The need for professional data science is currently at its peak. Almost every industry has started to process data to form models and high-value information. This is what makes data scientist the most sought after profession today.
When you start to step foot in the world of data, of course you need provisions and various skills. In this article, you will get tips, tricks, and hacks to help your career in data — especially data science.
Reasons why you should study data science
There are many roles and opportunities available to be achieved in the field of data science. Data science has many disciplines to pursue. You don’t have to worry if you don’t feel right in one field, you can still open up opportunities in other fields of data science.
This makes your career path will not be a dead end.
In most jobs, the problem that is often faced is that someone has difficulty being a decision maker.
This is also the main reason for the emergence of imbalances and so the work is not optimal.
However, by taking on a role in the field of data science, you don’t have to worry about this. Because of course data science has trained you to become a decision-maker so that it will be easier to deal with dynamic conditions.
Although this field is quite difficult for beginners, but when you succeed in it, you become the most sought-after worker. This is because data science is a new field, and is growing rapidly. This rate of growth has fueled a huge demand for the data scientist profession in the workforce.
However, the magnitude of the need is still not in line with the number of people skilled in this field. So, when you have taken a position in the field of data science, the opportunities you get are very large and have a golden opportunity in this profession.
How much do data scientists make?
It’s no longer a secret that data science is one of the most highly-earning fields.
Data scientists are paid so highly because there is a huge global demand for their skills, their work is very valuable to most companies, the supply of these professionals is scarce, they require some expert skills that take years to master, this is what makes a salary data scientists are huge.
One of the reasons why data scientists earn such high salaries is because they are very valuable to company owners.
It is very easy for companies to collect large amounts of data. They can do this in no time.
The problem comes when it’s time to process this data into useful information.
Companies understand that understanding this data properly and putting it to good use can greatly benefit them in the future.
The problem is that many CEOs and business leaders don’t know how to sort and analyze the data themselves.
But they are willing to pay a data scientist to help them with this.
If data scientists can provide them with insights from that data to make informed decisions and to actually reach their customers, then data scientists deserve high salaries that companies pay.
Based on the data from Glassdoor above, we can see that the average income of a data scientist reaches USD 2000 per month.
Fantastic value isn’t it?
The first step to learning data science and becoming a data scientist
The field of data science is vast, and there is a wealth of information available. This may make it difficult for you to focus.
The secret to directing this is:
Find reasons to study
Identify your motivation
Use them to guide your data learning journey. Also make it a habit to practice working on projects, participate in internships, and improve portfolios — learning by doing.
When you apply learning by doing, you get a reliable experience in the future. Take control of your learning by adapting it to your goals, not the other way around.
Along the way, it’s possible that you’ll get carried away with the desire to learn about difficult topics like machine learning, neural networks, image recognition, and more.
However, first strengthen your fundamental skills by learning the basics as best you can.
You can’t run until you learn to walk.
You will find it much easier when you master the simple things before spending your time on more expert topics.
Learn about linear regression, k-means clustering, and other data science fundamentals, then use what you know to complete projects and build portfolios.
Do data scientists have to be good at math?
A data science career requires knowledge of mathematics because, machine learning algorithms, performing analysis, and finding insights from data require mathematics.
While math won’t be the only requirement for your career in data science, it is often one of the most important.
Can a non-IT become a data scientist? Can you really get into data science without a math or IT background?
The answer, of course!
Although data science requires strong mathematical knowledge, mathematical skills in the field of data science are certainly not impossible for you to learn.
When it comes to studying math for data science, you can start by taking courses on topics such as:
Linear algebra
Applied statistics
Probability theory
Calculus
What skills must be mastered to become a data scientist?
In-depth knowledge of at least one of the analytical tools is essential.
For data science, R is generally preferred.
R is specifically designed for data science needs.
You can use R to solve any problem you come across in data science.
In fact, 43% of data scientists use R to solve statistical problems.
Python is the most common programming language usually required in the data science profession, along with Java, Perl, or C/C++.
Python is a great programming language for data scientists.
This is why 40% of respondents surveyed by O’Reilly use Python as their primary programming language.
Because of its versatility, you can use Python for almost any step you take in the data science process.
Python can take a variety of data formats and you can easily import SQL tables into your program.
Python also allows you to create datasets.
Although NoSQL and Hadoop have become popular in data science, it is still desirable that one can write and execute complex queries using SQL.
SQL (Structured Query Language) is a programming language that can help you to perform operations such as add, delete, and extract data from a database.
It can also help you perform analysis functions and design database structures.
You must be proficient in SQL as a data scientist.
This is because SQL is specifically designed to help you access, communicate, and work with data.
SQL has a short syntax that can help you save time and reduce the amount of programming you need to perform difficult queries.
Learning SQL will help you to better understand relational databases and raise your profile as a data scientist.
The field of data science has grown massively over the last few years.
Many companies find the importance of data collection and analysis, and they need a data scientist to study and understand it.
With so much growth potential and so many exciting opportunities for work, it’s no wonder that data scientist is one of the careers with a very bright future.