A warm welcome to the Data Science with Python course by Uplatz.
Data Science with Python involves not only using Python language to clean, analyze and visualize data, but also applying Python programming skills to predict and identify trends useful for decision-making.
Why Python for Data Science?
Since data revolution has made data as the new oil for organizations, today's decisions are driven by multidisciplinary approach of using data, mathematical models, statistics, graphs, databases for various business needs such as forecasting weather, customer segmentation, studying protein structures in biology, designing a marketing campaign, opening a new store, and the like. The modern data-powered technology systems are driven by identifying, integrating, storing and analyzing data for useful business decisions. Scientific logic backed with data provides solid understanding of the business and its analysis. Hence there is a need for a programming language that can cater to all these diverse needs of data science, machine learning, data analysis & visualization, and that can be applied to practical scenarios with efficiency. Python is a programming language that perfectly fits the bill here and shines bright as one such language due to its immense power, rich libraries and built in features that make it easy to tackle the various facets of Data Science.
This Data Science with Python course by Uplatz will take your journey from the fundamentals of Python to exploring simple and complex datasets and finally to predictive analysis & models development. In this Data Science using Python course, you will learn how to prepare data for analysis, perform complex statistical analyses, create meaningful data visualizations, predict future trends from data, develop machine learning & deep learning models, and more.
The Python programming part of the course will gradually take you from scratch to advanced programming in Python. You'll be able to write your own Python scripts and perform basic hands-on data analysis. If you aspire to become a data scientist and want to expand your horizons, then this is the perfect course for you. The primary goal of this course is to provide you a comprehensive learning framework to use Python for data science.
In the Data Science with Python training you will gain new insights into your data and will learn to apply data science methods and techniques, along with acquiring analytics skills. With understanding of the basic python taught in the initial part of this course, you will move on to understand the data science concepts, and eventually will gain skills to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular Python toolkits such as pandas, NumPy, matplotlib, scikit-learn, and so on.
The Data Science with Python training will help you learn and appreciate the fact that how this versatile language (Python) allows you to perform rich operations starting from import, cleansing, manipulation of data, to form a data lake or structured data sets, to finally visualize data - thus combining all integral skills for any aspiring data scientist, analyst, consultant, or researcher. In this Data Science using Python training, you will also work with real-world datasets and learn the statistical & machine learning techniques you need to train the decision trees and/or use natural language processing (NLP). Simply grow your Python skills, understand the concepts of data science, and begin your journey to becoming a top data scientist.
Data Science with Python Programming - Course Syllabus
1. 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?
2. 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
3. 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
4. Python Identifiers, Keywords, Reading Input, Output Formatting
What is an Identifier?
Keywords
Reading Input
Taking multiple inputs from user
Output Formatting
Python end parameter
5. Operators in Python
Operators and types of operators
- Arithmetic Operators
- Relational Operators
- Assignment Operators
- Logical Operators
- Membership Operators
- Identity Operators
- Bitwise Operators
Python programs for all types of operators
6. 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
7. 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
8. Lists
Python Lists
Accessing Values in Lists
Updating Lists
Deleting List Elements
Basic List Operations
Built-in List Functions and Methods for list
9. 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
10. 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
11. 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
12. Regular Expression
What is a Regular Expression?
Metacharacters
match() function
search() function
re match() vs re search()
findall() function
split() function
sub() function
13. 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
14. Components of Python Ecosystem
Components of Python Ecosystem
Using Pre-packaged Python Distribution: Anaconda
Jupyter Notebook
15. 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
16. 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
17. Three-Dimensional Plotting with Matplotlib
Three-Dimensional Plotting with Matplotlib
- 3D Line Plot
- 3D Scatter Plot
- 3D Contour Plot
- 3D Surface Plot
18. Data Visualisation with Seaborn
Introduction to seaborn
Seaborn Functionalities
Installing seaborn
Different categories of plot in Seaborn
Exploring Seaborn Plots
19. 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
20. 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
21. Data Science Methodology (Part-2)
Module 4: From Modeling to Evaluation
Modeling
Evaluation
Module 5: From Deployment to Feedback
Deployment
Feedback
Summary
22. 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
23. Regression Analysis
Regression Analysis
Linear Regression
Implementing Linear Regression
Multiple Linear Regression
Implementing Multiple Linear Regression
Polynomial Regression
Implementing Polynomial Regression
24. Classification
What is Classification?
Classification algorithms
Logistic Regression
Implementing Logistic Regression
Decision Tree
Implementing Decision Tree
Support Vector Machine (SVM)
Implementing SVM
25. 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
26. Association Rule Learning
Association Rule Learning
Apriori algorithm
Working of Apriori algorithm
Implementation of Apriori algorithm
End-to-end knowledge of Data Science
Prepare for a career path as Data Scientist / Consultant
Overview of Python programming and its application in Data Science
Detailed level programming in Python - Loops, Tuples, Dictionary, List, Functions & Modules, etc.
Decision-making and Regular Expressions
Introduction to Data Science Libraries
Components of Python Ecosystem
Analysing Data using Numpy and Pandas
Data Visualisation with Matplotlib
Three-Dimensional Plotting with Matplotlib
Data Visualisation with Seaborn
Introduction to Statistical Analysis - Math and Statistics
Terminologies & Categories of Statistics, Correlation, Mean, Median, Mode, Quartile
Data Science Methodology - From Problem to Approach, From Requirements to Collection, From Understanding to Preparation
Data Science Methodology - From Modeling to Evaluation, From Deployment to Feedback
Introduction to Machine Learning
Types of Machine Learning - Supervised, Unsupervised, Reinforcement
Regression Analysis - Linear Regression, Multiple Linear Regression, Polynomial Regression
Implementing Linear Regression, Multiple Linear Regression, Polynomial Regression
Classification, Classification algorithms, Logistic Regression
Decision Tree, Implementing Decision Tree, Support Vector Machine (SVM), Implementing SVM
Clustering, Clustering Algorithms, K-Means Clustering, Hierarchical Clustering
Agglomerative & Divisive Hierarchical clustering
Implementation of Agglomerative Hierarchical Clustering
Association Rule Learning
Apriori algorithm - working and implementation
Enthusiasm and determination to make your mark on the world!
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