Beyond the Curriculum: AllWomen’s Data Science/Analytics Course through the Eyes of Five Alumni
Hi, I am a Data Science alumni from AllWomen, an academy that provides a range of technical courses designed and taught by women, for women, to diversify the tech sector.
Since finishing the course, I have had a flurry of LinkedIn messages from other women wanting to know what it’s like, what the course covers, if it’s worth doing, and my advice to them if they choose to enroll. So, myself and a few other AllWomen Data Analytics/Data Science alumni have co-authored this article, to put the answers out there.
What’s it like?
The part-time courses are 12 hours of live class time per week, delivered remotely over video call. For Data Analytics, the course is 12 weeks long, and for Data Science, there are 12 further weeks to complete.
Two weekly classes were in the evening, from 17:30–20:30, and one weekly class was on Saturdays 08:00–14:00 (UCT). The course generally follows a structure where mathematical and programming theory is taught by the instructor for the first half of each session, and the second half of the sessions are dedicated to applying the concepts using Python code. Saturday sessions are sometimes exclusively used for practicing coding.
Often, breakout rooms were used after the theoretical part had been taught, so that we could learn the ropes by trial and error, problem-solve together and share our progress. This was usually the time when we could figure out our blind spots, and go back to the instructor with a few clarification questions.
What does it cover?
You can read all about what the courses cover by reading the syllabuses, so I won’t repeat that high-level summary here. I remember wanting to have more tangible illustrations of what I would actually do in the course before I enrolled, so here are some examples:
In Data Analytics:
- Become experts at ‘Exploratory Data Analysis’/’EDA’ (this can cover a wide range of tasks but as an overview this is where you meet your data, explore missing values, plot the distributions, find outliers and clean the data, then find key trends relevant for the specific business case).
- Discover the main factor determining whether a product was good or bad quality.
- Analyze historical data of the Olympic Games.
- Create visualizations to show how house prices in certain locations have changed across time.
- Generate customer segmentation with the help of Unsupervised Machine Learning.
In Data Science:
- Predict whether employees in a company will be retained or not; identify the main reasons for low retention-rates.
- Classify hundreds of thousands of emails as spam or non-spam.
- Find out the main topics occurring in huge textual datasets.
Advice to those considering enrolling — five perspectives
This is where we delve deeper into the perspectives of five alumni (including myself) from both the Data Analytics and the Data Science tracks, to show you an honest and diverse range of experiences.
Let us introduce ourselves :
Zoe Maggs (me): Data Science alumni. Charity sector evaluation professional in the UK. I took this course because I was interested in automating some data tasks using Python, and I wanted to be able to use Natural Language Processing (NLP) to detect safeguarding concerns in charity helplines such as online chat rooms.
Denise Demeter: Data Science alumni. Financial and Risk Management professional with experience predominantly in the Banking industry. Large datasets were not new to me; however, by joining the course I wanted to expand my skill set beyond using spreadsheets, and potentially make a career shift.
Laura Castro: Data Analytics alumni. Data visualization designer. Prior to taking this course, I already had experience as a data designer, but I wanted to learn Python as an additional tool to enhance my skill set. My goal in taking this course was not to change careers, but rather to expand my abilities and knowledge in data analysis and visualization.
Maria Aloy: Data Science alumni. Computational Linguist, PhD. The reason why I took this bootcamp was to boost my technical skills focused on Natural Language Processing and to be able to start my own personal project on automatic detection of harassment language in Spanish.
Gloria Spagnoli: Data Analytics alumni. Business owner and language teacher. I took this course because I was looking for a career change that could suit my personality better. I love researching, analyzing, and looking beyond the surface and this course gave me the tool to pursue my constant curiosity.
Advice to prospective students:
Understand that you will need more than just 12 hours of class time per week
Gloria: Something that can be discouraging is the fast pace of the course. Questions are welcome all the time, but time is limited and some topics require a certain amount of hours before they can be processed and digested. If there is a piece of advice that I can give to future students, come a little bit prepared and look for additional materials to study from during the course, if you have the time to do it. You may need more time in order to process a certain topic, so make sure you carve out a few hours for yourself to review and/or look for external resources.
(Over)protect your work-life balance
As Gloria says, you will put more than 12 hours per week into this course. With extra study hours, the course took around 16 hours per week for me. I am very grateful that my understanding employer really valued my ambition; we agreed that I could finish earlier on the days that I had evening classes, so I could keep some work-life balance and get away from my desk. Even with these adjustments, some weeks, I was effectively at my desk for 50 hours. To anyone interested in doing this course, I would say: think about your work-life balance. Try to negotiate with your employer on a set-up that you feel goes beyond what you need, so that when it is put under pressure, it still works for you.
Set non-negotiable study times
Denise: As my peers reflect, the course is demanding. However, the reason I chose this specific course over others is exactly because it offered so many hours of live classes and real-time support. I knew that if I wanted to make the most of the course, I would need the accountability of being there live and having a group of people I can share the ups and downs with. Therefore, I decided before the course even started that I would not rely on having the recordings to watch later; I wanted to be there during the live hours. What helped me immensely in this regard, was adding the 24 weeks’ classes to my calendar ahead of time, which made prioritizing and organizing the rest of my schedule much easier (and I was slightly more prepared to say no to other plans). The classes became my non-negotiables and I probably wouldn’t have attended almost all of them otherwise.
Similar to Denise, Laura also recommends being realistic about the demands the course is going to have on your time:
In total, with my job and the course, I dedicated between 55 and 60 hours a week during 12 weeks. Imagine the personal life I had left! Before starting the course, I prepared myself mentally for the effort and sacrifice that it was going to take.
And as a mother, Maria adds:
Motherhood puts an element of chaos when it comes to dedicating hours to study new things and to strengthen your professional profile. It was a long and encouraging learning process and you should be very aware of the time you have and the time you are willing to spend on the course.
Make full use of the flexibility of remote-learning: make it work around you
Denise: Without doubt, there will be days or weeks when you feel more overwhelmed and tired, and I think it’s worth noting that the fully online form gives you some flexibility in this respect. You can take an extra break during practice time, or log out of class earlier on the days when you feel you cannot digest any more information. The fact that you can connect from anywhere is obviously a huge plus as well. To me, the course setup was the perfect combination of learning within a community and still having flexibility location- and time-wise.
Consider if a sense of community helps you learn
Maria: An important reason for me to take this course in particular was that there are women from different backgrounds who want to learn the same skills. This gives an empowering atmosphere to every lesson we take and motivates women to find their path in technical roles/tech companies that have been overpowered by men for decades.
Laura adds:
The learning curve of the course affects you emotionally, it is important to have a strong positive attitude and to lean on your colleagues who are going through the same journey.
Did somebody say learning curve? … This is kinda what it feels like:
If community matters less to you, AllWomen have also recently started offering an On-Demand Data Analytics course.
Be clear about your goals from the beginning
Laura: When it comes to evaluating and taking on the course I think it’s important not to worry about whether your background is ‘right’ for the course. There have been people who have completely changed their career path having little experience with programming or even computers. What’s most important is that you know why you want to do the course — what your goal is after you get the skills you are going to learn. In my case, I was already working in the data world, I have been a data visualisation designer for nine years. My goal of the course was to learn how to do data analysis with Python, learn SQL and other technologies that I didn’t know before.
Similarly, Maria also reflects on the importance of having a goal before starting the course:
As a Computational Linguist, I used to work in an interdisciplinary and tech-related context. I decided to enroll in the Data Science course because being surrounded by engineers made me think of boosting my career and acquiring more technical abilities such as data mining skills and being able to train machine learning models focused on NLP. Also, I wanted to start a professional project and this course gave me the necessary capabilities to work on it.
Consider the seasons and timing of your graduation
The part-time Data Science course runs for 6 months, and I did mine from September to April, over the autumn and winter. I thought this was going to be preferable to me because I looked at it from the perspective of what I would be most happy to miss out on or give up: I don’t mind being locked away doing coding when it gets dark at 16:00 during the winter, but I may resent it a little if I look out the window to my local park and see barbecues and picnics going on late into the evening.
In hindsight, I could also have considered what it would feel like to try and garner the energy to join a 3-hour zoom session in the dark, after sometimes having already done 4 hours of this in the morning for work — safe to say that I chose to break my ‘no caffeine after 16:00’ rule for a few months!
You may also want to think about when you will graduate from the course — if you want to look for jobs right away, it may be better to finish the course outside of university academic term dates, so that you are not directly competing with those graduates as they flood the market.
If you decide to enrol, don’t be afraid to give feedback
Denise: We experienced first-hand that the instructors at AllWomen take feedback seriously. We were able to influence how the classes should be structured, and how much practice we wanted to do, depending on our own goals and what we wanted to get from the course. Every Saturday there was a chance to give feedback via an interactive whiteboard to reflect on how the week had gone, and often this would lead to clear changes in the instructor’s approach in the following week. We hope that all the feedback we gave during our time makes the course even better for you than it already was for us.
Understand that you will not be ‘done’ after the course: the tech sector requires life-long study
Maria notes that working on technology and AI is a long-distance race:
Technology evolves constantly and new investigations announce new insights and new ways to optimize algorithms every day. Learning how to code is like learning a new language with its structure and its logic. It’s a fascinating universe that can be very overwhelming.
As Denise summarizes:
You will never know everything but you can figure out anything. The course gives you enough of the foundations and skills, such that by the end you feel confident to continue learning on your own in the ‘digital wilderness’. Advice that stuck with me from one of our classes was to focus on one specific topic / model / technique when we go on to practise by ourselves. To me, this was a relief mentally — take it step by step and don’t feel discouraged by the vastness of the data world. You learned how to do ‘x, y, (and even z!), you will be able to learn the dozenth concept as well when you need it.
Laura agrees that mentally what the course teaches you is that :
“nobody knows everything” and you don’t need to know it all either. From the outside, the technology can seem scary, but in reality if you follow certain steps, especially at the beginning, there are many resources available to everyone and the key is to know how to look for those resources, how to talk to or ask for help from colleagues, or how to make that call to do a search on Google or use an AI tool like ChatGPT. Technologically, the course teaches you general basic principles that you will reinforce in the future by specializing in your particular sector.
It’s going to be hard, but if you have a goal and the right support, it will be worth it!
We hope that this article has given you some insight into the emotional journey of the AllWomen courses, and we hope we haven’t scared you off! While it was grueling at times, there was plenty of laughter and always hope within the course community. With AllWomen, there is no such thing as a stupid question, the instructors are very supportive and you really feel that you are learning in a safe space. If you have a clear goal, it can become the life-jacket that gets you through the late nights and long weekends. We wish you luck on your journey!
Get in touch with us on LinkedIn:
Zoe: https://www.linkedin.com/in/zoemaggs/
Denise: https://www.linkedin.com/in/denisedemeter/
Laura: https://www.linkedin.com/in/lauracastrosoto/
