not you will be considered for an entry-level data scientist position at a firm.
These variables are generally determined by your education and experience.
Many businesses consider experience to be the most important element, but because you’re reading about entering into the sector, I’ll presume you don’t have any.
Education isn’t a huge concern since you can always fill in the gaps by demonstrating the breadth of your abilities.
Keep in mind that some employers will refuse to hire someone without a tertiary education.
It’s worth noting that some employers may state in their job advertisements that only qualified individuals will be considered.
Conclusion
Following the different steps, such as:
- Acquiring the necessary knowledge
- Developing critical data science abilities
- Certificates of achievement
- Putting together a portfolio
- Taking part in a variety of competitions
Even if you don’t return to school, you will undoubtedly become a data scientist.
However, keep in mind that while some firms may advertise that they only hire individuals with degrees, they may become interested in you once they see your portfolio.
What exactly do I mean?
Okay, I mean that you should participate in hackathons to develop a strong portfolio.
This will help you develop your skills by allowing you to get experience by addressing a variety of data science challenges.
Did you find my advice on becoming a data scientist without a college certification helpful? If yes, then see the 7 Steps To Get Started in Machine Learning & Data Science. Cheers.
