How to Become a Data Scientist - The Sexiest Job of 21st Century

Asking the question of how to become a data scientist? Or thinking to start a career in data science?

Well, I am going to break everything down in detail. If you love challenges and willing to solve problems through data then a career in data science may be a great option for you. And of course, a job with a fat paycheck at the end of every month doesn’t hurt, does it?

According to Harvard Business Review, data science jobs are considered to be the “The sexiest job of the 21st Century”. So, let’s see and understand what it really takes to land the sexiest job of this century.

What is Data Science?

Data science is the study of data to understand, process and interpret it into valuable insights to positively affect business decisions.

It requires asking the right questions, collecting data from a variety of sources, storing, and analyzing, and processing data to extract key information.

One of the main goals of data science is to effectively gain business-oriented insights and knowledge from either structured or unstructured data. These insights help businesses to improve the quality of their service and products.

You can also think of data science as the uncovering of useful findings from data. As a result, organizations are able to understand complex behaviors, trends, and actions.

A simple use case of data science is recommendation engines in retail.

A recommendation engine or a recommender system is an application that recommends and makes a suggestion for something that a website user might be interested in, such as music, movies or even restaurant through analyzing data that is available.

Recommendation engines are found to play an important role for retailers. It is a powerful tool that can predict customers’ behavior through their purchase history.

More and more retailers are using recommendation engines as one of their main business tools.

Recommendation engines help them to extract useful insights from customers’ opinions. As a result, it allows them to identify customer behaviors and increase sales.

Not just that, but data science also applies to the health care industry.

Data scientists perform extensive data analysis to process complex clinical and laboratory reports. It helps doctors and health care facilities to conduct a more accurate diagnosis of patients.

Moreover, doctors can provide preventive care and better treatment to patients by detecting the early stages of illness through data science. This can turn out to be a vital part when it comes to curing illnesses such as cancer and diabetes among patients.

These are just some of the use cases and applications of data science. But the list can go on and on. Since every major industry involves working with huge bytes of data. Thus, data scientists in today’s world are high in demand.

Defining a Data Scientist

Before we learn how to become a data scientist, let’s start by defining a data scientist.

A data scientist can be defined in a number of different ways.

But from our previous explanation of data science, we can define a data scientist as an individual who studies data to understand, process and interpret it into valuable insights. These valuable insights help make better business decisions.

Data scientists collect, store and analyze data to process critical insights. They uncover useful findings from data. As a result, organizations are able to understand trends and perform actionable steps to improve the quality of their service.

According to Glassdoor, the average base salary for a data scientist is $120,495/yr as of December 2019. If a data scientist holds a managerial position then their average base salary bumps to more than $180,000/yr.

Common Job Duties

Let’s dig a little bit deeper when it comes to your job responsibilities as a data scientist. After that, I will break down some of the key skills that you must have in order to become a data scientist.

Here are some example of the common job duties and roles of a data scientist:

Find a range of valuable sources to collect data and automate the data collection process.
Convert and preprocess both structured and unstructured data into clean data sets to ensure the accuracy, effectiveness, and validity of the data.
The analysis of large amounts of raw data for a company to dictate trends and patterns.
Utilize AI & machine learning models for creating predictive models to increase and optimize business outcomes.
Communicate, interpret, and visualize key insights from data to decision-makers and stakeholders.
Present patterns and trends from data sets using data visualization techniques
Collaborate and communicate with engineering, software development, and product designing teams to help a business make a data-driven decision.

These are just some of the common job duties of a data scientist. The key takeaway from here is that working with data on a regular basis is the core duty of a data scientist.

Top 5 Technical Skills of a Data Scientist

Programming

Knowing how to code is an essential skill that every data scientist should consider.

Programming languages such as Python, R, or even querying language like SQL allows data scientists to analyze, manipulate, and understand data faster and more accurately.

Not only that but you can also easily apply certain algorithms on data to uncover meaningful insights and findings for a company.

The majority of the companies today know the value of data. Thus, they know how important it is to have someone on board who knows how to write algorithms. Algorithms to analyze and uncover useful findings to help them understand their customers, users, and stakeholders.

That’s why having solid programming skills is a must for every data scientist in order to become successful in this field.

Recommended: Coding for Beginners — Start Here

Statistics

As a data scientist, it is crucial that you have a decent understanding of statistics.

Some of the basic statistical concepts that you should be familiar with are Probability Distributions, Bayesian Statistics, Statistical Features, Hypothesis Testing, Regression, etc.

In the world of data science, analyzing and manipulating data requires at least the understanding of descriptive statistics and probability theory. Because knowing these concepts will help you to extract better insights for your business.

As a result, you will be able to help make better business decisions.

Machine Learning

Machine learning is the field of study or method that allows computers to learn from observational data and make predictions based on it. It gives the computer to learn without the need to be explicitly programmed.

Data science methodologies are heavily involved with machine learning & AI. So, it is important for data scientists to know common machine learning algorithms.

Having the skill to train data and use them to insight predictive relationships is a highly demanding skill.

If your goal is to work for big corporations such as Netflix, Google, Uber, etc where the products and services are data-driven then you must learn how to build predictive algorithms using data.

Some of the common machine learning algorithms you should know about are Linear Regression, Logistic Regression, Decision Tree, SVM (Support Vector Machine), Naive Bayes, etc.

Read: What is Machine Learning? – Supervised & Unsupervised

Mathematics

Well, you don’t need to be a math genius. Some of you may not even like math. And that is completely fine as most of the data science libraries and framework out there come with all the math that has been already figured out.

All you have to do is apply and learn to use the libraries.

But, here is the thing. If you know the basics of Multivariable Calculus & Linear Algebra, it is going to take you a long way in the field of data science.

Like I said before, you don’t have to be a master at these topics. Knowing the basics and understanding how they work helps a lot. Especially when it comes to choosing the right algorithm for certain problems and datasets.

Data Wrangling

Data wrangling is one of the key skills you need to have as a data scientist. If not some may consider the most important skill that you can possess.

The data you are analyzing may certainly be unorganized, messy and difficult to work with. That’s why you need to know how to clean, process and wrangle datasets that are messy.

I honestly believe clean datasets can either break or make a company. Especially if their products are data-driven.

So, as a data scientist a lot of times your task may require you to transform process and map raw data into a form that is more appropriate and valuable

It is definitely a must-have skill for a data scientist.

Data Visualization

Having the skill to graphically represent and communicate data is crucial for every data scientist. Especially in an age where a lot of companies make data-driven decisions.

One of the qualities that newer companies look for in a data scientist is the ability to not only analyze data but also visualize them. Since humans process visual content much faster than text.

Not just that but also, data visualization shows insights and findings that traditional reports may miss.

Knowing how to use tools like Matplotlib, Tableau, etc will definitely make you stand out as a data scientist. And in my experience data visualization is one of the fun aspects of data science.

4 Steps to Start a Career in Data Science

Step 1: Develop Data Science Skills

The first step on how to become a data scientist is to gather the knowledge and develop the data science skills.

Having a Bachelor’s degree in Computer Science or a related field may help. But, it is definitely not mandatory.

So, what skills should you focus on developing? Well, I have already listed the 5 technical data science skills in this article. You can start there.

If you are completely new to the world of data science and programming, then start by learning how to code.

I prefer that you start learning Python as this is one of the most widely used programming languages for data science. There are tons of free tutorials on Python over the internet.

You also have to learn some of the basic concepts in Statistics. And don’t forget about Machine Learning, Data Wrangling & Visualization.

I have already talked about these concepts. Feel free to go over them again.

All these may sound overwhelming. However, you don’t have to rush or neither learn everything at once. Start by learning a programming language. And if you are already familiar with programming, then apply your programming skills to Statistics and data analysis.

Take things slow and steady. One at a time.

Step 2: Apply Your Knowledge

The second step on how to become a data scientist is to apply your knowledge and skills.

You should not only be learning and practicing the skills but applying them through working on small real-world projects. These projects can be paid or free. Although it is difficult to land paid projects without the skills.

Although, I suggest that you don’t stress about getting paid work. Rather maybe work on small projects for free.

You can try out test projects and apply your data science skills to them. There are tons of test or sample projects on data science that will brush up your skills.

Look up “test data science projects for beginners” on Google and you will know what I am talking about.

One thing that I highly recommend that you try is to develop your own mini-project from open-source data. If you go to Kaggle.com, then you can find and play around with a vast amount of open-source data.

Other than that also try entering data science competitions to compete for prizes.

Participating in competitions is a great opportunity to apply your knowledge. Moreover, it is a fun way to meet and network with other data scientists.

Apply your skills and knowledge through projects and competitions. This is one of the best ways to showcase your data science expertise to recruiters.

Step 3: Build Your Resume

The third step on how to become a data scientist is to build a professional resume.

Building a professional resume is a crucial step when it comes to becoming a successful data scientist.

Employers will first take a look at your resume when you apply for jobs. You need to showcase the very best of yourself through your resume.

Your resume does not have to be perfect. Just identify your data science skillset and put them in order. Keep it nice and simple.

Here is a list of sections that your resume should cover:

● Contact info
● Professional Summary
● Key Skills
● Professional Experience
● Education
● Projects
● Social Media Profiles
● Interests, Hobbies, Extra-curricular achievements.

To get started, you can also use the following resume template as an example. Thanks to our friends from springboard.com.

Step 4: Apply For Data Science Jobs

This is the step where you will apply for data science jobs. Time to go after that bacon!

By this time of your journey, you should already have the key data science skills, some projects to show off your work and also a professional resume all under your belt.

Now it is time for you to look for data science jobs and apply to them. Here are five of my favorite job search websites where you can find companies that are looking for data scientists.

Indeed.com
Glassdoor.com
Monster.com
Dice.com
SimplyHired.com

Read: Five Must-Know Job Interview Tips (Ace Your Next Interview)

You can also try freelance data science jobs if you don’t want to work for
companies as a full-timer.

Here are some of the freelance platforms where people are actively seeking freelance data scientists.

Fiverr.com
Upwork.com
Toptal.com
Freelancer.com
PeoplePerHour.com

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4 Personal Qualities of a Successful Data Scientist

Honesty

Sounds cheesy? Well, you will be surprised to know the number of people that lack this basic human decency. If you want to be successful in any aspect of your life, then be an honest human being. It is not that hard.

The first and foremost quality of successful data scientists is that they are honest. A lot of data scientists deal with personal and private information. When this information is in your hand, you have to make sure that you treat the data with full confidentiality.

Most importantly be honest with the work you do with data.

Not just that but you also must be reliable with what you do, what you say and how you handle confidential data.

Whether you are freelancing or working for a company, you must put your best self forward. As a result, you will be able to build a good reputation not only as a data scientist but as a human being.

Life-long learner

Data science is a very exciting but yet challenging career. It is continuously evolving. At every step of the way, there is a learning curve.
Especially when it comes to working with big data.

A data scientist who is passionate about learning, exploring and experimenting with new technologies is always a step ahead in this field. You must have the will to constantly educate yourself.

One of the qualities of a successful data scientist is that they are always self-motivated in the pursuit of knowledge for either personal or professional reasons. Therefore, it not only increases personal development but also the quality of being competitive and employable.

Team Player

Companies require data science individuals to work in teams. It is nearly impossible for a single individual to analyze, process and manipulate all the big data that are out there today.

So, getting along and communicating with other people in your team is an essential quality of a successful data scientist. Well, I am not saying, that you have to please everybody or play the role of a ‘nice guy”. But being able to communicate and convey the right message to your team is important.

Rather than working alone, you have to work with your team to solve complex challenges related to projects. As a result, you grow to be a leader in your industry.

Problem Solver

Writing, developing and building models can be very challenging in some cases. Not only that but you also may have to spend hours in front of your computer just to clean and process datasets. This is where you have to thrive and find ways to make things work.

I think that everyone is naturally a problem solver. All you have to do is dig deep and use your creativity.

A successful data scientist is always ready to take on challenges.

Have a strong will to fix problems. So when that big data is thrown at your face, you will push yourself to rip it apart and find the most useful insights.

Where to Start?

If you have followed the guide till now! Well done!

It means that you are actually serious about educating yourself on how to become a data scientist. Moreover, you have an interest in a data science career.

And guess what? I have just the right platform to suggest where you can start learning data science immediately.

Udacity

If you are not aware, Udacity is one of the most popular online video course platforms on the internet. Their courses are project-based educational credential programs. Also known as the Nanodegrees.

A good thing is that they have a “Learn to Become a Data Scientist” Nanodegree program taught by industry experts in this field.

Not only that but you will also gain real-world data science experience with projects to build your portfolio and advance your data science career

Similar to their other Nanodegree programs, this program comes with:

Real-world projects in partnership with industry experts.
Access to personal career coaching sessions and services.
1-on-1 mentor to guide students and answering their questions.
Learning path designed to be flexible for busy individuals.

Udacity’s data science Nanodegree program is a great way for you to build the necessary skills to become a successful data scientist.

Click here to learn more about Udacity.

Conclusion

Hopefully, this gives you an overall idea of how to become a data scientist. It’s time for you to stop reading and start taking the first step to pursue a career in data science.

Pursuing a career in data science opens doors to a great deal of opportunity. A path filled with mystery, excitement, and challenges. And of course prestige.

Not only you will be earning more than the national average as a data scientist, but also expect the field to continuously grow over the coming decade.

More and more companies are looking for individuals that can help them to make data-driven decisions. And that’s why data scientists are in such demand.

And that’s probably one of the reasons why Harvard Business Review considers data science jobs as “The sexiest job of the 21st Century”.

What do you think is one of the most important qualities or skills of a data scientist?