How to survive a month as a beginner in Data Science in 5 steps?

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It’s great that you have worked hard and finally landed up in your dream or your first data science job! But, this could be a little abstract and mysterious too. I know it requires a lot of effort to study and get a job of your choice if you were not from a technical background. I have listed a few things that will help you survive the initial period of your data science job. You have to let people know about your talent and the skill you are bringing to the team. Your teammates might not immediately realize your importance as they might be used to follow their existing process. This is not to criticize you, but it’s quite natural to witness. Here, along with the five steps, we will provide a bonus step, hence ensuring that you read this blog until the end.

Step 1: Understanding the work and day to day activities:

Start with understanding your day-to-day activities and trying to get comfortable with them as quickly as you can. Once you can understand what exactly is going around you, then you can improvise. It is not wise to directly jump onto working and prove to your boss that you are the best one around. Often, this backfires as there will be a lot of silly mistakes that you ignore. Hence, when one writes down their day-to-day tasks and sub-tasks, it is less likely that they will miss a small step.

Step 2: Come up with a plan :

Come up with a plan, strategize your day properly. That should be educating your team/company about your next step. What exactly are you trying to do? Educate them about that. Probably, dropping an email would be a good idea. Most might argue that this is similar to the first step. However, the difference is that here, you are vocal about your ideas to your team. You can commit yourself to write an email and inviting people to get in touch with you if they have anything to share or any questions. If you feel uncomfortable doing this, you can reach out to your lead or buddy and seek help to initiate this.

Step 3: Grow within your organization:

Next, as soon as you have established a connection with your team and company, you can expand your network. For instance, you may try connecting with more people on one-to-one chats. It helps you explore more about the work, but it also opens a spectrum of knowledge that might be helpful in the future. You can go for a floor walk and can explore the different ideas or approaches that you can imply as per your interactions. Also, it will help you understand how the day of the other person looks like. In addition to that, it can help you making or altering a few changes to your approach accordingly.

Step 4: Know your sources:

There are various sources from which one can improve their skills. Some of those sources can be used to help your current work, and some of them can be used for practising your skills. Some sites help you to practice your skills and sharpen your data science abilities. Some of the sources are Kaggle, GitHub, HackerRank, which are exceptional places to learn and sharpen your skills. Sharpening your skills will allow you to update yourself about the daily changes in technology and changes in the processes that might be implemented in your organization.

Step 5 : Presentation:

Once you feel confident, presenting your idea to the team via presentation will be a good idea. This will provide you with a base and will also help to establish a base. Try to make the talk informative and relevant to the audience based on your research. It must also respond to the need for the project. In addition to that, it will be a good stage to demonstrate new ideas or any upcoming process changes. Be very mindful of not overselling or a lot of hype about data science or your talent. With time, you can implement your learnings on your work data sets and later let the team know what you did and how it can benefit the team if they start following it.

Bonus Step:

Nowadays, some students are recruited with their experience in computer science and asked to work on data science projects. This is a common misconception. It is not necessary to be a computer science student to become a data scientist. Most of the data scientist jobs involve mathematics, and that is the fundamental of the subject. Computer knowledge only acts as a medium of communicating to the computer for implementing the data science methods. If you are new to this field, it is advised to complete a certification. Ivy Professional School is a pioneer in data science and has committed itself to provide knowledge and certifications to its students.

Learn with Ivy:

Ivy Professional School’s collaboration with NASSCOM will unlock many opportunities for data science aspirants and help them towards an excellent career. The program endorsed by NASSCOM under this collaboration is Data Science, Machine Learning, Artificial Intelligence, and Deep Learning Certification Course. It includes high-demand tools like Python, R, Tableau, SQL, Adv Excel, VBA, etc., and you can apply here. Ivy Professional School has been consistently ranked among the top data science and analytics course institutes since 2008. Ivy has a pool of faculties who are industry experts with 10+ years of industry experience. It aims to provide specific learning experiences that organizations are looking for in their prospective data science candidates. If you are looking to get certified in Data Science and get recognized with your skills, we recommend choosing from our range of Data Science Certifications. You can also get in touch with us at +91-7676882222.

Following these will help you survive your initial days in your first data science job. You will gain confidence to come up with an improved version of yourself as a data scientist. It will also help your team to know you well and also to understand your importance.

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