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A Complete Guide to Building Your Career as a Data Scientist

We always wish to have someone, a mentor of sorts, who can guide us to start our professional careers. So, today, Infosectrain is here to help you towards your Data Science career. In this article, you will get some highly essential tips to start your career as a Data Scientist.

A Complete Guide to Building Your Career as a Data Scientist

Before going into the career guide, let me tell you the common responsibilities of a Data Scientist. Usually, Data Scientists work with the business stakeholders and determine how the data can be used to achieve their business goals. They are the ones who design the data modeling process and generate predictive models and algorithms to extract the required data. It’s their responsibility to analyze the data and share insights with fellow team members.

If you are ready to become a Data Scientist, here are some essential tips that will prepare you to start your career.

Choose your role: There are many roles a Data Scientist will fit into, for example, machine learning expert, data visualization expert, or data engineer. So you have to choose which role you would like to work in. Because there may be a few roles that don’t suit your background. For example, if you are a software engineer, a role like data engineer doesn’t suit you. So, make sure you choose a role that suits your background.

Consider taking the course: After deciding on the role you are willing to work in, it is essential to develop the skills required for that role. For example, suppose you are eager to become a machine learning expert. In that case, it is essential to develop your skills in applied mathematics, neural networks, natural language processing, data modeling and evaluation, programming, and computer science fundamentals. To sharpen the required skills, try to take up a course at InfosecTrain to get complete information and in-depth exploration of these topics.

Learn it practically: In many cases, when we are taking training, we will get theoretical knowledge. It is a good way to study, but getting hands-on training or practically learning something is always better. In this way, we remember things because we have done them by ourselves. Let us say you are learning to create a code for “palindrome.” You will understand it if someone teaches you by showing the code on a screen, but you will remember or understand it better if you are trained to write the code yourself. So try to learn things practically.

Get a good grasp on the programming language: A Data Scientist needs two essential skills. The first one is to have good math skills, and the second one is to have good programming skills, and having good programming skills doesn’t mean you must know every programming language. It means that you have to pick one programming language and stick to it. Here are some programming languages that you can choose to become a Data Scientist.

 

R: R is an open-source programming language primarily used for complex statistical and mathematical calculations. The R programming language is also used for data visualizations.

Python: The Python scripting language comes with libraries for manipulating, filtering, and transforming big data and unstructured data. Python is used in web development, deep learning, software development, and machine learning. Among data scientists, it is the most commonly used programming language.

SQL: Structured Query Language is mainly used as a tool by which Data Scientists can query for and merge data across various databases and tables.

SAS: Due to its cost, SAS is not recommended for individual users. Large corporations use SAS for statistical analysis, business intelligence, and predictive analytics. Once you have learned the other languages, you will easily be able to learn SAS on the job.

Do internships: Even if you are perfect at the required skills, it is vital to understand how your job will be, the stress you will have to take, and the time you have to spend to finish a particular task. And learning these is only possible by doing internships. One of the advantages of doing an internship is that you indirectly tell your employer that you are already aware of this game and can meet their expectations. By doing internships, you will stand out in the recruiter’s eyes.

Expand your network: Whatever the field you are in, expanding your network can help you very much, from increasing your knowledge to giving you job recommendations. So, try to make friends with fellow data scientists. to improve your knowledge and learn new things in data science.

Seven steps for becoming a Data Scientist

Tip 1: Find the position you’re looking for.

Tip 2: Getting a job entails coping with rejection. PERSISTENCE IS ESSENTIAL.

Tip 3: Research Statistics, Machine Learning, SQL, and Python.

Tip 4: Go above and above if you want the job.

Tip 5: Research the company’s culture, personnel, and business methods.

Tip 6: Negotiate and use leverage.

Tip 7: Select the position that is most suited to YOU.

Data Science

Final words:

Data science is a booming field that is not only helping organizations to understand their markets and make the correct decisions but also helping organizations understand their customers in a better manner. Data Scientists are the ones who are responsible for analyzing, cleaning, collecting, and organizing the data. So the jobs in the field of data science are rapidly growing in this era. No matter how small and big, each and every organization is looking for employees who can understand and analyze their data. So check out InfosecTrain for data science training.

AUTHOR
Yamuna Karumuri ( )
Content Writer
Yamuna Karumuri is a B.tech graduate in computer science. She likes to learn new things and enjoys spreading her knowledge through blogs. She is currently working as a content writer with Infosec Train.
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