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A Guide to Startup Compensation

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If you’re pursuing a job at a startup company, one of the most important factors you’ll need to consider is compensation, which is commonly structured differently than at a mature company. This is largely dependent on the life stage of a company, which can greatly impact compensation, as well as work-life balance, risk, and upside.

Compensation at a startup company is largely made up of three components: salary, benefits, and equity. The value of each depends on the stage of a company’s growth, the role, and an employee’s previous experience. A good rule of thumb, though, is this: The earlier a stage the company is in, the lower the salary and benefits will be, but the higher the equity will be. As the company matures, the scales start to tip in the other direction. Let’s talk in a bit more detail about each of these.

Salary

As mentioned above, salary is largely contingent on the company’s stage, the role, and the employee’s previous experience. There is no one-size-fits-all here. At an earlier-stage company, you can almost certainly expect a lower base salary than the industry norm, regardless of your previous experience. As the company matures, the salaries of all positions start to get closer and closer to market rate. If you’re curious what to expect, we recommend playing with the salaries and equity tool by AngelList or researching salary ranges at specific companies on Glassdoor.

Benefits

Benefits at a startup are also largely dependent on stage. If good benefits are important to you, then an early-stage startup is likely the wrong place to work. However, as a startup grows, its benefits often become an extension of its culture and are used in all recruiting efforts. Take, for instance, Airbnb, which offers a $2,000 travel stipend to all employees. Other startups may allow pets at the office, or offer gym and other discounts, catered lunches, generous vacation policies, or flexible remote-working options.

Equity: Stock and Vesting Schedules

Equity is often the most confusing and intriguing part of a compensation package at a startup. Equity refers to ownership of the company, and this can be extremely valuable if the company ever sells or goes public (learn more about startup fundraising here and in our eBook, How to Get a Job at a Startup).

What’s important to know here is that no employee is ever “given” equity. Instead, employees often receive stock options, which are the option to purchase equity in the company at a heavily discounted price. You also are not given all of your stock options up front; rather, you earn an increasing amount of options over a four-year period. That four-year period is often referred to as a vesting schedule. The typical vesting schedule gives you one-fourth of your options at the end of your first year, and then 1/48th every month after that. Once your options vest, you have the right to purchase them (or not).

Getting into a company early has a big impact on the amount of stock options you receive and at what price. If you join a company early, you are often rewarded with a higher number of options at a much lower price. As the company matures, the risk gets lower and its ability to pay market-rate salaries improve, so you will typically receive fewer stock options and at a higher purchase price.

The benefit of purchasing your options is that eventually — fingers crossed — the company will sell or go public and you will get a big payday. For example, early Instagram employees turned their stock options into an average profit of nearly $8 million! And there’s the famous example of the Facebook muralist who was compensated in stock options that were eventually worth north of $200 million. Of course, these examples are far on the ludicrous side of the scale, and many people don’t make any money from stock options — but risky or not, they’re part of what makes joining a startup so exciting.

How to Negotiate Your Startup Offer

There are special considerations to make when negotiating your compensation at a startup. Macia Batista, a career coach at General Assembly’s New York campus, walks you through essential steps for building your ideal job offer.

  • Know your minimum number. Leverage sites like PayScale and Glassdoor to learn to learn what employers in your city are paying for similar roles and industries. Do your research ahead of time to fully understand the fair market value for the position, taking into account background and experience. Know your worth!
  • Provide a salary range. Determine a range for yourself, then ask for the upper half of it, so you can negotiate down if needed. Giving a range demonstrates flexibility. It gives you the opportunity to ask for more when an offer is presented, and negotiate other variables, like 401k contribution, remote work options, or vacation days. Tell the hiring manager, “I’m targeting roles with a range of X, but I’m focused on the entirety of the package including culture, growth, and mission.”
  • Consider the whole package — not just salary. Compensation goes beyond your paycheck. When weighing a job offer, look at factors like bonuses, equity, health care and retirement plans, transportation costs, schedule flexibility (e.g., working from home and vacation time), and potential for growth at the company.
  • Ensure your pay increases with funding. If you’re joining an early-stage startup, equity (stock options) is oftentimes part of the compensation package, since these offers often fall below market salary. However, you should be be earning a fair market-value salary as soon as the company raises real money. I recommend signing a written agreement with your employer to guarantee a pay increase once the company has more capital.

How to Land Your Dream Startup Job

Working in the startup world can be one of the most challenging, exhilarating, sometimes heartbreaking, and oftentimes fulfilling journeys of your life. But before you find first startup job, there are terms to learn, steps to take, and skills to grow to make you a candidate who stands out from the crowd.

In our eBook, How to Get a Job at a Startup, we’ll help you find your dream startup job through the knowledge of startup job-hunters, founders, and employers. Get firsthand tips on how to break into a startup career, clear up confusing industry jargon, and learn about important resources that will aid you on your journey as a startup employee.

General Assembly believes that everyone should be empowered to pursue work they love. We hope you’ll find this book to be a helpful first step in getting there yourself.

How to Land a Job at a Startup

Learn how to start your journey with our exclusive guide.

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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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Understanding the Difference Between Data Analytics and Data Science

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Data analytics and data science are two key terms thrown around in the tech and business world. What do they mean, and what’s the difference between the two? Data analytics is concerned with performing statistical analysis on existing datasets to solve problems and find answers to current issues we don’t know the answers to. Data science focuses on creating actionable insights and predictions from raw and structured data, often in large quantities.

This article will discuss the critical differences between data analytics and data science. First, we’ll explain what big data is, followed by a little more information on each role: data analyst and data scientist.

What is Big Data?

Big data can often be challenging to comprehend. Big data is usually more extensive and more complex than other datasets and may contain multiple sources. Put simply, big data is too large to process and understand using traditional data processing methods. This is where data analysts and data scientists come in — their job is to interpret this data and present it to their company or organization.

The original definition of big data, prefaced by Gartner (2001) is as follows: “Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.”

The Three V’s of Big Data

To better understand big data, whether as a data analyst, scientist, or curious individual, we must apply the three V’s: Volume, Velocity, and Variety.

Volume

When it comes to an understanding of big data, the volume of this data matters significantly. Big data requires you to process an increased volume of unstructured data, e.g. Twitter feeds, sensor-enabled equipment, forum responses, or comments and reviews on webpages or mobile apps. This data can be difficult to comprehend; however, it’s crucial that there’s a lot of it in order to make valid claims. The volume of big data depends on the organization’s size and the questions that are being asked.

Velocity

In regards to big data, velocity is the speed at which data is received and then interpreted. Some pieces of software can do this automatically, depending on the complexity and structure of the data. However, this is not always possible, making the velocity much slower as it’s done manually by a data analyst or data scientist.

Variety

Finally, we have a variety. This refers to the different types of data that are available, both structured and unstructured. For example, data types may include audio, text, video, comments on forums, reviews, and other metadata. In the last few years, we’ve seen a rise in unstructured data (such as interviews, which then need to be transcribed), audio recordings, and video interviews.

Value & Veracity

Although the three V’s mentioned above are the go-to for big data, more recently, two new V’s have been introduced: value and veracity. For example, all data contains an intrinsic value, but this value cannot be understood until the data is understandable. Some data contains more intrinsic value than others, and this is determined by the data source and the truthfulness of the data, e.g. can you rely on the data source?

Big data is becoming more and more mainstream, especially for large tech companies (and others that deal in large quantities of data) to better understand their users and their products. For instance, companies such as Apple use big data to understand and map user experience and intentions, and to help create new products that customers will actually be interested in — solutions to problems that others don’t yet recognize as obstacles.

Data Analytics vs. Data Science

As mentioned previously, both data analytics and data science are somewhat similar and often confused. To eliminate this confusion and to better help you understand the difference, we’ve provided a brief description of each role below.

What does a data analyst do?

A data analyst’s job consists of sorting through data to provide visual and written reports to uncover insights in a dataset. These datasets could be on any topic, whether a crime, government funding, or within the sports performance industry. Often, many data scientists practice first as a data analyst, learning the ropes and better understanding data as a whole.

What does a data scientist do?

A data scientist’s role is to collect and analyze data to gather valuable insights, later sharing these with their organization or company. Similar to a data analyst, the role of a data scientist exists across many different industries.

Unlike data analysts who provide insights via representations of data, data scientists are more significantly involved by creating their own experiments, cleaning data, finding patterns, building algorithms, and finally, sharing their data and newly found insights with their team in an easy to understand process.

What is the difference between data analytics and data science?

This next section will explain several key differences between data analytics and data science to help you better understand each role in more detail.

1.   Data science is multidisciplinary

One of the main differences between data analytics and data science is that data science incorporates numerous disciplines, including data analytics, data engineering, machine learning, and software engineering, to name a few. In particular, data science relies heavily on machine learning and data analytics. Without traditional data analytics, whether performed by an analyst or a data scientist, it would be difficult and nearly impossible to understand big data.

Ultimately, a data scientist’s role is to understand and re-structure big data, identify patterns, and educate business leaders and decision-makers on their findings to adjust current practices for better, more effective results.

2.   The unknown vs. the known

A data scientist’s role is to predict future events or further data by analyzing past data patterns. On the other hand, a data analyst looks at current data and perspectives to better understand current events. This fundamental difference is paramount, and a critical distinction between the two sets of expertise. Essentially, data scientists focus on the future, and data analysts center their attention on the now.

3.   Hands-on machine learning experience

Data analysts are not expected or required to have hands-on machine learning experience. Similarly, those within this role are not likely to build statistical models or conduct advanced experiments to better understand big data.

Data scientists, on the other hand, are expected to have hands-on machine learning experience and are required to build their own statistical models and conduct their own experiments. As you can see, the roles are somewhat similar, but a data scientist’s role is more advanced and a step up from a data analyst. This is why many data scientists start out as data analysts.

4.   Addressing vs. formulating questions

Generally, data analysts are given questions to address by their business or organization. The request usually has to do with understanding a specific dataset to better benefit the business and their regular operations, e.g. cutting costs, increasing footfall, or understanding sales trends of distinct products or services.

Conversely, data scientists formulate these questions and provide solutions that will benefit the business. Usually, these questions are about events that haven’t happened yet; with greater focus on predicting the future as opposed to understanding current data and events.

5.   Multiple sources vs. single sources

Data analysts typically use and interpret data from a single source, such as a CRM system, while data scientists collect and gain insights from multiple data sources — sources that are often disconnected and more complex to understand. This is why processes such as machine learning and statistical models are used to better understand this big data.

6.   Visualization skills

Data analysts are not always required to possess business acumen or exceptional data visualization skills. Instead, their role is to interpret the data in an easy-to-understand fashion, not to implement changes to a business setting or real-world scenario. By comparison, data scientists are required to show business acumen and advanced data visualization skills, putting newly understood data to work in a business setting and contextualizing potential impacts on a business and its current decisions and processes.

Frequently Asked Questions

Can a data analyst become a data scientist?

Yes, data analysts can become data scientists. Many data scientists often start as data analysts, learning the big data world’s ropes and the various methods involved in interpreting and making sense of data. With this being said, an advanced degree is not necessary but may support you during the transition process.

Which is better for business: analytics or data science?

Business analytics is concerned with the analysis of data to make key business decisions, while data science uses statistics and various other methods to complement and inform business decisions. While there’s no correct answer, if you think you’d like to be more involved in a business decision, then a business analyst role is probably for you.

Data analyst vs. data scientist salary — which is better?

According to Glassdoor, the average salary for a data analyst ranges from $83,000 to $115,000, while data scientists earn, on average, upwards of $168,000 a year.

To conclude

Data analytics and data science have different roles within the same industry; however, they’re somewhat similar. As we’ve discussed, data analysts focus on sorting through current datasets to provide insights and visualizations in response to a business or organization’s question or current problem. On the other hand, data scientists formulate their questions as well as the subsequent answers and solutions that will benefit the business, focusing typically on events that have not yet happened.

Many data scientists often become data analysts first, helping them to better understand big data and the many processes involved in its analysis. Think of a data scientist as a more advanced data analyst — they ask questions, use machine-learning, build statistical models, and conduct experiments. However, both roles share the critical goal of a better understanding of big data.

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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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An Introduction to the Denver Tech Community

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Located at the foot of the Rocky Mountains and providing a vibrant culture and setting for active lifestyles, Denver is not only one of the most livable cities in the United States, it’s also a burgeoning tech hub. With a favorable tax rate, a lower cost of living, Denver is also one of the best places to start a business, and the best place for female founders. It’s just one of several cities in Colorado — including Boulder, Fort Collins and Colorado Springs, in close proximity — with a growing tech community.

And community is at the center of everything we do: GA Denver cultivates thousands of connections and learning opportunities throughout the year by leading expert-led classes and workshops and panel discussions each week. Since opening our doors, we’ve attracted more than 220 hiring partners across the state, many of whom have hired multiple GA graduates.

Companies and Jobs

  • Top industries: aviation, bioscience, financial services, energy, and more.
  • Companies like Ibotta, Fivetran, Guild Education, Home Advisor, Zayo, Gusto, Ball Corporation, and VF Corporation have large presences in Denver, and Silicon Valley giants like Amazon, Facebook, Slack, Salesforce and Google are growing their Denver based teams. 
  • With an unemployment rate of 1.9% in 2019, Denver’s job market grew in 2020, adding 80,000 jobs despite the pandemic, and is projected to grow another 12% over the next few years.¹

The Denver Tech Community

  • The Denver tech community fosters a sincere spirit of collaboration, with support from business associations like the Colorado Tech Association, the Downtown Denver Partnership, Commons on Champa and the Denver Metro Chamber.
  • Denver’s annual weeklong Startup Week is the largest free conference in North America and demonstrates this energy and excitement around tech growth and innovation.

Stay in the Know

Here are just a handful of resources to help you to dive deeper into Colorado tech:

  • Check out open jobs, salary data and more, over at Built In Colorado.
  • Colorado Inno has a great daily email spotlighting Denver tech news and trends.
  • The Downtown Denver Partnership provides an overview of initiatives and resources that make our vibrant city thrive. 
  • Denver Startup Week’s YouTube channel has posted all of its 2020 sessions and created video playlists so you can hear from local tech industry leaders directly.
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¹ CompTIA 2020 Tech Town Index

This Holiday Season: Learn. Give. Grow.

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Our Free Fridays are back — with a twist.

In the spirit of bettering our community and serving those in need, from November 20 to December 30, 2020, we donated $1 USD to the International Rescue Committee for every person who joined us at select free weekly workshops. While this promotion is now over, we always have free intro classes and eventscoming up. From coding to data, marketing, and career development, explore the tech skills that will keep you in demand and in the know in 2021.

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12 Must-Read Digital Marketing Books in 2021

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A question I often get asked by students is, “What is the best digital marketing book?” 

It’s not easy to answer; the majority of digital marketing books don’t have a long shelf life. The information around best practices needs to be fluid as algorithms change, tactics lose their effectiveness, and the platform rules constantly shift

While digital marketing books that are rich on tactics continue to be updated and recycled, there are a number that have managed to withstand the test of time. Included in the list below are also the books that every digital marketer should read for developing a well-rounded understanding of behavioral psychology, growth mindset, and a few other areas that will help you stay ahead of the pack.

1. Influence: The Psychology of Persuasion by Robert Cialdini

It may have first been published in 1984, but you’ll be hard-pressed to find a list of best marketing books that doesn’t include this ageless text.

Widely regarded as the marketer’s bible, “Influence: The Psychology of Persuasion” provides a succinct and effective outline for understanding what leads to us making decisions. Cialdini uses storytelling and real-world examples to seamlessly guide readers through six principles of persuasion of which many a marketer have called upon to compose email copy, frame social media ads, and devise practically every memorable marketing campaign in recent history.

While you can’t expect to learn specific channel tactics from this digital marketing book, the application of reciprocity, consistency, social proof, authority, liking, and scarcity will ensure your digital marketing strategy is laser-focused on achieving conversion outcomes.

2. Scientific Advertising by Claude Hopkins

Claude Hopkins was a man far ahead of his time. While A/B testing and statistical significance are commonplace in today’s digital marketing world, Hopkins was teaching early interpretations of these all the way back in 1923 in “Scientific Advertising.”

I find myself regularly returning to this book when looking to return to fundamentals surrounding ad creative and influencing buyers. At just 120 pages, you can almost read it in one go and won’t find a page that doesn’t offer a quick tip applicable to effective digital marketing today.

3. Expert Secrets by Russell Brunson

You will find iterated teachings of “Expert Secrets” within countless social media and digital marketing courses across the internet. Yes, it may have been published 4 years ago (which is like 40 years in digital marketing) but its valuable content is likely to remain a mainstay in the years ahead.

The appeal of “Expert Secrets” is that it provides a practical framework that takes the guesswork out of email marketing, content marketing, and copywriting. It helps you recognise expertise in areas and how your intimate knowledge of a subject can lead to the development of a successful business. Author and ClickFunnels founder Brunson is one of the most recognised figures in the digital marketing world, and the book really reads as a collection of the best practices he has discovered through the constant refinement of his own digital marketing strategy.

While everybody will have unique takeaways from this digital marketing book, I am constantly revisiting his tips towards the end on conducting the perfect webinar. He outlines the structure, the perfect timings between sections, and evergreen tips for keeping audiences engaged — a must read!

4. Hooked: How to Build Habit-Forming Products by Nir Eyal

I read this book cover to cover on a plane trip from Sydney to Los Angeles and it’s fair to say it had me, well, hooked! 

“Hooked: How to Build Habit-Forming Products” is an excellent product and marketing book for learning what it takes to create habits in consumers. You’ll learn how to create triggers, get customers to take action, reward them, and encourage investment following the fundamentals adopted by many of the world’s leading technology companies. There are few digital marketing books that will provide you with better end-to-end insights into optimising the user journey of your audience. 

It’s packed with relevant examples of these techniques in practice and I found it refreshing that author Nir Eyal ended the book with some wise words on how to apply these teachings ethically while keeping your consumer’s well-being top of mind.

5. Jab, Jab, Right Hook by Gary Vaynerchuk

In my opinion, this is the best book that’s been written on social media marketing strategy thus far. “Jab Jab Right Hook” was my first exposure to the teachings of Gary Vee, and his celebrity status should be of little surprise to those who have read about the common sense approach he preaches here.

The book asserts the importance of social media marketing in today’s landscape while providing a winning blueprint for developing an engaging community that will reward you in the long run. We all want sales, but it’s through adding value to our audience first that we earn the right to ask for something in return.

The audiobook is read by Gary Vee himself and he frequently deviates from the script to adding yet another nugget of social media gold. Whether you’re wanting to learn about creating content specifically for a social media platform or how to build an Instagram following from scratch, you’ll find something here to put into practice.

6. Content Machine by Dan Norris

“Content Machine” is an absolute must read for anyone looking to develop an epic content marketing strategy that drives commercial success.

The book details the exact content marketing strategy used by Norris to build a 7-figure business that was fuelled by an outstanding blog. You’ll learn that there is far more to winning the content marketing game than just creating the most blog posts, and the search engine optimization techniques and tools mentioned by Norris remain as relevant as ever in today’s digital marketing landscape.

7. Lean Analytics by Benjamin Yoskovitz and Alistair Croll

I won this book at a startup event and I’ll admit that the title didn’t win me over at first. However, after a colleague recommended it I decided to give it a try and couldn’t put it down.

I haven’t come across a book that better equips you for doing digital marketing in a tech startup than “Lean Analytics.” You’ll learn how to measure, but more importantly what to measure depending on the stage and focus of the company. 

If you’re intimidated by digital marketing jargon such as AARRR, CAC, CTR, and Virality, then this should be your first step. It’s as close to a startup digital marketing textbook as I have found, and will equally help B2B and B2C marketers level up.

8. Permission Marketing by Seth Godin

Any book by Seth Godin is a worthwhile read, but few have influenced my own approach to marketing strategy more than “Permission Marketing.”

While other digital marketing books will jump straight into tactics, Seth’s 1999 guide focuses on the importance of building a relationship with your customer over time. Marketing is most effective once your audience has given you permission to market to them, and to get to this stage we need to provide consistent value from the get-go.

A true highlight of this book for me was the variety of case studies Godin uses in detailing the evolution of marketing over time. You’ll certainly walk away with plenty of things to try for yourself.

9. StoryBrand by Donald Miller

In the words of Donald Miller, “Pretty websites don’t sell things. Words sell things.”

There are plenty of great books on copywriting, including classics like Gary Halbert’s “The Boron Letters” and David Ogilvy’s “Confessions of an Advertising Man.” My personal recommendation however would be to start with “StoryBrand” for a more holistic and modern take on how to delight your customers with your digital marketing creative.

Too often businesses position themselves as the hero in the story. What customers really need is a guide who can help them successfully solve their problems. Miller will help you use content to make your customer the hero of your story and how to create your digital marketing assets accordingly.

10. Getting Everything You Can Out of All You’ve Got by Jay Abrahams

This book helps us understand how incredibly simple it is to have an impact on the commercial success of a business.

While they’re not specifically about digital marketing, the teachings of this book will help shift your mindset to one that is always on the lookout for internal growth opportunities. You’ll end up with a range of ideas surrounding email marketing, search engine marketing, social media promotion, and conversion rate optimisation.

Abrahams helps us to identify the value of our customers, what we can do to increase that value, and how to find more of our ideal customers. So simple, yet so very effective!

11. Don’t Make Me Think by Steve Krug

It’s a mistake to consider a user who gets stuck on our website as foolish. If a potential buyer is unable to complete an action on our website, then it’s on us to change.

“Don’t Make Me Think” is a book you’ll find on virtually every UX designer’s bookcase and with so much of digital marketing depending on an excellent user experience, this is a book we simply can’t ignore. The journey from an ad click to conversion depends on reducing friction, limiting distractions, and maximising accessibility. You won’t find a better guide to achieving this than Krug’s classic, which remains the go-to resource on web design 20 years on from its first publication.

12. Hacking Growth by Sean Ellis and Morgan Brown

It’s only entered our vernacular in the past decade, but growth hacking has quickly made its way to the top of every company’s digital marketing wishlist. Growth hacking focuses on finding faster and more cost-effective solutions to success, and it’s only fitting that the godfather of the movement’s work makes the list of must-read digital marketing books.

Sean Ellis coined the term growth hacker in a blog post back in 2010, and went on to co-author “Hacking Growth” seven years later alongside renowned marketer Morgan Brown. The book walks through the humble beginnings of some of today’s biggest companies — Airbnb, Facebook, Uber — and the methodology behind their unprecedented growth. 

You won’t find a better methodology for attaining, retaining, engaging, and motivating customers than “Hacking Growth.” It will completely change the way you approach your digital marketing strategy and help you to use data to deliver driving cost-effective results.

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6 Must-Know Digital Marketing Trends of 2021

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Every business is trying to find that edge that sets them apart from competitors. Digital marketers are constantly looking at new channels and techniques that haven’t yet reached a point of oversaturation, and with more advertising dollars being spent on online mediums, these are becoming increasingly difficult to find. 

2020 has brought its own set of challenges for digital marketers. Email service providers have reported a four point increase in open rates, conversion rates have plummeted in certain industries, and for the first time ever both Google and Facebook have reported quarterly declines in ad revenue.

While the goalposts have shifted, there have been a number of emerging digital marketing trends that we’ve seen innovative marketers adopt with early signs of success. Here we’ll discuss 7 digital marketing trends that every business should explore.

1. Conversational Marketing

We’re increasingly seeing conversational marketing make its way into businesses’ digital marketing mixes. While this is not a brand new trend in 2020, it’s definitely something that more businesses are trialling as barriers to entry are reduced and customers become more comfortable with the interactions. 

It’s difficult to put an exact definition on conversational marketing, but the term essentially covers the use of conversations between brands and customers to personalise each step of the buyer journey. It commonly involves using targeted, personalised messaging combined with chatbots to engage with users via your website, your social media marketing pages, and anywhere else where conversations with customers take place.

In today’s always-on world, innovative marketers have looked to adopt conversational marketing to provide customers with an instant stream of personalised information. The effectiveness of such messages is unquestionable, with message platform open rates north of 70% and clickthrough rates averaging around 20%. Customer service is also becoming more comfortable with assisting clients via conversational marketing, with surveys showing that 54% of customers would prefer to choose a chatbot over a human if it saved them time.

Despite this, the potential of such technology is still being realised. Take Facebook Messenger for example, where over 1.3 billion people use the platform. Facebook has 9 billion advertisers, yet the last reported number of chatbots was just 300,000

Chatbots are moving beyond a mere text offering as well, with voice-based chatbots with advanced speech recognition capabilities set to become commonplace in 2021. We’ve also yet to see smart speakers such as Amazon Alexa and Google Home introduce conversational ads delivered by these devices. With voice search growing year on year, most believe it is only a matter of time. 

2. Personalisation

There isn’t a trend that has captured the imagination of digital marketing publications more in 2020 than personalisation. It’s usually a bit of a concern if too many marketers are all focusing on the same idea, but there’s no doubt that there are some definite benefits to having customers experience your products and services through a personalised lens. 

We’ll see many businesses try to personalise experiences for each individual customer with one key aim: conversions. As competition for customer attention continues to increase, any friction one can remove from the customer’s decision journey is an advantage. A web experience that focuses on showing you listings based on your preferences and previous activity will reduce the path to purchase and help to increase retention and customer loyalty.

When we think of personalisation we immediately think about some of the titans of the industry. Netflix has a hand-picked selection of shows for me at any moment, as does Amazon when it comes to products, or Spotify when it comes to songs. There’s also examples such as Cadbury, who recommended products for customers based on their Facebook profile, or even new influencer marketing tools like Influencersphere, which recommend Instagram influencers for your business based on your account. 

A Gartner study showed that companies making investments into personalisation technology are outselling competitors by 30%, and while many of us won’t be able to create recommendation engines, personalisation efforts can still be useful and effective. Companies such as conversational platform Intercom have adopted personalisation into their selling by sending prospects video demos of how their software looks when embedded into their website. There’s also software such as Bonjoro that allows you to easily send a quick personalised video to your customers or prospects to delight and convert.

3. Smarter Bid Strategies

There’s a lot more to Google Ads than just keyword bidding these days. The introduction of Smart Bidding allows advertisers to leverage Google’s machine learning and automate their bidding based on their advertising goal. It then looks to optimise towards a goal by adjusting bids based on a range of user signals, including location, time of day, audience interests and the type of device used.

Many ‘traditional’ digital marketers have steered away from smart bidding with a preference to own more control of their client’s budget. However as Google becomes more and more precise in their ability to predict, this is becoming harder to ignore. There is simply no match for a real-time bidding engine that works 24/7 to bring you the best results. 

Facebook has followed suit, announcing a strategy at the end of 2019 called the Power 5. The Power 5 tools place great emphasis on simplifying your ad account setup in order to best leverage the platform’s machine learning and drive better results.

These shifts to account simplification mean that the barrier to entry for new advertisers is significantly reduced. Take Google for example, where advertisers are now able to use the latest Smart Bidding strategies by simply providing a list of keywords to target and some ad creative to support this. The use of such technology puts greater emphasis on the quality of products and services and the usability of the website to ensure performance targets are achieved. 

In a Smart Bidding digital marketing landscape where we are all optimising towards ROI, it will become increasingly difficult to cut through the noise and have your message seen. This is likely to continue a shift back to the importance of effective creative that can stand out and pique your audience’s interest.

4. Interactive Content

Content marketing is here to stay. While buzzwords come and go from surveys looking at marketers’ focus for the year ahead, content is one of the few constants in every top digital marketing strategy.

The content marketing trend to watch relates to interactive content. I’m sure you’ve all had some kind of experience with interactive content, whether that be a poll, a quiz, a survey or something else. Interactive content is an attempt from marketers to cut through the clutter of content now available at our fingertips. Instead of writing another blog post on a topic, interactive content gives marketers the opportunity to keep their audience engaged for longer and have a more long-term impact on their decision making.

DemandGen found that interactive content delivers twice the engagement compared to that of static content, and we’ve seen the top platforms follow this trend as a means of keeping users engaged for longer. In the video marketing space, Facebook has rolled out video poll ads while YouTube announced in June 2020 a new ad format that turns video ads into shoppable experiences for viewers.

While the future of interactive content may lie in augmented reality (AR) or virtual reality (VR) experiences, there are some easy ways to see if interactive content will work for your content marketing strategy. Companies such as Typeform offer free, easy solutions for making quizzes while we can all run polls across Instagram Stories or Facebook Stories.

5. Marketing Automation

A study by Invesp found that 63% of marketers planned to increase their marketing automation budget in 2020, and despite everything that’s gone on throughout the year it’s hard to see a more pertinent use of these funds.

Never has there been a greater emphasis on marketing to your existing leads and customers. With advertising budgets reduced across the globe there’s been a shift in focus from organisations towards keeping customers engaged in an effort to increase lifetime value.

Marketing automation can cover all stages of the customer journey, although where it is most commonly utilised is at later stages of the customer lifecycle to prompt interactions that help us to gauge how warm the prospect is. This has seen marketers look to break apart the customer journey and create an omnichannel marketing experience, in which they include themselves as part of the conversation by means of email, content, social, push notifications, and retargeting. It also allows marketers to personalise the messages customers are receiving and to segment based on previous behavior. 

While this is commonplace among larger organisations, there is an increasing number of self-serve platforms that are bringing these capabilities to businesses of all sizes. Software such as Kit allows Shopify store owners to automatically send emails to customers based on their purchasing behaviors, while self-serve email marketing providers such as Mailchimp allow you to retarget customers you have sent emails to on Facebook with a few clicks. All signs point to a more even playing field in which those failing to automate are left behind.

6. More AI in Marketing

Applications of Artificial Intelligence (AI) are already widespread in marketing, and Gartner recently predicted that 80% of digital technology will be built on an AI foundation by 2021.

AI is already being leveraged to help B2B marketers score leads, converse with customers via chatbots, and improve conversion rates through variation testing. OpenAI’s GPT-3 technology has written content articles published by many organisations (including The Guardian) and companies such as VWO are A/B testing GPT-3 copy against human copy to determine which has a better impact on conversion rates.

As the data gathered from marketing campaigns and platforms continues to increase, AI in marketing looks set to grow exponentially. We’ll soon be able to hyper-personalise campaigns at scale, provide comprehensive persona research, and even use predictive scoring that could estimate the future value of your existing customers. This will help marketers to seamlessly create an effective infrastructure for their marketing strategy to be built on, allowing teams to focus on delighting the customer at all stages of the purchasing journey.

Conclusion

The bar is rising in digital marketing. Technology has made it easier than ever to connect with customers online, and with customers’ attention being increasingly divided, digital marketers are required to do more than just ‘show up.’ Audiences are expecting more of brands, and we need to shake up our digital marketing strategy in order to delight them in new and exciting ways. It’s time to take action before you get left behind.

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Free Lesson: Coding Essentials in 30 Minutes

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More than half of all jobs in the top income quartile show significant demand for coding skills.* Spend half an hour with expert GA instructor Madeline O’Moore to write your first lines of code and learn how coding knowledge applies to so many different fields. She’ll give you an overview of:

  • How HTML and CSS function together to form the backbone of the web.
  • Key coding terms and principles.
  • Tools you can use to practice.

If you’re curious to keep exploring, discover our popular short-form workshops like Programming for Non-Programmers. To dive deeper, check out our upcoming Front-End Web Development course to cement a versatile foundation in HTML, CSS, and JavaScript. Or start exploring what it takes to launch a career in web development with our Software Engineering Immersive career accelerator.

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*Source: Burning Glass, Beyond Tech