7 Ways to Improve Your Data Science Skills Working from Home

Last Updated: March 26, 2021 By

From one of the most demandable technologies of the 21st century, data science offers the best job packages in the market with sky-high salary packages. According to a recent survey, data science is the fastest-growing technology with a rise of 650% in the job profile since 2012. With a median salary of $125,000, this is significantly more than the other job.

Introduction

Many will remember 2020 as the COVID-19 year. The others will remember it as the opportunities for ‘Work from Home.’ Spending more time with families and upskilling their skills while learning and working from home. All you can say is trending technologies at their best. And a new revolution is already happening, along with 2021 as it is about to welcome us with open arms.

Plus, we all know how the whole world is suffering from COVID-19, and we are not over it yet. Everything looked paused in the eyes for a while. And the loss that is happening is inevitable in many ways. Life loss all around the world and the economy is getting all down. What could be worse than 2020 when laying off was happening in almost all industries. Thankfully, the job profiles in data science were different, and job securities were too high. And it did not alter so much.

While working at home has pushed many boundaries to think beyond and establish your tiny office-like environment at home, upgrading your data science skills is very much challenging.

Here are the Seven top-notch tips that will guide you through upgrading your skills parallelly while working from home. Let us explore them one after another.

Exploring Logic and Algorithms into More Details

There is no doubt that data science runs on algorithms, simple and complicated ones. Therefore, you need to understand how logic and algorithms work from the base level before jumping into the complex ones. You need to master some parts of machine learning too. When you will be able to write different steps by yourself and solve the puzzle inside it, you can easily understand how different logics and algorithms work so that you can frame them easily. And if you are wondering what logics and algorithms are. Then, they are a set of instructions that data scientists give to the computers to perform specific tasks.

Statistics Makes Data Science Even More Powerful

Statistics is a collection of multiple tools that answers all crucial questions about data. Statistics are something that makes data more sense. With side datasets, data scientists cannot figure out which sets of data can be more useful for them without statistics. It is why statistics even make data science more powerful. With statistics, you can predict many things like finding out meaningful insights, future trends through mathematical models, and computations. It is because data scientists, analysts use several statistical functions, principles, and algorithms to implement and analyze raw data and to build statistical models.

Brushing-Up Your Basics

It may sound so easy and odd to you, but we as humans tend to forget some of the prime concepts that we learned earlier, or we do not come across them too much. It is data science, and you never know when you need it, so practicing them on a regular interval is what will make you remember different concepts in data science. And even, it will give more ideas and opportunities to look at data in different visions when you gain more experience.

Therefore, keep brushing your basics in a regular interval and at the same time keep updating your skills with new trends and technologies. This way, you can build a lucrative career in data science and bag the highest paid job in no time.

Honing Programming Languages (R and Python)

Without R and Python, data science could be sturdy. Therefore, mastering these two programming languages can add immense hype to your career. Even the best part is they are platform-independent and give you multiple libraries so that you can finish up your data processing and mining instantly in some minutes rather than spending hours on it.

You can use R programming languages for statistical analysis; for general purposes, you can use Bag of Words with Python to process tons and tons of data. Learning both will be the only ideal solution.

Data Gathering & Data Processing

Collecting data is a humongous task where they are multiple sources around. And with a wide format of data, it is never easy for processing too. The first step is to collect the data; the second lies in storing them. Sometimes even the database falls short, and they use cloud computing to depot the rest of it. While all these things bring a headache to many data scientists, if you follow a few simple steps, it can make you feel amusing to solve the data puzzle.

With the help of R and Python, you can analyze which data can fulfill the aim and objectives of your research. Having those required data is better than having the whole database. The next thing is data cleaning, where you can delete those data that do not meet your business needs. Or there may be chances of redundancies, and you need to clean them all.

Mastering Data Visualization Tools

Consuming data is never as easy as it seems. Sometimes it feels tedious too. When you see a lot of data in table form, and there are chances that you may waste all your valuable time finding a solution when you cannot get over it. It is where data visualization tools help to consume the data and get multiple insights from it. And even from multiple POV.

Some of the best data visualization tools are Tableau, PowerBI, Infogram, ChartBlocks, DataWrapper, Google Charts, Fusion Charts, Polymaps. When you use these visualization tools, you can visualize the raw data in a pictorial form, which now becomes so easy to consume and discover new and meaningful opportunities.

Working on Various Data Science Projects

Working on various projects will give you multiple exposures and different ways of tackling similar projects. It will even boost up your skills, make you a better decision-maker and data scientists in the future. When you focus more on practical things than on theories, you will learn a lot, and new doors and opportunities will open for you.

Therefore, work more on projects, take advice from experts in your team. You can conduct multiple meetings with your team, make an outline about what should be different processes that are going to take place, and brainstorming about how you tackle various challenges that may pop out in the middle of the project. Once you start doing and invest more of your time in the project, you will get much exposure to showcase your skills.

Conclusion

COVID-19 pushed us to continue our work at home for an extended period. While many things are going on around us, technologies have turned our world upside down. So that when you work from home, it can be quite challenging. Still, you can figure out some of the best ways to do it. These are some helpful tips on how you can boost your data science skills while working from home. And at the same time, you can maintain your flow of learning and honing your data science skills.