How To Learn Data Science [Beginner’s Guide]

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Do you want to learn data science but are confused about how to get started? If yes, you have landed on the right page as here we will explain how to learn data science from a beginner’s perspective.

These days, Data science is the number one choice for anyone who wants to have a lucrative career in IT. The field of Data Sciences has surpassed all others when it comes to scope and prominence.

Data that is being generated at a massive scale is now readily available. The best part is that with the help of Data Science, we can now process it, analyze it, and finally convert it into useful information that businesses can use for better decision-making.

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How to Learn Data Science [Beginner’s Guide]

A lot of people are coming forward these days to pursue Data Science as a career. However, as a result of so many people, the competition has increased significantly. As a result, you will need to be more cautious about the different things you will now need to prepare in addition to your ordinary data science expertise and understanding.

The good thing is, nowadays, knowledge is abundant, and most of the time, it is also – free. As a newcomer, things may appear daunting at first, but you will ultimately become accustomed to the subject and its volatility. So let’s read on further to find out how to learn Data Science.

1. Determine what you’ll need to learn

It’s easier said than done to become a data scientist. To become a data scientist, you’ll need to acquire a wide range of skills. But first and foremost, you must learn these three technical skills that are incredibly essential –

  • A programming language like Python- If this is your first time learning a programming language, we strongly advise you to begin with Python.
  • SQL- For handling and manipulating data.
  • Machine learning and statistics fundamentals- We would recommend brushing up on your statistics principles and learn a little machine learning to do well and understand how the data ecosystem works.

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2. Peer group learning

It’s incredible how much you can learn from collaborating with others. In the field of data science, teamwork is also very crucial in the workplace.

Data scientists are frequently part of a team, and lone data scientists at smaller businesses often collaborate with other departments to tackle specific problems.

It’s not uncommon for a data scientist to shift from team to team as they work on data queries for various departments inside the organization. Therefore collaboration skills may be more crucial for data scientists than practically anybody else.

Here are some suggestions:

  • ‘Meetups’ can help you find collaborators.
  • Make contributions to open-source software programs.
  • Message reputed data analysis bloggers to see if you can work together.
  • See if you can find a buddy on ‘Kaggle,’ a machine learning competition platform.

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3. Continue to learn and practice

Find “the thing” that pushes you to put what you’ve learned into practice and to continue learning. Personal data science projects, Kaggle competitions, online courses, reading books, reading blogs, visiting meetings or conferences, and so on are all possibilities.

Kaggle tournaments are an excellent way to get some data science practice without having to come up with your problem. Don’t be concerned about your ranking; instead, concentrate on learning something new with each competition.

You can experience collaborating with others by contributing to open source projects. You should share your data science projects on GitHub and add write-ups. If you enjoy email newsletters, you can subscribe to Data Elixir, Data Science Weekly, Python Weekly, and PyCoder’s Weekly.

PyCon US is a must-attend for everyone interested in learning more about the Python community. You should also consider attending SciPy and the nearby PyData conference if you’re a data scientist or are striving to be one.

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4. Learn how to communicate insights

Data scientists are frequently required to present their findings to others. This can be the difference between being a good data scientist and becoming a great one.

In most cases, data analysis is only valuable for a business context if you can persuade others in your firm to act on what you’ve discovered, which requires learning to communicate data.

Understanding the topic and theory is an integral part of expressing insights; you’ll never be able to explain something to others if you don’t comprehend it yourself. Another aspect is knowing how to organize your results logically.

Hence, the ability to adequately describe your analysis is a crucial component. It’s challenging to master the art of enough expressing complex topics, but here are some strategies to try:

  • Make an effort to speak out during meetups.
  • Create a blog. Please share the findings of your data analysis. Alternatively, submit a pitch and contribute to Dataquest’s blog.
  • All of your analyses should be stored and shared on GitHub.
  • Try to educate data science principles to your less tech-savvy relatives and family. It’s remarkable how teaching can aid you with your comprehension skills.
  • Participate in online communities such as Quora, Dataquest, and the Machine Learning subreddit.

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5. Enhancing problem solving and critical thinking skills

Developing technical abilities is essential if you want to work as a data scientist in the future. However, creating your critical thinking and problem-solving skills is just as important as enhancing your technical skills.

When working with data sets where even a single decimal position can significantly influence, you must be alert, swift, and sensible. We are not expecting you to turn on a switch because these abilities take time to develop, but you may start working on them while learning technical skills.

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Conclusion

For people wondering how to learn data science, the above article is a set of recommendations to follow when starting your journey in this field. If you are a fresher in this field, your data science adventure is just getting started. In data science, there is so much to learn that mastering it would take a lifetime. Remember, you do not have to know everything to start; all you have to do is start.