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 have had profound positive impact on my life thus far.
Table of contents
- R
- Python
- Big Data
- Multi-course specialization
- Tableau
- SQL
- Agile Methodology
- Competitive achievements
- Miscellaneous
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 Scientist’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
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
- Applied Social Network Analysis in Python - University of Michigan - Coursera
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
- Applied Data Science with Python - University of Michigan - Coursera - 5 Courses - 20 Weeks
- 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 - Renewed 2022 on updated syllabus
Tableau
- Tableau Analyst - Tableau Software
SQL
- SQL (Advanced) - HackerRank
Agile Methodology
- Certified ScrumMaster (CSM) - Scrum Alliance
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
- Top 10 in ADL AI Summit PreHackathon Kaggle Competition public and private leaderboard with Team Chunoputi
Miscellaneous
- Continuous learning and practice - DataCamp Profile
- Continuous learning and practice - Coursera Profile
- Bengali translation of Data Import with R packages like readr,readxl and googlesheets4 published on official RStudio cheatsheets page (Scroll to bottom for translations list- my beloved bangla is there)
- Few assessments I took for benchmarking thyself in DataCamp (No shareable link - only for planning own learning route) - Scores are in percentile - Higher is better
- Understanding and Interpreting Data - 99% (Mar’21)
- R Programming - 96% (Dec’21)
- Statistics Fundamentals with R - 89% (Dec’21)
- Importing & Cleaning Data with R - 95% (Dec’21)
- Data Manipulation with R - 89% (Dec’21)
- Data Visualization with R - 99% (May’21)
- Data Analysis in SQL (PostgreSQL) -
74% (May’21)96% (Jul’23) - Data Management Theory - 82% (Jul’23)