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What is a Data Scientist?

Using various analytical tools and methods, Data Scientists find meaning and opportunities buried in gigabytes of data, which first they interpret and then translate into actionable business insights. Companies from across various sectors are looking to collaborate with Data Scientists, so let's talk about their missions and required skills!

What is a Data Scientist?

Up until 20 years ago, Data science was still in its early stages. But thanks to the internet and improvement in computer technology, the pace at which data is now being generated is mind-boggling. By 2025, it’s estimated that 463 exabytes of data will be created each day globally.

Clearly, the keyword here is data. And to unearth what secrets it holds, we have a breed of specialists called Data Scientists.

Definition of a Data Scientist

Using various analytical tools and methods, they find meaning and opportunities buried in gigabytes of data, which first they interpret and then translate into actionable business insights.

Simply put, Data Scientists challenge the status quo in order to come up with data-driven solutions that are profitable and lend businesses a competitive edge.

Take, for example, Netflix. Their team of Data Scientists investigate user data to identify patterns and understand viewers’ interests. This helps them make accurate, personalized recommendations that ensure long-term engagement (think Netflix and chill) and steadily growing revenue stream.

Statistia reports that Netflix subscribers grew from less than 22 million in 2011 to nearly 150 million in 2019. In fact, their service is so popular that 37% of the world’s internet users now use Netflix. Would that have been possible without the mounds of data and Data Scientists working on it? Not at all.

Key Differences Between a Data Scientist and a Data Analyst

Data Scientists are often confused with data analysts partly because different organizations have their own ways of defining Data Science roles.

In small organizations, for example, one person does almost everything data related, while in the big companies, there are bigger teams wherein every person is assigned a specific task. And although agreed, there’s an overlap of a few, basic data scientist skills, but there are some significant differences between Data Scientists and data analysts that can’t be completely overlooked.

  • A data analyst gathers data from multiple sources, analyzes it to find correlation and pattern, and creates reports and compelling visualizations. A Data Scientist on the other hand, can do all that as well as apply machine learning and predictive analysis to extract critical insights from data.
  • Key questions about data come from Data Scientists and it’s the data analysts job to find answers to those very questions.
  • Data Scientists are innovators and they scratch beneath the surface through experiments. They make hypotheses, overcome their biases, and use their experience to join the dots that are invisible to others. Data analysts aren’t usually expected to do this job: in fact, they build off of that input.
  • Owing to their high-level analytical skills, Data Scientists can create custom statistical models and algorithms which can data analysts use.

Why become a Data Scientist

Companies across all domains, be it finance, health, marketing, retail, aviation, transport, banking, etc., understand that data-driven decisions are profitable and can change peoples’ lives for the better too. And with the mounds of data they collect every second, now more than ever, they need Data Scientists by their side.

Anyway, even though the growing demand was predicted years ago, there’s still a huge talent gap, because there aren’t many people who know how to handle data that is unstructured and messy. In October 2018, Linkedin reported that the demand for Data Scientists is “off the charts nationally”, and continues to be so. IBM, too, had reported that the demand for Data Scientists will soar 28% by 2020.

So consider this as your chance to know how you can become the most sought-after Data Scientist.

Technical Skills Employers Look for in a Data Scientist

Let’s talk about degrees and basic competencies that Data Scientists must have.

Educational Qualifications

The academic background of most people working as Data Scientists is diverse. Generally, they have a four-year bachelor’s degree in a technical field, such as information technology, computer science, statistics, or mathematics. If you don’t want to engage in long studies, or if you are looking for a quick career change, another way to get all the skills required to become a data scientist is to complete an intensive bootcamp.

Benefits of joining a Data Science Bootcamp:

  • They are intensive, targeted training programs where you master key languages and frameworks.
  • Since the goal is to strengthen your practical skills, they give you a hands-on experience of using various tools and technology popularly used by Data Scientists.
  • You’ll be surrounded by your peers which will be an intellectually stimulating experience. Not only do you get to collaborate with them but also learn from some of the bright minds there.
  • Many bootcamps, like ours, offer career services, such as networking opportunities, coaching by alumni, and access to recruiters.
  • Cost wise, bootcamps are more affordable compared to getting a traditional degree.

Programming languages

Statistical programming languages help Data Scientists compute statistical output. By and large, companies use the ones that solve problems at hand, and between Python and R, the preferred one is Python.

  • Python is an open-source language that is easy-to-learn and read. It has a massive set of third-party libraries and a huge developer support. Knowing how to use Python tools, such as Panda, Numpy, and Scipy is highly appreciated.
  • SQL (Structured Query Language) although isn’t a programming language is important for Data Scientists to perform key tasks, such as update, manage, modify, delete or retrieve structured data from the database management system.

Other programming languages Data Scientists know or learn:

  • SAS language
  • C++
  • Java
  • Javascript
  • Scala
  • Swift
  • Julia

Data Processing Frameworks:

  • Hadoop
  • Spark
  • Flink
  • Storm
  • Samza
  • BigQuery
  • Hydra

Data visualization tools

Data Scientists use data visualization tools to present their complex findings graphically so that even the layman can understand and remember it. When you’re starting out, you might be intimidated and would want to learn everything, but don’t give in. Companies have their own set of preferences and chances are you’ll learn it on the job.

In any case, here are some of the tools Data Scientists use:

  • Qlik
  • SAS
  • Tableau
  • Microsoft Power BI
  • Google Fusion Tables
  • Talend
  • Microsoft Excel
  • D3.js
  • Matplotlib
  • Seaborn
  • Plotly
  • Jupyter Notebook

Machine learning algorithms:

Machine learning is the science of making computers learn how to act without explicitly programming them. Here’s a list of commonly used machine learning algorithms:

  • Linear Regression
  • Logistic Regression
  • Decision Tree
  • Random Forest
  • Gradient Boosting
  • KNN
  • SVM
  • Naive Bayes
  • Unsupervised Learning
    • Dimensionality Reduction
    • Clustering
  • Deep Learning
    • Neural Networks
    • Recurrent Neural Networks
    • Convolutional Neural Network

6 Non-technical skills Employers Value in a Data Scientist

When hiring Data Scientists, recruiters assess candidates on several factors other than their educational qualifications and top-notch technical know-how. It is this complex combination of traits that makes hiring the right person difficult. Luckily for you, if you have them, you definitely stand a better chance at getting hired.

Strong business acumen

Since Data Scientists occupy management seats they’re expected to have a sharp business sense. Put simply, you should be someone who sees everything through a critical lens, asks relevant and important questions, and thinks in business context. After all, no amount of perfect data wrangling is of any consequence if you can’t come up with actionable insights that address business priorities.

A good listener

Other than asking questions and giving their opinions, Data Scientists, because they play a cross-functional role, must be good listeners.Sure, you become a good listener with practice, but once you manage to have great conversations, you’ll be seen as an empath. So how do you become a good listener? You let people talk so that they feel heard and understood. Plus, don’t interject if you don’t agree. Wait your turn.

A storyteller with incredible communication skills

Most business owners are more interested, as any non-technical person, to understand how your proposed solution will impact their business. To get their attention, you should leverage your natural flair to create a cohesive, persuasive narrative and present data. That’s because stories have the power to influence decision-makers and bring about a change. Even if you aren’t a great story-teller, you can hone this skill over time.

A Team Player

Data Scientists don’t work in silos. Instead right from the C-suite member, data analysts, data engineers, product managers to designers, marketers, software developers, they work with everyone who plays even the smallest role in decision-making. So mould yourself into a collaborator who is ready to walk everyone together.

Smart Intuition about Data

A lot of Data Science concepts are driven by complex mathematics and statistics. Ironic as it might sound, when you sit down to deconstruct data you need equal parts intuition that can guide you. Of course, this is one skill that comes with years of experience, but as long as you’re ready to test your hunches along the way, know that you’re on the right track.

A Quest to Never stop learning

Data Scientists are a curious lot and never stop learning. It’s what guides them to look in places and directions where no one can ever think of. Naturally, so that you keep growing, here’s what you can do to make your personal development project never end:

– Attend networking events: It’s important to stay connected with industry insiders, leaders, and researchers. Given your availability and budget, network all you can. If you can’t, how about organize an event locally with your other Data Scientist friends and acquaintances to make new connections? – Join discussion forums and groups: Think LinkedIN, Quora, Reddit, and Twitter. Data Scientists love to chatter and hang out on multiple platforms to ask questions and also help members of their own tribes with advice and opinion. – Participate in hackathons: It’s an incredible learning and training opportunity even if you’re a student. Everyone gathers with a combination of tools and experience to solve big problems together. Often at Data Science hackathons there are workshops that you can attend and mentors who you can meet to guide you along the way. – Step outside the box: Just so you don’t get too comfortable, start using new/never tried before set of tools and frameworks. Then blog about it or even vlog your experience. For all you know, your hits and misses can spark a new discussion in the community of Data Scientists. – Listen to data science podcasts: The topics range from data optimization, storytelling, data visualization, machine learning to innovations, and much more going on in the field of Data Science. If interested, check out this podcast recommendations article and be sure to tune into the conversations that you can pause and play at will. – Read books and blogs: Given that you’ll work in a field that’s skyrocketing to the future, books will actually help you both slow down and catch up. Here’s an excellent article on books Data Scientists must read. Also, there are bloggers who write about everything Data Science. Some good ones are listed here. Give them a follow.

How Much Does a Data Scientist Earn

Glassdoor reports that junior Data Scientists in the United States with 1-3 years of experience can expect an average salary around $86,672/yr. And those with 15+ years of experience can expect to earn an average salary for data scientist around $135,683.

Clearly, you can earn well if you have what it takes to be a stand-out Data Scientist, and grow leaps and bounds financially through the length of your career.

Time to Kickstart your Journey as a Data Scientist

The Data Science field is thriving. Companies from across various sectors are looking to collaborate with Data Scientists. To top it all, as you would have made out by now, it’s also a lucrative job. How about we help you launch your career in Data Science?

At Le Wagon, we offer a Data Science course, where you’ll learn everything step-by-step. Right from the basic programming language, Python to deep learning, and so much more, the course will be taught by industry experts, who’ll prepare you for the real world and help you crack interviews in the biggest tech companies. And if you feel comfortable with everything you learned during the bootcamp, you’ll definitely be successful as a Data Scientist.

Even after you graduate, you can access our online platform and community. Additionally, you can attend job fairs and networking events, gain access to our hiring partners and land your dream job. Aside from that, you can leverage our fast-growing alumni community and get coached by Data Science course alumni.

Want to learn more about Le Wagon’s Data Science course?

Download our syllabus below to discover the program and learn more about our alumni and community! And for answers to frequently asked questions, head here.

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