Follow these steps to become a data scientist without a degree


by Sumana Bhattacharya

September 30, 2021

Is it possible to work as a data scientist without a formal qualification? “Yes” is the answer. Without a formal qualification, you can work as a data scientist. The most requested area is data science. Because data science is such a new field, there is very little chance that applicants will have a degree in this field. To compensate for this, you need to follow the steps outlined below, which will allow you to work as a data scientist without a degree.

Acquire the prior knowledge

Data science is a broad term that encompasses a variety of fields, including math, computer science, and statistics. There are a variety of books available to help you learn more about these topics. You can also learn about mathematical principles such as calculus, linear algebra, probability, and discrete mathematics. If you want to learn data science, you need to understand math and statistics first. You can study Python and R, the two most important languages ​​in data science, to gain a fundamental understanding of the principles of computing.

Learn what data science is

The second crucial step in becoming a data scientist is to begin to understand the subject. What exactly is data science? Data science has several components, including data mining, data processing, cleansing, visualization, and prediction. Each of these elements requires a distinct skill. Storytelling is another essential component of data science. You must be familiar with a variety of tools to learn these abilities. For example, to visualize data, you will need to be familiar with programs such as matplotlib, seaborn, and ggplot2. Machine learning is another essential part of data science. While there are many Blackbox tools, such as sci-kit-learn and TensorFlow, that allow you to build machine learning algorithms using condensed functions, you should at least be familiar with fundamental techniques such as that linear regression, logistic regression and k- stands for grouping.

Gain a basic understanding of Big Data and database management

Accessing, collecting, cleaning, discussing and storing structured and unstructured data is an important part of a data scientist’s job description. Knowing how to use relational databases like MySQL or MongoDB is essential. Mastering big data storage and processing platforms like Hadoop or Spark can also help you advance in your data science profession.

Learn programming while studying data science

Aspirants often think that a data scientist only works with data and therefore does not code. It’s a misunderstanding. Data scientists are as much responsible for developing algorithms as they are for deploying code in production. To do this, they will need to know one or more programming languages. Python is a popular programming language for cleaning, manipulating, and visualizing data. Many data scientists also use R for statistical inference and analysis, SQL for database activities, Perl for text manipulation and structure, and Scala for data ingestion, storage, and processing. big data.

Practice data visualization

Data scientists also pass their findings on to business leaders and technically unqualified stakeholders. They can understand the meaning and message of data through data visualization without needing to master the underlying processes. As a data scientist, you will need to know how to create simple visualizations based on Excel and PowerPoint, as well as how to create dynamic visualizations using Python, R and other programming languages, and how to create dashboards. using Tableau or PowerBI.

Investigate ongoing case studies

Once you have a basic understanding of data science and the many tools used in the data science process, you need to examine and discover many case studies of large companies using data science to help them improve. their operations and profitability. Exploring more case studies can help you identify challenges and determine how to tackle each problem.

Work on real world projects

Data science is a more hands-on discipline where you have to tackle real issues while working on real projects to gain meaningful expertise. You will gain hands-on experience by taking on real-world challenges, which will help you improve your data science skills. As a youngster, finding a job in data science can be difficult, so be sure to work on solid, concrete projects and keep improving your abilities.

Take part in hackathons

Several online platforms allow active participation in data science competitions. You can gain experience that will be added to your CV and portfolio by participating in these contests. You can gain a good understanding of using data science in real contexts by doing a lot of data cleansing, transformation, analysis, and visualization. You can develop your skills by tackling a variety of data science issues and gaining experience.

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