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An Easy-to-Follow Guide to Starting Your Data Science Journey

ByDave Stopher

Nov 10, 2021 #Business

Data scientists identify important questions, then collect the relevant data in order to answer them. If you would like to become a data scientist, then you have your work cut out for you. It is an incredibly complex and difficult job, but also a very rewarding one. It is also in high demand. To become a data scientist, you will need to work on your education, data analytical skills, and technical computer skills, but we will cover all of that here.

This article will present an easy-to-follow guide for becoming a data scientist:

Data Organization

Becoming a data scientist isn’t easy. It is a very high-level career. There are some fundamentals that you will need to know before you can apply for work. These fundamentals may not be taught to you in university or college. Instead, you will have to learn them yourself. These fundamentals are knowledge of Python, Excel, and SQL. In order to work with and organize raw data, you will need to be familiar with these things. In addition to taking a course on data science, you may also want to take a course in these areas. Understanding these subjects will help you to think like a data scientist, which will, in turn, help you to become a data scientist. 

Acquire Qualifications

As one might expect, data scientists are highly educated. You will need to also have qualifications before applying for any jobs in data science. Research shows that over 85% of data scientists have at least a Master of Data Science degree [or another relevant master’s degree like social sciences, computer science, and physical science, and over 45% have PhDs. You can take courses in data sciences at a community college, university, or online. Considering the global pandemic and its impact on the educational sector, taking courses online is probably your best bet because they are mostly uninterrupted.  

R Programming

When you are studying at university, college, or online, you may also want to take a course in R programming. R is a programming language designed specifically for data science. Over 40% of data scientists use R programming to solve statistical problems. However, it can be an incredibly difficult language to learn. You will need to commit a lot of your time to learning about it in order to master it. Having an understanding of R programming will be very beneficial when it comes to applying for jobs because many candidates will not have taken the time to learn about it.

Computer Skills

Computer skills are very important if you want to become a data scientist. Like we previously mentioned, you will need to be able to use SQL, Excel, and Python. Python is a very common coding language required for most data science jobs. SQL is a domain-specific programming language, and Excel is a data analysis and documentation program. You may also need to be familiar with the Hadoop platform, as well as Apache Spark. All of these skills can be learned through online videos, tutorials, and guides. Taking the time to learn these things before applying for a job will make you look good in the eyes of employers.

Machine Learning

An area many would-be data scientists overlook is machine learning, which is extremely important nowadays. Data science uses machine learning in many different areas, from recommendation engines to reinforcement learning. There are lots of courses online that will teach you about machine learning. When you have qualifications pertaining to machine learning on your resume, employers will likely give your application precedence over those that do not. The skills acquired when learning about machine learning will also benefit you in your career as a data scientist.

Data Visualization

As a data scientist, it’s fair to say that you will encounter vast amounts of raw data. This data needs to be translated. If you aren’t good at working with raw data, then you will struggle to become a data scientist. You will be able to visualize data with the help of data visualization tools. Prior to applying for jobs in the data sciences, it’s definitely worth learning about data visualization tools. Being able to understand visualize raw data prior to entering the industry will ensure that your transition into a data scientist role is seamless.

Unstructured Data

In addition to data visualization, you will need to be able to work with unstructured data. This type of data will not fit into database tables, which will make it difficult to work with. Instead of struggling to contend with unstructured data in a data scientist role, take the time to learn about it beforehand. Unstructured data includes videos, audio, and social media posts. Working with unstructured data can be very difficult. However, you will be expected to be able to work with it in a data scientist position.

Create a Portfolio

When you have completed your studies, and you have acquired all of the relevant skills, then you can create a portfolio. Some would even suggest creating a portfolio while you are studying. On your portfolio, you can include all of the work that you have completed outside of your course, and any stand-out pieces of work that you completed while on your course. A portfolio is an absolute necessity so that companies can see what skills you have. Make sure to showcase everything you have done relevant to data sciences. If you follow this article’s guidance then you should already have more skills than other candidates, so by showing employers this you will set yourself up for success.

Applying for Jobs

When you have created your portfolio, and it has gone live, you can begin searching for [and applying to] jobs. Make sure to carefully write your resume and cover letter, so that you stand out. Most would recommend hosting your portfolio on a website, and most applications will give you an opportunity to link your portfolio. If you have a specific company in mind then you can visit their website to apply for available positions, otherwise, you can apply through job search websites.

If you want to become a data scientist, then you need only follow this article’s advice. Cracking the data science industry can be difficult, but with time, determination, and the relevant skills, you will be able to.

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