Career Change Journey — Month 1
Data Science Mentorship with She Code Africa
Recently, I was admitted to the She Code Africa Mentoring Program, Cohort IV, Data Science Track. If you have read my previous posts, you know that this milestone has marked the next phase of my career transition journey this year.
To be honest, the transition has not been easy. There are areas that I have really struggled with. Especially when I learned that other than programming, I needed to remind myself of some probability and statistics, linear algebra et al. I’d say 8 years since undergrad, is a long time to remember.
It has been a month since I started the remote mentorship program by She Code Africa. The peer-guided program has enabled me to build fundamental areas that I lacked from other resources I used before. I have learned so much more not only from the mentor but mentees in the group.
Let’s dive into what I have tackled so far:
Learning Path — Month 1
- Week 1: Introduction to Python Programming
This week I covered python data types, variables, use of conditionals and loops, python data structures i.e. lists, sets, dictionaries, and tuples, functions, writing scripts, and handling errors. Programming language happens to be an important skill for a data scientist. Some people prefer R, but let me stick to Python for now.
The assignment for this week was on writing a python code to guess a number and to generate a password. You can find my code on my GitHub Repository. - Week 2: Basic Mathematics for Data Science
This week I learned probability & statistics and fundamental mathematical concepts that one requires as a data scientist. This was an interesting learning week, seeing as I had not covered these topics in the past. I did need a refresher on a few of these concepts and Khan Academy came in handy.
These basic concepts as advised by our mentor, are useful when we start handling machine learning algorithms and concepts.
In the assignment this week I learned how to write and publish my first medium article. - Week 3: Python Libraries for Data Analysis
This week I learned about some python libraries like Numpy and Pandas that are essential for data manipulation and analysis. I also covered Matplotlib and Seaborn, which is are great tools for data visualization.
Based on what I had learned, we were given a .txt dataset which we were meant to merge and explore using current topics we had covered. You can check out my week 3 assignment on my GitHub Repository. - Week 4: Data Wrangling
This happens to be the final week of my first-month mentorship journey. I have tackled data cleansing and manipulation. Here I learned the removing of unwanted columns, identifying and removing duplicates, replacing null values, and ensuring that the data is well-formatted. This session also touched a little bit on Introduction to Machine Learning and I got to work on the Titanic dataset.
I learned that data wrangling requires one to understand the data they are going to model and the steps of building a model which is fitting, predicting, and evaluating.
This learning experience has been extremely useful. Apart from the technical skills I have also learned time management, consistency, and teamwork. Thanks to my mentor Kolawole Precious.
Applying for this opportunity has been the best decision I have made during this career transition journey. The journey has somewhat been tough, because of the content to be covered in a week, but guess what, I am tougher. I am super excited about the learning experience in the following months.
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