This is my tech learning journal for skills ranging from HTML/CSS to ML Libraries like Scikit Learn/XGBoost to DevOps topics.
Web Development
Machine Learning Projects
Kaggle
Business Intelligence: PowerBI & Tableau
SQL / NoSQL
Algorithms
Python / Advanced Python
R / Advanced R
Docker
Statistics
Linear Algebra
- To learn to create my own website.
- To learn to use a cloud service such as AWS.
Angela Yu's Complete Web Development Course
| Day | Lessons/Tasks Done | Reference Links |
|---|---|---|
| Day 1 | Github Setup & Introduction to HTML | Angela Yu - Sections 1-2 |
| Day 2 | Intermediate HTML & Multi-page Websites | Angela Yu - Section 3-4 |
| Day 3 | CSS - Beginner to Advanced | Angela Yu - Sections 5-8 |
| Day 4 | Flexbox, Grid & Bootstrap | Angela Yu - Sections 9-11 |
| Day 5 | Javascript & Document Object Model | Angela Yu - Sections 12-16 |
| Day 6 | Domain name purchase and hosting with Hostinger | |
| Day 7 | Domain name purchase with Namecheap and getting started with AWS EC2 | |
| Day 8 | AWS EC2 Apache2 server setup and SSL Certificate procurement | |
| Day 9 | CI/CD with Github Actions | |
| Day 10 | Folder structure for keeping an archive of old website designs | |
| Day 11 | Galssmorphism CSS Aesthetic |
- To familiarize with datasets from various disciplines.
- To gain intuition on approaches to analyzing and producing models from datasets from various disciplines.
| Day | Lessons/Tasks Done | Reference Links |
|---|---|---|
| Day 1 | Titanic Surivival Prediction in Python | Youtube-NeuralNine |
| Day 2 | Titanic Cont'd [Seaborn, Numpy, Matplotlib, SKLearn, GridSearchCV] |
- To learn entire project lifecycle of a machine learning project.
- To learn application of advanced python methods in a project.
- To develop intuition for solving errors and exceptions.
End-to-End Machine Learning Project YT Playlist by Krish Naik
| Day | Lessons/Tasks Done | Reference Links |
|---|---|---|
| Day 1 | VSCode, Github and setup.py | Krish Naik's ML Tutorial 1 |
| Day 2 | Project/Folder Structure, Logging and Exception Handling | Krish Naik's ML Tutorial 2 |
| Day 3 | EDA & Model Training win Jupyter Notebook | Krish Naik's ML Tutorial 3 |
| Day 4 | data_ingestion.py | Krish Naik's ML Tutorial 4 |
| Day 5 | data_transformation.py | Krish Naik's ML Tutorial 5 |
| Day 6 | Model Training and Evaluation | Krish Naik's ML Tutorial 6 |
| Day 7 | Hyperparameter Tuning | Krish Naik's ML Tutorial 7 |
| Day 8 | Model test deployment with Flask | Krish Naik's ML Tutorial 8 |
- To learn no-code data visualization.
- To create easy-to-use interactive dashboards.
- To learn the Microsoft Business Intelligence ecosystem.
- To learn how Microsoft Data Analytics is integrated with Azure and Fabric.
Microsoft Learn - Power BI Data Analyst
Tablea Training for Data Science (Udemy)
The Big Book of Dashboards by S.Wexler, J.Shaffer & A.Cotgreave
| Day | Lessons/Tasks Done | Reference Links |
|---|---|---|
| Day 1 | Tableau: Importing CSV Files, Calculated Fields and Exporting Worksheets | Udemy Ch. 1 |
| Day 2 | Tableau: Time Series Data, Area Charts, Highlighting, Aggregation, Level of Detail and Filters | Udemy Ch. 2 |
| Day 3 | Tableau: Maps, Scatterplots and Dashboards | Udemy Ch. 3 |
| Day 4 | Tableau: Joining, Blending, Relationships and Dual Axis Charts | Udemy Ch. 4 |
| Day 5 | Tableau: Table Calculations, Advanced Dashboards and Storyline | Udemy Ch. 5 |
| Day 6 | Tableau: Advanced Data Preparation, Column Splitting, Pivoting, Geographical Errors | Udemy Ch. 6 |
| Day 7 | Tableau: Course Completion: Clusters, Custom Territories, Design Features | Udemy Ch. 7 |
| Day 8 | Power BI: Interface, Data Sources and Power Query Tool | Microsoft Learn -Lesson 1 |
| Day 9 | Power BI: Clean, transform and load data | Microsoft Learn - Lesson 2 |
| Day 10 | Power BI Personal Project: Analyzing Relations between Country Obesity Rates, Country Weather and Country Economics | |
| Day 11 | Power BI Personal Project Cont'd: Transforming CIA World Factbook Dataset [258 Rows x 1071 Cols] | |
| Day 12 | Power BI Personal Project Cont'd: Interactive Dashboards | |
| Day 13 | Case Study: The Big Book of Dashboards |
- To learn how to handle SQL databases and make queries.
- To learn how to handle NoSQL databases such as MongoDB.
University of Colorado Boulder: The Structured Query Language W3Schools SQL PostgreSQL Tutorial
| Day | Lessons/Tasks Done | Reference Links |
|---|---|---|
| Day 1 | The Origins of SQL, The Relational Algebra, The SQL Standard | |
| Day 2 | SELECT, WHERE, BOOLEAN, ORDER BY, DISCTINCT, DATES & NULLS | |
| Day 3 | Subqueries, Co-related Subqeuries | |
| Day 4 | Aliases, Implicit/Explicit Inner Joins, Caertesian Product Error, Outer Join Discrepancy Checking | |
| Day 5 | DDL, DML, CREATE TABLE, INSERT INTO, ALTER, Data Types, Constraints, VIEWs |
- To learn programming fundamentals behind functions.
- To learn first-principles of algorithms to enable ground-up implementation at work.
University of Colorado Boulder - Foundation of Data Structures and Algorithms (Python)
- Algorithms for Searching, Sorting, and Indexing
- Trees and Graphs: Basics
- Dynamic Programming, Greedy Algorithms
| Day | Lessons/Tasks Done | Reference Links |
|---|---|---|
| Day 1 | Insertion Sort Algorithm | |
| Day 2 | Time and Space Complexity, Big O Notation | |
| Day 3 | Binary Search, Merge Sort | |
| Day 4 | Assignment: Introduction to Algorithms | |
| Day 5 | Dyanmaic Array | |
| Day 6 | Heap, Min/Max Heaps and Properties of Heaps | |
| Day 7 | Priority Queues, Heapify and Heapsort | |
| Day 8 | Hashtables | |
| Day 9 | Assignment: Heap Data Structure Assignment | |
| Day 10 | Partition and Quicksort Algorithm | |
| Day 11 | Hash Functions, Universal Hash Functions and Analysis | |
| Day 12 | Application of Hashtables: Bloom Filters, Count-Min and String Matching using Hashing | |
| Day 13 | Assignment: Hash Applications | |
| Day 14 | Binary Search Trees | |
| Day 15 | Red-Black Trees | |
| Day 16 | Graphs, Graph Traversal and Breadth First Search | |
| Day 17 | Depth First Search | |
| Day 18 | Topological Sorting and Strongly Connected Components | |
| Day 18 | Assignment: Graphs | |
| Day 19 | Amortized Analysis of Data Structures | |
| Day 20 | Spanning Trees and Minimal Spanning Trees | |
| Day 21 | Kruskal's Algorithm | |
| Day 22 | Union-Find Data Structures and Rank Compression | |
| Day 23 | Assignment: Spanning Trees & Union-Find Data Structure | |
| Day 24 | Shortest Path Problems and their Properties | |
| Day 25 | Bellman-Ford Algorithm | |
| Day 26 | Dijkstra's Algorithm | |
| Day 27 | Assignment: Shortest Path Algorithms | |
| Day 28 | Divide and Conquer Algorithms | |
| Day 29 | Max Subarray Problem using Divide and Conquer | |
| Day 30 | Karatsuba's Multiplication Algorithm | |
| Day 31 | Fast Fourier Transform Parts I, II, III | |
| Day 32 | Data Analysis and Fast Polynomial Multiplication using FTT | |
| Day 33 | Basics of Complex Numbers | |
| Day 34 | Master Method Revisited | |
| Day 35 | Assignment: Divide and Conquer Algorithms | |
| Day 36 | Dynamic Programming Algorithms | |
| Day 37 | Extra-corricular: Why is Dynamic Programming called Dynamic Programming? | |
| Day 38 | Rod Cutting Problem | |
| Day 39 | Memoization | |
| Day 40 | Coin Changing Problem | |
| Day 41 | Knapsack Problem | |
| Day 42 | When Optimal Substructure Fails | |
| Day 43 | Longest Common Subsequence | |
| Day 44 | Assignment: Dynamic Programming | |
| Day 45 | Greedy Algorithms, Greedy Interval Scheduing | |
| Day 46 | Prefix Codes, Huffman Codes | |
| Day 47 | Assignment: Greedy Algorithms | |
| Day 48 | Intractability, P Vs NP | |
| Day 49 | Computation and Physics, Qubits and Operations | |
| Day 50 | Bell's Inequality, Grover's Search Algorithm | |
| Day 51 | Assignment: Problem Set 4 |
- To learn quality-of-life improvements to make coding easier.
Complete Python Playlist by Krish Naik
| Day | Lessons/Tasks Done | Reference Links |
|---|---|---|
| Day 1 | Krish Naik's Advanced Python Tutorial |
- To learn a language purpose-built for Data Science.
R Programming by John Hopkings University
| Day | Lessons/Tasks Done | Reference Links |
|---|---|---|
| Day 1 | John Hopkin's University - R Programming |
- To learn to use Docker for containerization of projects.
- To learn Kubernetes afterwards.
| Day | Lessons/Tasks Done | Reference Links |
|---|---|---|
| Day 1 | Docker Desktop Installation, creating Images and Containers | Docker Getting Started Guide |
- To learn fundamentals of statistics.
- To learn intuition on which statistical method to apply to which problems or questions.
Statistics by Professor Leonard, Merced College Callifornia
| Day | Lessons/Tasks Done | Reference Links |
|---|---|---|
| Day 1 | Elementary Statistics, Categories of Data, Sampling Techniques | |
| Day 2 | Frequency Distribution, Historgrams; Mean, Median & Mode | |
| Day 3 | Standard Deviation | |
| Day 4 | Z-Score, Percentiles, Quartiles and Comparing Standard Deviation | |
| Day 5 | Introduction to Probability | |
| Day 6 | Probability: Addition Rule, Multiplication Rule | |
| Day 7 | Complementary Events | |
| Day 8 | Permutations & Combinations |
- To learn fundamentals of statistics.
- To learn intuition on which statistical method to apply to which problems or questions.
Linear Algebra by Dr. Trefor Bazett, University of Victoria, Canada
| Day | Lessons/Tasks Done | Reference Links |
|---|---|---|
| Day 1 | Elementary Statistics, Categories of Data, Sampling Techniques | |
| Day 2 | Matrix Notation, Elementary Row Operation, Row Echelon Form | |
| Day 3 | Matrix-Vector Multiplication | |
| Day 4 | Homogeneous Systems of Linear Equations | |
| Day 5 | Transformations and Matrix Transformations | |
| Day 6 | Matrix Inverse | |
| Day 7 | Vector Space, Span, Subspace | |
| Day 8 | Eigenvalues and Eigenvectors |