This is the only way to become a data scientist without any experience
by Apoorva Komarraju
April 29, 2021
Progress to the best data scientist positions by following these steps.
A data scientist collects and cleans large amounts of data, manages dashboards, interprets data to solve problems, performs experiments, creates algorithms, and presents visualized data to stakeholders. If all this interests you, here are some news for you, you can become a data scientist without experience.
You don’t need advanced degrees to become a data scientist
Although most of the job postings you will come across mention a master’s or doctorate degree. in engineering, computer science, math or statistics, it is possible to land a job without all of these. There are many online courses and certification programs that can give you the knowledge.
Step 1: improve your math skills
If you have a quantitative background, switching from your old job to data science should be easy. But before moving on to high-tech tools, it’s important to master the basics, like plotting data points on graphs and finding correlations. As a checklist, here are the things you should build a solid foundation on:
- Statistics and probabilities
- Multivariate calculation
- Linear algebra
- Hypothesis tests
- Descriptive statistics
- Regression analysis
- Markov chains
Step 2: Learn Important Programming Languages
To be a data scientist, it is important to know and master the necessary skills rather than getting a brilliant degree from a university. The interview process is skill-based and these are the languages you need to master:
- Python – Knowledge will help you filter and transfer large, unstructured data. Python can be used for web development, software development, deep learning, and machine learning.
- A – An open source programming language, R is useful for calculating complicated mathematical and statistical problems. It will also help with data visualization.
- SQL – This is a relationship management tool with which you can query and join data across multiple tables and databases.
- SAS – Large companies use this tool for statistical analysis, business intelligence and predictive analysis.
Step 3: Build your CV with internships
Companies are looking for people with practical experience. Once you have the basics, putting it into practice and dealing with job issues will strengthen your case and impress recruiters with real-time skills. These internships are easy to find because the criteria for internships start with an experience without a foundation.
Step 4: Start by being a data analyst
First, a data scientist and a data analyst are two different professions. Data analysts manage data collection and identify trends in data, while data scientists also interpret data using coding and mathematical modeling. Therefore, a data analyst role is the best way to get started in the field.
Step 5: Have a reason for your change
Data science is a booming field and many might have the idea to change due to lucrative positions. However, you must be able to explain your career transition. Mention your past roles in a way that highlights common aspects of the field. If you are a pro at using Microsoft Excel or have developed business, communication and collaboration skills, mention these skills and explain how you improved them to apply for this job.
With all of this in mind, you can become an inexperienced data scientist. Another important thing to keep in mind is to network with people who can influence your position in this field. The more you network, the more opportunities will knock on your door.
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