Course Outline

Python Fundamentals – Part 1 

1. Installing and setting up Python environment
2. Variables, data types, and type conversions
3. Working with Python strings and string methods
4. String formatters and escape sequences
5. Operators (Arithmetic, Comparison, Logical, Assignment, Boolean, Membership)

Assignment #1 

Python Fundamentals – Part 2 

1. Conditional statements (if, if-else, if-elif-else, nested conditions)
2. Loops in Python (for loop, while loop, nested loops)
3. Break, continue, and pass statements
4. Python’s built-in data structures (Lists, Tuples, Sets, Dictionaries)
5. Indexing, slicing, and negative indexing

Assignment #2 

Functions and File Handling 

1. Defining and using functions in Python
2. Function arguments and recursion
3. Lambda expressions (anonymous functions)
4. File handling (opening, reading, writing files)
5. Exception management (try-except, try-except-else, try-except-finally)

Assignment #3 

Modules, Packages & OOP Basics 

1. Modules and packages (creating and using)
2. Built-in modules (e.g., datetime)
3. Using pip and PyPI
4. Object-Oriented Programming: Classes, objects, and methods
5. Inheritance, polymorphism, constructors, and destructors

Assignment #4 

Git, GitHub & SQL Fundamentals – Part 1 

1. Introduction to Git and GitHub
2. Creating repositories and basic bash commands
3. Overview of RDBMS and database normalization
4. SQL statements: SELECT, WHERE clause, filtering
5. Sorting data with DISTINCT, TOP, LIKE keywords

Assignment #5 

SQL Fundamentals – Part 2 & NumPy Introduction 

1. Different types of joins in SQL
2. Modifying data using SQL syntax
3. Introduction to NumPy library
4. Working with NumPy arrays and built-in methods
5. Indexing, slicing, and broadcasting in NumPy

Assignment #6 

Data Analysis with NumPy & Pandas – Part 1 

1. Array methods, attributes, and Boolean masking
2. Arithmetic operations and universal functions (ufuncs)
3. Array aggregations and statistical operations (sum, mean, std, min, max)
4. Introduction to Pandas library
5. Pandas data structures: Series and DataFrames
6. Creating DataFrames from dictionaries, lists, and arrays
7. Basic DataFrame operations (head, tail, info, describe)
8. Selecting columns and rows in DataFrames
9. Conditional selection and filtering

Assignment #7 

Working with Pandas – Part 2 

1. Hierarchical indexing in Pandas (MultiIndex)
2. Handling missing data (isna, dropna, fillna)
3. Data wrangling with Pandas (merge, join, concatenate)
4. Groupby operations and aggregations
5. Pivot tables and cross-tabulation
6. Sorting and ranking data
7. Useful Pandas methods and operations (apply, map, applymap)
8. Practical data manipulation techniques
9. Working with dates and time series data
10. String operations in Pandas

Assignment #8 

Data Analysis Project 

1. Project: Download a CSV file from Kaggle
2. Perform comprehensive data analysis using NumPy and Pandas
3. Data cleaning and preprocessing
4. Exploratory data analysis (EDA)
5. Drawing insights from data

Assignment #9 

Introduction to Statistics 

1. Quantitative analysis and frequency distribution
2. Data presentation: Bar graphs vs histograms
3. Central tendency measures (Mean, Median, Mode)
4. Dispersion measures (Range, Variance, Standard Deviation)
5. Quartiles, percentiles, box plots, coefficient of variation
6. Correlation coefficient and standard scores (Z-score, T-score)
7. Normal distribution and hypothesis testing (Z-test, T-test)

Assignment #10 

Data Visualization with Matplotlib 

1. Creating multiple plots on a single canvas
2. Matplotlib’s object-oriented approach
3. Creating figures, subplots, and inset plots
4. Saving and enhancing figures
5. Built-in data visualization in Pandas
6. Creating area plots, bar charts, histograms, line charts
7. Scatter plots, box plots, hexagonal bin plots, pie charts, and KDE plots

Assignment #11 

Data Visualization with Seaborn 

1. Introduction to Seaborn: Distribution Plot, Lmplot
2. Jointplot, Pairplot, and Kdeplot
3. Stripplot, Swarmplot, and Boxplot
4. Violinplot and Pointplot
5. Axis Grids, Matrix Plot, and Heatmap
6. Understanding Seaborn figure styles

Assignment #12 

Machine Learning Fundamentals 

1. Introduction to machine learning: Definition and importance
2. Applications of machine learning
3. Understanding supervised learning
4. Introduction to unsupervised learning
5. Overview of machine learning models
6. Data splitting: Training and test sets
7. Understanding K-fold cross-validation
8. Handling underfitting and overfitting
9. Confusion matrix metrics: Precision, recall, and F1 score

Assignment #13 

Feature Engineering with Scikit-learn 

1. Understanding feature scaling
2. Hands-on with feature scaling techniques
3. Introduction to Principal Component Analysis (PCA)
4. Working with PCA in real-world examples
5. Practical exercises with label encoding
6. Hands-on with ordinal encoding
7. Working with one-hot encoding through practical examples
8. Removing outliers in real-world datasets

Assignment #14 

Linear Regression vs Multiple Regression in Scikit-learn 

1. Theory of linear regression
2. Applying a simple linear regression model
3. Theory of multiple linear regression
4. Applying a multiple linear regression model
5. Project 01: Overview of a data project
6. Project 01: Solutions to the data project

Assignment #15 

K-Nearest Neighbors and Logistic Regression with Scikit-learn 

1. Theory behind binary logistic regression
2. Algorithm of binary logistic regression
3. Hands-on with a binary logistic regression model
4. Understanding K-Nearest Neighbors (KNN) theory
5. Algorithm of K-Nearest Neighbors
6. Pen-and-paper exercise for K-Nearest Neighbors
7. Hands-on with K-Nearest Neighbors
8. Project overview: K-Nearest Neighbors
9. Solutions to the K-Nearest Neighbors project

Assignment #16 

Naive Bayes Classification using Scikit-learn 

1. Saving and Loading Trained Machine Learning Models
2. Implementing K-Fold Cross Validation
3. Introduction to Kaggle Platform
4. Introduction to Google Colab
5. Naive Bayes Classification Theory
6. Naive Bayes Classification Algorithm
7. Pen & Paper Exercise for Naive Bayes Classification
8. Hands-on with Naive Bayes Classification

Assignment #17 

Scikit-learn: Decision Trees, Random Forests, and Ensemble Learning 

1. Theory of Decision Trees: Entropy, Information Gain
2. Hands-on with Decision Trees
3. Introduction to Ensemble Learning: Bagging, Random Forests, Boosting
4. Hands-on with Bagging
5. Hands-on with Random Forests

Assignment #18 

Scikit-learn – Support Vector Machines (SVM) 

1. Utilizing Grid Search CV for Finding the Best Model and Hyperparameter Tuning
2. Theory of Support Vector Machines
3. Algorithm for Support Vector Machines
4. Hands-on with Support Vector Machines (SVMs)
5. Project Overview: Support Vector Machines
6. Solutions for Support Vector Machines Project

Assignment #19 

Scikit-learn – Clustering with K Means 

1. Theory of K-Means Clustering
2. Algorithm for K-Means Clustering
3. Modified Algorithm for K-Means Clustering
4. Pen & Paper Exercise for K-Means Clustering
5. Hands-on with K-Means Clustering
6. Projects Overview: K-Means Clustering
7. Solutions for K-Means Clustering Project

Assignment #20 

Natural Language Processing (NLP) 

1. What is Natural Language Processing?
2. Practical Uses of Natural Language Processing (NLP)
3. Practical Uses of Natural Language Processing (NLP) Overview

Assignment #21 

Deep Learning 

1. Understanding Neurons
2. Biological Neural Networks (BNNs)
3. Artificial Neural Networks (ANNs)

Assignment #22 

Python with Data Science and Machine Learning Course Overview & Career Path 

Discussion 

1. Overview the Course
2. Writing CV
3. Job Searching

Final Course Completion