SHE CODE AFRICA: Concepts, Techniques and Tools learnt
She Code Africa Mentorship Program is a 3-month mentorship program designed to help women to acquire both hard and soft skills in various tech fields under the guidance of a mentor.
I joined the data science track of the program and would like to share the techniques, concepts and tools learnt during the program.
During the first month of the program, I was introduced to basic programming concepts in python such as basic data types, python built-in functions, python operators, control flow statements as well as python libraries used in data science. Later, we proceeded to learning mathematical and statistical methods used in data analysis such descriptive statistics which help summarize the data, inferential statistics which helps draw conclusions from that data and probability theories.
By the end of the first month, I had learnt to use python libraries such as pandas, matplotlib, seaborn in analyzing various data sets and creating visualizations. I had also learnt the concept of article writing and I got to publish my first article.
At the second month of the program, we got deeper into data analysis. This time around we worked as a team sourcing for data, cleaning the data, performing exploratory data analysis, creating visualizations with tableau, and giving analysis report using G suites as a means of collaboration. Afterwards, we delved into learning how to query relational database management systems (RDBMS) like MySQL, Microsoft SQL server, Oracle using a structured query language SQL and getting familiar with various SQL syntax for each.
By the end of the second month, I had learnt to use tableau effectively, efficiently query relational database management systems by participating in solving hacker rank challenges and acquired team collaboration skill.
Finally, during the last month, we learnt machine learning techniques such as supervised learning which trains a model on known input and output data so that it can make future predictions and unsupervised learning which shows hidden patterns in a data. We solved some regression problems and worked on a machine learning project using some regression algorithms like Linear regression, Random Forest Trees and Extra-trees regressor. We also got familiar with other machine learning algorithms and learnt to use various evaluation metrics like Root Square Error (RMSE), Mean Squared Error (MSE), Mean Absolute Error (MAE) and R2 score to test the accuracy of the models and how they best fit in solving some machine learning problems.
By the end of the program, I had gained some level of confidence as a data scientist, developed my communication skills, critical thinking and analytical skills, presentation skill and team work.
In conclusion, learning never ends, the journey continues and I am grateful to She Code Africa and my mentor, Countess Olufunmilayo for the role you played in my career development.
Thank you for reading.
