Logit Academy is a 12-week full-time data science bootcamp in Los Angeles, CA. The program aims to train students to build and analyze data and prepare them for a career in data science. Course topics include SQL, Python, Pandas, Scikit-Learn, D3, Hadoop, Hive, Spark, NoSQL, linear algebra, linear regression, LASSO, tree-based methods, spectral analysis, topic modeling, and more. Students will also receive job preparation including interviewing techniques, resume reviews, and networking events.
Logit Academy is part of data consulting company Logit Data Science, which uses data insights to help clients grow their businesses. The company also builds custom designed software solutions for clients.
Recent Logit Academy News
0 reviews sorted by:
- Only Applicants, Students, and Graduates are permitted to leave reviews on Course Report.
- Post clear, valuable, and honest information that will be useful and informative to future coding bootcampers. Think about what your bootcamp excelled at and what might have been better.
- Be nice to others; don't attack others.
- Use good grammar and check your spelling.
- Don't post reviews on behalf of other students or impersonate any person, or falsely state or otherwise misrepresent your affiliation with a person or entity.
- Don't spam or post fake reviews intended to boost or lower ratings.
- Don't post or link to content that is sexually explicit.
- Don't post or link to content that is abusive or hateful or threatens or harasses others.
- Please do not submit duplicate or multiple reviews. These will be deleted. Email moderators to revise a review or click the link in the email you receive when submitting a review.
- Please note that we reserve the right to review and remove commentary that violates our policies.
Click here to log in or sign up and continue.
Our latest on Logit Academy
Taylor originally studied chemical engineering and was exposed to programming through the scientific research programming language Mathematica. After working in pharmaceuticals and therapeutics, Taylor realized that her background in engineering, statistics, and programming was ideal for data science. So she enrolled at Logit Data Science Academy in LA with the aim to get into a technical quantitative role in bioinformatics and AI. We spoke to Taylor about why she chose to study at Logit, what she has learned so far, and why she is excited about the data science industry.
What is your pre-Logit story? What is your educational and career background?
I received a master's degree from the University of Rochester in 2013 in chemical engineering. When I graduated I decided to apply my research-driven engineering education to biotechnology, so I joined a pharmaceutical company in Boston as a development engineer. I was a technical liaison between process development and manufacturing departments, which was fairly analytical. I was applying statistical and data analysis techniques to company business initiatives.
I then moved to the West Coast to work at the Emerald Cloud Lab. There I gained insight into a computer science aspect of therapeutics, and received training in computer science. There was a definite computer science element to understanding the technology, as the lab itself was powered by the Wolfram language. After I was exposed to this programming language, I decided to investigate how I could leverage my engineering degree and my statistics background with programming. I realized that data science is essentially rooted in computer science and statistics, so my background was actually very relevant.
How much programming knowledge did you have before this? Did you teach yourself about programming and/or data science? What types of resources did you use?
I had previous programming experience in mathematica through my engineering degree, but hadn’t actually implemented programming until I started at the Emerald Cloud lab. There I interfaced programming with advanced robotics.
I had also been teaching myself Ruby through MOOCs, including courses offered by Coursera, Datacamp, and Dataquest. I would definitely encourage all applicants to look at the online resources available, especially with Coderbyte and Codecademy. Also, there were a few textbooks, like Python Playground, and a variety of different resources you could use to understand what the scope of the course could be and what to expect.
Did you look at other data science bootcamps or just this one? What attracted you to Logit?
I was reviewing and comparing several bootcamp opportunities. A lot of them were online, and I actually utilized Course Report to scope out what opportunities were available. There are several other data science bootcamps emerging here in the market but their prerequisite requirements include a Ph.D. or extensive software engineering experience. So what you’re seeing in the market is around 40% of current data scientists have a Ph.D. according to recent reports, and the remainder have a bachelor's or masters. It’s not a requirement to get into the field, so Logit is actually reflecting what we are seeing in the market, and that is what predominantly attracted me.
I also liked the fact that Logit is a branch of an actual data consulting firm. And the founder is the host of Logit’s Hollywood Data Science meetup group, so he has networking connections, he knows what the field demands are, and what the responsibilities of a typical data scientist are.
Did you think about going back to college to study data science?
Right now, the market and demand for data scientists is surpassing what academia can support. The open source model, especially like you’re seeing in AI, is accelerating innovation of the tools that data scientists are using and expected to implement. There are a handful of masters and bachelor's programs in data science, but the scene is evolving so rapidly, that bootcamps are necessary to keep up with this technology. So bootcamps are doing very well because of the amount of skills you can learn in a condensed time period. The number of data scientists has doubled within the last four years, and there is a 50% higher rate of new data scientists than that of software engineers, so there is a huge demand.
What was the application and interview process like for Logit?
First, there was more of an informal conversation to discuss my expectations, what I was hoping to achieve, and my background. Students are required to have a degree in a quantitative field, programming experience and an understanding of basic probability and statistics. Then there was a more formal written application and a brief coding exam which I had a week to complete. There was no required language for the exam, you could code in any type of language that you knew, which was very appealing. I actually used Ruby, because I had been looking at web development, and I had been completing coding exams in Ruby. I have since been doing all my code in Python.
Was there a reason that you chose a data science bootcamp which taught Python?
Yes. There have been several studies which surveyed different tools that data scientists predominantly use in the market, and Python has come up as one of the most popular tools, so it’s a very relevant language.
What’s your class like in terms of size, diversity, gender, race, life and career backgrounds?
There are about 18 students and it is quite diverse. Students have a wide variety of STEM backgrounds including math majors, physics, CS majors, meteorology, and electrical engineering. So this particular course is attracting a diverse perspective. In terms of female:male ratio, there is me and one other female in the course.
What is the learning experience like at Logit so far — typical day and teaching style?
On a typical day there is a lecture and theory in the mornings, and labs in the afternoons. We have four professors who are rotating and teaching specific weeks of the curriculum. We are in the third week now, and the first two weeks we were taught by instructor Mike McKerns. He brought with him a vast amount of experience in the industry. He has authored 12 python packages, so we were able to ask him very specific questions about what he’s observed in his Python consulting.
So far we’ve been learning the fundamentals of Python, and Mike has shown us the back end of Python, how to understand the underlying C code of the functions, how to develop a pipeline of data analysis, how to approach solving large data problems, and how to streamline that workflow. Along the way, Mike has been using his industry experience to teach us these fundamentals and increase our understanding of the sort of real world problems we’ll be encountering. We’re eventually going to be covering machine learning and neural networking, which require these fundamentals.
Do you work on exercises or projects throughout the program?
We work on problem sets in labs in the afternoons, which reinforce some of the concepts we covered in lectures in the morning. Our course instructor teaches the lab, and we either work independently or in groups to problem solve and complete the problem sets. Towards the end of the program in weeks 11 and 12, we will have the opportunity to work on capstone projects, which will demonstrate our new skills.
What’s been the biggest challenge so far?
At this point, one of the more challenging parts is learning and understanding the new syntax at a very fast pace. The great thing about this course is we have such a diverse set of backgrounds, that people bring different strengths. My strengths are more in statistics and I’m able to leverage that experience. Everyone has different challenges, but we are all trying to learn at the same rate.
Could you give me an example of the sort of problem sets you’ve been working on?
A lot of the Big Data problems that people are encountering in the workplace are complex. We’re learning how to access public data through SQL, and in some of the examples we’ve been parsing data and giving summary statistics for particular subsets of this data. So we can find underlying statistical themes in these subsets, by performing analytical analysis with Python packages. We can also construct models by performing regression-based techniques on our data, which we will learn in the coming weeks.
A lot of these problems require a statistical approach, but we also need to be able to present them to clients. We’ll have the opportunity to hone our presentation skills during our individual mid-course projects.
What is the Logit campus like? Where is it and how big is it?
We are in the Broadway Hollywood building on the corner of Hollywood and Vine, it’s actually quite iconic. The Hollywood walk of fame is right outside our door, and on the rooftop there is a view of the Hollywood sign. We have lunches on the rooftop occasionally, as well as the meetup group event the last Thursday of every month. Classes are in a retrofitted loft.
Have you been able to give feedback about the program so far?
The director of the program and the founder have been excellent in collecting and receiving feedback. We’ve been filling out surveys, and they’ve been tailoring the program to the student responses. It’s really been very helpful.
How does the bootcamp prepare you for job hunting?
To ensure all the students are going to be finding jobs after this, Logit has partnered with a recruiting firm that specializes in data science. Towards the end of the course we will be refining our resumes, learning how to interview well and understanding how we can leverage our previous experience. Periodically throughout the program there are lectures from people in the industry. This week we have a data scientist from IBM coming to give a talk, which is going to be very helpful.
What was your goal in attending a data science bootcamp — get a job as a data scientist, start a business, get a promotion? What are your plans after you graduate?
I’d like to pivot into the biotech industry and do more of a technical quantitative role in bioinformatics. Especially in biotech and in pharma in general, you’ll start seeing that there is a cluster of opportunities to design experiments, find genetic markers for diseases, and other opportunities, especially in clinical trials, for determining how to select patients for those trials. So that is one potential implication of the skills I will be developing.
What advice do you have for people who are considering a data science bootcamp?
One of the motifs I’ve found is having the ability to adjust, adapt, and be creative. There are always multiple approaches that one can take to solve some of these problems. A lot of the data we’re going to look at is highly unstructured, so finding unique ways to implement the tools we are going to be learning will be extremely pivotal, and will definitely benefit future applicants to this program.
At this point the data science field is in its nascent stages. The name itself stems from a hybrid of a data analyst with a research scientist, and that came from Facebook’s first data group. So with a background in fundamental statistics, or computing, anyone can actually leverage these skills and apply them to this field. This is why data science is massively attracting people with quantitative backgrounds. It’s very encouraging and I would encourage anyone who has that type of experience to look into this field, especially people who are passionate about AI.
Mike McKerns is a data science veteran and instructor at Los Angeles data science bootcamp Logit Academy which starts its first cohort on June 13. In his 37 years as a data scientist, Mike has pioneered new techniques, and founded a nonprofit for advancing the maths behind data science. Mike tells us about his extensive experience, why he’s excited to teach at Logit, and why Python is an ideal language for data scientists to learn.
Tell me about your background and experience in data science.
Data science is a relatively new term. I have been a researcher in data science longer than the field has been called “data science.” For the last 37 years I’ve been using simulations to fit models of experimental data. In machine learning terms, model fitting is generally called regression and/or classification.
I’m an optimization specialist. I write Python programs to solve optimization problems and I build models as well as tools that other scientists and data scientists can use to solve problems. I’m the author of Mystic, which is used for large complex nonlinear optimization problems in both academia and industry. I’m also the author of few Python packages for parallel computing. Some of my core packages are leveraged by popular Python libraries, both in data science libraries and parallel computing.
I work in computing and mathematical science at Caltech. I’m not really a data scientist but somebody who does research that aids data science. I’m a coauthor of Optimal Uncertainty Quantification, OUQ, which is a theory that can rigorously determine the optimal model and optimal bounds for the data and other information you have. I started the UQ Foundation with a few of my colleagues from Caltech with the idea that we could help support, promote, and advance the mathematics and software tools for data science.
I’ve also been teaching data science courses as contract work for a few years.
What did you study at college?
I studied applied physics and I have a PhD in Physics. Back then, you couldn’t get a degree in “data science.” You either had a theoretical or applied degree, and you stuck to your particular domain. There was no real institutional recognition of computational science or data science. So those of us doing it were pioneers, of sorts.
You mentioned that when you were studying, there was no data science major. Do you know if there’s something like that now?
Yes, they exist. I don’t know if those degrees are called data science but, they’ll often say applied and computational math which is basically statistics and data science. Degrees in finance or economics can also involve a lot of data science courses. For theory or a general background, you could get a degree in computational or applied mathematics.
What companies have you worked for over the years?
I’ve worked at J.P Morgan, and more recently I’ve had several contract jobs at companies like Shell, and some hedge funds, and for the US Government. I also have worked for Enthought, and of course for Caltech for many years.
Can you tell me about your experience as a teacher?
I had 7 or 8 years of teaching experience as a graduate student, teaching astronomy and general physics classes. I got my first job at Caltech as a postdoctoral student but I didn’t teach there because I was focusing on research.
I started missing teaching a lot. I was friends with the president and CEO of Enthought, a global company which teaches Python for scientists, engineers, and data scientists. I started teaching again at Enthought. Over the past five years I’ve taught 10 to 15 one-week courses per year at Enthought, and also at Python Academy, all over the world.
What do you think of the idea of this intensive, career-driven data science bootcamp that Logit Data Science offers?
I think it’s a good idea and I’m interested to see how it will work. I know that when I teach a weeklong class, my students by the end of it feel they’ve crammed a lot of knowledge into their brain and that’s tiring. The Logit program classes run for 12 weeks, so it’ll be interesting to see how the amount of information balances out with people’s ability to absorb it.
I think it’s a fantastic idea to take people like me who are working in the field and have experience that can be conveyed to someone not just through a textbook, but with a presentation and hands-on knowledge. I think that’s invaluable. They key is to have interactivity around subjects, then go out and do hands-on learning and problem solving. That’s a very important and critical experience that you don’t get elsewhere and I’m really excited to do that. When I teach weeklong classes, you can’t go to into that depth. You can do one or two example problems, but you can’t go to the depths of the sort of problem you might do if you were working in the field. That part of the lab should be pretty exciting.
Are you going to be teaching full-time or will there be other instructors as well?
The plan is that the first bootcamp will be split up into three instructors. I’m teaching one week at the beginning, one in the middle and one near the end.
What will you be teaching in your three weeks?
In the first week I will do an introduction to data science; what data science is and the different types of data science. Then in the middle I’ll do a section on time series problems - time evolution and things relevant to stock market information and making predictions on things that are dynamically evolving.
Then near the end I’ll cover applications to text or corpus problems; basically text based processing, or scraping. A lot of marketing companies take email responses or posts, scrape them for customer reactions, and then do market analytics based on the customer reactions.
Why is Logit teaching Python rather than R or another data science language?
There are a number of reasons:
- Python is probably a more pervasive language than R. If you look at the use of Python in industry, Python is used in big banks and hedge funds more than R.
- R is a more tightly specific kind of language so if you’re going to be doing statistics, you might be doing R. It’s got a very tight scope. Once you get outside of it and start to apply it to other problems, you have to start working in another language.
- Python is very good at binding and interplaying with other languages, technologies and tools. What I mean by binding, is that it can communicate back and forth between these other languages. We have Python bind to C and to R and all sorts of other languages that people use.
- Python’s used a lot in high-performance computing, which data science typically needs.
- Python’s more generalist so there are more programs in Python for optimization than there might be in R. If you’re solving a predictive problem, you need optimizers.
Overall, it’s a more general toolset that is used more in the larger scale industry. In certain cases, people will use things on a smaller scale like R and Excel. But if you’re looking at an integrated technology where you want to do bigger problems, in bigger companies, you find that they almost always use a language like Python.
What will the students’ schedule be like?
In the mornings, we’ll cover general theory so it’ll be lecture style with slides and walkthroughs, where the instructor is working through problems, and discussing user stories. Afternoons are basically labs where the students get hands-on with real data sets and real problems.
Logit Data Science classes are 9:30am to 6:30pm, Monday to Friday.
What’s your personal teaching style?
I’m an extremely informal instructor. If it's just me talking all the time, it’s a failure. Basically, I want interactivity and I want people to ask questions. The more questions somebody asks the better the class is, so it’s my responsibility to move the class through the entire curriculum. When a student asks a question, another student probably has the same question and it helps enhance the learning process.
In my own experience I found that most of my instructors weren’t able to communicate subjects to me very well. I learned best by hearing examples rather than seeing derivations. In a traditional setting you don’t get a lot of that. So I teach how I learn, which is to introduce one or two guiding mathematical expressions, then tear them down, and get a picture of what the thing looks like so you can start to translate it to real life. It’s not remembering, it’s actually learning how the equation is part of the picture of real life and how you can use that in code to solve a problem.
How will Logit help students find jobs?
Logit is partnered with a recruiting firm and resume experts who will help ensure that students are prepared for interviews and exposed to job opportunities. We’ll have a few hiring managers and recruiters coming to speak to students, and there will also be networking events. A senior data scientist from Google is one of the first speakers.
What sort of jobs might students be prepared for when they graduate?
If you want to be general about it, the students are being prepared to be data scientists. However, in a lot of other industries you might get a different name for what you’re doing. The type of education and problems we’ll get into in this class will put you on the right learning trajectory for tech companies like Google and Facebook. It should also make you very attractive to hedge funds and banks, which use a lot of data science modeling.
Data science is pretty pervasive in any field that does predictive analytics. We’ll try to cover problems in the different sections that are specific to each of those different fields so people will get a taste and see how data science applies to it.
What sort of students do you expect to see studying with Logit?
I would expect two types of student. First, I would expect someone primarily looking for a job in data science. Second, we might see people who already have jobs that entail some data science, so they use it as training for data science within a particular company. You’d be surprised that a lot of big companies don’t offer data science within their own training program.
What sort of resources or meetups are there in Los Angeles about data science where people can get a taste of what it would be like to study data science?
There are a number of data science meetups in LA, including the Hollywood Data Science Meetup which is hosted by Logit Academy.
How are you feeling about starting as an instructor there?
I think the people running Logit are very nice, genuine people. They’ve been working in data science for a while. They’re not necessarily data scientists but more in data science management. There are a lot of data science positions in Los Angeles, and they know it’s important to build the community of data scientists in Los Angeles. They saw the opportunity and not only are they in the right place at the right time but they really care about getting students work in the field.
If you're a college student, an incoming freshman, or a teacher with a summer break, you have tons of summer coding bootcamp options, as well as several code schools that continue their normal offerings in the summer months.Continue Reading →