Louisa Barrett of Haught Codeworks tells us about how to become an effective junior developer. We also speak in depth about how to become a better teacher.

Here's what to listen for:

• 01:54 - Who is a junior developer?
• 02:59 - How did you go from going to an art school to wanting to learn about code?
• 06:43 - How long did it take to become a junior developer?
• 08:14 - How comfortable were you when you first started working out of bootcamp?
• 09:16 - How well do you feel that the dev bootcamp prepared you for starting a job?
• 11:39 - Why is networking so important?
• 14:02 - Why is it so hard to find a mentor?
• 15:17 - What does a mentor help with?
• 21:27 - What is the difference between a helper and a teacher?
• 26:04 - How does one become a better teacher?
• 28:56 - Once you get out of a boot camp, what kind of companies are best to join?
• 36:18 - How to junior developers get off on the right foot?
• 37:48 - Why is pair programming important?

Diana Smith of Segment tells us how to get the most out of our analytics tools. In the pursuit of trying to be data-driven, we have been conditioned to track everything. Diana tells us why this can be dangerous if we want to draw useful insights from our data.

Here's what to listen for:

• 00:49 - What specifically are we talking about when we are talking about analytics in this context?
• 01:55 - What is the difference between user path and funnel tracking?
• 03:10 - Are there tools similar to Kissmetrics Path Report tool?
• 03:57 - If I’ve got my own database, why should I be using some sort of other analytics tool when I could just easily track events that happen on my database as it is?
• 05:43 - What events should I be tracking?
• 07:22 - When I set up what these events are, does it matter how I name them?
• 08:19 - What is the best naming convention?
• 09:14 - Why should I start only with just tracking a few events?
• 10:53 - What kind of info should I be putting in these properties?
• 13:16 - How do you connect and keep track of the who the referrer is? How does that work?
• 14:45 - How important are user demographics for data and tracking?
• 19:59 - How should I make use of the data that I’m collecting?
• 23:25 - Do you recommend that people create a bunch of accounts on these different sites and then choose one? How do you deal with the paradox of choice?
• 24:57 - What types of other analytics tools are out there?
• 26:33 - How do you decide which of these tools to use? What sort of questions should I be asking around “which one is right for me”?
• 31:54 - Are there any can-not-ignore metrics?
• 33:03 - How do I actually try and make a connection between the action that I’ve taken and the results that I’m seeing?
• 34:34 - Do you recommend waiting and focusing on qualitative things over quantitative?
• 36:42 - Is there a number you should be looking for in terms of when things should be statistically significant?
• 37:19 - In terms of doing the qualitative work that you talk about, and maybe trying to use quantitative data to make it match up with the qualitative data, or at least help … are there any specific strategies that you recommend for going out and getting that qualitative data?
• 38:51 - Let’s say that I am collecting enough data at this point. Even though I have a baseline for myself and my company, how do I know whether or not that’s a good baseline?

Sarah Doody, a UX designer, consultant, and writer, tells us how to build products with great user experiences. We will hear why user experience is far more important than design.

Here's what to listen for:

• 00:46 - What is Sarah’s background in UX (User Experience)?
• 03:09 - What is the distinction between the experience and the interface?
• 05:53 - How do you create the ideal team at a startup?
• 10:53 - What is the distinction between experiential design and visual design?
• 13:14 - Which comes first: experiential design or visual design?
• 14:34 - What’s the process for evaluating the UX of an app?
• 19:01 - How do you get customers to use your product the way that it’s intended to be used?
• 20:22 - What mistakes do you see in the UX of an app once you are past the onboarding flow?
• 24:26 - How does user experience get compromised?
• 29:10 - How can you get back on track?
• 33:00 - How is product development like storytelling?
• 37:39 - What is a storyboard?
• 41:30 - Is a storyboard like a comic strip?
• 43:16 - How do you evaluate whether a developer is good or not at UX design?

Mubashar Iqbal, the #1 product maker on ProductHunt, tells us about how he launches products that people use in weeks, not months.

Here's what to listen for:

• 00:57 - What is Mubashar’s Background?
• 02:11 - What does being the most featured maker on Product Hunt mean?
• 03:25 - What helped you to become #1 and have so many products features on Product Hunt?
• 05:22 - What does the “featured” product distinction mean?
• 06:27 - What was important in different products that ended up making them get featured?
• 07:40 - What is it that you do to ensure not overbuilding?
• 12:13 - What is your feature-building process?
• 14:13 - Where do you draw the line between building a feature now vs later?
• 17:38 - How do you make yourself comfortable with pushing products out when they’re ready to be pushed out?
• 19:43 - How do you handle requests for features?
• 26:01 - How has the adding features metric changed since you originally launched the project?
• 27:00 - Do you use quantitative data in addition to doing qualitative customer development?
• 28:06 - What kind of long-term success have you seen with your products and what has made a difference between the ones that are ones that are successful over time and the ones that go wayside?
• 31:50 - Is there an example of an app that you built in the past where you built way too much?

Ben Orenstein of Upcase tells us how to go from a junior to a senior developer. He reveals a number of things senior developers do that junior developers don't.

Here's what to listen for:

• 02:34 - Would a degree in computer science benefit somebody who is interested in starting programming?
• 03:23 - How do you convince people that getting a computer science degree isn’t necessary?
• 08:41 - What is the path from zero to junior developer?
• 14:16 - How do you define what a junior developer is?
• 15:35 - What goals are junior developers making?
• 17:24 - How was Upcase started? What was the focus/goal?
• 19:43 - What might an intermediate developer be doing that a junior developer isn’t?
• 21:50 - What is the difference between TDD (test-driven development) and writing tests after you write your code?
• 26:15 - Where do you look for your first job? How do you go about getting hired?
• 30:01 - How do deal with impostor syndrome when applying for a job?
• 32:46 - What kind of qualities that you look for when making a hiring decision for junior developers?
• 33:55 - How can you create a work environment for junior developers that helps them get better?
• 35:33 - What did Ben mean by, “To become a better programmer, one should practice like a musician.”?

Lincoln Ritter, director of engineering at Animoto, shares how they use data to make more informed product decisions.

Here's what to listen for:

• 02:01 What can we do with data?
• 04:21 Why should a company care about data and trends?
• 08:02 How can you become more data-driven?
• 12:28 How can you get more people involved in caring about data?
• 16:41 What are the tradeoffs when it comes to agility in software development?
• 18:29 How do you combat paralysis and help people on your team understand the data better?
• 20:58 Is data-supported a better term than data-driven?
• 22:50 What’s the difference between data engineering and data science?
• 24:59 How do you get data in the right format?
• 25:59 What kind of question might you have that requires information from multiple sources?
• 30:13 How do you decouple, consolidate, and keep data separate?
• 33:01 Do you think real-time data is necessary?
• 35:06 How would Animoto look at data and make a product decision?
• 39:04 When is split-testing useful and not useful?

Abby Covert, author of How to Make Sense of Any Mess, teaches us about information architecture, a subject she strongly feels is a core life skill. She's seen people get fired over language and informs us that – quite obviously in hindsight – architecture is less expensive than design.

Here's what to listen for:

• 00:44 What is information architecture?
• 01:52 How is information architecture used specifically in building software?
• 04:25 Is information architecture synonymous with customer development?
• 04:52 Is information architecture as a practice pervasive and can it be used in multiple contexts?
• 06:28 How do we make sure everybody’s on the same page?
• 10:24 What does deciding what language to use entail?
• 13:27 How do you get started with information architecture?
• 15:06 Does everybody on the team need to be involved in the information architecture/design process?
• 17:49 Are there a range of emotions/feelings about people’s involvement in architecture design?
• 19:34 What is meant by a “mess”?
• 20:53 How do you get customers involved in the information architecture process?
• 24:01 Why should you consider architecture before design?
• 25:18 How can we make sure we’re going about naming things properly?

Sven Lenaerts joins us to share his expertise on product design and user experience. This conversation includes some thoughts on MVPs, when to hire a designer, and what a product person really does.

Here's what to listen for:

• 00:46 What do you do as a product designer and UI/UX designer?
• 04:18 What should I figure out before I talk to a designer or developer about building a product?
• 07:01 What things can I do inexpensively that are lower-risk to see if building an app is the right solution for the problem I am trying to solve?
• 12:35 What are the biggest mistakes people make when trying to define their Minimum Viable Product (MVP)?
• 15:29 Should you do much experimentation before the product goes out into the wild?
• 17:53 What do you do when clients are including features in their MVP that they shouldn’t be?
• 21:22 How do you formulate conversations to make sure the vision of the product is realized? What do those conversations look like?
• 27:13 Does your vision document have a narrative start/finish to it?
• 31:17 What “expensive mistakes” can be made when building products?
• 34:58 Are there any qualities that would distinguish a good product person from a bad one?
• 38:38 What can I do if I want to stretch my skills and be a little risky without feeling like I’m putting a client at risk?
• 44:32 What do you do when you find yourself becoming cynical about product ideas and features?
• 50:06 What can I do to help a UI/UX design person do their best work?

James Ward shares how the hosting landscape has changed for web applications over the years and how you can avoid some of his middle-of-the-night pager nightmares.

Here's what to listen for:

• 00:47 What are the differences between hosting services?
• 05:19 What is a sysadmin?
• 07:14 What is the advantage of having people or a service do system administration for you?
• 09:23 What are some things that Heroku does for you that a lower-level server won’t?
• 13:36 Given all the choices out there, how do you think about where to host your application?
• 17:28 Why might one want to switch between servers?
• 21:58 What are container technologies and why do they matter?
• 24:42 What is virtualization?
• 26:44 Why are app servers fading away?
• 30:02 Are add-ons the way to think about services that are broken out?
• 32:06 Should startups use services like Heroku to start out or roll it yourself?
• 35:13 Why do people want to/think it’s easy to manage production systems themselves?
• 37:01 When should you begin thinking about the scalability of an app?
• 45:33 What is the right way to handle multithreading in Ruby?
• 48:12 What does “stateless” mean? Why should app servers be stateless?

Jonathan Cornelissen tells us about DataCamp, the need for data scientists, and how to become one yourself. We also learn about some popular languages and libraries for analyzing data.

Here's what to listen for:

• 00:43 What is the story behind DataCamp?
• 02:06 What is data science?
• 02:52 What kind of xdata is out there that can be analyzed?
• 04:46 Do I need a scientific or statistical background to work with data science?
• 05:26 Does DataCamp help establish a theoretical background?
• 06:21 Do only big companies need data science?
• 07:16 What is big data?
• 07:58 Can the term big data be used interchangeably with data science?
• 09:08 Do you need a “billion dollar budget” to build a data science team? What kind of people do I need to build that kind of team?
• 12:08 What is behind the shortage of data scientists?
• 12:48 What can a startup do to incorporate data science into their team?
• 13:45 What is meant by data savvy?
• 14:10 What do you do with the data once it’s collected?
• 14:50 What is cohort analysis?
• 15:42 Once users are segmented, what could you do at that point?
• 16:21 Are correlations the primary sort of analysis?
• 17:14 Are people trying to make causative claims out of correlative data?
• 18:23 What are some other examples of techniques in addition to correlation?
• 18:55 Are there any other interesting algorithms out there that people are using?
• 20:07 Are these analyses run offline or real-time?
• 20:37 What is the Spark framework?
• 21:10 What is the R language?
• 24:09 Where does R fit in in a company?
• 24:47 Is R being run by a human or is there also a sense of R running on the server to serve up recommendations?
• 25:30 Is R still evolving as a language?
• 25:58 Is there anything people should try to learn before trying to tackle R as a language?
• 26:52 Why learn a language like R?
• 28:27 Does R allow you the ability to communicate the insights that you’re getting from the data that you’ve analyzed to build a narrative to help the non-technical people on your team?
• 29:19 Is visualizing the data that we get back important to our understanding of that data? Why?
• 29:57 Does DataCamp help people visualize data?
• 30:51 Aside from R, what other tools are out there that a data scientist would use?
• 31:23 What is Hadoop?
• 33:09 What is the concept of MapReduce?
• 33:42 What is the mark of a good data scientist?
• 35:30 Why do you need domain expertise?
• 38:30 How are people becoming aware of data science? Where do these people start?

Jeff Patton, author of User Story Mapping, teaches us how to map user stories by focusing on the user's journey to an outcome. He shares his opinion on the notorious "MVP" and how he helped Gary Levitt build his MVP with Mad Mimi.

Here's what to listen for:

• 00:49 What is a user story?
• 02:07 What does a user story look like?
• 02:57 When people refer to user stories do they mean the documentation around the conversations they’ve had?
• 03:44 Why is just having stories written down in a document not sufficient?
• 05:47 What is a good user story template?
• 09:17 What was the motivation for writing User Story Mapping?
• 11:44 Is the concept of a “map” about the narrative of a user’s journey?
• 17:36 How did Jeff help Gary from Mad Mimi get clarity on what he was doing?
• 20:54 Why were things taking longer for Gary when he came to you?
• 23:31 What does Jeff’s road mapping process look like?
• 26:47 What was it about Jeff and Gary’s conversation that took him from having a giant backlog to organizing user stories?
• 29:36 What is your definition of a “minimum viable product” (MVP)?
• 34:41 Why do you want to build something “less than minimal” before building the MVP?
• 39:34 Why is so difficult to put a time estimate on when software will be done?
• 44:53 What is meant by “scope doesn’t creep, understanding grows”?