syllabus.md

Data Science Using Python

Objectives:

• To introduce students to the basic concepts and techniques of Data Science and Machine Learning.
• To develop skills of using recent machine learning software for solving practical problems.
• To gain experience in doing independent study and research.

Course Content

Chapter No.	Topic Name	Sub Topics
1	Overview, content and Introduction to Data Science	Introduction to Data Science What is Data Science Programming Languages used for DS Applications of Data Science
2	Introduction to Python	Python Introduction Literate Programming Jupyter Notebook Environment Markdown format for documentation Python basics Input and output statements in python
3	Introduction to Version Control System	Purpose of Version Control System Types of VCS Introduction to Git and History Git Terminology Git Bash Installation and Unix bash commands
4	Git Basics	Initializing repositories Accessing Existing Repositories Adding/Removing files from Staging area Committing the changes to repository Undoing the commits that are made
5	Remote Repository	Introduction to GitHub Creating an Account on GitHub Create a remote repository Adding the remotes Push, pull and fetch commands.
6	GitHub Pages	Creation of personal portfolio site Creating a GitHub Page using Markdown and Jekyll themes for repositories.
7	Identifiers and Operators in Python	Identifiers in Python Properties for Declaring Identifiers Type Conversions Operators in Python Examples
8	Data Types in Python	Numbers int, float, complex bool, None Strings Accessing the characters from strings String Methods
9	Conditional Statements & Loops in Python	Conditional Statements For and While Loop Break, continue keywords
10	Data Structures in Python	Lists List Methods Tuples Tuple Methods Dictionaries Dictionary Methods
11	Functions	Different types of Functions - Built-in Functions - User Defined Lambda function Call by value Call by Reference
12	File Handling in Python	Open and Closing Files in Python Writing to Files in Python Reading Files in Python File Methods
13	Modules and Packages in Python	Types of Modules and Packages - Built-in Packages and Modules - Math, Random, OS, sys module - User Defined Examples
14	Comprehensions and Functional Programming	List, Dictionary & set Comprehensions map(), filter(), reduce()
15	Object-Oriented Programming - 1	Object-orientation - Class, Objects, Methods, Encapsulation Inheritance: Single, Multiple, Multilevel, Hierarchical, Hybrid Inheritance
16	Object-Oriented Programming - 2	Polymorphism Method overriding, Variable Overriding
17	Introduction to Data Analysis	Introduction to Data Types of Data in Statistics (Numerical & Categorical) Overview of Python Concepts
18	Data Manipulation with NumPy	Introduction NumPy Arrays NumPy Basics Math Random Indexing Filtering Statistics Aggregation Saving Data
19	Data Analysis with pandas	Introduction Series DataFrame Combining Indexing File I/O Grouping Features Filtering Sorting statistics Plotting
20	Data Cleaning With Pandas	Working with Duplicates and Missing Values Which values should be replace with missing values based on data Identifying and Eliminating Outliers Applying on raw dataset and introduction to Kaggle and other data sources
21	Data Preprocessing with Scikit-Learn	Introduction Standardizing Data Data Range Robust Scaling Normalizing Data Data Imputation
22	Introduction to Data Visualization and Matplotlib	Introduction to Visualization and Python packages Matplotlib history Introduction to plotting Line Plot Scatter Plot Bar Graph Histogram Pie Chart Box Plot Tasks
23	Data Visualization using Seaborn	Using Seaborn Styles Setting the default style Color Palettes Creating Custom Palettes stripplot() and swarmplot() boxplots, violinplots and lvplots barplots, pointplots and countplots
24	Data Visualization using Seaborn	Using Seaborn Styles Setting the default style Color Palettes Regression Plots Binning data Pairplots Creating heatmaps
25	Introduction to Machine Learning	What is Machine Learning Machine Learning Classification Types of Algorithms
26	Regression Models	Linear Regression with One variable Evaluation Metrics in Regression Models Train/Test splitting of data & Cross Validation Linear Regression with Multiple Variables Polynomial Features Non-Linear Regression with One variable Non-Linear Regression with Multiple variable
27	Regularization Models	Under fitting Overfitting Best fit Applying Ridge Regression Lasso Regression Algorithms
28	Classification models - 1	Introduction to categorical types of data Types of classification K-Nearest Neighbors Classifier Evaluation Metrics for classification Models Logistic regression Support Vector Machines
29	Unsupervised Machine Learning	Introduction to Unsupervised Learning Types of Unsupervised Learning
30	Clustering	Introduction to clustering Types of Clustering Methods K-Means Clustering Hierarchical Clustering Applications
31	Dimensionality Reduction	Dimensionality Reduction: Principal Component Analysis (PCA)

Hardware Requirements:

• i3 or above Processor is required
• 4 GB or above RAM is recommended
• Good Internet Connectivity
• OS-Windows 10 is Preferable

Duration :

45 Days (2 hours each day)

Entry Requirements:

• Students must have Knowledge of basic computer.
• Students must have Knowledge on Statistics Algebra, and Probability.