Info of all the courses I’ve done in the past and currently going after.

### In Progress ⏳

#### [Freecodecamp] Docker & Kubernetes

### Completed ✔️

#### [Udemy] **The Git & GitHub Bootcamp**

**The Git & GitHub Bootcamp**

Adding and Committing Git Merge and resolving conflicts, Stashing, Rebasing(squash, clean and rewrite history), Git Aliases, Hosting Static Pages, Github Gists, Different Git Objects: (tree, blobs,) Commits, Annotated Tags, Working with Branches, Git Diff command, Understanding of Git Restore, Revert, Reset, Collaboration Workflows: Pull requests, Fork and Clone, Git Reflogs, Git Tags and Markdown of READMEs.

#### [Kaggle] Python Course

Hello python, Functions and getting help, Booleans and Conditionals, Lists, Loops, and List Comprehensions, Strings and Dictionaries, Working with external Libraries.

#### [DataCamp] **Data Scientist with Python**

**Data Scientist with Python**

Python, Pandas, Statistics, Matplotlib, Seaborn, Numpy, Importing Data, Cleaning Data, Data and Time in Python, EDA, Regression with Statsmodel, Sampling, Hypothesis Testing, Supervised Learning, Unsupervised Learning, M.L. with Tree Models.

**Projects:**Netflix Movies, Github History using Scala Language, The Android App market on Google Play, A Visual History of Nobel Prize Winners, Discovering of Handwashing, Predicting Credit card Approvals

#### [ZeroToMastery] **Complete Machine Learning & Data Science Bootcamp 2022**

**Complete Machine Learning & Data Science Bootcamp 2022**

Data Exploration and Visualizations, Neural Networks and Deep Learning, Model Evaluation and Analysis, Python 3, Tensorflow 2.0, Numpy, Scikit-Learn, Data Science and Machine Learning Projects and Workflows, Data Visualization in Python with MatPlotLib and Seaborn, Transfer Learning, Image recognition and classification, Train/Test and cross-validation, Supervised Learning: Classification, Regression and Time Series, Decision Trees and Random Forests, Ensemble Learning, Hyperparameter Tuning, Using Pandas Data Frames to solve complex tasks, Use Pandas to handle CSV Files, Deep Learning / Neural Networks with TensorFlow 2.0 and Keras, Using Kaggle and entering Machine Learning competitions, How to present your findings and impress your boss, How to clean and prepare your data for analysis, K Nearest Neighbours, Support Vector Machines, Regression analysis (Linear Regression/Polynomial Regression), How Hadoop, Apache Spark, Kafka, and Apache Flink are used, Setting up your environment with Conda, MiniConda, and Jupyter Notebooks, Using GPUs with Google Colab

#### [Udemy] **Python for Data Science and Machine Learning Bootcamp**

**Python for Data Science and Machine Learning Bootcamp**

Programming with Python, NumPy with Python, Using pandas Data Frames to solve complex tasks, Using pandas to handle Excel Files, Web scraping with python, Connect Python to SQL, Using matplotlib and seaborn for data visualizations, Use Plotly for interactive visualizations, Machine Learning with SciKit Learn, including Linear Regression, K Nearest Neighbors, K Means Clustering, Decision Trees, Random Forests, Natural Language Processing, Neural Nets, and Deep Learning, Support Vector Machines

#### [Udemy] The Complete SQL Bootcamp 2022: Go from Zero to Hero

SQL statement command(SELECT, COUNT, WHERE, ORDER BY, IN, LIKE etc), GROUP BY, JOINS(inner, outer, left, right), Advance SQL commands(Timestamps, TO_CHAR, SubQuery), Creating database and tables(Primary and foreign keys , constraints, INSERT, CREATE, UPDATE, ALTER, DROP), Conditional expressions(CASE, CAST, NULLIF, Import and export)

#### [ZeroToMastery] **Master the Coding Interview: Data Structures + Algorithms**

**Master the Coding Interview: Data Structures + Algorithms**

**Big O notation**

**Data structures**: Arrays, Hash Tables, Singly Linked Lists, Doubly Linked Lists, Queues, Stacks, Trees (BST, AVL Trees, Red Black Trees, Binary Heaps), Tries, Graphs

**Algorithms**: Recursion, Sorting, Searching, Tree Traversal, Breadth First Search, Depth First Search, Dynamic Programming

**Non-Technical**: How to get more interviews, What to do during interviews, What to do after the interview, How to answer interview questions, How to handle offers, How to negotiate your salary, How to get a raise

#### [Udemy] **Data Structures & Algorithms - Python**

**Data Structures & Algorithms - Python**

**Big O notation**

**Data Structures**: List, Linked List, Doubly Linked List, Stacks & Queue, Binary Tree, Hash Table, Graphs

**Algorithms**: Sorting, Bubble Sort, Selection Sort, Insertion Sort, Merge Sort, Quick Sort

**Searching**: Breadth First Search, Depth First Search

#### [YouTube] Live Statistics Playlist

Introduction, Descriptive Statistics, Inferential Stats, What is Statistics, Types of Statistics, Population And Sample, Sampling Techniques, What are Variables? , Variable Measurement Scales, Mean, Median, Mode, Measure of Dispersion with Variance And SD, Percentiles and Quartiles, Five number summary and Boxplot, Gaussian And Normal Distribution, Stats Interview Question 1, Finding Outliers In Python, Probability, Additive Rule, Multiplicative Rule, Permutation And Combination, p-value, Hypothesis Testing, Confidence Interval, Significance Values, Type 1 and Type 2 Error, Confidence Interval, One sample z test, one sample t-test, Chi-square test, Inferential Stats with Python, Covariance, Pearson correlation, Spearman Rank Correlation, Deriving p values and significance value, Other types of Distribution.

#### [YouTube] **Microsoft Excel Tutorial for Beginners - Full Course**

Enter Data, Navigate through a Spreadsheet, Create Formulas to solve problems, Create Charts and Graphs, Understand Relative vs Absolute References, Import and Export Data, Implement VLOOKUP, Use Pivot Tables, Split and Concatenate text

### [AWS Training & Certification 1h ]**Machine Learning for Business Challenges**

**Machine Learning for Business Challenges**

Machine Learning for Business Leaders, How to Define and Scope a Machine Learning Problem, When is Machine Learning a Good Solution? When is Machine Learning Not Good Solution?, Image Classification: Vocabulary and Example, Reinforcement Learning: Robot Programming Example, Machine Learning in Action: The Pollexy Project

### [AWS Training & Certification 12h]**The Elements of Data Science**

**The Elements of Data Science**

The Elements of Data Science, What is Data Science? Problem Formulation and Exploratory Data Analysis, Data Processing and Feature Engineering, Model Training, Tuning, and Debugging, Model Evaluation and Model Productionizing

### [Linkdin Learning]**Become a Data Scientist**

A Day In The Life of a Data Scientist, The Non-Technical Skills of Effective Data Scientists, Data Science & Analytics Career Paths & Certifications: First Steps, Data Science Foundations: Fundamentals (2019), Statistics Foundations 1: The Basics, Statistics Foundations 2: Probability, Data Visualization: Storytelling, Big Data in the Age of AI, Learning Data Governance, Data Fluency: Exploring and Describing Data, Lessons from Data Scientists, Side Hustle Strategies for Data Science and Analytics Experts