Inside LearningFuze’s Part-Time Data Science Program for Working Adults

LearningFuze now offers a Part-Time Data Science Bootcamp aimed at working adults making a career change. We caught up with LearningFuze Director of Operations, Bill Cunningham, to learn more about this program and how the curriculum is different from the full-time bootcamp. Bill shares his tips for acing LearningFuze’s technical challenge in the application process, and how to get the most out of the part-time program. Plus, learn how to balance working while learning data science!

Meet the Expert: Bill Cunningham, Director of Operations at LearningFuze

• Bill Cunningham works closely with the instructional team to ensure LearningFuze is developing bootcamps that meet student and employer needs

LearningFuze has been offering full-time data science bootcamps since 2020 – why launch this part-time immersive data science bootcamp?

The part time program has been put in place based on feedback from our students. Students are looking for a data science bootcamp they can enroll in while they continue to work.  

Why is data a good career path now?

Data is the new oil for businesses. Companies are trying to be data-driven in order to compete in the marketplace to take advantage of opportunities, solve problems and grow. Data is growing at a great pace — We as a human race generate a lot more data on a daily basis than ever before. Companies have huge volumes of data and are interested in driving information from the data, which is driving the demand for people who can analyze and make inferences based on data. Due to this increase in data availability, processing power of computers and reliable internet, data science has become a fast growing and in-demand skill.

What can students expect from the Part-Time Data Science Bootcamp curriculum? 

It will cover the same curriculum as the full immersion program, but the part-time curriculum will be broken into two modules of 12 weeks each. 

• In Module 1, we will focus on machine learning and Python, including Python libraries such as Pandas, Scikit-Learn, Matplotlib and more. It will also cover SQL, data scraping, data cleansing, and statistical analysis so students learn how to glean usable information from different data sets.
• In Module 2, the curriculum focuses on deep learning and artificial intelligence. Students will delve into Keras, Tensorflow, PyTorch as well as classification, regression, and time series. The second module really accelerates the learning to build on Module 1 as students will dig into Natural Language Processing (NLP), image recognition, and recommendation systems. Students will even touch on the cloud and deployment by learning more about AWS and Spark. 

Who are the instructors for the part-time bootcamp? 

The lead instructor for the program is Zia Khan, and he has constructed the curriculum. Zia has a computer science degree and over 20 years of commercial engineering experience. He has been working as a cloud architect over the last 12 years as well as functioning as a data scientist is his current role. One of the keys to his success is that he has honed his teaching over the last 4 years, so he understands the importance of students learning this as a skill. Zia understands the importance of “hands on the keyboard” teaching, asking students to work through exercises and projects to gain well-ingrained skills

Who is the ideal student for the Part-Time Data Science Bootcamp?

Since the part-time program is truly teaching data science concepts (and not just data analytics), we need to ensure students are ready for the program. Students that would be an optimal fit for the part-time program would already have some degree of software engineering (specifically Python) skills, though not necessarily working professionally. Additionally, the ideal applicant would have some experience in data analytics or business analytics. Incoming students who want to learn skills they need to take their career to the next level will be a good fit for the part-time program. Those students that have a computer science degree or a focus in mathematics would also be a good fit. 

Is this part-time bootcamp appropriate for beginners?

The course is appropriate for beginners, but they will need to have a foundation in the fundamentals of both Python and statistics. Anyone that has a fundamental understanding of a programming language (preferably Python), exposure to statistics, and enjoys working with data to find business solutions is very likely a good fit for the program.

We will provide resources to help incoming students prepare for the bootcamp, plus students can enroll in our Data Science Prep course or the Python for Data Scientists short course. 

What is your advice for a working adult who is trying to balance the bootcamp with their full-time job? 

The total number of hours students should plan to invest in the part-time program is 20 hours per week, and this includes the 10½ hours of instructional time. We encourage students to be well-organized each week to ensure that they set aside the necessary time to work on projects and any exercises outside of class. To support our part-time students, we have set aside extensive office hours with an instructor either on-site or remotely, so students can receive live help throughout the week. 

To get the most out of LearningFuze part-time, the key is to put in the time to prepare prior to the start of the program. It’s especially important to have an understanding of Python. During the program, students that put in focused time on their craft and even go above and beyond the program out of passion and curiosity typically are the most successful. They clearly understand that the more that they put in the more they are going to get out of the education. 

What can part-time applicants expect from the LearningFuze admissions process?

Due to our application and enrollment process, we strongly encourage students to submit their application as soon as possible so that we can work to have a plan in place to help them best prepare for the program. We will provide resources that help them to ensure they possess the fundamental skills to not only enter the program, but also to successfully complete the technical assessment. 

The technical assessment is focused largely on Python. We believe it’s important for students to have confidence in their Python skills so they are ready for the program before investing their time and money. We want to ensure that all of our students get the most out of the instruction. We recommend our Data Science Prep course and our Python for Data Scientists course to help students prepare for the program.

What’s the difference between a software engineering vs data science career? Is there a certain type of person who is suited for each career path?

Software engineering is essentially explicit programming, which means software engineers build something for a defined purpose. Data science focuses on more implicit work, so data scientists utilize data to extract information, drive decision-making, and conceptualize how to automate systems and processes. Data scientists need to have a grasp of programming and statistics as well as some business acumen. Those skills can certainly apply to software engineering, but they are central to data science engineers. If you like to take data and organize it into usable information then Data Science might be the field that is a good fit for you.    

What have you noticed about the most successful, part-time career changers? 

As with all things engineering, those who gravitate towards data tend to think logically, enjoy problem-solving, and work with data. Students interested in data science really like to use data to gain insight around business opportunities and problems. 

To pivot to another career, our most successful students are curious, want to constantly learn, and are gritty, not only in their education but also in the job search. Students that are passionate and willing to overcome any obstacle will invariably be very successful. This ultimately leads to a highly lucrative career as a result. 

For our readers who are data beginners, which online resources do you recommend?

We really like to introduce our LearningFuze students that are new to data science to Kaggle.  Freecodecamp is always a good place to learn. Both Kaggle and Freecodecamp are great resources that will help you get a strong grounding in the fundamentals (specifically Python) before entering the program. W3Schools is also a great resource as students are working in Python or any other programming language. 

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