8 tips before Starting a Career in Data Science

29Jul - by aiuniverse - 0 - In Data Science

Source: newcivilengineer.com

Data Science can look like an intimidating field to some, especially if you have just started your journey. What tool to learn? What techniques to focus on? Do you learn to code? How much statistics is required? There are several such questions that you will find answers during your journey. Here are some tips that will help you start a career in the field of Data Science:  

1. Select the right role 

In the data science industry, there are several varied roles. You can be a data engineer, a data scientist, a machine learning expert, a data visualization expert, etc. These are just a few roles that you can go into. Depending on your work experience and background, getting into a certain role can be easier than others. For example, for a software developer, shifting into data engineering is not that difficult. However, if you are not clear regarding the path you should be taking, you will be confused. If you are unclear about the differences between the roles and want to figure out what to do, follow the below-mentioned steps: 

  • Talk to the people already working in the field of data science to determine what roles to entail. 
  • You can also take mentorship from people. Take a small amount of time and ask the questions. 
  • Figure out the area that you are interested in and select the role that is best suited for your field of study. 

An important tip that you should keep in mind while deciding your role is that you should not jump to a role. You must understand the requirements of the field and then prepare for it. 

2. Take up a training program and finish it 

Once you have decided the role for you, the next thing that you have to do is make efforts to understand the role. This includes more than just going through the requirements. There are several courses available for data scientists. Finding the resources to learn is not difficult. However, learning them is a different story. 

If you decide to take up the training program, make sure that you go through it actively. You have to follow the coursework, discussions, and assignments that happen throughout the course. For example, if you want to be a data scientist, you have to take up a training program for data science certification . For the machine learning engineering role, you should enroll in a machine learning training program. You have to follow the course materials diligently. This also means that the assignments in the course are as important as going through the lectures. When you complete a course from one end to another, you will get a clearer picture of the field. 
 

3. Choosing your tools and language 

As mentioned above, it is important for you to complete the course from end-to-end of the topic that you are pursuing. However, a common question that students face is on selecting the tools and language. The straight answer is that you have to select mainstream tools and languages and start your journey. Remember that tools are just a method of implementation. What is more important is understanding the concept.  

However, if you are still not sure about what to use, you can start with the simplest language or the one you are familiar with. However, if you are not well-versed with coding, you can use GUI tools. As you cover the concepts, you can start coding as well.  

4. Join the peer group 

Now that you are aware of the role that you want and are prepared for it, the next important thing to do is join a peer group. This step is important as it helps in keeping you motivated. When you take on a new field, the whole process can be daunting if you do it alone. But when you have friends or colleagues beside you, the task can get a little easier. You can either join a group or connect with people online. Even if you don’t want to join a group, you can have a meaningful discussion over the internet. There are several online forums that can provide you with this form of environment. 
 

5. Focus on practical applications as well as the theory 

While you are undergoing training, it is important that you pay attention to the practical applications of all the concepts. This will help you in understanding the concept better and have a deeper sense of how you can apply these concepts in reality. Here are a few tips that you can follow: 

  • Make sure that you complete all the assignments and exercises for understanding the applications better. 
  • Work on data sets to apply your learning. Even if you don’t have any idea of the math behind a technique, you can understand what it does, its assumptions, and interpretation of the results. At a later stage, you can develop a better understanding.  
  • Take a look at the solutions created by the people who are working in the field.  

6. Follow the right resources 

If you want to learn Data Science, you must engulf every source of knowledge that you can find. Some of the most influential data scientists have blogs where you can find several useful sources of information. All of these data scientists are active and update the followers regarding their findings. They also post frequently about Data Science’s recent advancements.  
 

You have to make a habit of reading about data science every day and stay updated with the recent findings. However, you also have to make sure that you don’t follow incorrect practices. 

7. Improve your communication skills 

Communication skills are an important part of the data scientist role. Even if you are technically profound, it is possible that you get rejected because of your poor communication skills. What you can do is take the help of a friend who has good communication skills and ask them for honest feedback. You need this skill for sharing your idea or proving your point. 

8. Network  

When you have just started studying, it is better to focus on learning and not do too many things at the initial stage. Only after you have a hang of the field should you attend industry conferences and events. You can also participate in hackathons and meetups.  

Data Science is in huge demand right now. And this is the reason why employers have been investing significant time, effort, and money. If you take the right steps, it can lead to significant growth.  

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