Learning How to Learn

This article on “Learning How To Learn” is part of a series developed by Curriculum Design to guide students through the Flatiron School program experience.

We believe that when learners feel autonomous and in control of their learning, they achieve greater success both academically and motivationally. Learning to Learn is designed to offer a variety of resources and tools to help you take control of your online learning journey and life beyond Flatiron School.

Take Ownership Of Your Learning

Taking ownership of your learning journey, through personalized learning, means finding your motivation, being engaged, and personalizing your learning experience with complete autonomy, choice, and responsibility in how you approach your online learning journey. Every learner has a fundamental need to feel in control of what they do versus only being told what to do. When this autonomy is exercised, the motivation to learn and the desire to perform well academically are much stronger.

As you go through the Learning to Learn series, our goal is to encourage you to take ownership of your learning journey- make decisions that matter, pursue directions that feel meaningful, and hold a sense of responsibility and control for both your learning successes and setbacks.

Connect The Dots

Taking the leap to build technical skills takes courage and determination. It can be intimidating to dive into new skill sets and knowledge, but the rewards and sacrifice will be worth it. As you learn, your horizon will expand and the information you collect along the way will start to connect in unexpected ways.

The saying goes, knowledge is power, and when it comes to personal and professional growth, this couldn’t be more true. When we actively seek knowledge through experiences or formal education, we add another “dot” to our mental map. These dots, connected, generate new ideas and help to solve problems in unique ways. Some of the greatest innovators credit their success to continue expanding their knowledge base through both life experiences and deliberate learning sessions.

Continue adding dots to your map.

TL;DR

  • Personalized learning is a great way to improve your skills and knowledge base.
  • Learning on your own can be intimidating to start, but the rewards are worth it.
  • Seek out new experiences and resources to challenge yourself and broaden your perspectives.

Insider Guide: Flatiron School’s Admissions Assessment

When you choose to start a program at Flatiron School, we know that you are investing — both financially and an investment of your time. That’s why it’s important that you are a right fit for the program and vice versa — that our program is the right fit for you.

One way we make sure that the program is a good fit is with an admissions assessment test.

The admissions assessment is a cognitive aptitude test that analyzes your problem-solving skills, critical thinking skills, your attention to detail, and your ability to learn new information. There are three different styles of questions — verbal, math and logic, and spatial reasoning. Think of the questions more like brain teasers, not about coding, computers, or cybersecurity.

After all, in addition to your experience and skills so far (if any!), we are more interested in understanding your ability to learn and pick up the skills that will be taught in our courses.

The test is 15 minutes long and can include up to 50 questions. But don’t stress. We don’t expect you to complete all the questions. Less than 1% of people complete all 50 questions.

How many questions should I complete?

Try to answer as many questions as possible in the allotted 15 minutes, with the minimum goal of answering at least 25. 

Don’t get caught up on any one question though. If you’re feeling stumped, take a guess and move on. It’s more important to maintain a decent pace and keep moving through the questions, rather than to stress over scoring perfectly on one question.

Remember, you have a 15-minute time cap so you’ll want to move through as many questions as you can efficiently.  Again, less than 1% of people complete all 50 questions so don’t stress yourself out about finishing all the questions.

Here are two examples of the types of questions you might see on the admissions assessment.

1. Sample Verbal Question: (Source)

Choose the word that is most nearly OPPOSITE to the word in capital letters: LENGTHEN

  • abdicate
  • truncate
  • elongate
  • stifle
  • resist

2. Sample Math Question: (Source)

A group of 3 numbers has an average of 17. The first two numbers are 12 and 19. What is the third number?

  • 17
  • 19
  • 20
  • 23
  • 30

How to prepare for the admissions assessment

  • Complete the assessment on a laptop or desktop as it is not mobile-friendly. 
  • Set aside 15 minutes of uninterrupted, dedicated time.
  • Remove any distractions so you can focus for 15 minutes.
  • Have a piece of paper and a pencil for notes.
  • Relax and don’t overthink it.

Remember, it’s not about being perfect; it’s about getting the best score you can. Don’t get caught up on one question. Keep moving at a decent pace. 

There is a time clock on the page so you will know how many questions you have completed and how much time remains.

 

How does the test affect my admissions decision?

Our admissions process includes three phases — a written application, the admissions test, and an admissions interview. The test is a factor in the admissions process, but ultimately, we will consider all three phases of your application to determine an admissions decision.

Wondering what score you should get? We do have a target score for each one of our study programs but don’t worry about that upfront. Only worry about making sure you have 15 minutes of dedicated time, and then do your best.

Your score will be measured against the target score to determine if you will be a good candidate for the program. Remember, we don’t want you to commit to one of our programs unless we know you have the potential to be successful in that career field.

How do we determine target scores?

We asked our current students and graduates of our program to take the admissions test. And created our target scores based on how well our successful students scored.

Then, the company that prepares the test provided scores from successful professional software engineers, data scientists, cybersecurity engineers and analysts, and product designers. And that’s how we came up with the target score for applicants.

What happens after I take the admissions test?

After you finish and submit the admissions test, your score is recorded in our system and you will receive a link to schedule your interview at the end of the assessment. In that interview, your admissions rep will share your score and discuss your next steps.

Remember, the test is a factor in your admission decision, but we will make our final decision based on the combination of your application, interview, and assessment test.

Ready to start your admissions process? Apply now.

Disclaimer: The information in this blog is current as of 01 Dec 2022. For updated information visit https://flatironschool.com/.

How Financial Services Prepares For The Holiday Season

With the holidays right around the corner, financial service companies are preparing for a busy shopping season. 

Public trust in the security of digital purchases has had a hand in the boom of online shopping. Consumers are now more likely to visit digital storefronts, instead of brick-and-mortar locations. As of 2022, an estimated 2.14 billion purchase goods online and at least 75% of consumers shop online at least once a month.

With every online transaction, gift purchase, or swipe of a card, financial services are tasked with keeping data secure.

So, with holiday shopping ramping up, what challenges should financial services be prepared to tackle?

Problem #1: How To Prevent Data Breaches?

A data breach can cost millions, tarnish a company’s reputation, and leave customers doubting that their information is safe. In fact, according to the Ponemon Institute and IBM’s Cost of a Data Breach Report, the average total cost of a data breach increased from $3.86 million to $4.24 million in 2021.

Cybersecurity can feel like an endless game of one-upping bad actors. Even large companies struggle to keep up with digital innovation which has resulted in an ever-increasing number and complexity of cyber attacks. 

In a digital world where automated attacks can quickly overwhelm manual monitoring attempts, having adept and skilled professionals in place is critical to a company’s continued prosperity and longevity.

Solution: Invest In Cybersecurity Preparation and Plan Ahead

To tackle the cybersecurity threats attempting to infiltrate your organization, it’s crucial to develop a two-pronged plan – a prevention strategy and a response procedure.

Prevention Strategy

You’ve likely heard that an ounce of prevention is better than a pound of treatment, which is particularly relevant to preventing cybercrime. 

Financial services should reinforce their cyber protocols and ensure that their team is skilled, supplied with appropriate software and platforms, and has the bandwidth necessary to handle the deluge of attacks. This can be accomplished either by outsourcing to third-party providers or investing in internal infrastructure and employees by hiring new employees with up-to-date skills or upskilling your existing workforce. 

Response Procedure

Should bad actors breach your organization’s data stores, it’s vital to have a plan of action in place. 

Shockingly few companies have a solid breach response plan in place, and time wasted scrambling to decide what to do, who has access to what, and which files may have been compromised lets whoever has infiltrated run amock in your system. 

To be fully prepared, financial services should develop, test, and implement an incident response plan to minimize the potential fallout of a breach. 

Related reading: Top 3 Cybersecurity Pain Points in 2022

Problem #2: How To Reach New Customers?

No matter the industry, product, or company size, the goal is ultimately the same – growth.

But for financial services, a saturated market can make it difficult to differentiate themselves from competitors and attract new customers. Many organizations find themselves pondering as the biggest shopping season ramps up, “how do we stay ahead of the competition and reach new customers who prefer online experiences?”

Solution: Leverage The Power of Data

To attract new customers, financial services can use the data they collect, which we discussed protecting above. 

Invest in Data Scientists who are able to decipher actionable insights from data collected about existing customers and use models to forecast emerging market trends. By making data-backed, research-based decisions, your organization can develop targeted promotions and bring in new customers, all with information you already had on hand.

Related reading: The (Data) Science Behind Netflix Recommendations

Tech Talent Solutions Made For Financial Services

Whether it’s the holiday shopping season or not, financial services have no shortage of challenges: legacy technology, cybercrime, and connecting data across brick-and-mortar and digital products. 

Let Flatiron School help modernize your business with training and talent services across Cybersecurity, Data Science, Software Engineering, and Product Design.

Contact us to get started.

Disclaimer: Information in this blog is current as of November 07, 2022. For more information, visit FlatironSchool.com.

How Retailers Prepare For The Holiday Season

With the holidays right around the corner, retailers are preparing for a busy shopping season. Each year, more consumers are shopping online. As a result, retailers must invest in their digital storefronts, backend security, and supporting software to keep customers coming back.

So, with holiday shopping ramping up, what challenges should retailers be prepared to tackle this year?

Problem #1: How To Prevent Data Breaches?

A data breach can cost millions of dollars, tarnish a company’s reputation, and leave customers with little trust that their information will be kept safe. In fact, according to the Ponemon Institute and IBM’s Cost of a Data Breach Report, the average total cost of a data breach increased from $3.86 million to $4.24 million in 2021.

Cybersecurity can feel like an endless game of one-upping bad actors, with even large companies struggling to keep up with digital innovation that has resulted in an ever-increasing number and complexity of cyber attacks. 

In a digital world where automated attacks can quickly overwhelm manual monitoring attempts, having adept and skilled professionals in place is critical to a company’s continued prosperity and longevity.

Solution: Invest In Cybersecurity Preparation and Plan Ahead

To tackle the cybersecurity threats attempting to infiltrate your organization, it’s crucial to develop a two-pronged plan – a prevention strategy and a response procedure.

Prevention Strategy

You’ve likely heard that an ounce of prevention is better than a pound of treatment, which is particularly relevant to preventing cybercrime. 

Retailers should reinforce their cyber protocols and ensure that their team is skilled, supplied with appropriate software and platforms, and has the bandwidth necessary to handle the deluge of attacks. This can be accomplished either by outsourcing to third-party providers or investing in internal infrastructure and employees by hiring new employees with up-to-date skills or upskilling your existing workforce. 

Response Procedure

Should bad actors breach your organization’s data stores, it’s vital to have a plan of action in place. 

Shockingly few companies have a solid breach response plan in place, and time wasted scrambling to decide what to do, who has access to what, and which files may have been compromised lets whoever has infiltrated run amok in your system. 

To be fully prepared, retailers should develop, test, and implement an incident response plan to minimize the potential fallout of a breach. 

Related reading: Top 3 Cybersecurity Pain Points in 2022

Problem #2: How To Increase Customer Loyalty?

With the rise of the digital-first era, shoppers are no longer walking into physical stores for their goods. Instead, they are logging onto their computers or opening a mobile app, credit card in hand.

Customer touch points now feature everything from brand-owned mobile apps to traditional website storefronts and social media platforms. Each channel is a chance for retailers’ brand messaging to reinforce customer loyalty.

So, how do retailers elevate the customer experience across all these touchpoints?

Solution: Develop Supportive Software

With so many brand touchpoints across multiple platforms and channels, delivering a seamless, omnichannel experience is key. To achieve this, retailers can invest in software engineering to develop custom retail software. 

Company-specific software creates IT solutions that automate the retail business process, streamlining offerings and product delivery. Everything from sales notification, invoice delivery, shipping, and returns/refunds can be accomplished by one, overarching system. 

That way, no matter where a customer interacts with your brand, they’ll be met with a consistent, pleasant, and easy-to-navigate system they know well. 

Related reading: Top 3 Retail Tech Trends in 2022

Problem #3: How To Quantify Consumer Behavior?

With trends that change at the pace of social media algorithms, it can feel just about impossible to predict trends. What was trendy one moment can be “last season” the next, with product and marketing teams struggling to keep up. 

Many retailers are asking, “how can we quantify customers’ behavior and translate it into sales?”

Solution: Leverage The Power Of Data

To predict consumer behavior and deliver tailored experiences that convert, retailers need simply to harness the power of data. 

Without a doubt, somewhere in every retailer’s system is a mountain of data. This data is generated by customers each time they interact with a brand. Data Scientists use models and machine learning to connect data points from multiple sources and generate actionable insights that can be incorporated into a retailer’s strategy at scale. 

To improve conversion rates, retailers should invest in a team of skilled data scientists – or upskill their current team. Data Scientists can use data-driven insights to create recommendations catered to each customer. That way, they’ll keep them coming back to the company that, somehow, knows them so well.  

Related reading: The (Data) Science Behind Netflix Recommendations

Tech Talent Solutions Made For Retailers

The tech that powers digital shopping is only as effective as the talent behind the scenes. 

For 10 years, Flatiron School has been teaching the tech skills that retailers need most. Let us help accelerate your business with our talent and training solutions.

Contact us today to get started.

The Holiday Season: Brought To You By Tech Workers

The holiday shopping season is quickly approaching, and retailers are ramping up operations in preparation for the biggest retail events of the year – Black Friday and Cyber Monday. With sales consistently shifting to predominantly online year over year, retailers need to evolve their digital storefronts to keep up with modern shopping trends.

For retailers looking forward to these influxes of customers, preparation is key to success and keeping bottom lines black. The tech that powers online shopping, provides a seamless customer experience, and keeps data secure is only as effective as the engineers behind the scenes.

Retailers need a technically trained team with up-to-date skills to keep up and meet four critical needs: the need for websites that perform, the need to predict trends, the need for pages to convert, and the need to keep data secure. 

Here’s how the four disciplines Flatiron School teaches – Software Engineering, Data Science, Product Design, and Cybersecurity – support the holiday season. 

Need Websites That Perform (Software Engineering)

For websites to perform well, load quickly, and deliver an enjoyable online shopping experience, the engineers behind them must be well-versed in the languages used for Back-End and Front-End Software Engineering

In fact, in a recent study retailers reported software development as the #1 desired technical skill for new hires. Java, software engineering, SQL, Python, JavaScript, and data science also made the list.  

So, how do retailers build out a technical team to get their digital storefront live and profitable? Sourcing recent graduates from technical training institutions ensure that new hires are up to date on the newest software, platforms, and best practices in the online marketplace. 

Big box and clothing retailers in particular source our Software Engineering and Data Science graduates for their skills in Python, Java, JavaScript, and SQL. These languages are used in online interfaces such as cashier-less checkout, virtual storefronts, virtual dressing rooms, and marrying online and offline data to personalize shopping experiences and increase profitability.

Related reading: In-demand skills taught to our Software Engineering students

Need To Personalize and Predict Behavior (Data Science)

No matter how optimized a digital store-front functionality is or how easy to navigate a user interface is, a consumer won’t buy from you unless they see something they like enough to part with their hard-earned dollars. Item recommendations and promotions, whenever possible, should be personalized to individual customers to increase conversions and sale amounts.  

Data Scientists are tackling this task by taking advantage of big data – the mountain-sized amount of information points generated by customers interacting with your brand. 

Our Data Science graduates use models and machine learning to connect data points from multiple sources and generate actionable insights that can be implemented at scale. 

Unleashing the power of data-based decisions can have wide-reaching impacts on your business and increase conversion rates with recommendations catered to each customer and keep them coming back to the company that, somehow, knows them so well.  

Related reading: The (Data) Science Behind Netflix Recommendations

Need Websites That Convert (Product Design)

UX / UI design is a critical success factor for successful digital storefronts. User experience and user interface can make or break mobile viability, and nothing bottoms out the performance of a website or mobile app faster than a difficult-to-use interface.

Retailers utilize UX / UI Product Designers to revamp user interfaces and outfit brand-owned digital touch-points with easy-to-use features to ensure a seamless experience that will keep users coming back and clicking ‘buy’.

Related reading: What Is Design Thinking?

Need To Protect Data (Cybersecurity)

While not a new topic and certainly not unique to retailers, recent cybersecurity trends and high-profile breaches have resulted in several pain points for brands that hold personally identifiable information (PII). 

Many retailers are realizing new vulnerabilities including cloud hosting platforms, an increased number of access points, more frequent cyber attacks, and a lack of internal resources struggling to keep up. 

In the digital age where automated attacks can quickly overwhelm retailers, having adept and skilled professionals in place is critical to a company’s continued prosperity and longevity.

Essential cybersecurity skills for the digital age include SQL, which attackers could use to steal confidential data, compromise data stores, and execute web-based attacks, as well as Python, which helps to scan and analyze malware, and Java, which can be used in penetration (pen) testing.

For retailers to ensure their databases are secure for the rush of the holiday season, recruiting cybersecurity professionals with up-to-date and relevant skills or upskilling in-house teams is critical.

Related reading: Top 3 Cybersecurity Pain Points in 2022

Join Santa’s Workshop Of Tech Workers

No matter your area of interest or expertise, you can have a hand in bringing the holiday season to life. So, if you’d like to apply to Santa’s workshop, we have good news and bad news. 

The good news is that you can acquire the skills you need to join Santa’s team of tech workers by attending one of Flatiron School’s programs in Software Engineering, Data Science, Product Design, or Cybersecurity.  

In fact, many Flatiron School graduates have been hired at some pretty magical companies that can have a hand in making the winter season feel like magic

The bad news is that Santa cross-checks the naughty list. Good luck! (Kidding.)

Apply Today to start making some magic. 

What is the difference between a data analyst and a data scientist?

While these positions have different responsibilities, which we cover below, the people that fill them often have similarities that lead them to excel in these roles. 

Data analysts and data scientists tend to be people with investigative minds, are very curious, and enjoy solving puzzles. They blend logic and creativity to look for pieces of data that fit together to tell a compelling story. Ultimately, they are storytellers through data. 

The roles of data analysts and data scientists are often used interchangeably in conversation, but in practicality, they have very different responsibilities. 

A data analyst’s primary role is to scan and analyze data, where a data scientist collects, cleans, and explains the data. An easy way to think of it is that an analyst is often more of a beginner-level role, and a data scientist may have more experience or more advanced education.

What Does A Data Analyst Do?

Data analysts do exactly what their title says – they analyze data sets to identify trends and draw conclusions. 

Analysts provide explanations and reports and show visualizations to illustrate insights to decision-makers. Data analysts are proficient in SQL and business intelligence software. They use these software to interpret structured data and analyze trends and patterns.

However, data analysts do not need to have any extensive programming abilities. Their expertise is more in the realm of analytics and data management. For instance, they can generate marketing reports and future sales projections and show the success of ad campaigns.

To become a data analyst, you need to learn how to interpret trends from historical data, prepare summary reports, information management, data cleansing, data mining, and develop data pipelines.

What Does A Data Scientist Do?

A Data Scientist collects, cleans, and explains data. Their primary role is to adjust statistical and mathematical models and apply them to the data. Due to the higher reasoning and programming skills used to extrapolate findings from complex data sets, Data Scientists are often considered to be more senior than Data Analysts. 

Data Scientists are responsible for translating formal business problems into workable data questions. They build predictive models for upcoming data and can theorize, implement, and acquire data effectively.

These professionals are often creative and must work to display their findings to tell a transparent, understandable, and compelling story. 

‌In short, a Data Scientist interprets data in a similar way to a Data Analyst. But, Data Scientists also code models and algorithms to gain more insight into the data. Data Analysts act like “translators” while Data Scientists act in a hybrid capacity, helping companies turn data into practical and actionable information.

Breaking Into The Field

No matter which position you would like to pursue in Data Science, it is critical to learn the skills required for your target role to be a competitive candidate.

This can be achieved in several methods – including with university classes or self-teaching. But by far the most time-conscious and cost-effective way is to enroll in a technical training course

These courses are completed in months, not years. They also just cost a fraction of traditional university tuition and provide practical, real-world applicable training. 

Most importantly, short-term, intensive courses teach you up-to-date skills that won’t be obsolete when you graduate. 

Related Reading: See the programming languages and skills Flatiron School will teach you. 

Ready to take the next step? Apply Today to get started towards your next role in Data Science.

If you’re not ready to apply, then start with a Free Data Science Prep Work. Or, check out the Data Science Course Syllabus that will set you up for success with the skills to launch you into a fulfilling and lucrative career.

Disclaimer: The information in this blog is current as of 16 September 2022. For updated information visit https://flatironschool.com/

How Is Data Science Used?

Data science has been a persistently popular field lately, cropping up in job reports and listicle articles for recommended career paths. With all these headlines, you may wonder though – how is data science used, and where can you see its implications in everyday life? 

At its heart, data science is the study of information. Companies use it to help make business decisions, solve complex problems, and create strategies to improve results and performance. It is essential to every industry, from understanding water use and air quality to creating a more sustainable society and helping insurance companies mitigate risk.

The data science field is evolving and growing almost as quickly as the amount of information generated and collected across the globe increases. 

Every industry — from retail and social media to sports and space travel — uses data science to make intelligent, data-driven decisions.

How Is Data Science Used?

Data science deals with the enormous amount of information generated from business operations, medical research, and nearly every other endeavor. Businesses and corporations are drowning in data. They want to use this data to unlock insights and drive growth.

Data science uses complex machine learning algorithms to build predictive models. It blends tools, algorithms, and machine learning principles to uncover patterns from raw data and visualize them for non-technical audiences.

This analytical field is also the cornerstone of artificial intelligence (AI), deep learning, and machine learning.

Deep learning analyzes videos, images, and unstructured data in ways machine learning cannot. Machine learning is more about computers that can think and act with less human intervention. AI encompasses all of this and attempts to make machines think and act more like humans.

Sub-categories for the field subcategories include:

  • Data Engineering
  • Data Mining
  • Mathematics
  • Statistics
  • Advanced Computing
  • Model Visualization

Data science collects your actions, organizes them with your other activities and those of others, looks for patterns, and creates an output, like a recommendation, based on the information found.

How Is Data Science Changing The World?

Data science enables those using it to make more informed decisions about the future. 

Its use improves decision-making processes in business, medicine, science, and almost every other field or industry. It helps uncover patterns and provides predictive analysis so that organizations can anticipate the results of their next move.

From treating drug and alcohol addiction to fighting poverty, data science is used for the greater good of society.

The world already uses it to predict disruptions in travel and optimize the airline industry. It’s used for decisions in retail, like furniture shopping, and streamlines treatment discovery in medicine. For instance, Operation Warp Speed – the worldwide development effort of the COVID-19 vaccine – would not have been possible without this field.‌

If you want a career where you can use your analytical skills to change the world, this field can lead to a lucrative career with vast employment and career growth opportunities. Demand continues to rise, and the average pay in the U.S. for a Data Scientist is around $119,686* annually, according to ZipRecruiter.

Will You Change The World?

If you want to join this high-growth, impactful, and lucrative field, then you’ll likely need to acquire some new skills first. Flatiron School’s Data Science course can get you industry-ready in as little as 15 weeks. 

With curriculum tailored to in-demand skills, experienced instructors, and individualized career coaching for up to 180 days after graduation, Flatiron prepares you for success. 

Apply to Flatiron School today.

*Salaries cited current as of September 2022 

Disclaimer: The information in this blog is current as of 16 September 2022. For updated information visit https://flatironschool.com/

Jeffrey Hinkle: From Chef to Data Science Curriculum Writer

Jeffrey Hinkle, a Junior Curriculum Writer for Data Science, spent more than two decades in the restaurant industry as a chef before pivoting into tech. The driving force behind his life change was the desire to spend more time with his family, and the work/life balance he now has as a Data Science Curriculum Writer allows him to do just that. 

Jeffrey shares his journey from Chef to Data Scientist below.

Give me an overview of your experience – where did you start and how did you get where you are today?

I was a professional chef for over 20 years and needed a career that would allow me to spend more time with my wife who is a middle school teacher. I found data science and fell in love with using information to gain insight into real-world problems. 

As a result, I attended [Flatiron School’s] Online Data Science Bootcamp. Upon graduation and at the beginning of the pandemic, I began my job search where I was hired as the Data Science Interview Coach. 

The role eventually changed into a traditional coaching role and I transitioned to the curriculum team as a Curriculum Developer.

What are some notable projects you’ve worked on?

I migrated a majority of the Data Science curriculum to the canvas platform. I developed a few Python scripts to help to automate some of the processes along the way.

What are you most proud of in your career (so far)?

I am proud to have the opportunity to work for an organization such as Flatiron School.

The instructors’ dedication and hard work helped me succeed in the program and make it possible for me to be where I am today, still learning every day and helping keep the curriculum up to date with what is happening in the industry.

Do you have any advice for current or prospective Data Science students?

Don’t give in to “imposter syndrome,” if you are uncomfortable with something you are doing or working on, you are expanding your knowledge. 

Staying in your comfort zone will not allow you to push yourself.

Inspired by Jeffrey Hinkle’s career pivot story? Apply Today to our Data Science Course to take charge of your future in as little as 15 weeks.

Not quite ready to apply? Book a 10-minute chat with admissions to see if you qualify, or test-drive the material with Data Science Prep

Disclaimer: The information in this blog is current as of 02 September 2022. For updated information visit https://flatironschool.com/

Day In The Life Of A Data Science Student

Thinking of enrolling in a data science course but not sure what to expect from the day-to-day as a data science student? 

In this post, we’ll cover the typical day-to-day schedule of a full-time student from when you sign on at 9 a.m. to when the day ends at around 6 p.m.

Morning Check-In

Every day begins with a group check-in. The goal of the morning check-in is to get you ready for the day, set goals, and remove any blockers that may come up. 

Sometimes you’ll answer icebreaker questions or solve short warm-up questions to get the gears in your brain turning and primed and ready for the day!

This sets you up for success so you can make the most of your time in the classroom today.

Lectures

A typical day will involve two lectures. You should come ready to engage and participate – our lectures are interactive! These live instructor-led sessions are deliberately engaging to help move your learning out of the theoretical and practice what you’ve read in the written curriculum materials.

Related reading: Data Science Course Syllabus

Pair Programming

This is dedicated time to work on core curriculum content, with the support of your peers. Pair programming helps you practice verbalizing concepts and walking through your thinking process for technical interviews, communicating with others, and solving problems together.

Lunch Time

Rest, refuel, go for a run – whatever you need to do to come back afterward ready to take on the remainder of the day!

Individual Check-Ins

Now is your chance to chat one-on-one with your instructors. You can use this time to discuss your progress, goals, and understanding of core content. It’s also a great time to review labs or checkpoints to go more in-depth into how the code works and how you can bring that understanding to new problems.

Canvas Time

Canvas acts as our textbook, and going through lessons and labs, plus taking quizzes to check your understanding, allows you to familiarize yourself with concepts before diving into the lectures to practice what you’ve learned.

Assessments

We check your understanding of key concepts and tools throughout the course, including during timed assessments that help mimic the pressure of a technical interview (but in an environment we design to be much more supportive to learning and growing your skills).

Afternoon Check Out

The daily wrap-up sets expectations for the next day. You can also use this time block to share current work and personal projects, or discuss struggles and successes with your peers and instructor.

Community Events

Community events are optional events designed to connect you with your peers and the broader Flatiron School network. These often feature workshops and guest speakers, but also yoga and happy hours – there’s always something going on that you can opt into!

Regular Course Activities

Checkpoints

These are mini-assessments to check your understanding and key proficiencies. You can expect them about twice per week. Checkpoints give you a chance to practice coding in a time-boxed session without the pressure and check your understanding of the course material.

Code Challenges

As a data science student, you’ll regularly face coding challenges meant to simulate real-world workflows or technical interviews. This will feel just like the checkpoints, but you will have 1 hour to complete the code challenge. This is one place where no collaboration is allowed. We encourage you to ask instructors questions, but these are solely your work.

Projects

Either solo or in groups, you will tackle real problems to develop portfolio-ready projects you can showcase to potential employers. Projects will consist of both technical and non-technical deliverables as well as presentations for each to gain experience presenting your findings to a non-technical audience.

Blog Posts

Building a digital presence demonstrating your Data Science chops and establishing a personal brand is essential in the job search. To prepare you for graduation you’ll write four blog posts during the course (one for each phase, except the Capstone).

How To Get Started

Ready to take the next step and become a data science student? Apply Now to our Data Science course or schedule a 10-minute chat with admissions to see how you can take charge of your future in as little as 15 weeks.

Data Science Career Paths

In the modern digital age, data is now the currency of business, and data science career paths are proving to be both in-demand and numerous.

More and more, the rise of big data means big opportunities for those possessing specific data science skill sets. It pays to know how to collect, clean, sort, and analyze data in a way that is valuable and provides actionable insights. 

In this post, we’ve collected some standard job titles, their typical requirements, average salary, and the required skillset to hold them. If you’re interested in data science career paths, here’s what to look for.

What is Data Science?

In simple terms, data science is using and preparing data for analysis. It is a data scientist’s job to clean and analyze it to provide digestible and actionable insights to decision-makers and business leaders.

There is a growing need for data scientists and analysts globally to help navigate a digital-first and data-driven global market. Data science is used in just about every corner of the economy – from political forecasts and predicting sports outcomes to forecasting media trends and warning of business slowdowns. Data scientists turn mountains of captured data into neatly packaged, connected dots that detect trends, make predictions, and provide insights into an organization’s goals.

Why pursue a career in Data Science?

The current marketplace combines a high demand for data scientists with a shortage of qualified applicants, making it the perfect opportunity for those interested in entering the field.

Research shows there was a shortage of 250,000 data science professionals in 2020. In addition, 35% of organizations surveyed said they anticipated difficulty finding skilled candidates for data science roles. (1) What’s more, the U.S. Bureau of Labor Statistics reports that the demand for data science skills will drive a 27.9 percent rise in employment in the field through 2026. (2) 

For those with the needed skill sets, companies are paying top dollar, especially for candidates familiar with emerging technologies such as cloud computing, A.I., and machine learning. (3)

Entry-Level Roles

Data Analyst

Average salary: $93,262 USD*

Typical job requirements: A Data Analyst collects and stores data on sales numbers, market research, logistics, linguistics, or other behaviors. They ensure the quality and accuracy of data, then process, design, and present it to help stakeholders make better decisions.

Typical skillset required: Java, Python, SQL, R, Scala

Junior Data Scientist

Average salary: $115,586 USD*

Typical job requirements: Junior Data Scientistsinterpret and manage data and solve complex problems with the help of various data software. A typical job description for a Junior Data Scientist would include things such as having an extreme passion for data science and data analysis, being able to conduct data mining, and working in teams.

Typical skillset required: Java, Python, SQL, R, Scala 

Data Engineer

Average salary: $116,206 USD*

Typical job requirements: Data Engineers are responsible for finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise. This IT role requires a significant set of technical skills, including a deep knowledge of SQL database design and multiple programming languages.

Typical skillset required: SQL, Python, R, and Scala

Database Administrator

Average salary: $101,097 USD*

Typical job requirements: Database Administrators are responsible for the management and maintenance of company databases. Database Administrators’ duties include maintaining adherence to a data management policy and ensuring that company databases are functional and backed up in the event of memory loss.

Typical skillset required: SQL, PHP, Python, R, C#

Mid-Level Roles

Data Mining Engineer

Average salary: $114,682 USD*

Typical job requirements: A Data Mining Engineer is an advocate for both the database system and its manager. They advise company executives on the best equipment and software to meet the company’s needs and look for opportunities to improve the system and increase its relevance to company goals.

Typical skillset required: Python, Java, R, MapReduce

Data Scientist

Average salary: $118,537 USD* 

Typical job requirements: Data Scientists work closely with business stakeholders. They work to understand their goals and determine how data can be used to achieve those goals. They design data modeling processes and create algorithms and predictive models to analyze data. Combining computer science, modeling, statistics, analytics, and math skills data scientists help organizations make objective, data-driven decisions.

Typical skillset required: Python, SQL, Java, R, Scala

Senior Level Roles

Data Architect

Average salary: $133,823 USD* 

Typical job requirements: Data Architects build and maintain a company’s database by identifying structural and installation solutions. They work with database administrators and analysts to secure easy access to company data. Duties include creating database solutions, evaluating requirements, and preparing design reports.

Typical skillset required: Python, Java, C, C++

Machine Learning Engineer

Average salary: $122,844 USD*

Typical job requirements: Machine Learning Engineers develop self-running AI software. This software automates predictive models for recommended searches, virtual assistants, translation apps, chatbots, and driverless cars. They design machine learning systems, apply algorithms to generate accurate predictions, and resolve data set problems.

Typical skillset required: Python, Java, R, Julia, LISP 

Breaking Into The Field

If you want to break into any of these data science career paths, it’s critical to learn the required programming languages for your target title. 

This can be achieved in several methods – including with university classes or self-teaching – but by far the most time-conscious and cost-effective way is by enrolling in a technical training course that will get you to your goals faster. 

These courses are completed in months, not years, cost a fraction of traditional university tuition, and provide practical training to prepare graduates to jump headfirst into their first position. Short-term, intensive courses teach you up-to-date skills that won’t be obsolete when you graduate. 

Check out all the programming languages and skills Flatiron School will teach you. 

Ready to take the next step? Start with a Free Data Science Prep Work, or check out the Data Science Course Syllabus that will set you up for success with the skills to launch you into a fulfilling and lucrative career.

* Salaries cited current as of June 2022 

Sources:

  1. https://quanthub.com/data-scientist-shortage-2020/
  2. https://www.bls.gov/
  3. https://fortune.com/education/business/articles/2022/02/24/a-hot-market-for-data-scientists-means-starting-salaries-of-125k-and-up-this-year/#:~:text=Data%20scientists%20made%20a%20median,%24152%2C500%20median%20salary%20in%202019
  4. https://www.glassdoor.com/index.htm

Disclaimer: The information in this blog is current as of 28 June 2022. For updated information visit https://flatironschool.com/