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Machines are Learning but Humans are not

You keep working hard and fixing goals to land that new Data Science job or completing a topic by this week. But you still fail in your Data Science Career path. Ever wondered why? Well here are a few reasons which might have led you to failure.

Not choosing the Right Data Science Career path

There are various diverse roles in the data science industry. Data Visualization Specialist, Machine Learning Specialist, Data Scientist, Data Engineer, Data Analyst, etc. are some of the many roles you can go for. Depending on your work experience, getting into one role might be preferable than getting in another. For example, if you are a software developer, switching to data engineering is not difficult for you. So, if you are not clear about what you want, you will be confused about the path you should take and the skills to improve.

What if you are not clear about the differences or do not know what you want? Some things we suggest:

  • Talk to people in the industry to understand the profile under each designation
  • Take proper mentoring from people working in industry – request them for their time and ask relevant questions. We know for a fact that no one refuses to help someone in need!
  • Identify what you want and what you are good at and choose a role that suits your objective.

Took a course but did not complete 

You have to make sure that you take a MOOC or join an accreditation program, which will take you through all the twists and turns. Free vs paid option is not an issue, the main goal is that the course will clear your basics and bring you to an appropriate level, from which you can move further to your data science career path.

When you take a course, actively go through it. Follow the course work, tasks, and all the discussions going on around the course. For example, if you want to become a machine learning engineer, you can take machine learning by Andrew Ng. ,of course there are pre-requisites. Now you have to carefully follow all the course contents provided in the course. It also means assignments in an important course as you go through the videos. Only following end-to-end of the course will give you a clear picture of the field.

You are not able to choose the right tool and stick with it

The question always remains, what language would be a good choice to start with?

There are various guidelines/discussions on the Internet to address this particular question.

The gist is to start with simple language or a language you are familiar with. If you are not familiar with coding, you should now prefer GUI based tools. Then when you master the concepts, you can get your hands on the coding part.

Focusing only on the theory

When taking courses and training, you need to focus on the practical applications of what you are learning. It not only helps you understand the concept but also gives you a deeper idea of ​​how it applies

Here are some tips you can do while following the course:

  • Make sure you have done all the exercises and tasks to understand the applications.
  • Work on some open data sets and apply your skills. Even if you do not understand the mathematics behind a technique at first, understand
  • what it does. In the next step, you can always develop a deeper understanding.
  • Examine the solutions of people who have worked in this field. They will be able to identify you faster with the right approach.

Not following the right resources 

To keep learning forever, you need to surround yourself with every source of knowledge you can find. The most useful source of this information is blogs run by very influential data scientists. These data scientists are active and keep followers updated on their findings and often post about recent progress in this field.

Read about data science every day and get in the habit of being updated with recent events. But with so many resources, you have to be sure that you are not following the wrong ones. So it is mandatory to follow the right resources.

Communication skills not taken seriously

People generally do not associate communication science skills with rejection in data science roles. If they are technically expert, they will ace the interview. This is a myth.

Give this activity a try; Listen to your introduction to your friend who has good communication skills and ask for an honest opinion. He will show you the mirror.

Communication skills are even more important when you are working in this field. To share your thoughts with a coworker or to prove yourself in a meeting, you need to know how to do it. So learn to communicate effectively.

Wasting more time in just networking and not learning

In the beginning, your whole focus should be on learning. Doing too much, in the beginning, will eventually lead you to give up.

Gradually, once you are confident in the field, you can attend industry events, conferences, and hackathons in your area – even if you only know a little. You do not know who, when, and where will help you in your data science career path.

Other mistakes to avoid are:

  • Giving up too easily.
  • Being lazy and procrastinating.
  • Not practicing enough.
  • Learning just for the sake of learning.
  • Being impatient in learning too much in a short time

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