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# Data Science With Python (Beginner To Expert)

Start your career as a Data Scientist from scratch. Learn Data Science with Python and predict trends with advanced analytics

Development,Data Science,Python

Lectures -**51**

Duration -**40 hours **

30-days **Money-Back Guarantee**

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## Course Description

A warm welcome to the Data Science with Python course by Uplatz.

Data science with Python includes using Python programming skills to forecast and uncover trends that can be useful in making business choices, in addition to using Python to clean, analyze, and visualize data.

## Data Science With Python (Beginner To Expert) Course Overview

The Data Science with Python course from Uplatz will guide you through the Python language basics, easy and complicated dataset exploration, and model construction and predictive analysis. You will learn how to prepare data for analysis, conduct intricate statistical analyses, produce insightful data visualizations, forecast future trends from data, design machine learning & deep learning models, and more in this Data Science with Python course.

You will steadily go from beginner Python programming to advanced Python programming in the course's Python programming section. It will be able to create your own Python scripts and carry out simple practical data analysis. This course is ideal for you if you want to broaden your horizons and pursue a career as a data scientist. This course's main objective is to give you a thorough educational framework for using Python for data science.

You will learn to apply data science methodologies and techniques while taking the Data Science with Python program. You will also develop your analytics abilities. You will be able to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques once you have a firm grasp of the fundamentals of Python that were covered in the course's first section. Popular Python toolkits like pandas, NumPy, matplotlib, scikit-learn, and others will help you do this.

The Data Science with Python training will assist you in learning and appreciating how this flexible language (Python) enables you to perform rich operations beginning with the import, cleansing, and manipulation, to form a data lake or structured data sets, and finally to visualize data - thus combining all integral skills for any aspiring data scientist, analyst, consultant, or researcher.

Also, you will interact with real-world datasets and learn the statistical & machine learning methods required to train decision trees and/or employ natural language processing during this Data Science using Python training (NLP). Simply improve your Python abilities, comprehend data science principles, and start on the path to becoming a great data scientist.

### Why Python for Data Science?

Today's decisions are based on a multidisciplinary approach that uses data, mathematical models, statistics, graphs, and databases for various business needs, such as weather forecasting, customer segmentation, studying protein structures in biology, designing a marketing campaign, opening a new store, and the like. This is because the data revolution has made data the new oil for organizations. Identification, integration, storing, and analysis of data drive modern data-powered technology systems so they can make valuable business decisions.

Data-supported scientific reasoning offers a thorough understanding of the business and its analysis. So, there is a need for a programming language that can efficiently be applied to real-world situations and can meet all these varied needs of data science, machine learning, data analysis, and visualization. Given its enormous capability, extensive library support, and built-in capabilities that make it simple to handle the numerous parts of Data Science, Python is a programming language that precisely fits the bill in this context. It stands out as one such language.

### What you will learn?

**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 a 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 ifelif statement

**7. Loops**

Introduction to Loops

Types of loops

- for loop

- while loop

- nested loopLoop 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-learnLibraries for Data Visualization

- Matplotlib

- Seaborn

- Plotly

**14. Components of the Python Ecosystem**

Components of the 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 MatplotlibTypes 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

**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

### Who this course is for:

Data Scientists

Data Analysts / Data Consultants

Senior Data Scientists / Data Analytics Consultants

Newbies and beginners aspiring for a career in Data Science

Data Engineers

Machine Learning Engineers

Software Engineers and Programmers

Python Developers

Data Science Managers

Machine Learning / Data Science SMEs

Digital Data Analysts

Anyone interested in Data Science, Data Analytics, Data Engineering

### Goals

What will you learn in this course:

End-to-end knowledge of Data Science

Prepare for a career path as a 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 the 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

### Prerequisites

What are the prerequisites for this course?

Enthusiasm and determination to make your mark on the world!

## Curriculum

Check out the detailed breakdown of whatâ€™s inside the course

##
Introduction to Data Science

1 Lectures

- Introduction to Data Science 01:01:14 01:01:14

##
Introduction to Python Programming

1 Lectures

##
Variables and Data Types

2 Lectures

##
Input-Output, Keywords, Identifiers

2 Lectures

##
Operators and Types of Operators

2 Lectures

##
Decision-Making

1 Lectures

##
Loops in Python

3 Lectures

##
List in Python

2 Lectures

##
Tuples in Dictionary

2 Lectures

##
Functions and Modules

3 Lectures

##
Working with Files

2 Lectures

##
Regular Expression

1 Lectures

##
Introduction to Data Science Libraries

1 Lectures

##
Components of Python Ecosystem

1 Lectures

##
Analysing Data using Numpy and Pandas

5 Lectures

##
Data Visualisation with Matplotlib

3 Lectures

##
Three-Dimensional Plotting with Matplotlib

1 Lectures

##
Data Visualisation with Seaborn

2 Lectures

##
Introduction to Statistical Analysis

1 Lectures

##
Data Science Methodology

3 Lectures

##
Introduction to Machine Learning and its Types

1 Lectures

##
Regression Analysis in Data Science

3 Lectures

##
Classification in Data Science

3 Lectures

##
Clustering in Data Science

3 Lectures

##
Association Rule Learning in Data Science

2 Lectures

## Instructor Details

Uplatz

## Course Certificate

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## Our students work

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