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How to Get a Job in Data Science Fast

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You want to get a data science job fast. Obviously, no one wants one to get a job slowly. But the time it takes to find a job is relative to you and your situation. When I was seeking my first data science job, I had normal just Kevin bills and things to budget for, plus a growing family who was hoping I’d get a job fast. This was different from some of my classmates, while others had their own versions of why they needed a job fast, too. I believe that when writing a how-to guide on getting a data science job quickly, we should really acknowledge that we’re talking about getting you, the reader, a job faster. Throughout this article, we’ll discuss how to get a job as a data scientist faster than you might otherwise, all things considered.

Getting a job faster is not an easy task in any industry, and getting a job faster as a data scientist has additional encumbrances. Some jobs, extremely well-paying jobs, require a nebulous skill set that most adults could acquire after several years in the professional working world. Data science is not one of those jobs. For all the talk about what a data scientist actually does, there’s a definite understanding that the set of skills necessary to successfully execute any version of the job are markedly technical, a bit esoteric, and specialized. This has pros and cons, which we’ll discuss. The community of people who aspire to join this field, as well as people already in the field, is fairly narrow which also has pros and cons.

Throughout this article, we’ll cover two main ways to speed up the time it takes to get a data science job: becoming aware of the wealth of opportunities, and increasing the likelihood that you could be considered employable.

Becoming Aware of the Wealth of Opportunities

Data science is a growing, in-demand field. See for yourself in Camm, Bowers, and Davenport’s article, “The Recession’s Impact on Analytics and Data Science” and “Why data scientist is the most promising job of 2019” by Alison DeNisco Rayome. It’s no secret however that these reports often only consider formal data science job board posts. You may have heard or already know that there exists a hidden job market. It stands to reason that if this hidden job market exists, there may also be a number of companies who have not identified their need for a data scientist yet, but likely need some portion of data science work. Here’s your action plan, assuming you already have the requisite skills to be a data scientist:

1. Find a company local to your region. This is easier if you know someone at that company, but if you don’t know anyone, just think through the industries that you’d like to build a career in. Search for several companies in those fields and consider a list of problems that might be faced by that organization, or even those industries at large.

2. Do some data work. Try to keep the scope of the project limited to something you could accomplish in one to two weekends. The idea here is not to create a thesis on some topic, but rather to add to your list of projects you can comfortably talk about in a future interview. This also does not have to be groundbreaking, bleeding edge work. Planning, setting up, and executing a hypothesis test for a company who is considering two discount rates for an upcoming sale will give you a ton more fodder for interviews over a half-baked computer vision model with no clear deliverable or impact on a business.

3. You have now done data science work. If you didn’t charge money for your services on the first run, shame on you. Charge more next time.

4. Repeat this process. The nice thing about these mini projects is that you can queue up your next potential projects while you execute the work for your current project at the same time.

Alternatively, you could consider jobs that are what I call the “yeah but there’s this thing…” type jobs. For example, let’s say you’re setting up a database for a non-profit and really that’s all they need. The thing is… it’s really your friend’s non-profit, all they need is their website to log some info into a database, and they can’t pay you. Of course you should not do things that compromise your morals or leave you feeling as though you’ve lowered your self worth in any way. Of course you’d help out your friend. Of course you would love some experience setting up a database, even if you don’t get to play with big data. Does that mean that you need to explain all of those in your next job interview? Of course not! Take the job and continue to interview for others. Do work as a data engineer. Almost everyone’s jobs have a “yeah but” element to them; it’s about whether the role will help increase your likelihood of being considered employable in the future.

Increasing the Likelihood That You Could Be Considered Employable

Thought experiment: a CTO comes to you with a vague list of Python libraries, deep learning frameworks, and several models which seem relevant to some problems your company is facing and tasks you with finding someone who can help solve those issues. Who would you turn to if you had to pick a partner in this scenario? I’ll give you a hint — you picked the person who satisfied three, maybe four criteria on what you and that team are capable of.

Recruiting in the real world is no different. Recruiters are mitigating their risk of hiring someone that won’t be able to perform the duties of the position. The way they execute is by figuring out the skills (usually indicated by demonstrated use of a particular library) necessary for the position, then finding the person who seems like they can execute on the highest number of the listed skills. In other words, a recruiter is looking to check a lot of boxes that limit the risk of you as a candidate. As a candidate, the mindset shift you need to come to terms with is that they want and need to hire someone. The recruiter is trying to find the lowest risk person, because the CTO likely has some sort of bearing on that recruiter’s position. You need to basically become the least risky hire, which makes you the best hire, amongst a pool of candidates.

There are several ways to check these boxes if you’re the recruiter. The first is obvious: find out where a group of people who successfully complete the functions of the job were trained, and then hire them. In data science, we see many candidates with training from a bootcamp, a master’s program, or PhDs. Does that mean that you need these degrees to successfully perform the function of the job? I’d argue no — it just means that people who are capable of attaining those relevant degrees are less risky to hire. Attending General Assembly is a fantastic way to show that you have acquired the relevant skills for the job.

Instead of having your resume alone speak to your skill, you can have someone in your network speak to your skills. Building a community of people who recognize your value in the field is incredibly powerful. While joining other pre-built networks is great, and opens doors to new opportunities, I’ve personally found that the communities I co-created are the strongest for me when it comes to finding a job as a data scientist. These have taken two forms: natural communities (making friends), and curated communities. Natural communities are your coworkers, friends, and fellow classmates. They become your community who can eventually speak up and advocate for you when you’re checking off those boxes. Curated communities might be a Meetup group that gathers once a month to talk about machine learning, or an email newsletter of interesting papers on Arxiv, or a Slack group you start with former classmates and data scientists you meet in the industry. In my opinion, the channel matters less, as long as your community is in a similar space as you.

Once you have the community, you can rely on them to pass things your way and you can do the same. Another benefit of General Assembly is its focus on turning thinkers into a community of creators. It’s almost guaranteed that someone in your cohort, or at a workshop or event has a similar interest as you. I’ve made contacts that passed alongside gig opportunities, and I’ve met my cofounder inside the walls of General Assembly! It’s all there, just waiting for you to act.

Regardless of what your job hunt looks like, it’s important to remember that it’s your job hunt. You might be looking for a side gig to last while you live nomadically, a job that’s a stepping stone, or a new career as a data scientist. You might approach the job hunt with a six-pack of post-graduate degrees; you might be switching from a dead end role or industry, or you might be trying out a machine learning bootcamp after finishing your PhD. Regardless of your unique situation, you’ll get a job in data science fast as long as you acknowledge where you’re currently at, and work ridiculously hard to move forward.

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What is Data Science?

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It’s been anointed “the sexiest job of the 21st century”, companies are rushing to invest billions of dollars into it, and it’s going to change the world — but what do people mean when they mention “data science”? There’s been a lot of hype about data science and deservedly so, but the excitement has helped obfuscate the fundamental identity of the field. Anyone looking to involve themselves in data science needs to understand what it actually is and is not.

In this article, we’ll lay out a deep definition of the field, complete descriptions of the data science workflow, and data science tasks used in the real world. We hope that any would-be entrants into this line of work will come away reading this article with a nuanced understanding of data science that can help them decide to enter and navigate this exciting line of work.

So What Actually is Data Science?

A quick definition of data science might be articulated as an interdisciplinary field that primarily uses statistics and computer programming to derive insights from and base decisions from a collection of information represented as numerical figures. The “science” part in data science is quite apt because data science very much follows a scientific process that involves formulating a hypothesis and using a specific toolset to confirm or dispel that hypothesis. At the end of the day, data science is about turning a problem into a question and a question into an answer and/or solution.

Tackling the meaning of data science also means interrogating the meaning of data. Data can be easily described as “information encoded as numbers” but that doesn’t tell us why it’s important. The value of data stems from the notion that data is a tangible manifestation of the intangible. Data provides solid support to aid our interpretations of the world. For example, a weather app can tell you it’s cold outside but telling you that the temperature is 38 degrees fahrenheit provides you with a stronger and specific understanding of the weather.

Data comes in two forms: qualitative and quantitative.

Qualitative data is categorical data that does not naturally come in the form of numbers, such as demographic labels that you can select on a census form to indicate gender, state, and ethnicity.

Quantitative data is numerical data that can be processed through mathematical functions; for example stock prices, sports stats, and biometric information.

Quantitative can be subdivided into smaller categories such as ordinal, discrete, and continuous.

Ordinal: A sort of qualitative and quantitative hybrid variable in which the values have a hierarchical ranking. Any sort of star rating system of reviews is a perfect example of this; we know that a four-star review is greater than a three-star review, but can’t say for sure that a four- star review is twice as good as a two-star review.

Discrete: These are countable and finite values that often appear in the form of integers. Examples include number of franchises owned by a company and number of votes cast in an election. It’s important to remember discrete variables have a finite range of numbers and can never be negative.

Continuous: Unlike discrete variables, continuous can appear in decimal form and have an infinite range of possibilities. Things like company profit, temperature, and weight can all be described as continuous. 

What Does Data Science Look Like?

Now that we’ve established a base understanding of data science, it’s time to delve into what data science actually looks like. To answer this question, we need to go over the data science workflow, which encapsulates what a data science project looks like from start to finish. We’ll touch on typical questions at the heart of data science projects and then examine an example data science workflow to see how data science was used to achieve success.

The Data Science Checklist

A good data science project is one that satisfies the following criteria:

Specificity: Derive a hypothesis and/or question that’s specific and to the point. Having a vague approach can often lead to a waste of time with no end product.

Attainability: Can your questions be answered? Do you have access to the required data? It’s easy to come up with an interesting question but if it can’t be answered then it has no value. The same goes for data, which is only useful if you can get your hands on it.

Measurability: Can what you’re applying data science to be quantified? Can the problem you’re addressing be represented in numerical form? Are there quantifiable benchmarks for success? 

As previously mentioned, a core aspect of data science is the process of deriving a question, especially one that is specific and achievable. Typical data science questions ask things like, does X predict Y and what are the distinct groups in our data? To get a sense of data science questions, let’s take a look at some business-world-appropriate ones:

  • What is the likelihood that a customer will buy this product?
  • Did we observe an increase in sales after implementing a new policy?
  • Is this a good or bad review?
  • How much demand will there be for my service tomorrow?
  • Is this the cheapest way to deliver our goods?
  • Is there a better way to segment our marketing strategies?
  • What groups of products are customers purchasing together?
  • Can we automate this simple yes/no decision?

All eight of these questions are excellent examples of how businesses use data science to advance themselves. Each question addresses a problem or issue in a way that can be answered using data science.

The Data Science Workflow

Once we’ve established our hypothesis and questions, we can now move onto what I like to call the data science workflow, a step-by-step description of a typical data science project process.

After asking a question, the next steps are:

  1. Get and Understand the Data. We obviously need to acquire data for our project, but sometimes that can be more difficult than expected if you need to scrape for it or if privacy issues are involved. Make sure you understand how the data was sampled and the population it represents. This will be crucial in the interpretation of your results.
  1. Data Cleaning and Exploration. The dirty secret of data science is that data is often quite dirty so you can expect to do significant cleaning which often involves constructing your variables in a way that makes your project doable. Get to know your data through exploratory data analysis. Establish a base understanding of the patterns in your dataset through charts and graphs.
  1. Modeling. This represents the main course of the data science process; it’s where you get to use the fancy powerful tools. In this part, you build a model that can help you answer a question such as can we predict future sales of a product from your dataset.
  1. Presentation. Now it’s time to present the results of your findings. Did you confirm or dispel your hypothesis? What are the answers to the questions you started off with? How do your results advance our understanding of the issue at hand? Articulate your project in a clear and concise manner that makes it digestible for your audience, which could be another team in your company or your company’s executives.

Data Science Workflow Example: Predicting Neonatal Infection

Now let’s parse out an example of how data science can affect meaningful real-world impact, taken from the book Big Data: A Revolution That Will Transform How We Live, Work, and Think.

We start with a problem: Children born prematurely are at high risk of developing infections, many of which are not detected until after a child is sick.

Then we turn that problem into a question: Can we detect patterns in the data that accurately predict infection before it occurs?

Next, we gather relevant data: variables such as heart rate, respiration rate, blood pressure, and more.

Then we decide on the appropriate tool: a machine learning model that uses past data to predict future outcomes.

Finally, what impact do our methods have? The model is able to predict the onset of infection before symptoms appear, thus allowing doctors to administer treatment earlier in the infection process and increasing the chances of survival for patients.

This is a fantastic example of data science in action because every step in the process has a clear and easily understandable function towards a beneficial outcome.

Data Science Tasks

Data scientists are basically Swiss Army knives, in that they possess a wide range of abilities — it’s why they’re so valuable. Let’s go over the specific tasks that data scientists typically perform on the job.

Data acquisition: For data scientists, this usually involves querying databases set up by their companies to provide easy access to reams of data. Data scientists frequently write SQL queries to retrieve data. Outside of querying databases, data scientists can use APIs or web scraping to acquire data.

Data cleaning: We touched on this before, but it can’t be emphasized enough that data cleaning will take up the vast majority of your time. Cleaning oftens means dealing with null values, dropping irrelevant variables, and feature engineering which means transforming data in a way so that it can be processed by a model.

Data visualization: Crafting and presenting visually appealing and understandable charts is a hugely valuable skill. Visualization has an uncanny ability to communicate important bits of information from a mass of data. Good data scientists will use data visualization to help themselves and their audiences better understand what’s going on.

Statistical analysis: Statistical tests are used to confirm and/or dispel a data scientist’s hypothesis. A t-test or chi-square are used to evaluate the existence of certain relationships. A/B testing is a popular use case of statistical analysis; if a team wants to know which of two website designs leads to more clicks, then an A/B test is the right solution.

Machine learning: This is where data scientists use models that make predictions based on past observations. If a bank wants to know which customers are likely to pay back loans, then they can use a machine learning model trained on past loans to answer that question.

Computer science: Data scientists need adequate computer programming skills because many of the tasks they undertake involve writing code. In addition, some data science roles require data scientists to function as software engineers because data scientists have to implement their methodologies into their company’s backend servers.

Communication: You can be a math and computer whiz, but if you can’t explain your work to a novice audience, your talents might as well be useless. A great data scientist can distill digestible insights from complex analyses for a non-technical audience, translating how a p-value or correlation score is relevant to a part of the company’s business. If your company is going to make a potentially costly or lucrative decision based on your data science work, then it’s incumbent on you to make sure they understand your process and results as much as possible.

Conclusion

We hope this article helped to demystify this exciting and increasingly important line of work. It’s pertinent to anyone who’s curious about data science — whether it’s a college student or an executive thinking about hiring a data science team — that they understand what this field is about and what it can and cannot do.

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How is Python Used in Data Science?

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Python is a popular programming language used by both developers and data scientists. But what makes it so popular and why are so many data scientists choosing Python over other programming languages? In this article, we’ll explore the advantages of Python programming and why it’s useful for data science.

What is Python?

No, we’re not talking about the giant, tropical snake. Python is a general-purpose, high-level programming language. It supports object oriented, structured, and functional programming paradigms.

Python was created in the late 1980s by the Dutch programmer Guido van Rossum who wanted a project to fill his time over the holiday break. His goal was to create a programming language that was a descendant of the ABC programming language but would appeal to Unix/C hackers. Van Rossum writes that he chose the name Python for this language, “being in a slightly irreverent mood (and a big fan of Monty Python’s Flying Circus).”

Python went through many updates and iterations and by the year 2008, Python 3.0 was released. This was designed to fix many of the design flaws in the language, with an emphasis on removing redundant features. While this update had some growing pains as it was not backwards compatible, the new updates made way for Python as we know it today. It continues to be well-maintained and supported as a popular, open source programming language.

In “The Zen of Python,” developer Tim Peters summarizes van Rossum’s guiding principles for writing code in Python:

Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren’t special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one– and preferably only one –obvious way to do it.
Although that way may not be obvious at first unless you’re Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it’s a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea — let’s do more of those!

These principles touch on some of the advantages of Python in data science. Python is designed to be readable, simple, explicit, and explainable. Even the first principle states that Python code should be beautiful. In general, Python is a great programming language for many tasks and is becoming increasingly popular for developers. But now you may be wondering, why learn Python for data science?

Why Python for Data Science?

The first of many benefits of Python in data science is its simplicity. While some data scientists come from a computer science background or know other programming languages, many come from backgrounds in statistics, mathematics, or other technical fields and may not have as much coding experience when they enter the field of data science. Python syntax is easy to follow and write, which makes it a simple programming language to get started with and learn quickly. 

In addition, there are plenty of free resources available online to learn Python and get help if you get stuck. Python is an open source language, meaning the language is open to the public and freely available. This is beneficial for data scientists looking to learn a new language because there is no up-front cost to start learning Python. This also means that there are a lot of data scientists already using Python, so there is a strong community of both developers and data scientists who use and love Python.

The Python community is large, thriving, and welcoming. Python is the fourth most popular language among all developers based on a 2020 Stack Overflow survey of nearly 65,000 developers. Python is especially popular among data scientists. According to SlashData, there are 8.2 million active Python users with “a whopping 69% of machine learning developers and data scientists now us[ing] Python (compared to 24% of them using R).”4 A large community brings a wealth of available resources to Python users. Not only are there numerous books and tutorials available, there are also conferences such as PyCon where Python users across the world can come together to share knowledge and connect. Python has created a supportive and welcoming community of data scientists willing to share new ideas and help one another. 

If the sheer number of people using Python doesn’t convince you of the importance of Python for data science, maybe the libraries available to make your data science coding easier will. A library in Python is a collection of modules with pre-built code to help with common tasks. They essentially allow us to benefit from and build on top of the work of others. In other languages, some data science tasks would be cumbersome and time consuming to code from scratch. There are countless libraries like NumPy, Pandas, and Matplotlib available in Python to make data cleaning, data analysis, data visualization, and machine learning tasks easier. Some of the most popular libraries include:

  • NumPy: NumPy is a Python library that provides support for many mathematical tasks on large, multidimensional arrays and matrices.
  • Pandas: The Pandas library is one of the most popular and easy-to-use libraries available. It allows for easy manipulation of tabular data for data cleaning and data analysis.
  • Matplotlib: This library provides simple ways to create static or interactive boxplots, scatterplots, line graphs, and bar charts. It’s useful for simplifying your data visualization tasks.
  • Seaborn: Seaborn is another data visualization library built on top of Matplotlib that allows for visually appealing statistical graphs. It allows you to easily visualize beautiful confidence intervals, distributions, and other graphs.
  • Statsmodels: This statistical modeling library builds all of your statistical models and statistical tests including linear regression, generalized linear models, and time series analysis models.
  • Scipy: Scipy is a library used for scientific computing that helps with linear algebra, optimization, and statistical tasks.
  • Requests: This is a useful library for scraping data from websites. It provides a user-friendly and responsive way to configure HTTP requests.

In addition to all of the general data manipulation libraries available in Python, a major advantage of Python in data science is the availability of powerful machine learning libraries. These machine learning libraries make data scientists’ lives easier by providing robust, open source libraries for any machine learning algorithm desired. These libraries offer simplicity without sacrificing performance. You can easily build a powerful and accurate neural network using these frameworks. Some of the most popular machine learning and deep learning libraries in Python include:

  • Scikit-learn: This popular machine learning library is a one-stop-shop for all of your machine learning needs with support for both supervised and unsupervised tasks. Some of the machine learning algorithms available are logistic regression, k-nearest neighbors, support vector machine, random forest, gradient boosting, k-means, DBSCAN, and principal component analysis.
  • Tensorflow: Tensorflow is a high-level library for building neural networks. Since it was mostly written in C++, this library provides us with the simplicity of Python without sacrificing power and performance. However, working with raw Tensorflow is not suited for beginners.
  • Keras: Keras is a popular high-level API that acts as an interface for the Tensorflow library. It’s a tool for building neural networks using a Tensorflow backend that’s extremely user friendly and easy to get started with.
  • Pytorch: Pytorch is another framework for deep learning created by Facebook’s AI research group. It provides more flexibility and speed than Keras, but since it has a low-level API, it is more complex and may be a little bit less beginner friendly than Keras. 

What Other Programming Languages are Used for Data Science?

Python is the most popular programming language for data science. If you’re looking for a new job as a data scientist, you’ll find that Python is also required in most job postings for data science roles. Jeff Hale, a General Assembly data science instructor, scraped job postings from popular job posting sites to see what was required for jobs with the title of “Data Scientist.” Hale found that Python appears in nearly 75% of all job postings. Python libraries including Tensorflow, Scikit-learn, Pandas, Keras, Pytorch, and Numpy also appear in many data science job postings.

Image source: The Most In-Demand Tech Skills for Data Scientists by Jeff Hale

R, another popular programming language for data science, appeared in roughly 55% of the job postings. While R is a useful tool for data science and has many benefits including data cleaning, data visualization, and statistical analysis, Python continues to become more popular and preferred among data scientists for a majority of tasks. In fact, the average percentage of job postings requiring R dropped by about 7% between 2018 and 2019, while Python increased in the percentage of job postings requiring the language. This isn’t to say that learning R is a waste of time; data scientists that know both of these languages can benefit from the strengths of both languages for different purposes. However, since Python is becoming increasingly popular, there’s a high chance that your team uses Python, and it’s important to use the language that your team is comfortable with and prefers.

What is the Future of Python for Data Science?

As Python continues to grow in popularity and as the number of data scientists continues to increase, the use of Python for data science will inevitably continue to grow. As we advance machine learning, deep learning, and other data science tasks, we’ll likely see these advancements available for our use as libraries in Python. Python has been well-maintained and continuously growing in popularity for years, and many of the top companies use Python today. With its continued popularity and growing support, Python will be used in the industry for years to come.

Whether you’ve been a data scientist for years or you are just beginning your data science journey, you can benefit from learning Python for data science. The simplicity, readability, support, community, and popularity of the language — as well as the libraries available for data cleaning, visualization, and machine learning — all set Python apart from other programming languages. If you aren’t already using Python for your work, give it a try and see how it can simplify your data science workflow.

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How to Quickly get an Internship in Data Science

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After studying statistics, probability, programming, algorithms, and data structures for long hours, putting all the knowledge in action is essential. An internship at a great company is a great way to practice your skills, but at the same time is one of the most difficult jobs. Especially with such vast competition.  

Nowadays, many other opportunities are branded as “internship experiences” but they’re not actually internships. A key distinction is as follows: if you’re asked to pay for an internship, then it’s not an internship. An internship is a free opportunity to work in a specific industry for a short period of time, usually shadowing an existing employee or team.

This article will provide you with five tips to help you secure your first data science internship. However, first we’ll discuss what exactly data science is and what the job entails.

What is data science?

Data science focuses on obtaining actionable insights from data, both raw and unstructured, often in large quantities. This big data is analyzed by data analysts as it’s so complex it cannot be understood by existing software or machines.

Ultimately, data science is concerned with providing solutions to problems we don’t yet know are problems or concerns. It’s essentially about looking into the future and finding fixes for things that may happen or might be implemented. On the other hand, a data analyst’s role is to investigate current data and how this impacts the now.

What is the role of a data scientist?

As a data science intern, you will be responsible for collecting, cleaning, and analyzing various datasets to gather valuable insights. Later, with the help of other data scientists, these insights will be shared with the company in an effort to contribute to business strategies or product development. Within the role of a data scientist, you will be expected to be independent in your work collecting and cleaning data, finding patterns, building algorithms, and even conducting your own experiments and sharing these with your team.

5 Tips to Finding Your First Data Science Internship

Now that you know what data science is and what a data science analyst does, you may be wondering how to get a data science internship. Here are five actionable tips to land your first data science internship, beginning with a more obvious one: acquiring the right skills.

1.   Acquire the right skills

As a data scientist, you’re expected to possess a variety of complex skills. Therefore, you should begin learning these now to set yourself aside from your competition and increase the likelihood of landing a data science internship.

In fact, regardless of your internship role, you should be actively learning new skills all of the time, preferably skills that are related to your industry (e.g. data science). There’s no set formula to acquire skills; there are numerous ways to get started, such as online data science courses (some of which are free), additional University modules, or conducting some data science work yourself, perhaps in your free time.

The more relevant data science skills you have, the more appealing you’ll be to employees looking for a data science intern. So, start learning now and distinguish yourself from your competition; you won’t regret it.

2.   Customize each data science application

A common problem many graduate students make when applying for internships online is bulk-applying and using the same CV and cover letter for each application. This is a lengthy and tedious process, and rarely pays dividends.

Instead, students should customize each data science application to each company or organization that they’re applying for. Not all data science jobs are the same — their requirements are somewhat different, both in the industry and the company’s goals and beliefs. To increase your likelihood of landing a data science internship, you need to be genuinely interested in the company you are applying for, and show this in your application. Be sure to read through their website, look at their previous work, initiatives, goals, and beliefs. And finally, make sure that the companies you are applying for are places you actually want to work at, or else the sincerity of your application may be cast in a negative light, even if you don’t realize this.

3.   Create a portfolio

To stand out in such a saturated market, it’s essential to create your very own portfolio. Ideally, your portfolio should consist of one or several of your own projects where you collect your own data. It’s good to indicate you have the experience on paper, but showing this to potential employers first-hand shows that you’re willing to go above and beyond, and that you truly do understand datasets and other data scientist tasks.

Your portfolio project(s) should be demonstrable, covering all typical steps of machine learning and general data science tasks such as collecting and cleaning data, looking for outliers, building models, evaluating models, and drawing conclusions based on your data and findings.  Furthermore, go ahead and create a short brief to explain your project(s), to include as a preface to your portfolio.

4.   Practicing for interviews is crucial

While your application may land you an interview, your interview is the penultimate deciding factor as to whether or not you get the data science internship. Therefore, it’s essential to prepare the best you can. 

There are several things you can do to prepare:

●  Research what to expect in the interview.

●  Know your project and portfolio like the back of your hand.

●  Research common interview questions and company information.

●  Practice interview questions and scenarios with a friend or family member. 

Let’s break down each of these points further.

Research what to expect in the interview.

Every interview is different, but you can research roughly what to expect. For example, you could educate yourself on the company’s latest policies and events, ongoing initiatives, or their plans for the coming months. Taking the time to research the company will come through in your interview and show the interviewer that you’re dedicated and willing to do the work.

Know your project and portfolio like the back of your hand.

To show your competence and expertise, it’s essential to have a deep and thorough understanding of your project and portfolio. You’ll need to be able to answer any questions your interviewer asks, and provide detailed and knowledgeable answers.

Prior to the interview, familiarize yourself with your project, revisiting past data, experiments, and conclusions. The more you know, the better equipped you’ll be.

Research common interview questions and company information.

Most data science internship interviews follow a similar series of questions. Before your interview, research these, create a list of the most popular and difficult questions, and prepare your answers for each question. Even if these exact questions may not come up, similar ones are likely to. Preparing thoughtful answers in advance provides you with the best opportunity to express professional and knowledgeable answers that are sure to impress your interviewers.

This leads us to our next point: practicing these questions.

Practice interview questions and scenarios with a friend or family member.

Once you’ve researched a variety of different questions, try answering these with a friend or family member, ideally in a similar environment as the interview. Practicing your answers to these questions will help you be more confident and less nervous. 

Be sure to go over the more difficult questions, just in case they come up in your actual data science internship interview.

Ask whomever is interviewing you (the friend or family member, for example) to ask some of their own questions, too, catching you off guard and forcing you to think on your feet. This too helps you get ready for the interview, since this is likely to happen regardless of how well you prepare.

5.   Don’t be afraid to ask for feedback

You’re not going to get every data science internship you apply for. Even if you did, you wouldn’t be able to take them all. Therefore, we recommend asking for feedback on your interview and application in general.

If you didn’t land the internship the first time, you can use this feedback and perhaps re-apply at a future date. Most organizations and companies will be happy to offer feedback unless they have policies in place preventing them. With clear feedback, you’ll be able to work on potential weaknesses in your application and interview and identify areas of improvement for next time.

Over time, after embracing and implementing this feedback, you’ll become more confident and better suited to the interview environment — a skill that will undoubtedly help you out later in life.

Frequently Asked Questions

What do data analyst interns do?

Data analyst interns are responsible for collecting and analyzing data and creating visualizations of this data, such as written reports, graphs, and presentations.

How do I get a data science job with no experience?

Getting a data science job with no experience will be very difficult. Therefore, we recommend obtaining a degree in a relevant subject (e.g. computer science) if possible and creating your own portfolio to showcase your expertise to potential employers.

What does a data science intern do?

Data science interns perform very similar roles and tasks to full-time data scientists. However, the main difference here is that interns often shadow or work with another data scientist, not alone. As an intern, you can expect to collect and clean data, create experiments, find patterns in data, build algorithms, and more.

To Conclude

Data science internships are few and far between, and landing one can be difficult. But it’s not impossible and the demand for these roles is slowly increasing as the field becomes more popular.

The role of a data scientist intern includes analyzing data, creating experiments, building algorithms, and utilizing machine learning, amongst a variety of other tasks. To successfully get a data science internship, you should begin acquiring the right skills now, customize each application, create your very own portfolio and project, practice for interviews, and don’t be afraid to ask for feedback on unsuccessful applications.

Best of luck to all those applying, and remember: preparation is key.

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User Experience Jobs: 7 Options & How to Choose a UX Career You Love

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If you have ever done a quick job search for “user experience design,” chances are you’ve seen a number of titles and descriptions that aren’t always as simple as “UX designer.”

User experience has a variety of specializations, and as a job seeker and practitioner, you should know the skills and applications that come with each. Understanding these differences will help you decide your UX career path and and help you find the appropriate job to fit your interests and skill set.

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Best Resources for Learning Digital Marketing in 2021

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Whether you’re looking to learn to do your own digital marketing for your business, get into the life of a digital marketer, or add new skills to your existing arsenal, there are plenty of free and affordable resources out there to help you learn your CPCs from your CPMs, and how to master the tools of the trade.

Below is a list of our favorite resources and certifications to help you learn digital marketing in 2020!

Guides + Blogs

Moz

Search Engine Optimization is key to any digital marketing strategy, and Moz is the go-to free resource for all things SEO. It’s got everything you need whether you’re a complete newbie to keyword research and optimization, or an experienced digital marketer looking to refresh your skill set.

Best for: SEO

Our pick: The One Hour Guide to SEO is a quickfire lesson in 6 easy-to-digest videos, covering all the need-to-know SEO essentials in just one hour.

Content Marketing Institute

Explore blogs, resources, and guides on all things content marketing with the Content Marketing Institute. They also have a killer daily newsletter that you should definitely sign up for to keep on top of all the latest trends in content marketing.

Best for: Content marketing

Our pick: Getting Started in Content Marketing is a “back to basics” series designed to get you started, offering content marketing essentials, processes to implement, and helpful templates.

Ahrefs 

Ahrefs is one of the best hubs full of tutorials, case studies, and opinion pieces from some of the best in the industry. Check out some of their great free tools for when you’ve mastered your SEO skills!

Best for: SEO

Our pick: Once you’ve learned the SEO basics, one of the best free tools out there is the Ahrefs SEO toolbar, a chrome extension that allows you to do top level SEO audits of any website with the click of a button.

Neil Patel

An icon in the digital world, Neil Patel hosts an amazing comprehensive suite of educational content on anything and everything you need to learn digital marketing.

Best for: SEO, content marketing, email marketing, social media, e-commerce, and search.

Our pick: Instagram Unlocked is part of the free digital marketing training series, and offers a free two-week training module to help you learn social media marketing strategies specifically for Instagram growth — something everybody wants.

AdEspresso Academy 

AdEspresso Academy includes step-by-step guides to learn both Facebook and Google Ads that are easy to understand, as well as regular webinars, blogs, and downloadable ebooks full of great free content.

Best for: Facebook Ads and Google Ads

Our pick: On the Academy page, there’s a great list of 6 easy steps to getting on top of Facebook Ads; start with an eight part guide that covers everything from setting up an account, all the way through to reporting and optimisation.

Social Media Examiner

With guides, studies, webinars, and a great podcast to help you keep up to date with the world of social, Social Media Examiner is your hub for social media knowledge. 

Best for: Social media marketing

Our pick: While we typically hear a lot about Facebook and Instagram, it’s not often people talk about the power of social media marketing on LinkedIn — a no brainer for B2B companies. This guide to LinkedIn ads is a great starting point for anyone new to LinkedIn ads, and provides a huge number of helpful Linkedin articles and strategy templates.

Search Engine Land

What started as a major resource for all things search-related, Search Engine Land has now branched into email, social, and retail. It offers free webinars, how-to guides, handy resources, and tools for auditing to help you understand almost all aspects of digital marketing.

Best for: Search, Email, Social and Retail

Our pick: Google Ads can be confusing (don’t worry, we get it!) but this beginner’s guide to paid search is incredibly easy to follow and understand, with things like glossaries for common terms and how to do keyword research — a must read for those who are new to paid search!

Unbounce

UnBounce is a landing page building platform, but also has a very good resource and learning centre to help you understand everything you need to know about landing pages, conversion optimisation, and where landing pages sit within the wider digital marketing landscape. 

Best for: Landing pages and conversion rate optimisation

Our pick: Never given landing pages a thought until now? This 8 module introduction is a great way to understand the fundamentals of landing pages, why they matter, and how to use them.

Certifications

While there’s plenty of free guides, resources and blogs out there, a certification can help you stand out from the crowd when looking for a job as a digital marketer, or give you an easy to follow holistic overview of a topic, coming out with the confidence to action your learnings. Here’s our picks for the best online certifications out there:

Google Analytics Academy + Google Digital Garage

Get certified in Google Ads, Google Analytics, and Google My Business while also completing non-certification short courses in more niche areas, or explore courses on topics like Google Shopping and YouTube.

Cost: Free

Facebook Blueprint

After utilising the library of free resources Facebook offers through their learning centre (there are over 90 courses!), you can apply your knowledge of social media marketing and beyond to their Blueprint Exams and obtain a Facebook certification in a few key areas. The best part? They’ll guide you through exactly what you need to learn for each course.  

Cost: $150 USD

Hubspot Academy

With both short courses and certifications, HubSpot Academy is globally recognised, and has many different digital marketing courses to help you learn digital marketing essentials, covering almost all areas including social media marketing, SEO, and business analytics.

Cost: Free

Hootsuite Academy

Hootsuite Academy offers socially focused certifications and courses with an exam at the end of each certification. As a leading social media platform, the Hootsuite brand is very well respected within the industry, and their certifications are too.

Cost: $99–$999 USD

And lucky last, we can’t go past one of the best resources for learning digital marketing — General Assembly! GA offers part-time and full-time digital marketing courses, as well as short hands-on workshops across all areas of digital marketing, and is one of the industry’s most respected education providers. Want to know more? Get in touch!

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Top 15 Skills Every Digital Marketer Needs to Master

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Tech is booming, and the recent COVID-19 pandemic has accelerated the need for organizations of all sizes to move away from traditional marketing and establish a competitive online presence as swiftly as possible. This need fuels the demand for skilled digital marketing professionals worldwide.

In fact, at this very moment, there are over 150K digital marketing jobs available on LinkedIn alone, which makes digital marketing a perfect opportunity for young professionals and career changers to enter the tech industry and future-proof their job prospects for the years to come. But what is digital marketing? What are the digital marketing skills needed to get started? Is digital marketing a technical or creative skill?

First of all, digital marketing is not a monolith, but rather a collection of skills and competencies. As a senior digital marketer, you need to combine creative analytical and technical skills to communicate the right message at the right time to the right audience. At the same time, you need to understand the nuances of how various digital channels work to be able to track, analyse, and optimise your marketing plans.

Digital marketers come in many shapes and sizes. As a digital marketer, you will be required to wear many hats and work on a diverse range of projects and challenges during your career. In principal, there are two broad types of digital marketing:

  1. Performance Marketing
  2. Content Marketing

Each type requires digital professionals with a different set of skills to succeed. However, there are some digital marketing skills that both types need to “check” as prerequisites for the role. Here’s a digital marketing skills list that every digital marketer needs to master:

Digital Marketing Foundational Skills

Required for performance, content, and social media marketing roles.

1. Search Engine Marketing and SEO

Understanding how search engines index websites and rank pages will not only enable you to grasp one of the most sought-after digital marketing skills in the market, but also provide you with all the foundational knowledge required to project manage web development and content marketing projects. Moreover, SEO skills are essential for optimising product pages within e-commerce ecosystems such as Amazon, Lazada, and eBay.

2. Copywriting

Copywriting is an absolute essential skill for every digital marketing professional. Digital marketing is all about communicating the right message to the right audience at the right time. The art of crafting compelling messages is at the heart of everything a digital professional does. Whether it’s for social media advertising, building landing pages, developing banner ads, or crafting paid search ads, there is always an element of copywriting involved.

3. Data Analytics and Visualization

Data-driven marketing is not only a recent buzzword but an essential digital marketing skill. Every digital marketing activity comes with data, so at minimum, digital marketing professionals ought to know how to work with and visualize data using tools like Excel or Google Analytics. In today’s digital marketing industry, every role comes with a wealth of data to be collected and analysed. For example, a social media marketer will need to report on the effectiveness of social media campaigns, the same way a pay-per-click (PPC) executive is required to report on paid media performance.

4. Basics of Business and Finance

Understanding the basics of business and finance is an absolute must-have to succeed in the digital marketing industry. The end objective of digital marketing is to generate profit for the business. Upon entering the digital marketing space, you will be bombarded with jargon such as CPA (cost per acquisition), CPL (cost per lead), CPM (cost per 1,000 impressions) and more! The ability to understand these metrics and connect them with the “big picture” is one of the very first skills you will need to master.

Performance Marketing Skills

Required for media buying and analytical roles.

1. Pay-per-Click Fundamentals

Pay-per-click or PPC covers the most popular kinds of digital advertising such as Paid Search, Facebook Advertising, Amazon advertising, etc. Every digital marketer needs to understand the PPC advertising auction logic as well as some platform fundamentals to be able to set up and optimise PPC campaigns successfully on various digital marketing channels.

2. Media Planning and Buying

Media planning and buying are some of the oldest advertising skills that are still relevant in the market. Understanding how to purchase media inventory directly or via programmatic advertising, the targeting options, as well as the pros and cons of each approach, is essential for every marketer who wants to build a career in the numerical side of digital marketing. Lastly, being able to deliver a complete media plan is an absolute must for both agency and in-house digital marketing roles.

3. Digital Tracking and Analytics

Performance marketers need to be experts in digital tracking — meaning they should be able to put together and implement a digital measurement plan. Moreover, they should understand how to set up conversion tracking on various platforms, make use of UTM tags or various tracking codes effectively, and how to take advantage of third-party tracking tools if necessary.

Content Marketing Skills

Required for content marketing and social media roles.

1. Social Media Marketing Know-How

Social media has become an integral part of our lives. At the same time, the social media marketing landscape is constantly expanding and evolving. Every content marketing professional should understand the basics of how social media algorithms operate to be able to conceptualise and develop impactful, relevant, and attention-grabbing social media content. Moreover, as a social media professional you should be the first to embrace and explore new social media channels and tactics.

2. Intermediate Design Skills

In an ever-expanding digital marketing ecosystem, the need for marketing visuals is greater than ever. The ability to ideate, develop, and modify marketing assets and collateral on the fly is a must-have skill for every content marketing professional. Experience with tools like Photoshop and online platforms such as Canva or equivalent will give you a competitive advantage in the digital recruitment market.

3. Endless Creativity

Marketing and creativity go hand in hand! As a digital content marketer, you should be able to conceptualise, project manage, and implement creative digital marketing campaigns as needed. Furthermore, you should familiarise yourself with concepts such as marketing seasonality and campaign-thinking, as well as being able to deliver click-worthy creatives for various advertising purposes. Experience with video production and editing will be a huge plus in the years to come.

How can I improve my digital marketing skills?

Digital marketing is evolving fast! No matter how senior you may become, always remember that every digital marketer needs to upskill and reskill on a yearly basis to stay relevant in an ever-changing industry. On this note, it’s worth pointing out the skills required to improve your digital marketing know-how for future trends:

1. Project Management and Collaboration

Digital marketing is a fast-paced and multi-faceted job. You’ll need to be on top of various projects, channels, and marketing initiatives at the same time. Moreover, you’ll have to communicate effectively with a diverse range of internal and external stakeholders. Consider actively investing in and growing “soft skills” such as teamwork, empathy, adaptability, and problem solving.

2. Customer Relationship Management (CRM)

As mentioned, data is at the heart of every digital marketing initiative. The ever-growing data protectionism and the rise of marketing automation means that the internet will be a safer place for all of us, but it also fuels the need for customer relationship management (CRM) as a key skill within the digital marketing space. Understanding how to work with first-party data, the media opportunities they open, and the fundamentals of marketing automation, is an essential skill for all senior digital marketers.

3. Email Marketing

Email is still the number one most effective digital marketing channel. Why? There is a lot more than meets the eye to strategizing and implementing an effective email marketing campaign. Crafting an intriguing subject line, writing an engaging click-worthy email, and leveraging marketing automation in the context of email marketing are extremely valuable skills in the digital marketing industry.

4. User Experience Design (UX)

UX or user experience design is a relatively new entry in the long list of digital marketing skills to master. UX is the area of digital marketing or product design that ensures intuitive, meaningful, and positive interactions throughout a customer’s journey. Think of UXers as the architects of the digital space. Understanding how to best structure a website or mobile app, the empathetic design thinking involved, and what a good user experience entails is a very practical must-have skill for any senior digital marketer, product manager, or project manager.

5. Presentation and Communication Skills

Last but not least, whether you end up working in-house, within an agency environment, or running your own business, you will always have to present your ideas to various stakeholders, teammates, clients, or investors. The ability to deliver clean, clear, and impactful presentation documents, as well as being able to communicate with confidence, are key skills you should aim to master.

Conclusion

Digital marketing includes a diverse collection of skills and competencies you should aim to develop depending on which part of the industry you’d like to build your career on. Assuming you are a beginner in the space, the safest way to land your dream digital marketing role is to invest in a structured course, launch your own side-hustle to gain practical experience in the above areas, or both! As an experienced digital marketer, you should aim to regularly upskill yourself through credible workshops, seminars, and industry-specific events.

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What Is an Income Share Agreement? (ISA)

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Financing education is a huge decision at any point of life—even more so in such uncertain times. That’s where an income share agreement (ISA) might be a great option to invest in yourself. In professional education, an ISA is not a loan, but rather a financial structure where tuition is repaid as a percentage of your monthly income for a fixed number of years.

How do Income Share Agreements Work?

At General Assembly, our ISA, Catalyst, allows students to learn in-demand tech skills in our full-time immersive courses and land a job with the help of our career services team. Repayment begins only once you secure a role earning at least $40,000 per year. After you’ve reached the minimum income threshold, you’ll start paying back 10% percent of your monthly earned income over 48 months.

Learn More About Other Financial Aid Options

While ISAs offer repayment flexibility, if you’re still wondering how to afford General Assembly we reccomend checking out our various financial aid options. Our Admissions team can guide you through scholarships, grants, and more.

Get Started Today

With our ISA, you can confidently invest in tech skills without immediate financial strain. Our full-time bootcamp and short courses offer varied financing options to make starting this educational journey accessible. Connect with the admissions team to learn more about General Assembly’s financing options.

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A Day in Class With a Remote Career Changer

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For many students, enrolling in a career-accelerating bootcamp can be a daunting decision, especially when it’s conducted entirely online. How do students stay engaged and accountable while learning remotely? We connected with GA student Fletcher Jones to walk us through his day-to-day in our Software Engineering Immersive program. He graduated in July 2020 and landed a job as a software engineer at Safe & Reliable Healthcare shortly after.

Before coming to GA, I was an actor, a model, and a recording artist. I also had experience as a former student ambassador for the U.S. State Department, and after graduating from college, I worked as a marketing consultant. Later, I worked closely with Senator Bernie Sanders during his 2020 campaign for president.

After the presidential race changed, I — like many others — found myself out of the job. And that’s not all: At this point, the pandemic had begun, and the U.S. entered a tumultuous period of race relations. It was a difficult decision but I decided it was best to take on the challenge of a career change while spending some time at my parents’ home in North Carolina. I wanted a path with more job security that also strengthened my problem-solving skills — following my passion for computer science at GA seemed like the best solution. It was.

My instructor was based on the West Coast, so by being on the East Coast during the course (and being a night owl), this provided amazing flexibility. Given the time difference, my schedule probably isn’t typical for a GA student, but learning remotely at GA gives you even more control over your day and how you use your time when you’re not in class. Plus, all the sessions are recorded, so you can revisit at any point. For me, that was a huge benefit to learning online because the recorded lessons were so helpful for taking notes. Online learning was not my first choice, but it was definitely the best one. I’d absolutely do it again.

Here’s what my average day looked like during the course:

7:30 a.m. — Rise & Shine 

Given the noon start time on the East Coast, I was able to enjoy a relaxed morning routine. This really helped me start class with energy and a positive attitude every day.

8–11 a.m. — Morning Routine

I would start my day with a walk around the neighborhood — sometimes with my mom, and sometimes solo while listening to music. When I returned home, I’d eat breakfast and do some stretching, too.

11:30 a.m. – Check the Day’s Schedule

Every day, we’d have lectures on at least two topics concerning front-end or back-end programming. They would be split into a morning exercise, module one, lunch, and then module two. Here’s a sample of the schedule:

GA Alum Fletcher Jones’ Software Engineering Immersive Remote Schedule, Week 10

12 p.m. — Sign On for Coding Exercises

We’d often begin with a morning exercise (or afternoon in my case). These could range from an assigned coding challenge, to a quick lab exercise, or a breakout group discussing an engineering topic. After these exercises, one member would present the group’s learnings. Everyone comes into the class at different levels of experience, so these sessions were really valuable to learn from students who might have more background in coding.

Here’s an example of a coding challenge — I especially appreciated this one because it showed up on a technical interview during my job search! I was able to complete it in class and present my solution to the instructor for feedback.

12:30 p.m. — First Module Begins

Module 1 is a mix of instructor lecture and (depending on how intensive it is) related lab exercises. These are never solo — you’re always working in pairs or small groups. We would share computer screens using Zoom to work through these, in addition to other tools like our computers’ terminals, Chrome browser, and Visual Studio Code (or another preferred text editor).

The lectures on React really stood out to me — I instantly fell in love with them. It’s such a useful library that allows you to build out robust apps that remain scalable with relative ease. I’m grateful that Dalton, my lead instructor, did such a great job capturing my attention with React and the MERN stack because these are what I currently use at my job. Dalton was always eager to answer questions and would always make sure his students completely understood the topics.

These lectures started with a walkthrough of how React is implemented on Facebook (which it was created for). That visual was really helpful in understanding the fundamentals. Dalton would highlight specific parts of posts, comments, or profiles — things we were already familiar with — and explain to us how they were coded in React. Later in the week, we put all the basics together to create a fully functional app using React and other technologies from earlier in the course (MongoDB, Express.js, and Node.js).

1:30 p.m.— 15-Minute Break

Just the right amount of time to brew a cup of Yerba Mate to get me through the rest of the day. After, we would reconvene to wrap up Module 1.

3:30 p.m.—First Module Ends, One-Hour break

Here I would eat with my family, sometimes take a walk, or on really rough days…  take a nap! 

4:30 p.m. — Second Module Begins

For the majority of the course, the time allotted for second modules was usually spent in a lab to get hands-on practice and dive deeper into the ideas we learned during the first module. For instance, our first module on React was followed with a lab exercise that brought our app prototypes to life.

5 p.m. — 5-Minute Break

Sometimes our instructor would see people yawning, and we’d have a five minute break. Or sometimes we’d get a bio break if a lecture was really long. It’s nice that our instructor paid attention to little things like that.

6 p.m. — Presenting Group Work  

After a brief break, we’d present group work. Sometimes you’d get assigned into groups to work through an activity, or in Slack, you could use reactions to request teammates. Using Zoom’s breakout sessions, this kind of group work was engaging and motivating. It’s so valuable to troubleshoot with people from (seemingly) unrelated backgrounds to learn how they problem-solve.

One person from each group would agree to present. Sometimes, it was intimidating to see the progress others were making, but most times, I felt that I “got” a concept or solved a problem more quickly. Ups and downs are just part of the day-to-day, and everyone progresses differently throughout the course.

7 p.m. — 15-Minute Break 2.0

During these breaks, I’d interact with my family or just chill for a few minutes.

8 p.m. — Class Ends

Crushed it.

8 p.m. — Dinner

My parents would wait for me to finish class, and we’d sit down together to catch up on the day and what happened in class — easily the best part of my day.

9 p.m. — After-Hours Support

After-hours support is something students can take advantage of a few times a week if necessary. Adonis, our teaching assistant, was great and had wide-ranging knowledge in both front-end and back-end development. Adonis helped me get a better grasp on working on servers, specifically using MongoDB with Express. I was having trouble with the database for one of my portfolio projects, Notify, which was a streaming music service using the SoundCloud API. Adonis spent about an hour helping me figure out the bug.

10:00 p.m. — Start Homework

At this point I would complete any unfinished labs and review exercises that need more attention.

Midnight — Ideal Bedtime

Eight hours of sleep was everything. Sometimes, I wouldn’t get to bed until even later; it felt good to go to bed knowing that I did the best as I could, and that nothing was hanging over my head. I was actually doing something and making progress — with the pandemic and all, I hadn’t felt that in a long time.

Two other key areas where I spent time throughout the course were prepping with my career coach and working on my final project.  

Meeting With Career Coaches and Portfolio Development

Rashid Campbell, a member of the Outcomes team, was my career coach at GA. Rashid did more than just prepare us for our job search — he was our frontline defense against burnout and genuinely cared about how I was doing as a human being, not just as a student. Learning in an Immersive is intense to begin with, but during the pandemic there was added stress!

On Tuesdays we would meet for two to three hours to work on my resume, personal branding, job applications, and technical interview prep. Additionally, one requirement for students to receive Outcomes support was that we had to create a portfolio summarizing our five projects. I would make time for this kind of work toward the end of the course on many days.

The Capstone Project

For their capstone projects, students mimic a team-client interaction, collaborating to build and deploy a full-stack application that fulfills provided specs. The final result integrates functionality from a third-party API. Instructors urge students to choose a capstone project grounded in a personal passion or a problem they’re excited to tackle.

During the last week of the course, the schedule was very open to allow for deep focus on your project. Any lectures were mostly optional, and we could take breaks whenever we needed. We had an open classroom policy — almost like a workplace environment — so that we could focus solely on the project.

My Final Project

My capstone project was inspired by my background in acting. A lot of people in the arts lack a centralized place to find fellow creatives to collaborate with on projects or events (or promote them). I created a wireframe for a website called Accolade, which helps creatives and artists stay connected and collaborate. Creatives can post and spread the word about their upcoming performances, showcases, or premieres on the site. They can also post an ad looking for actors, models, photographers, or videographers, and more.

First, I had to draft a wireframe of what it would look like and document its features, user interface, and tech dependencies — like a map API to display event locations.

On the day of presentations, students would give praise and “grows” — constructive criticism grounded in an empathetic understanding of how hard it can be to put yourself out there. This approach helped some students feel more comfortable with having their work in the spotlight.

Learning remotely at GA offered more support from fellow students than I ever expected. Everyone was so understanding when there were two deaths in my family during the course. When I got my job offer, Rashid helped with salary negotiations. I still keep in touch with students from my class as they get started on their new career paths. This was a period of my life that I will never forget — through the people I met. It was an authentic milestone.

Over the years, I used to feel anxious about all my loose ends. I have done so many things: I earned a journalism degree after 4 years of college; I jumped from entertainment, to politics, to whatever paid the bills. I looked at my peers who stuck to one thing and admired how far they went. After this experience, I realized that my diverse experiences are my superpower. I can literally do anything I put my mind to.

And you can too.

Take the Leap Into Software Engineering

Should You Consider a Career in Digital Marketing?

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These days you would be hard-pressed to find a business, regardless of its size, that isn’t investing in digital marketing to assist in their promotional efforts. Businesses must try their best to keep up with their fast-paced consumer markets and are challenged with staying in tune with the ever-evolving digital marketing technologies and strategies available to them.

As a result, digital marketing budgets are increasing by double digit increments year after year, projected to hit a total global spend of 306 billion by 2020, keeping the field of digital marketing both challenging and exciting.

What exactly is digital marketing, anyway?

Well, it’s not too far off from what you might think of as traditional marketing: businesses or organizations connecting with their audiences to promote their brands, services, and/or products, ideally bringing them closer to purchase as they span the customer journey. However, as consumers consistently spend more time online, marketers are shifting their promotional efforts to meet consumers where they are. Thus, digital marketing has come to the forefront, with marketing strategies spanning a variety of online channels such as social media, search engines, email, online publications, and other key business websites.

Today, the field of digital marketing is more interesting than ever and encompasses a wide range of knowledge and skill sets. It appeals to those that consider themselves creative types as well as those who are more analytically or technically minded. A digital marketing career includes a mix of desired skills to be successful in the field — such as data analysis, automation software expertise, and user experience design — as represented in the Altimeter State of Digital Marketing Report.

Digital Marketing Career Opportunities

While the skill sets required of digital marketing specialists are vast and diverse, it’s typically not expected that a single digital marketing role take on all of these skills. Instead, digital marketing careers are more commonly made up of a variety of roles and responsibilities that span areas such as:

Content Marketing 

Content marketing entails the creation and distribution of consistent, valuable, and engaging content — emails, blog posts, videos, ads, social media posts — to clearly defined audiences. It’s the content marketing manager’s job to decide what kinds of content will resonate most with key audiences and keep them coming back for more. Content marketing managers work with their team members to decide how to use or repurpose pieces of content to suit the various digital channels leveraged by the business, ensuring that the content created has a long shelf life and reaches as many viewers as possible.

Search Engine Marketing

While a solid content marketing strategy is important for digital marketers to develop, it’s just as important for them to optimize their content and websites for search engines, as search engines are primarily what people use to find the information they need. Digital marketers have put various search engine optimization (SEO) techniques in place to improve the ranking of their content on search engines like Google. SEO can be a full time job; it’s the SEO Manager’s job to ensure content and websites are optimized as much as possible and are adapting to the requirements of continually changing search engine algorithms, such as Google’s PageRank.

Pay Per Click (PPC) marketing takes SEO one step further, applying a lot of the foundational aspects, but offering content through a digital ad on the search engine that viewers click on to access. Advertisers are charged per each click on the ad, hence the name of the practice. Putting money behind these ads yields a higher chance that the ad content will be seen. PPC managers are hired to determine which keywords to associate with the promoted ads, how large of a budget to allocate towards the advertising campaign, and which content to promote as part of the advertisement itself.

Social Media Marketing

Social media platforms such as Facebook, Twitter, Instagram, and LinkedIn are available for digital marketers to use to promote their brands, generate followers, and drive traffic to their websites for future lead generation. It’s the role of the social media marketing manager to determine which social media platforms are best suited for the company’s audience, what content should be shared at what cadence and time of day, and which topics will interest followers based on monitoring conversations through specific keywords, phrases, or hashtags. Social media is an exciting part of digital marketing for people new to the field to dive into, and its use cases and features are always shifting and expanding.

Email Marketing 

Email is another channel digital marketers can use to reach their prospects and customers. When done right, it allows email marketing managers to strategically send emails that rise above the noise of crowded inboxes and provide a relevant and personalized touch to their subscribers. Emails can come in many forms such as monthly newsletters, event promotions, educational product tips and tricks, and holiday discounts. Email marketing is often in place to point subscribers to a company’s website, with the hopes of driving further engagement or product purchases. Email is a tried and true digital marketing method that’s always improving and challenging digital marketers to do better, ensuring that email marketers stay challenged and subscribers stay informed and engaged.

Marketing Automation

As the options available to digital marketing professionals continue to evolve and campaigns become more sophisticated, so must the technologies that digital marketers use to maintain them. Enter marketing automation: the ability to utilize software to automate marketing operations that might otherwise be done manually. For example, marketing automation can allow digital marketers to set up processes on the back end of their various marketing tools to automatically send welcome emails to their new newsletter subscribers or schedule their daily social media posts. Marketing automation managers collaborate with many of the above mentioned roles and are most effective when they’re able to fully leverage both their creative and analytical attributes.

The Earnings of Digital Marketers in 2020

Digital Marketing, no matter which direction you go within the field, is in high demand and the earnings that can be made are in direct alignment. According to Mondo’s 2020 Tech, Digital Marketing, & Creative Salary Guide, you can expect to make upwards of $60,000 USD as a starting salary in most areas within digital marketing, progressing (upwards of $110,000 USD in some cases) as you develop in your career. This of course varies across regions and disciplines, with more technical roles tending to align with higher earnings.

Plan for a Digital Marketing Career

Digital marketing is an exciting field to get into and is only going to get more exciting over time as technology continues to advance. Should you find yourself interested in pursuing a career in digital marketing, don’t be afraid to explore the various ways you can dive into the career path. You’ll find that there are a number of great resources you can invest in to get you on your way. Whether you just recently finished school or you’re switching careers, digital marketing holds un-capped potential that’s yours to take advantage of in 2020.

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