Tips for People Planning to Get into Data Science

The decision to get into data science can be confusing and leave you overwhelmed. For instance, which tool or language should I start with? What are the necessary skills I should acquire?  Which course should I take? These are some questions that will brim your mind.
Given that data science is relatively new in the industry, it is easier to be confused, especially when different roles in the field have overlapping skills. So how do you decide a path to easily enter the field of data science.

Here’s a small guide to help you navigate the field of data science and get you ready for a data science career.

1. Choose a role:Data scientist/ Data Architect/ Data Engineer 
   Data science is a new field and various roles in this field tend to share a few skills. For instance, data scientists and data engineers both are required to have coding skills. However, data scientists are required to have stronger programming skills than data engineers.
   
   What does this mean for you? It is important for you to figure out which role suits you the best or fits your skill set. Of course, you will have to learn new skills but the role which is closest to your previous experience will do you far better than other. For instance, if you have software engineering experience, a data engineer role would fit you better. Here are ways in which you can decide a better role for yourself.
   
   – Speak to experienced data science professionals. Ask them to help you with some questions. Nobody would mind taking out a few minutes from their schedule to answer your questions.
   – Ask about different roles and responsibilities from people who are already in the field.
   – In the process of doing above, figure out what you would be comfortable at and go for it.
   
2. Do a data science certification
   Once you’ve decided the role you want to go for, take a data science course or a certification. A data science certification will prepare you for all the responsibilities that you will be required to undertake as part of your job. Focus on gaining practical skills and exposing yourself to challenges of the job by working on real industry projects. The more rigorously you work during your course the better you will be at your job, and faster you will be able to find opportunities. As data science is heavily application oriented, you should focus on application more than theory.
   
   – Do more case studies to understand your work and results expected out of you. A data science certification will add valuable credential to your experience, helping employers know your worth.
   
   3. Choose a language/tool
   
   Currently several languages and tools are used in data science. The choice of language and tools varies across organizations depending on their requirements. You should go with the most widely used language as it will give you more opportunities. Plus, a bigger community related to the tools and language will give you more exposure and the ease to understand and learn because of its large community. Once you have developed a good understanding of a particular language, move to other niche languages and tools to broaden your skills and scope of exposure.
   
   4. Focus on practical applications
   This point can’t be emphasized enough.As mentioned above, data science is heavily application oriented. As a data science professional, you are expected to deliver solutions that can produce tangible result. Unless you focus on practical applications, it would be difficult to bring value. So you should focus on doing more practical work.

Here are a few ways you can get more hands-on experience for any data science role —

1. As part of your data science course certification, do all the exercises and assignments to understand applications.

2. Several datasets are available on the internet as well as on platforms like Kaggle, GitHub, etc. Work on a few data sets and implement them in a real world scenario.

Data science requires knowledge of some complex mathematical and statistical techniques. It’s fine if you don’t understand it at first. Try and learn the assumptions, later you can get better understanding of techniques.

5. Focus on communication skills

Communication skills is one of the most important skills for data science professionals especially data scientists as they are required to communicate their ideas clearly. Try to develop a fluency and impeccable skills. As you pursue your course or data science certification, try to communicate your ideas and be as articulate as possible. This will help you develop impeccable communication skill necessary for the job.

Final words

Data science is one of the most coveted fields in the industry. As with all-things excellent, this field requires practice to reach perfection. In case of data science, practice can take a long time and unimaginable level of hard work before you can put your skills to real use. So stay at it.

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