Data scientists are a new breed of data analyst with the technical ability to solve complicated problems – as well as the curiosity to figure out what problems need to be solved.
They're a mix of mathematicians, computer scientists, and trend-spotters. They're also in high demand and well-paid because they work in both the business and IT worlds. Who wouldn't want to be a member of this elite group?
Data science is currently one of the most talked-about fields, and is in high demand. Given all of the exciting applications, it's no surprise that data science is a highly sought-after profession. Data science is used in a variety of industries, including the development of self-driving vehicles.
People learn in different ways; some prefer to read, while others prefer to do things practically. Some people, instead of learning a checklist of skills, they focus on creating projects more. This learning method is not only very motivating, but it also closely resembles the work you'll be doing as a Data Scientist.
The majority of articles on "how to become a data scientist" begin by listing a long list of supposed skills, software, and concepts that you'll need to master.
Say- Spark, Hadoop, Hive, Python, R, SQL, NoSQL etc.
Due to the overwhelming number of things to learn, candidates often feel overwhelmed and don't know how or where to begin. However, they'll feel very busy once they've started, they won’t even know if they've made any real progress.
But before anything and everything, make sure that you're passionate about the process of data science before moving on to the next step. There's no way to overemphasise. In order to become a data scientist, you'll need to put in months of hard work.
If you're interested in becoming a data scientist, I'll share some tips with you in this post. Even if the journey isn't easy, it will surely be more helpful than following the conventional wisdoms.
You must be aware of the fact that each industry requires a different set of skills. Finding a job in one industry is a good start, not only will you be able to focus on fewer topics, but you will also be able to begin building valuable domain knowledge. As a result of that, you'll have a huge advantage over other candidates while cracking the interviews.
Some of the most popular industries as per Glassdoor:
Internet & Tech
Media & Publishing
Biotech & Pharmaceuticals
Marketing & Advertising
Banking & Financial Services etc.
Let’s say, you may not enjoy formulating abstract questions but you might enjoy analysing health or education data. This way you will be able to niche down to one industry in the initial phase where the most important thing is to get industry experience.
Search for data science Jobs in your preferred domain on a job platform like Glassdoor, LinkedIn Jobs, or Indeed. Don't just look for "data scientist" in your search. Other terms to look for are data analyst, machine learning engineer, and quantitative analyst.
The problem, as you'll see, is that there are too many alternatives rather than too few, so we'll be eliminating a lot of them. Begin by skimming through the listings to gain a sense of the work's quality (this works best while preparing for an interview in literally any job). You gotta work smart, not hard. Write down all the skills/ requirements that employers demand.
Make sure to eliminate skills which aren't ones you'd be excited about, or any unrealistic expectations you can’t achieve within your time frame.
These specific positions will most likely be filled by then, but that is not the point. Do not forget to keep in mind that only 60-80% of the required qualifications are usually required to land a job. It is understood by employers that the majority of employees will need to continue to learn during their job.
It's time to start building your future, these tips will help you in that:
A) Doing is the best way to learn Data Science
You should always work on a data science project in order to learn. Data science projects are the best way to show off your skills after you've learned the necessary technical skills.
In addition to learning about machine learning algorithms, statistics and probability, building projects can help you gain a better understanding of real data science work and enhance your skills.
If you're looking for a job, it can help you create a portfolio. Always start building projects around smaller problems, then, as your skills improve, make the problem more difficult.
To become a full-stack data scientist, you'll need real-world experience in both analytics and coding, as well as knowledge on how to use modern technology.
Pro tip: Find the one-and-only project concept that can help you expand your portfolio.
B) For long-term benefits, master the fundamentals of data science:
Programming Language- R and Python are the most popular programming languages for Data Science. Between the two, Python is the more popular coding language, with the majority of Data Scientists using it. It is simple to understand, versatile, and supports a variety of in-built libraries such as Numpy, Pandas, MatplotLib, Seaborn, Scipy, and many others). Just ensure that you are great with fundamentals.
Machine Learning (this is the most important step in a data scientist's life cycle because one must build various models using machine learning algorithms and must be able to predict and come up with the most optimum solution to solve any problem)
Probability and statistics (data Science is based on fundamental ideas such as probability and statistics) There's no need to delve too deeply into these topics for the most part.
The objective is to understand the fundamentals and then apply what you've learned to some of the questions you've come up with during the last few weeks. This will aid in the consolidation of your knowledge and the development of a portfolio.
C) Share Your Work: Once you've completed a few projects, it's time to show them off! Uploading them to GitHub, where others may see them. Uploading projects will:
Make you think about how to present in the best way possible, which is exactly what you'd do in a data science capacity.
Give employers access to your projects.
Allow your peers to see and comment on your creations.
Participating in online forums can assist you in identifying opportunities and expanding your expertise by allowing you to learn from others. Reddit subreddits such as /r/datascience, Quora, Kaggle are a few more good online communities to think about.
It's not easy to learn data science, but the key is to stay motivated and appreciate what you're doing. You'll develop your knowledge and land the data scientist job you want if you continuously build projects and share them.
I haven't provided you a step-by-step guide to understanding data science, but if you follow this procedure, you'll be able to go further than you ever anticipated. If you're motivated enough, anyone, even you and me, can become a data scientist.