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How to become a data scientist

If you're looking to make a career in data science, you're in luck. It's one of the most exciting industries out there and has no shortage of job opportunities. We talked to data scientists to hear what they had to say, in terms of what to expect from their job on a daily basis and what job employers look for when hiring.

If you don't know where to start, here are some tips on how to begin your data science journey:

The regular day of a data scientist

As a data scientist, you will spend much of your time gathering and analyzing data. You'll work with a variety of tools to find patterns and trends in datasets, uncover insights that can be used to make decisions, create algorithms and models for forecasting outcomes, build visualizations that help others understand the information they're given, communicate recommendations to other teams as well as senior staff members.

In large, the work can be split into two – acquiring the required domain knowledge and getting valuable insights from your data.

Having domain knowledge is essential in truly understanding your data. General background knowledge of the field you’re working with whether say, finance or retail, and the specific sub-area your data is concerned is required so that you can know how to work with that data. For example, knowing how the data is collected will inform how you can process the data.

After processing, you can analyze your data and obtain insights from it. You can play and experiment with data to get the results you want.

The following list contains some of the tasks that data scientists are asked to complete:

  • Gathering extensive amounts of raw data from various sources

  • Cleaning and transforming data

  • Finding patterns within large amounts of data

  • Building algorithms that transform unstructured textual data into structured formats such as JSON or SQL databases

  • Creating data visualizations

Of course, insights aren’t helpful to your end user by themselves. With data storytelling and visualizations to aid the process, you can compress and simplify complex and technical work for the end user. Only then can the business make better data-driven decisions. So, whether it’s to increase your knowledge on a domain, work with your team or relay your findings to the end user, communication skills are important in helping you perform your job efficiently.

What you need to have Below is the necessary knowledge you’re expected to have:

  • Strong understanding of various concepts in data science

  • Solid foundation in statistics, probability, and mathematical analysis

  • Expertise in at least one programming language such as Python, R, SQL (Python being currently the most popular)

  • Understanding of the various tools involved in the end-to-end data stack

This serves as a great base for anyone looking to start learning data science. Beyond this, you can add value to your profile by being well-versed in the cloud space whether it be Microsoft Azure, AWS, or Google Cloud, understanding machine learning and deep learning concepts, being able to work with Big Data via technologies like Azure Synapse, Spark and Hadoop, and having an aptitude for data visualization, with tools like Power BI and Tableau to aid you in this task.

How to go about it

While a degree in data science would be the most formal way to go about it, it isn’t completely necessary. You can acquire an education in data science through the many wonderful sources available online. Data science courses on websites like Coursera, Udemy, and Simplilearn can be a cheap and effective way to quickly boost your knowledge. Some of these are even offered for free, making this a viable way to dip your feet into a new topic. Prestigious universities also offer their courses online, with some uploading video lessons to sites like YouTube.

Having a theoretical grip on your topic is needed, but the only way to become a successful data scientist is to learn by doing. You need to work through examples and practice in your own time. For a data scientist, learning is a continuous process as you encounter new and different datasets which throw up new learning opportunities. Kaggle, an online community of data scientists where you can find and publish datasets, offers you a large number and variety of datasets to practice on.

Case-study-oriented learning can help you understand what you need to know before you start working with actual problems that could be solved by using your skillset or knowledge base effectively. You can get better at solving problems as you complete more case studies as well as get an idea of where your strengths lie when given a real-world problem.

It is also important to complete a genuine and interesting data science project to add to your portfolio and display the skills that you want to advertise. Further, to improve your profile and highlight your expertise in the field, you can get certified. Companies that offer certifications in Data Science are Microsoft, Cloudera, SAS, etc. Here is a list of some of these certifications –

Data science is a rapidly growing field and there is no one way to get into it.

If you're thinking about learning how to be a data scientist, we have some advice for you: don't worry too much about which path will get you there. There are plenty of ways that people learn data science, AI, and machine learning, including courses at universities and boot camps (which are more like coding camps than traditional institutions). There's also plenty of great open-source software out there—if nothing else, it's free! And finally...there's always YouTube tutorials!

What’s most important is that you keep practicing with different datasets. Making mistakes and failing is part of the process. That’s how you learn where you need to improve and how not to repeat the same mistakes. While you’re learning your theory, you should always practice alongside, so you can marry your theory to your practice.

The more data you work with, the better you become as a data scientist, so keep practicing!

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