Become Data Scientist without a Degree – 7 steps to build your Career
How to become a data scientist? The hottest topic on every platform like Quora, Reddit, Google, Medium, etc. And for this question, there are amazing answers also. But talking about “Become a Data Scientist Without a Degree” – is it possible? So, the answer is YES! Now you can be a data scientist without a formal degree. You just need to follow this DataFlair’s guide, only 5 steps towards your data scientist journey without a master’s degree.
How to Become a Data Scientist Without a Degree?
Data Science is the most sought after field. Since it is a recent field, there is a very low probability of candidates having an actual degree in Data Science. Therefore, in order to compensate for that, below are five key steps that will allow you to become a data scientist without any degree. Step are –
- Gain Necessary Prerequisite Knowledge
- Learn Data Science
- Explore real-time case studies
- Work on live projects
- Get Certified
- Build Portfolio
- Participate in Hackathons
1. Gain Necessary Prerequisite Knowledge
Data Science is a vast field that stems from multiple disciplines of Mathematics, Computer Science and Statistics. There are various books through which you can acquire the knowledge of these subjects. Some of the important concepts of statistics that are useful for Data Science can be learned from DataFlair’s detailed guide on Statistics for Data Science.
Furthermore, you can gain knowledge about mathematical concepts like calculus, linear algebra, probability, discrete math etc. For attaining the knowledge of the basic concepts of Computer Science, you can learn Python and R, the two most popular languages in the field of Data Science.
2. Learn Data Science
Now, the second important step for becoming a data scientist is to start learning data science. What essentially is Data Science? There are various components in Data Science like data extraction, data transformation, cleaning, visualization, and prediction. Each of these components requires a separate mastery. Another important aspect of data science is storytelling. In order to acquire these skills, you must be well versed with various tools. For example, for visualization you must know tools like matplotlib, seaborn, ggplot2 etc.
You can master all these concepts for FREE at DataFlair’s Data Science Tutorials Library. Bookmark the link now and thank me later.
Another important aspect of Data Science is Machine Learning. While there are several blackbox tools like scikit-learn and TensorFlow that allow you to implement machine learning algorithms through condensed functions, it is important for you to at least know basic algorithms like linear regression, logistic regression, k-means clustering, etc.
3. Explore real-time Case Studies
Once you have got a good grasp on Data science and the various tools used in Data Science process, you should research and read about different case studies of how big enterprises are using data science to help them improve the organization and its profits.
Exploring more case studies will help you in finding out problems to solve, and how to approach towards solving a particular problem.
DataFlair has numerous Data Science Case Studies for you. Read them and grasp a good knowledge of this field.
4. Work on live projects
Data science is more of a practical field, in which to attain the true knowledge you have to actually solve real problems by working on live projects. You will get hands-on experience in solving real-world problems and this will improve your Data Science skills. Getting data science job as a fresher can be tedious, so make sure you work on good live projects and enhance your skills.
Now, the question is where to practice these live projects? So, the answer is DataFlair. We have published a series of Free Data Science Projects with Source Code for beginners and experience both. Start working on it and don’t forget to share your experience with us.
5. Get Certified
This step is optional, but getting a certificate will only improve your chances of becoming a Data scientist. An official certification will showcase your skills in Data Science that you have implemented. 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
6. Build a Portfolio
Your portfolio reflects your work that is performed in the field of Data Science. You can enrich your portfolio through several Data Science projects. Through building your presence on websites like Github, Linkedin, Kaggle, Tableau Public, etc. you can draw attention of many job recruiters.
You can craft your portfolio based on the type of job. For example, a job role demanding machine learning will require you to have a portfolio that reflects projects involving machine learning algorithms. Another type of portfolio is the data analysis portfolio through which you can demonstrate data transformation, cleaning, visualization, etc. The third type of portfolio is storytelling portfolio which is a comprehensive project that translates a business problem into data science.
DataFlair is always ready to help you. For those who searching how to build a data science portfolio, we have recently added an article on our list. Check this – Guide to build a Data Science Portfolio.
7. Participate in Hackathons
The best way to learn Data Science is by doing it. There are various online platforms like Kaggle that allow active participation in data science competitions. Through these competitions, you can gain experience that will be appended to your resume and it will augment your portfolio. Through intensive data cleaning, transformation, analysis, visualization, you can have an in-depth idea of implementing data science in real-life scenarios.
You can build your expertise by gaining experience through solving data science problems of varying degrees.
We went through various steps like gaining pre-requisite knowledge, learning key data science skills, earning certificates, building a portfolio and participating in various competitions. Through these steps, one can become a data scientist even without a degree.
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