Data Science with Python
- Python Programming
- Data Visualisation
- Data Analysis
- Machine Learning
- Data Science Methodology
- Types of machine learning with implementation of various algorithms
Data Science with Python
Data Science with Python Programming - syllabus
Introduction to Data Science
Introduction to Data Science
Python in Data Science
Why is Data Science so Important?
Application of Data Science
What will you learn in this course?
Introduction to Python Programming
What is Python Programming?
History of Python Programming
Features of Python Programming
Application of Python Programming
Setup of Python Programming
Getting started with the first Python program
Variables and Data types
What is a variable?
Declaration of variable
Variable assignment
Data types in Python
Checking Data type
Data types Conversion
Python programs for Variables and Data types.
Python Identifiers, Keywords, Reading Input, Output Formatting
What is an Identifier?
Keywords
Reading Input
Taking multiple inputs from user
Output Formatting
Python end parameter
Operators in Python
Operators and types of operators
1. Arithmetic Operators
2. Relational Operators
3. Assignment Operators
4. Logical Operators
5. Membership Operators
6. Identity Operators
7. Bitwise Operators
Python programs for all types of operators
DECISION MAKING
Introduction to Decision making
Types of decision making statements
Introduction, syntax, flowchart and programs for
- if statement
- if…else statement
- nested if
elif statement
Loops
Introduction to loops
Types of loops
- for loop
- while loop
- nested loop
Loop Control Statements
Break, continue and pass statement
Python programs for all types of loops
LISTS
Python Lists
Accessing Values in Lists
Updating Lists
Deleting List Elements
Basic List Operations
Built-in List Functions and Methods for list
Tuples and Dictionary
Python Tuple
Accessing, Deleting Tuple Elements
Basic Tuples Operations
Built-in Tuple Functions & methods
Difference between List and Tuple
Python Dictionary
Accessing, Updating, Deleting Dictionary Elements
Built-in Functions and Methods for Dictionary
Functions and Modules
What is a Function?
Defining a Function and Calling a Function
Ways to write a function
Types of functions
Anonymous Functions
Recursive function
What is a module?
Creating a module
import Statement
Locating modules
Working with Files
Opening and Closing Files
The open Function
The file Object Attributes
The close() Method
Reading and Writing Files
MORE OPERATIONS ON FILES
REGULAR EXPRESSION
What is a REGULAR EXPRESSION?
Metacharacters
match() function
search() function
re.match() vs re.search()
findall() function
split() function
sub() function
Introduction to Python Data Science Libraries
Data Science Libraries
Libraries for Data Processing and Modeling
· Pandas
· Numpy
· SciPy
· Scikit-learn
Libraries for Data Visualization
· Matplotlib
· Seaborn
· Plotly
Components of Python Ecosystem
Components of Python Ecosystem
Using Pre-packaged Python Distribution: Anaconda
Jupyter Notebook
Analysing Data using Numpy and Pandas
Analysing Data using Numpy & Pandas
· What is numpy? Why use numpy?
· Installation of numpy
· Examples of numpy
· What is ‘pandas’?
· Key features of pandas
· Python Pandas - Environment Setup
· Pandas – Data Structure with example
· Data Analysis using Pandas
Data Visualisation with Matplotlib
Data Visualisation with Matplotlib
• What is Data Visualisation?
• Introduction to Matplotlib
• Installation of Matplotlib
Types of data visualization charts/plots
• Line chart, Scatter plot
• Bar chart, Histogram
• Area Plot, Pie chart
• Boxplot, Contour plot
Three-Dimensional Plotting with Matplotlib
Three-Dimensional Plotting with Matplotlib
• 3D Line Plot
• 3D Scatter Plot
• 3D Contour Plot
• 3D Surface Plot
Data Visualisation with Seaborn
Introduction to seaborn
Seaborn Functionalities
Installing seaborn
Different categories of plot in Seaborn
Exploring Seaborn Plots
Introduction to Statistical Analysis
What is Statistical Analysis?
Introduction to Math and Statistics for Data Science
Terminologies in Statistics – Statistics for Data Science
Categories in Statistics
Correlation
Mean, Median, and Mode
Quartile
Data Science Methodology (Part-1)
Module 1: From Problem to Approach
· Business Understanding
· Analytic Approach
Module 2: From Requirements to Collection
· Data Requirements
· Data Collection
Module 3: From Understanding to Preparation
· Data Understanding
· Data Preparation
Data Science Methodology (Part-2)
Module 4: From Modeling to Evaluation
· Modeling
· Evaluation
Module 5: From Deployment to Feedback
· Deployment
· Feedback
Summary
Introduction to Machine Learning and its types
What is a Machine Learning?
Need for Machine Learning
Application of Machine Learning
Types of Machine Learning
· Supervised learning
· Unsupervised learning
· Reinforcement learning
Regression Analysis
Regression Analysis
Linear Regression
Implementing Linear Regression
Multiple Linear Regression
Implementing Multiple Linear Regression
Polynomial Regression
Implementing Polynomial Regression
Classification
What is Classification?
Classification algorithms
Logistic Regression
Implementing Logistic Regression
Decision Tree
Implementing Decision Tree
Support Vector Machine (SVM)
Implementing SVM
Clustering
What is Clustering?
Clustering Algorithms
K-Means Clustering
How does K-Means Clustering work?
Implementing K-Means Clustering
Hierarchical Clustering
Agglomerative Hierarchical clustering
How does Agglomerative Hierarchical clustering Work?
Divisive Hierarchical Clustering
Implementation of Agglomerative Hierarchical Clustering
Association Rule Learning
Association Rule Learning
Apriori algorithm
Working of Apriori algorithm
Implementation of Apriori algorithm
Project
Problem Statement
Dataset
Exploratory Data Analysis
Implementation of Project
After the completion of this course, you will get the certification.
The Data Scientist is an expert in various underlying fields of Statistics and Computer Science. He uses his analytical aptitude to solve business problems.
Data Scientist is well versed with problem-solving and is assigned to find patterns in data. His goal is to recognize redundant samples and draw insights from it. Data Science requires a variety of tools to extract information from the data. A Data Scientist is responsible for collecting, storing and maintaining the structured and unstructured form of data.The report from Indeed showed a 29% increase in demand for data scientists year over year and a 344% increase since 2013. Demand for data science professionals is growing, as organizations maintain themselves through data-driven insights. Once you have acquired the right Data science skills, here are the top five promising career paths that you can aspire for:
1. Machine Learning Engineer
2. Data Scientist
3. Software Developer/Engineer (AI/ML)
4. Data Engineer
5. Data Analyst