I believe that knowledge is the ultimate treasure and it should not be confined by mere academic boundaries imposed by ancient people. With democratization of learning, it has never been this easier as it is now. A new topic needs just the willingness and attention to learn, all other resources are waiting there for anyone who knows where to look for it.

This is my non-exhaustive learning log with verifiable links - not only to show off (obvious for a mere mortal) what I have learned so far but also to remind me of how they had profound positive impact on my life thus far.

# Table of contents

### **Python**

#### Basics

- Introduction to Data Science in Python - University of Michigan - Coursera
- Applied Plotting, Charting & Data Representation in Python - University of Michigan - Coursera
- An Introduction to Interactive Programming in Python - Rice University - Coursera

#### Machine Learning

- Applied Machine Learning in Python - University of Michigan - Coursera

#### Text Processing

- Applied Text Mining in Python - University of Michigan - Coursera

#### Network Science

### **R**

#### Basics

- Exploratory Data Analysis - Johns Hopkins University - Coursera
- Regression Models - Johns Hopkins University - Coursera
- Getting and Cleaning Data - Johns Hopkins University - Coursera
- Statistical Inference - Johns Hopkins University - Coursera
- Reproducible Research - Johns Hopkins University - Coursera
- R Programming - Johns Hopkins University - Coursera
- The Data Scientistâ€™s Toolbox - Johns Hopkins University - Coursera
- Introduction to the Tidyverse - Datacamp
- Correlation and Regression - Datacamp
- Exploratory Data Analysis - Datacamp
- Reporting with R Markdown - Datacamp
- Multiple and Logistic Regression - Datacamp
- Data Visualization in R - Datacamp
- Statistical Modeling in R - Datacamp
- Fundamentals of Bayesian Data Analysis in R - Datacamp

#### Machine Learning

- Supervised Learning in R: Classication - Datacamp
- Cluster Analysis in R - Datacamp
- Unsupervised Learning in R - Datacamp
- Machine Learning with Tree-Based Models in R - Datacamp

#### Network Science

- Predictive Analytics using Networked Data in R - Datacamp
- Network Analysis in R - Datacamp

### **Big Data**

- Data Science Hands-On with Open Source Tools - Cognitive Class - An IBM initiative
- Hadoop 101 - Cognitive Class - An IBM initiative
- Big Data 101 - Cognitive Class - An IBM initiative
- Introduction to Spark in R using sparklyr - Datacamp

### **Multi-course specialization**

- Python Programming Track - Datacamp - 4 Course, 15 Hours
- R Programming Track - Datacamp - 4 Course, 18 Hours
- Statistics Fundamentals with R Track - Datacamp - 5 Course, 20 Hours
- Importing & Cleaning Data with R Track - Datacamp - 4 Course, 14 Hours
- Data Scientist with R Track - Datacamp - 23 Course, 94 Hours

### **Competitive achievements**

- Special recognition for outstanding performance in first public
**Datathon Bangladesh**in 2019 organized by Axiata Analytics Centre in collaboration with Robi Axiata Limited