Wandering on A Different New Path!

My Journey in the field of Data Science & Machine Learning.

I have always had an interest in the way things work  together to solve everyday problems  and science has been one of my  favorite  subjects.  My interest in sciences, particularly in  Mathematics has developed throughout my life time at school and I have always wanted to   use the various theorems studied in the various courses  in solving real world problems. This desire to have the feeling of contributing to society pushed me to further studies in Mathematics up to the PhD level. After my PhD I joined the teaching and research community and that is where I have been for the past five years. 

During these years I have been teaching fundamental courses like Real Analysis, Measure Theory, Functional Analysis and Ordinary Differential Equations to Maths majors; and Engineering Maths courses like Linear Algebra, Calculus and Probability and Statistics to Science and Engineering students. One question that keeps coming back from students is; ” Sir why do we even need to study this? What is it used for? Where is it applied?” If you have ever been faced with such questions you can imagine how I felt each time. As a pure mathematician, I enjoy using theorems to prove theorems. But you don’t expect a Math major with less enthusiasm to understand that. You need to go in an area the student is familiar with to find examples of application, concepts that cannot be well understood without a solid foundation in Math. Which other area could possibly be better than the field of Information Technology to explain the importance of Math to a millennial? As I began using Machine Learning as one area in which optimization techniques are used, to explain to the students the importance of their multivariate Calculus, I realized that I got the necessary Math background to venture into this trending field of Data Science and Machine Learning. So after much thinking, I decided to embark on the journey. A Different New Path.

Having taught Numerical Analysis with Matlab for a while, I thought it wise to start my journey with a programming language I am most familiar with, MATLAB. So few weeks ago I successfully completed the Exploratory Data Analysis with Matlab by MathWorks on Coursera. In this course I learned importing data, visualizing and and filtering data as well as performing statistical computation. The final project was quite interesting, as given storm events data I was able to perform analysis and visualization on the data and make recommendation for the most affected areas. The experience was great and I can assure you folks that I am seriously on board for the journey and you are going to be reading from me often. 

• After that I got feedback from friends on LinkedIn suggesting Python as a preferable programming language for Data Science and Machine Learning. I found out they were right, so I decided to try that too and I bought the course Python for Data Science and Machine Learning bootcamp on Udemy taught by Jose Portilla. In this course I learned how to :
• Use Python for Data Science and Machine Learning
• Use Spark for Big Data Analysis
• Implement Machine Learning Algorithms
• Use NumPy for Numerical Data
• Use Pandas for Data Analysis
• Use Matplotlib for Python Plotting
• Use Seaborn for statistical plots
• Use Plotly for interactive dynamic visualizations
• Use SciKit-Learn for Machine Learning Tasks

I also learned Machine Learning algorithms such as

• K-Means Clustering
• Logistic Regression
• Linear Regression
• Random Forest and Decision Trees
• Natural Language Processing and Spam Filters
• Neural Networks
• Support Vector Machines

It was really a great experience and I will keep you posted as I learn new things.

Apart from Teaching and Research I want to do Consultancy in Data Science and Machine Learning in the long run. Mastering Machine Learning will not only give me the opportunity to do Consultancy as another source of income, but it will most importantly help me redirect my research interest towards Data driven research for more impact.