Python & Introduction to Data Science

Python & Introduction to Data Science, available at $64.99, has an average rating of 4.15, with 68 lectures, based on 1130 reviews, and has 45882 subscribers.

You will learn about Basic Notebook commands Variables and conversions in Python Variables, lists, dictionaries, sets, classes in Python Definition of a function Date management Reading and writing files Mathematical functions in Numpy Functions to create random data Indexing methods Pivot tables in Pandas Display options RAM memory optimization for large amounts of data This course is ideal for individuals who are Researchers in the field of data analysis, machine learning and data mining, who want to consolidate the basics or Beginners who want to start learning the Python programming language or Programmers who already have experience with other languages and want to learn the Python language or Any student wishing to pursue a career in the field of Data Science or Anyone who wants to approach this new field for work or personal growth It is particularly useful for Researchers in the field of data analysis, machine learning and data mining, who want to consolidate the basics or Beginners who want to start learning the Python programming language or Programmers who already have experience with other languages and want to learn the Python language or Any student wishing to pursue a career in the field of Data Science or Anyone who wants to approach this new field for work or personal growth.

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Summary

Title: Python & Introduction to Data Science

Price: $64.99

Average Rating: 4.15

Number of Lectures: 68

Number of Published Lectures: 68

Number of Curriculum Items: 68

Number of Published Curriculum Objects: 68

Original Price: $84.99

Quality Status: approved

Status: Live

What You Will Learn

Basic Notebook commands
Variables and conversions in Python
Variables, lists, dictionaries, sets, classes in Python
Definition of a function
Date management
Reading and writing files
Mathematical functions in Numpy
Functions to create random data
Indexing methods
Pivot tables in Pandas
Display options
RAM memory optimization for large amounts of data

Who Should Attend

Researchers in the field of data analysis, machine learning and data mining, who want to consolidate the basics
Beginners who want to start learning the Python programming language
Programmers who already have experience with other languages and want to learn the Python language
Any student wishing to pursue a career in the field of Data Science
Anyone who wants to approach this new field for work or personal growth

Target Audiences

Researchers in the field of data analysis, machine learning and data mining, who want to consolidate the basics
Beginners who want to start learning the Python programming language
Programmers who already have experience with other languages and want to learn the Python language
Any student wishing to pursue a career in the field of Data Science
Anyone who wants to approach this new field for work or personal growth

Python is the most important language in the field of data, and its libraries for analysis and modeling are the most relevant tools to use.

In this course we will start building the basics of Python and then going to deepen the fundamental libraries like Numpy, Pandas, and Matplotlib.

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The four main features of this course are:

1. Clear and simplified language, suitable for everyone

2. Practical and efficient

3. Examples, illustrations and demonstrations with relative explanations

4. Continuous updating of contents and exercises

Course Curriculum

Chapter 1: Introduction

Lecture 1: 01 Python & Introduction to Data Science

Chapter 2: Python

Lecture 1: 2.01 Configuration of the development environment

Lecture 2: 2.02 How to install Python libraries

Lecture 3: 2.03 Basic Notebook Controls

Lecture 4: 2.04 Introduction to Python

Lecture 5: 2.05 Operations in Python

Lecture 6: 2.06 Variables and conversions in Python

Lecture 7: 2.07 Strings and functions of modifications

Lecture 8: 2.08 Python’s Lists

Lecture 9: 2.09 Functions with lists

Lecture 10: 2.10 Dictionaries in Python

Lecture 11: 2.11 Functions with dictionaries

Lecture 12: 2.12 Set in Python

Lecture 13: 2.13 Assignment mechanism in Python

Lecture 14: 2.14 Conditional instructions in Python

Lecture 15: 2.15 Python iteration instructions

Lecture 16: 2.16 Creating functions in Python

Lecture 17: 2.17 Scripts and modules in Python

Lecture 18: 2.18 Error handling in Python

Lecture 19: 2.19 Reading and writing files in Python

Lecture 20: 2.20 Classes in Python

Lecture 21: 2.21 Inheritance of classes in Python

Lecture 22: 2.22 Time management functions

Lecture 23: 2.23 Practical exercises with Python (1)

Lecture 24: 2.24 Practical exercises with Python (2)

Lecture 25: 2.25 Practical exercises with Python (3)

Lecture 26: 2.26 Practical exercises with Python (4)

Lecture 27: 2.27 Practical exercises with Python (5)

Lecture 28: 2.28 Practical exercises with Python (6)

Chapter 3: Numpy

Lecture 1: 3.01 Introduction to Numpy

Lecture 2: 3.02 Arrays in Numpy

Lecture 3: 3.03 Indexing of matrices in Numpy

Lecture 4: 3.04 Copy, arange and random in Numpy

Lecture 5: 3.05 Data type and conversion to Numpy

Lecture 6: 3.06 Mathematical Functions in Numpy

Lecture 7: 3.07 Order functions in Numpy

Lecture 8: 3.08 Data management functions in Numpy

Lecture 9: 3.09 Functions to create arrays in Numpy (1)

Lecture 10: 3.10 Functions to create arrays in Numpy (2)

Lecture 11: 3.11 Logical operations in Numpy

Lecture 12: 3.12 Random in Numpy

Lecture 13: 3.13 Reading files in Numpy

Lecture 14: 3.14 Writing files in Numpy

Lecture 15: 3.15 Practical exercises with Numpy (1)

Lecture 16: 3.16 Practical exercises with Numpy (2)

Lecture 17: 3.17 Practical exercises with Numpy (3)

Lecture 18: 3.18 Practical exercises with Numpy (4)

Lecture 19: 3.19 Practical exercises with Numpy (5)

Lecture 20: 3.20 Practical exercises with Numpy (6)

Lecture 21: 3.21 Practical exercises with Numpy (7)

Chapter 4: Pandas

Lecture 1: 4.01 Introduction to Pandas

Lecture 2: 4.02 DataFrame and Series in Pandas

Lecture 3: 4.03 Indexing methods in Pandas

Lecture 4: 4.04 Groupby in Pandas

Lecture 5: 4.05 Mathematical Operations in Pandas

Lecture 6: 4.06 Indexing and editing of a Series data

Lecture 7: 4.07 Indexing, editing and deletion of a DataFrame

Lecture 8: 4.08 Merge in DataFrame

Lecture 9: 4.09 Display options in Pandas

Lecture 10: 4.10 Pivot chart in Pandas

Lecture 11: 4.11 Managing dates in Pandas (1)

Lecture 12: 4.12 Managing dates in Pandas (2)

Lecture 13: 4.13 Processing data in Pandas (1)

Lecture 14: 4.14 Processing of data in Pandas (2)

Lecture 15: 4.15 Methods for editing strings in Pandas

Lecture 16: 4.16 Advanced indexing methods in Pandas

Lecture 17: 4.17 Create graphs in Pandas

Lecture 18: 4.18 Memory management for large data

Instructors

AI 4 MY
AI Educational Company

Rating Distribution

1 stars: 19 votes
2 stars: 35 votes
3 stars: 175 votes
4 stars: 447 votes
5 stars: 454 votes

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