Python Programming for Beginners in Data Science

Python Programming for Beginners in Data Science

What you’ll learn

Learn just enough Python Programming to do Data Science, Machine Learning and Deep Learning
Have a good understanding of the core concepts of Python Programming
Form a good programming base to be able to apply it to Machine Learning Algorithms
Just enough Object Oriented Python as well

Requirements

None in general – This is a beginner’s course
A PC or Mac with good internet connections.
All required software (like Python executable, IDE etc) can be downloaded
Enough enthusiasm to learn the course through its quizzes and exercises.

Description

Data Science, Machine Learning, Deep Learning & AI are hot areas right now. But to learn these, for some of us programming is a bit of a problem. Not all of us are from a programming background. Or some come from a Java background and might not know Python. These days, Python is the de-facto ( almost ) programming language for Data Science. So,  to fill that gap, we have created a course that covers just enough Python for you to start up and running with any of you the Machine learning algorithms you are interested in. Python Programming – Python programming is one of the core skills required for any Data Scientist. However, not all wanna-be data scientists have the required programming background let alone Python skills. This Python online training program is designed to let you start all the way from the basics. It teaches you the basic skills in python. Here are some of the topics we will discuss in the course. You don’t have to understand these topics just yet. The listing is to just give a good inventory of the topics that we will be covering in this Python course. variables, type conversions, flow control, operators & Expressions. Loops – for & while loops , nested loops, for else loopsStrings, built-in and user defined functionsData Structures – Lists, Dictionaries, Tuples, SetsObject Oriented PythonI/O, exceptionsStandard library – date/time, file I/O, math, statistics & random numbers.For any data scientist, these are the absolute essentials of python. What about Data Science & Machine Learning ? This course does NOT teach you data science or machine learning. Python is a broad purpose programming langauge. It can be  used for a variety of purposes like building websites, process automation, devops, Data science etc. However, this Python programming course is designed specifically to cater to the needs of the Machine Learning or Data Science learner. By the end of this course, you will be in a good position to apply your python skills to apply to any of the Machine Learning or Data Science algorithms in Python. Who this course is not for ?Although most newbies or experienced folks will benefit from this course, it is not suitable for those experienced in Python already. those who already have some Python programming experience, but wish to learn more about its application in Data Science or Machine learning. Free PreviewWe have deliberately kept quite a number of videos for free preview. Hopefully, this will enable you to judge our Python Programming course before you take it. Either way, Udemy’s 30 day return program will hopefully help you with a refund in case you don’t like the course. However, we are absolutely positive you will like the course.

Overview

Section 1: Day 0 – Python Setup

Lecture 1 Why Python

Lecture 2 About the Course

Lecture 3 Python Setup

Lecture 4 Hello World in Python

Lecture 5 Python IDE Setup

Section 2: Day 1 – Python Basics

Lecture 6 What are Variables

Lecture 7 Variables – Types of Numbers

Lecture 8 Variables – Strings, Boolean & Reserved Keywords

Lecture 9 Variables – Quiz

Lecture 10 Variables – Recap

Lecture 11 Variables – Challenge – Discussion

Lecture 12 Type Conversion

Lecture 13 Type Conversion Quiz Discussion

Lecture 14 Arithmetic Operators

Lecture 15 Comparision Operators

Lecture 16 Operator Precedence

Lecture 17 Logical Operators

Section 3: Day 1 (contd) – Flow Control

Lecture 18 if statement

Lecture 19 python blocks

Lecture 20 nested if statement

Lecture 21 elif statement

Lecture 22 else statement

Lecture 23 flow control quiz – discussion

Lecture 24 flow control challenges – discussion

Section 4: Day 2 – Loops

Lecture 25 for loop

Lecture 26 While loop

Lecture 27 Challenge Discussion – 1

Lecture 28 Challenge Discussion – 2

Lecture 29 Challenge Discussion – 3

Lecture 30 for vs while loop

Lecture 31 Break Statement – Theory

Lecture 32 Break Statement – Program

Lecture 33 for-else statement

Lecture 34 Nested loops

Section 5: Day 3 – Strings & Functions

Lecture 35 What are Strings

Lecture 36 Sub-strings

Lecture 37 Split strings

Lecture 38 Strip strings

Lecture 39 Other String Functions

Lecture 40 Cheatsheet

Lecture 41 Challenges

Lecture 42 Python Functions

Lecture 43 Create your own Function

Lecture 44 doc string

Lecture 45 function arguments

Lecture 46 Python functions – Summary

Lecture 47 Python Built-in Functions

Lecture 48 Python Built-in functions Summary

Section 6: Day 4 – Data Structures – Lists

Lecture 49 What are Lists

Lecture 50 Challenge

Lecture 51 List Indexing and Merging

Lecture 52 List Manipulation

Lecture 53 Challenge – Average Grades v3

Lecture 54 Challenge contd.

Lecture 55 Challenge contd.

Lecture 56 Nested Lists

Lecture 57 Enumerate Lists

Lecture 58 Merge and Sort Lists

Lecture 59 List Slicing

Lecture 60 Python Dictionary

Lecture 61 get-vs-index

Lecture 62 Challenge – Vowels

Lecture 63 Dictionary access

Lecture 64 Dictionary – Key & Value objects

Lecture 65 Challenge – 1

Lecture 66 Challenge – 2

Lecture 67 Challenge – 2 ( contd)

Lecture 68 Dictionary – Deletion

Section 7: Day 5 – Data Structures (contd.)

Lecture 69 Python Tuples

Lecture 70 Python Tuples ( contd. )

Lecture 71 Python Sets

Lecture 72 Set Operations (Union, Intersection, Difference etc )

Lecture 73 Python Sets – (contd)

Lecture 74 Python Sets – Summary

Section 8: Day 6 – Object Oriented Python

Lecture 75 What is Object Oriented Python

Lecture 76 Write your first Python Class

Lecture 77 Attributes & Methods in a class

Section 9: Day 7 – I/O & Exceptions

Lecture 78 I/O – Input / Output

Lecture 79 I/O – contd.

Lecture 80 Exceptions

Section 10: Day 8 – Python Standard Library

Lecture 81 Date Object

Lecture 82 Quiz Discussion

Lecture 83 Time delta

Lecture 84 Time

Lecture 85 Date time

Lecture 86 File Operations – Read files

Lecture 87 File operations – Write & Append files

Lecture 88 File Operations – Exception Handling

Lecture 89 Math Module

Section 11: NumPy – Numeric Python

Lecture 90 What is NumPy

Lecture 91 What makes NumPy faster

Lecture 92 How to Create Arrays in NumPy

Lecture 93 How to Reshape NumPy Arrays

Lecture 94 Element wise operations in NumPy

Lecture 95 Aggregate Operations in NumPy

Lecture 96 Array Indexing in NumPy

Lecture 97 Array Slicing in NumPy

Lecture 98 Append rows/columns in NumPy

Lecture 99 Insert rows/columns in NumPy

Lecture 100 Array Manipulation in NumPy

Section 12: Pandas

Lecture 101 What is Pandas

Lecture 102 Pandas Installation & Sample file creation

Lecture 103 Dataframe Creation methods

Section 13: Frequently Asked Questions

Lecture 104 Course Completion Certificate

Section 14: Troubleshooting

Lecture 105 Jupyter command not recognized

Non-Programmers interested to learn Python as their first language,Non-Python Programmers interested in learning Python for Machine Learning and Data Science

Course Information:

Udemy | English | 14h 7m | 4.79 GB
Created by: Ajay Tech

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