Inside the new Lighthouse Labs Intro to Data Analytics Course

After teaching web development for years and seeing the tech landscape evolve in Canada, Lighthouse Labs is taking the next step into data analytics and data science. We caught up with co-founder Khurram Virani to find out where he sees the need for data skills, what the curriculum for their new Part-Time Data Analytics Course will cover, and how to make the most of your time at a data bootcamp. 

What is your role with Lighthouse Labs?

I'm a co-founder of Lighthouse Labs and now work as the Head of Education. I lead our program development and iteration. 

Lighthouse Labs has been teaching Web Development for over six years now. Why are you moving into Data? 

Essentially, all software is made up of code and data. We see companies making more data-driven decisions, gathering more data, and empowering their decision-makers (aka their staff) to be able to do that. Everyone needs that foundational data literacy that this course is going to tackle. Aligning with the higher goals at Lighthouse Labs, this course is a way to upskill the workforce in a way that creates opportunities from technology and data. Teaching data makes a lot of sense for us as a next step. 

Should everyone learn data? 

There’s controversy about whether everyone should learn to code or not. We feel strongly that everyone should have the opportunity to learn to code. But should everyone learn Python? People have different answers to that. 

However, most people will agree that everyone should learn data. No matter what role you're in, even if you're not doing the SQL or Python level analysis, being able to understand what questions to ask, how to interpret the data, and how it aligns with business objectives are all key things that a lot of people don't yet fully know how to do. 

Which types of jobs would benefit from learning data analytics? 

If you're interested in data but want to continue in your domain of expertise, a part-time course like our Intro to Data Analytics is a perfect course for that. Anyone from a Marketing Manager, Journalist, Human Resources Manager, Product Manager, even a Business Owner would benefit from this course. 

If you're a Data Analyst or a Financial Analyst already, chances are you're already using Excel and Tableau on the job and this course is not for you. Somebody in those roles would be better suited for an SQL workshop.

What will be taught in the curriculum for the Intro Data Course? How far will it get a student?

The Intro course is 36-hours of part-time, in-person learning about data collection, data preparation, analysis, visualization, and presentation. It will be taught through five modules, the last of which is a capstone project. Students will be working with Excel and Tableau. Ultimately, the goal of the course is to turn findings into conclusions, linking them to business solutions, and creating actionable recommendations for the business. They'll learn technical functions like Trim, Proper, Clean, IF statements, and V Lookups. They'll also be learning more analysis skills like basic clustering analysis, anomaly detection, optimization modeling, correlations, and linear regression, because ultimately those functions are only as useful as your analytical skills. 

Is there anything that this course specifically will not be covering?

Yes. Python and SQL are major tools that are being used by industry professionals, but this intro course will not go into those. Python and SQL will be taught in our twelve-week full-time Data Science Immersive bootcamp launching in July 2020. That bootcamp will teach data science and graduates will be job-ready to work as Junior Data Scientists in much the same way our Web Development Immersive graduates are job-ready to become Junior Web Developers. 

In the full-time bootcamp, we’ll teach data wrangling, data preparation, and data engineering. We’ll also dive into machine learning including deep learning, supervised and unsupervised learning. We will also be using Jupyter Notebook which is the main development environment that they will use to actually create their solutions.

What is the difference between SQL and Tableau?

Tableau is a visual tool with a drag-and-drop environment where you can create custom reports and visualizations from a data source. The data source can be a relational database such as an SQL database. It's successful because it’s quite flexible. Tableau allows you to create different types of charts and it helps you pivot your data. Excel offers some similar functionalities but the visualization technology in Tableau is much further ahead. 

What is the admissions process going to look like for the Intro to Data Analytics course?

The admissions process is similar to our existing part-time offerings (Intro to Web Development and Intro to Front End Development). It's more open than our immersive bootcamps. As long as the course details align with the goal of the student, they are able to register online. We don't actually do a rigorous admissions process for our intro courses.

We're still finalizing what the admissions process will look like for the immersive bootcamp but we know that it will be extensive. Our web bootcamp application process has a reputation in Canada of being the most rigorous. We believe that the data science bootcamp admissions process will be even more rigorous than the web bootcamp. 

Do you need to be a math whiz to do data analytics?

Students don't need to have a university-level of calculus or statistics because the intro program covers the important bits that are needed to do numeric calculations like standard deviation, mean mode average, understanding regression, etc. If you don't have that math background or you're maybe even scared of math you don't have to worry. This isn't a math-heavy program. We touch on just enough math so that things make sense. 

How many projects will students build during the part-time course?

Each module gives students the opportunity to play with different data sets. We want to make sure that everything students do in this course is applicable to their job.

During the capstone project in the final module, students have an option to bring in data from their work to solve an interesting problem that would add value to the organization for which they're working. 

Some courses tend to work with already cleaned data so you don't really get the real-world experience of working with actual messy data where there are missing fields, incorrect data, and fields that don't even need to be included. Playing with real data is a big priority in this course. Data wrangling and data prep are skills that are interwoven throughout the entire twelve-week program. 

Who are the instructors for this part-time Intro to Data Analytics class?

The instructors will be subject matter experts who have been working in the field as Data Analysts and or Data Scientists. They'll be able to bring relatable examples from their work. We're looking for instructors who have cross-domain knowledge. They can speak to anything from marketing to banking. We're also doing extensive training for the instructors of these courses. 

What’s your advice for someone who wants to change careers into a more data-focused career?

It's a great step to make the decision to add data to the list of things that you are comfortable with. The question after this is how deep do you want to go? Do you actually want to specialize in being a Data Analyst, Data Scientist, Data Engineer, Development Operations, or a Machine Learning Engineer? 

My other suggestion is to ask your peers who are already in those data roles. Ask what they're doing in the field. A friend like that can help you evaluate whether the curriculum makes sense, what's in scope, how far you'll get, and whether you might need to take more courses later. The unbiased opinions are going to come from your peers. 

Regardless of which program you choose, don't be afraid to call and talk to the school. Ask detailed questions about the program to find the best fit for you. If you end up talking to Lighthouse Labs or another school and the contact person doesn't have all the answers, they should be finding out those answers and getting back to you. 

What is the difference between a software engineering career and a data science career? 

Software engineering is creating products like web applications, mobile applications, embedded applications by using code. These web applications tend to generate a lot of data, and that's where Data Scientists and Data Analysts come in. Data Scientists and Data Analysts also write code, but their code is focused on making sense of the data. Everything from creating static reports to analyzing the data to making business decisions. Data Scientists also use tools like Python, Jupyter Notebook, and machine learning models to create predicted models based on data. 

The similarity is that both professions, whether it's data science or software engineering, require a strong comfort with code. Data Scientists need to understand Python and write functions and work with data, similar to web or mobile developers but they also need to understand data a lot better as well as machine learning techniques. That's where it diverges. 

How do we see the roles of Software Engineer and Data Scientist working together in the real world?

The truth is, Software Engineers and Data Scientists aren't mutually exclusive. There are many instances where these roles or teams would work together to create a substantial, production-ready product. A good example is a recommendation engine that powers a service like Netflix to suggest what users should watch next. That's a data science problem because the data has to be modeled, then there's the data pipeline and deployment which is done in partnership with Data Engineers and Software Engineers. When it comes to delivering a major feature like that, or a major product, you have Software Engineers working with Data Engineers and Data Scientists to make that happen. 

How can students get the most out of an Intro to Data Analytics program like this?

With an intro class, it's about enjoying the challenges. Consider making a list of the things that you wish you could do but can't yet. Those are things you can focus on in class and ask questions about. Try to map those things with what the actual course is about. If the course is about data visualization, data prep, and data analysis, then the problems you write down should be relatable to that. This should help you answer some business questions, make strategic decisions, or know whether you should recommend something. What are those burning questions that you are dying to answer and are not able to without the help of a Data Analyst? 

The other thing I would recommend is a bit of preparation. The more the student is actually doing on their own to prepare themselves and get comfortable the better. If you're somebody who is not comfortable with excel yet, it might make sense to do some prep work on your own to get comfortable with that.   

For someone who doesn't even feel ready for an Intro to Data Analytics course, are there any online resources that you recommend? 

I'd suggest focusing on Excel and BI tools like Tableau for preparing. There are plenty of resources for that. 

For those who are actually looking to change careers, SQL and Python are a good place to start to get into the more advanced analysis. I'd strongly recommend Khan Academy's SQL Introduction as a great primer. For Python, free resources from Twilio have come out recently. Digital Ocean has done some amazing work in the past few years in creating some phenomenal tutorials. 

Find out more and read Lighthouse Labs reviews on Course Report. This article was produced by the Course Report team in partnership with Lighthouse Labs.