Python & Data Science With R | Python & R Programming

R Programming Language & Python Programming for Data Science & Data Analytics all in one from scratch with real projects

What you'll learn

Installing Anaconda Distribution for Windows

Installing Anaconda Distribution for MacOs

Installing Anaconda Distribution for Linux

Reviewing The Jupyter Notebook

Reviewing The Jupyter Lab

Python Introduction

First Step to Coding

Using Quotation Marks in Python Coding

How Should the Coding Form and Style Be (Pep8)

Introduction to Basic Data Structures in Python

Performing Assignment to Variables

Performing Complex Assignment to Variables

Type Conversion

Arithmetic Operations in Python

Examining the Print Function in Depth

Escape Sequence Operations

Boolean Logic Expressions

Order Of Operations In Boolean Operators

Practice with Python

Examining Strings Specifically

Accessing Length Information (Len Method)

Search Method In Strings Startswith(), Endswith()

Character Change Method In Strings Replace()

Spelling Substitution Methods in String

Character Clipping Methods in String

Indexing and Slicing Character String

Complex Indexing and Slicing Operations

String Formatting with Arithmetic Operations

String Formatting With % Operator

String Formatting With String Format Method

String Formatting With f-string

Method Creation of List

Reaching List Elements – Indexing and Slicing

Adding & Modifying & Deleting Elements of List

Adding and Deleting by Methods

Adding and Deleting by Index

Other List Methods

Creation of Tuple

Reaching Tuple Elements Indexing And Slicing

Creation of Dictionary

Reaching Dictionary Elements

Adding & Changing & Deleting Elements in Dictionary

Dictionary Methods

Creation of Set

Adding & Removing Elements Methods in Sets

Difference Operation Methods In Sets

Intersection & Union Methods In Sets

Asking Questions to Sets with Methods

Comparison Operators

Structure of “if” Statements

Structure of “if-else” Statements

Structure of “if-elif-else” Statements

Structure of Nested “if-elif-else” Statements

Coordinated Programming with “IF” and “INPUT”

Ternary Condition

For Loop in Python

For Loop in Python(Reinforcing the Topic)

Using Conditional Expressions and For Loop Together

Continue Command

Break Command

List Comprehension

While Loop in Python

While Loops in Python Reinforcing the Topic

Getting know to the Functions

How to Write Function

Return Expression in Functions

Writing Functions with Multiple Argument

Writing Docstring in Functions

Using Functions and Conditional Expressions Together

Arguments and Parameters

High Level Operations with Arguments

all(), any() Functions

map() Function

filter() Function

zip() Function

enumerate() Function

max(), min() Functions

sum() Function

round() Function

Lambda Function

Local and Global Variables

Features of Class

Instantiation of Class

Attribute of Instantiation

Write Function in the Class

Inheritance Structure

Requirements

A working computer (Windows, Mac, or Linux)

No prior knowledge of Python for beginners is required

Motivation to learn the the second largest number of job postings relative program language among all others

Desire to learn machine learning python

Curiosity for python programming

Desire to learn python programming, pycharm, python pycharm

Nothing else! It’s just you, your computer and your ambition to get started today

Description

Welcome to "Python & Data Science with R | Python & R Programming" course.R Programming Language & Python Programming for Data Science & Data Analytics all in one from scratch with real projectsThe R programming language is a powerful open source platform designed for heavy data analytics. It is a popular language with data scientists, statisticians, and business analysts for its data analysis and visualization capabilities. R is also used extensively in machine learning, the foundational concept behind AI. R training can familiarize you with the concepts and methods R applies to artificial intelligence and analytics. Python and r, r and python, python, r programming, python data science, data science, data science with r, r python, python r, data science with r and python, data science course,OAK Academy offers highly-rated data science courses that will help you learn how to visualize and respond to new data, as well as develop innovative new technologies. Whether you’re interested in machine learning, data mining, or data analysis, Oak Academy has a course for you.Python instructors at OAK Academy specialize in everything from software development to data analysis and are known for their effective, friendly instruction for students of all levels.Whether you work in machine learning or finance or are pursuing a career in web development or data science, Python is one of the most important skills you can learn. Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasks.Machine learning and data analysis are big businesses. The former shows up in new interactive and predictive smartphone technologies, while the latter is changing the way businesses reach customers. Learning R from a top-rated OAK Academy instructor will give you a leg up in either industry.R is the programming language of choice for statistical computing. Machine learning, data visualization, and data analysis projects increasingly rely on R for its built-in functionality and tools. And despite its steep learning curve, R pays to know.Data science is everywhere. Better data science practices are allowing corporations to cut unnecessary costs, automate computing, and analyze markets. Essentially, data science is the key to getting ahead in a competitive global climate. python programming, oak academy, data literacy, python and r programming, data science python, python r data, data science r, python and r for data science, data transformation, python & r, python data science, python for data science, python r programming, data science python, pandas, r data science, r and python programming, r course, data science r and python, NumPy, python r data science, data science in r, data science with python and r, python with r, r studio, programming, r courses, programming for data scienceReady for a Data Science career? Are you curious about Data Science and looking to start your self-learning journey into the world of data?Are you an experienced developer looking for a landing in Data Science!In both cases, you are at the right place! The two most popular programming tools for data science work are Python and R at the moment. It is hard to pick one out of those two amazingly flexible data analytics languages. Both are free and open-source.R for statistical analysis and Python as a general-purpose programming language. For anyone interested in machine learning, working with large datasets, or creating complex data visualizations, they are absolutely essential.With my full-stack Data Science course, you will be able to learn R and Python together.If you have some programming experience, Python might be the language for you. R was built as a statistical language, it suits much better to do statistical learning with R programming.But do not worry! In this course, you will have a chance to learn both and will decide to which one fits your niche! Throughout the course's first part, you will learn the most important tools in R that will allow you to do data science. By using the tools, you will be easily handling big data, manipulating it, and producing meaningful outcomes.Throughout the course's second part, we will teach you how to use Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms and we will also do a variety of exercises to reinforce what we have learned in this Python for Data Science course.We will open the door of the Data Science world and will move deeper.  You will learn the fundamentals of Python and its beautiful libraries such as Numpy, Pandas, and Matplotlib step by step. Then, we will transform and manipulate real data. For the manipulation, we will use the tidyverse package, which involves dplyr and other necessary packages. At the end of the course, you will be able to select columns, filter rows, arrange the order, create new variables, and group by and summarize your data simultaneously.In this course you will learn;How to use Anaconda and Jupyter notebook,Fundamentals of Python such asDatatypes in Python,Lots of datatype operators, methods, and how to use them,Conditional concept, if statementsThe logic of Loops and control statementsFunctions and how to use themHow to use modules and create your own modulesData science and Data literacy conceptsFundamentals of Numpy for Data manipulation such asNumpy arrays and their featuresHow to do indexing and slicing on ArraysLots of stuff about Pandas for data manipulation such asPandas series and their featuresDataframes and their featuresHierarchical indexing concept and theoryGroupby operationsThe logic of Data MungingHow to deal effectively with missing data effectivelyCombining the Data FramesHow to work with Dataset filesAnd also you will learn fundamentals thing about the Matplotlib library such asPyplot, Pylab and Matplotlb conceptsWhat Figure, Subplot, and Axes areHow to do figure and plot customizationExamining and Managing Data Structures in RAtomic vectors Lists ArraysMatricesData framesTibblesFactorsData Transformation in RTransform and manipulate a deal dataTidyverse and morePython and rR programmingdata sciencedata science with rr pythondata science with r and pythonpython r programmingnumpy pythonpython r data sciencepython data scienceAnd we will do many exercises.  Finally, we will also have 4 different final projects covering all of Python subjects.What is data science?We have more data than ever before. But data alone cannot tell us much about the world around us. We need to interpret the information and discover hidden patterns. This is where data science comes in. Data science python uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction. Python data science seeks to find patterns in data and use those patterns to predict future data. It draws on machine learning to process large amounts of data, discover patterns, and predict trends. Data science using python includes preparing, analyzing, and processing data. It draws from many scientific fields, and as a python for data science, it progresses by creating new algorithms to analyze data and validate current methods.What does a data scientist do?Data Scientists use machine learning to discover hidden patterns in large amounts of raw data to shed light on real problems. This requires several steps. First, they must identify a suitable problem. Next, they determine what data are needed to solve such a situation and figure out how to get the data. Once they obtain the data, they need to clean the data. The data may not be formatted correctly, it might have additional unnecessary data, it might be missing entries, or some data might be incorrect. Data Scientists must, therefore, make sure the data is clean before they analyze the data. To analyze the data, they use machine learning techniques to build models. Once they create a model, they test, refine, and finally put it into production.What are the most popular coding languages for data science?Python for data science is the most popular programming language for data science. It is a universal language that has a lot of libraries available. It is also a good beginner language. R is also popular; however, it is more complex and designed for statistical analysis. It might be a good choice if you want to specialize in statistical analysis. You will want to know either Python or R and SQL. SQL is a query language designed for relational databases. Data scientists deal with large amounts of data, and they store a lot of that data in relational databases. Those are the three most-used programming languages. Other languages such as Java, C++, JavaScript, and Scala are also used, albeit less so. If you already have a background in those languages, you can explore the tools available in those languages. However, if you already know another programming language, you will likely be able to pick up.Does data science require coding?The jury is still out on this one. Some people believe that it is possible to become a data scientist without knowing how to code, but others disagree. A lot of algorithms have been developed and optimized in the field. You could argue that it is more important to understand how to use the algorithms than how to code them yourself. As the field grows, more platforms are available that automate much of the process. However, as it stands now, employers are primarily looking for people who can code, and you need basic programming skills. The data scientist role is continuing to evolve, so that might not be true in the future. The best advice would be to find the path that fits your skillset.What skills should a data scientist know?A data scientist requires many skills. They need a strong understanding of statistical analysis and mathematics, which are essential pillars of data science. A good understanding of these concepts will help you understand the basic premises of data science. Familiarity with machine learning is also important. Machine learning is a valuable tool to find patterns in large data sets. To manage large data sets, data scientists must be familiar with databases. Structured query language (SQL) is a must-have skill for data scientists. However, nonrelational databases (NoSQL) are growing in popularity, so a greater understanding of database structures is beneficial. The dominant programming language in Data Science is Python — although R is also popular. A basis in at least one of these languages is a good starting point. Finally, to communicate findings.What is python?Machine learning python is a general-purpose, object-oriented, high-level programming language. Whether you work in artificial intelligence or finance or are pursuing a career in web development or data science, Python bootcamp is one of the most important skills you can learn. Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed on the premise that there should be only one way (and preferably, one obvious way) to do things, a philosophy that resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing different tools for programmers suited for a variety of tasks.What is R and why is it useful?The R programming language was created specifically for statistical programming. Many find it useful for data handling, cleaning, analysis, and representation. R is also a popular language for data science projects. Much of the data used for data science can be messy and complex. The programming language has features and libraries available geared toward cleaning up unorganized data and making complex data structures easier to handle that can't be found in other languages. It also provides powerful data visualization tools to help data scientists find patterns in large sets of data and present the results in expressive reports. Machine learning is another area where the R language is useful. R gives developers an extensive selection of machine learning libraries that will help them find trends in data and predict future events.What careers use R?R is a popular programming language for data science, business intelligence, and financial analysis. Academic, scientific, and non-profit researchers use the R language to glean answers from data. R is also widely used in market research and advertising to analyze the results of marketing campaigns and user data. The language is used in quantitative analysis, where its data analysis capabilities give financial experts the tools they need to manage portfolios of stocks, bonds, and other assets. Data scientists use R in many industries to turn data into insights and predict future trends with its machine learning capabilities. Data analysts use R to extract data, analyze it, and turn it into reports that can help enterprises make better business decisions. Data visualization experts use R to turn data into visually appealing graphs and charts.Is R difficult to learn?Whether R is hard to learn depends on your experience. After all, R is a programming language designed for mathematicians, statisticians, and business analysts who may have no coding experience. For some beginning users, it is relatively simple to learn R. It can have a learning curve if you are a business analyst who is only familiar with graphical user interfaces since R is a text-based programming language. But compared to other programming languages, users usually find R easier to understand. R also may have an unfamiliar syntax for programmers who are used to other programming languages, but once they learn the syntax, the learning process becomes more straightforward. Beginners will also find that having some knowledge of mathematics, statistics, and probabilities makes learning R easier.Python vs. R: What is the Difference?Python and R are two of today's most popular programming tools. When deciding between Python and R, you need to think about your specific needs. On one hand, Python is relatively easy for beginners to learn, is applicable across many disciplines, has a strict syntax that will help you become a better coder, and is fast to process large datasets. On the other hand, R has over 10,000 packages for data manipulation, is capable of easily making publication-quality graphics, boasts superior capability for statistical modeling, and is more widely used in academia, healthcare, and finance.What does it mean that Python is object-oriented?Python is a multi-paradigm language, which means that it supports many data analysis programming approaches. Along with procedural and functional programming styles, Python also supports the object-oriented style of programming. In object-oriented programming, a developer completes a programming project by creating Python objects in code that represent objects in the actual world. These objects can contain both the data and functionality of the real-world object. To generate an object in Python you need a class. You can think of a class as a template. You create the template once, and then use the template to create as many objects as you need. Python classes have attributes to represent data and methods that add functionality. A class representing a car may have attributes like color, speed, and seats and methods like driving, steering, and stopping.What are the limitations of Python?Python is a widely used, general-purpose programming language, but it has some limitations. Because Python in machine learning is an interpreted, dynamically typed language, it is slow compared to a compiled, statically typed language like C. Therefore, Python is useful when speed is not that important. Python's dynamic type system also makes it use more memory than some other programming languages, so it is not suited to memory-intensive applications. The Python virtual engine that runs Python code runs single-threaded, making concurrency another limitation of the programming language. Though Python is popular for some types of game development, its higher memory and CPU usage limits its usage for high-quality 3D game development. That being said, computer hardware is getting better and better, and the speed and memory limitations of Python are getting less and less relevant.How is Python used?Python is a general programming language used widely across many industries and platforms. One common use of Python is scripting, which means automating tasks in the background. Many of the scripts that ship with Linux operating systems are Python scripts. Python is also a popular language for machine learning, data analytics, data visualization, and data science because its simple syntax makes it easy to quickly build real applications. You can use Python to create desktop applications. Many developers use it to write Linux desktop applications, and it is also an excellent choice for web and game development. Python web frameworks like Flask and Django are a popular choice for developing web applications. Recently, Python is also being used as a language for mobile development via the Kivy third-party library.What jobs use Python?Python is a popular language that is used across many industries and in many programming disciplines. DevOps engineers use Python to script website and server deployments. Web developers use Python to build web applications, usually with one of Python's popular web frameworks like Flask or Django. Data scientists and data analysts use Python to build machine learning models, generate data visualizations, and analyze big data. Financial advisors and quants (quantitative analysts) use Python to predict the market and manage money. Data journalists use Python to sort through information and create stories. Machine learning engineers use Python to develop neural networks and artificial intelligent systems.How do I learn Python on my own?Python has a simple syntax that makes it an excellent programming language for a beginner to learn. To learn Python on your own, you first must become familiar with the syntax. But you only need to know a little bit about Python syntax to get started writing real code; you will pick up the rest as you go. Depending on the purpose of using it, you can then find a good Python tutorial, book, or course that will teach you the programming language by building a complete application that fits your goals. If you want to develop games, then learn Python game development. If you're going to build web applications, you can find many courses that can teach you that, too. Udemy’s online courses are a great place to start if you want to learn Python on your own.Why would you want to take this course?Our answer is simple: The quality of teaching.When you enroll, you will feel the OAK Academy's seasoned instructors' expertise.Fresh Content It’s no secret how technology is advancing at a rapid rate and it’s crucial to stay on top of the latest knowledge. With this course, you will always have a chance to follow the latest data science trends.Video and Audio Production QualityAll our content is created/produced as high-quality video/audio to provide you the best learning experience.You will be,Seeing clearlyHearing clearlyMoving through the course without distractionsYou'll also get:Lifetime Access to The CourseFast & Friendly Support in the Q&A sectionUdemy Certificate of Completion Ready for DownloadDive in now into the "Python & Data Science with R | Python & R Programming" course.R Programming Language & Python Programming for Data Science & Data Analytics all in one from scratch with real projectsWe offer full support, answering any questions.See you in the course!

Overview

Section 1: Installations

Lecture 1 Installing Anaconda Distribution for Windows

Lecture 2 Installing Anaconda Distribution for MacOs

Lecture 3 Installing Anaconda Distribution for Linux

Lecture 4 Reviewing The Jupyter Notebook

Lecture 5 Reviewing The Jupyter Lab

Lecture 6 Basics of Jupyter Notebook for Mac - python data science, r programming

Section 2: First Step to Coding

Lecture 7 Python Introduction

Lecture 8 Python Project Files

Lecture 9 First Step to Coding

Lecture 10 Using Quotation Marks in Python Coding

Lecture 11 How Should the Coding Form and Style Be (Pep8)

Section 3: Basic Operations with Python

Lecture 12 Introduction to Basic Data Structures in Python

Lecture 13 Performing Assignment to Variables

Lecture 14 Performing Complex Assignment to Variables

Lecture 15 Type Conversion

Lecture 16 Arithmetic Operations in Python

Lecture 17 Examining the Print Function in Depth

Lecture 18 Escape Sequence Operations

Section 4: Boolean Data Type in Python Programming Language

Lecture 19 Boolean Logic Expressions

Lecture 20 Order Of Operations In Boolean Operators

Lecture 21 Practice with Python

Section 5: String Data Type in Python Programming Language

Lecture 22 Examining Strings Specifically

Lecture 23 Accessing Length Information (Len Method)

Lecture 24 Search Method In Strings Startswith(), Endswith()

Lecture 25 Character Change Method In Strings Replace()

Lecture 26 Spelling Substitution Methods in String

Lecture 27 Character Clipping Methods in String

Lecture 28 Indexing and Slicing Character String

Lecture 29 Complex Indexing and Slicing Operations

Lecture 30 String Formatting with Arithmetic Operations

Lecture 31 String Formatting With % Operator

Lecture 32 String Formatting With String.Format Method

Lecture 33 String Formatting With f-string Method

Section 6: List Data Structure in Python Programming Language

Lecture 34 Creation of List

Lecture 35 Reaching List Elements – Indexing and Slicing

Lecture 36 Adding & Modifying & Deleting Elements of List

Lecture 37 Adding and Deleting by Methods

Lecture 38 Adding and Deleting by Index

Lecture 39 Other List Methods

Section 7: Tuple Data Structure in Python Programming Language

Lecture 40 Creation of Tuple

Lecture 41 Reaching Tuple Elements Indexing And Slicing

Section 8: Dictionary Data Structure in Python Programming Language

Lecture 42 Creation of Dictionary

Lecture 43 Reaching Dictionary Elements

Lecture 44 Adding & Changing & Deleting Elements in Dictionary

Lecture 45 Dictionary Methods

Section 9: Set Data Structure in Python Programming Language

Lecture 46 Creation of Set

Lecture 47 Adding & Removing Elements Methods in Sets

Lecture 48 Difference Operation Methods In Sets

Lecture 49 Intersection & Union Methods In Sets

Lecture 50 Asking Questions to Sets with Methods

Section 10: Conditional Expressions in Python Programming Language

Lecture 51 Comparison Operators

Lecture 52 Structure of “if” Statements

Lecture 53 Structure of “if-else” Statements

Lecture 54 Structure of “if-elif-else” Statements

Lecture 55 Structure of Nested “if-elif-else” Statements

Lecture 56 Coordinated Programming with “IF” and “INPUT”

Lecture 57 Ternary Condition

Section 11: For Loop in Python Programming Language

Lecture 58 For Loop in Python

Lecture 59 For Loop in Python(Reinforcing the Topic)

Lecture 60 Using Conditional Expressions and For Loop Together

Lecture 61 Continue Command

Lecture 62 Break Command

Lecture 63 List Comprehension

Section 12: While Loop in Python Programming Language

Lecture 64 While Loop in Python

Lecture 65 While Loops in Python Reinforcing the Topic

Section 13: Functions in Python Programming Language

Lecture 66 Getting know to the Functions

Lecture 67 How to Write Function

Lecture 68 Return Expression in Functions

Lecture 69 Writing Functions with Multiple Argument

Lecture 70 Writing Docstring in Functions

Lecture 71 Using Functions and Conditional Expressions Together

Section 14: Arguments And Parameters in Python Programming Language

Lecture 72 Arguments and Parameters

Lecture 73 High Level Operations with Arguments

Section 15: Most Used Functions in Python Programming Language

Lecture 74 all(), any() Functions

Lecture 75 map() Function

Lecture 76 filter() Function

Lecture 77 zip() Function

Lecture 78 enumerate() Function

Lecture 79 max(), min() Functions

Lecture 80 sum() Function

Lecture 81 round() Function

Lecture 82 Lambda Function

Section 16: Class Structure in Python Programming Language

Lecture 83 Local and Global Variables

Lecture 84 Features of Class

Lecture 85 Instantiation of Class

Lecture 86 Attribute of Instantiation

Lecture 87 Write Function in the Class

Lecture 88 Inheritance Structure

Section 17: Python For Data Science: Data Science

Lecture 89 What Is Data Science?

Lecture 90 Data Literacy in Python

Section 18: Using Numpy for Data Manipulation

Lecture 91 Introduction to NumPy Library

Lecture 92 Numpy Project Files

Lecture 93 The Power of NumPy

Lecture 94 Creating NumPy Array with The Array() Function

Lecture 95 Creating NumPy Array with Zeros() Function

Lecture 96 Creating NumPy Array with Ones() Function

Lecture 97 Creating NumPy Array with Full() Function

Lecture 98 Creating NumPy Array with Arange() Function

Lecture 99 Creating NumPy Array with Eye() Function

Lecture 100 Creating NumPy Array with Linspace() Function

Lecture 101 Creating NumPy Array with Random() Function

Lecture 102 Properties of NumPy Array

Lecture 103 Reshaping a NumPy Array: Reshape() Function

Lecture 104 Identifying the Largest Element of a Numpy Array

Lecture 105 Detecting Least Element of Numpy Array: Min(), Ar

Lecture 106 Concatenating Numpy Arrays: Concatenate() Functio

Lecture 107 Splitting One-Dimensional Numpy Arrays: The Split

Lecture 108 Splitting Two-Dimensional Numpy Arrays: Split(),

Lecture 109 Sorting Numpy Arrays: Sort() Function

Lecture 110 Indexing Numpy Arrays

Lecture 111 Slicing One-Dimensional Numpy Arrays

Lecture 112 Slicing Two-Dimensional Numpy Arrays

Lecture 113 Assigning Value to One-Dimensional Arrays

Lecture 114 Assigning Value to Two-Dimensional Array

Lecture 115 Fancy Indexing of One-Dimensional Arrrays

Lecture 116 Fancy Indexing of Two-Dimensional Arrrays

Lecture 117 Combining Fancy Index with Normal Indexing

Lecture 118 Combining Fancy Index with Normal Slicing

Lecture 119 Operations with Comparison Operators

Lecture 120 Arithmetic Operations in Numpy

Lecture 121 Statistical Operations in Numpy

Lecture 122 Solving Second-Degree Equations with NumPy

Section 19: (Optional) Recap, Exercises, and Bonus İnfo from the Numpy Library

Lecture 123 What is Numpy?

Lecture 124 Array and Features in Python Numpy

Lecture 125 Array Operators in Python Numpy

Lecture 126 Indexing and Slicing in Python Numpy

Lecture 127 Numpy Exercises in Python Numpy

Section 20: Pandas: Using Pandas for Data Manipulation

Lecture 128 Introduction to Pandas Library

Lecture 129 Pandas Project Files Link

Lecture 130 Creating a Pandas Series with a List

Lecture 131 Creating a Pandas Series with a Dictionary

Lecture 132 Creating Pandas Series with NumPy Array

Lecture 133 Object Types in Series

Lecture 134 Examining the Primary Features of the Pandas Seri

Lecture 135 Most Applied Methods on Pandas Series

Lecture 136 Indexing and Slicing Pandas Series

Lecture 137 Creating Pandas DataFrame with List

Lecture 138 Creating Pandas DataFrame with NumPy Array

Lecture 139 Creating Pandas DataFrame with Dictionary

Lecture 140 Examining the Properties of Pandas DataFrames

Lecture 141 Element Selection Operations in Pandas DataFrames: Lesson 1

Lecture 142 Element Selection Operations in Pandas DataFrames: Lesson 2

Lecture 143 Top Level Element Selection in Pandas DataFrames:Lesson 1

Lecture 144 Top Level Element Selection in Pandas DataFrames:Lesson 2

Lecture 145 Top Level Element Selection in Pandas DataFrames:Lesson 3

Lecture 146 Element Selection with Conditional Operations in

Lecture 147 Adding Columns to Pandas Data Frames

Lecture 148 Removing Rows and Columns from Pandas Data frames

Lecture 149 Null Values in Pandas Dataframes

Lecture 150 Dropping Null Values: Dropna() Function

Lecture 151 Filling Null Values: Fillna() Function

Lecture 152 Setting Index in Pandas DataFrames

Lecture 153 Multi-Index and Index Hierarchy in Pandas DataFrames

Lecture 154 Element Selection in Multi-Indexed DataFrames

Lecture 155 Selecting Elements Using the xs() Function in Multi-Indexed DataFrames

Lecture 156 Concatenating Pandas Dataframes: Concat Function

Lecture 157 Merge Pandas Dataframes: Merge() Function: Lesson 1

Lecture 158 Merge Pandas Dataframes: Merge() Function: Lesson 2

Lecture 159 Merge Pandas Dataframes: Merge() Function: Lesson 3

Lecture 160 Merge Pandas Dataframes: Merge() Function: Lesson 4

Lecture 161 Joining Pandas Dataframes: Join() Function

Lecture 162 Loading a Dataset from the Seaborn Library

Lecture 163 Examining the Data Set 1

Lecture 164 Aggregation Functions in Pandas DataFrames

Lecture 165 Examining the Data Set 2

Lecture 166 Coordinated Use of Grouping and Aggregation Functions in Pandas Dataframes

Lecture 167 Advanced Aggregation Functions: Aggregate() Function

Lecture 168 Advanced Aggregation Functions: Filter() Function

Lecture 169 Advanced Aggregation Functions: Transform() Function

Lecture 170 Advanced Aggregation Functions: Apply() Function

Lecture 171 Examining the Data Set 3

Lecture 172 Pivot Tables in Pandas Library

Lecture 173 Accessing and Making Files Available

Lecture 174 Data Entry with Csv and Txt Files

Lecture 175 Data Entry with Excel Files

Lecture 176 Outputting as an CSV Extension

Lecture 177 Outputting as an Excel File

Section 21: (Optional) Recap, Exercises, and Bonus İnfo from the Pandas Library

Lecture 178 What is Pandas?

Lecture 179 Series and Features in Pandas

Lecture 180 Data Frame Attributes and Methods in Pandas Python

Lecture 181 Data Frame Attributes and Methods Part – II in Pandas Python

Lecture 182 Data Frame Attributes and Methods Part – III in Pandas Python

Lecture 183 Multi Index in Pandas Python

Lecture 184 Groupby Operations in Pandas Python

Lecture 185 Missing Data and Data Munging in Pandas Python

Lecture 186 Missing Data and Data Munging Part II in Pandas Python

Lecture 187 How We Deal with Missing Data in Pandas Python?

Lecture 188 Combining Data Frames in Pandas Python

Lecture 189 Combining Data Frames Part – II in Pandas Python

Lecture 190 Work with Dataset Files in Pandas Python

Section 22: Python For Data Science: Data Visualization

Lecture 191 What is Matplotlib?

Lecture 192 Using Matplotlib

Lecture 193 Pyplot – Pylab - Matplotlib

Lecture 194 Figure, Subplot and Axes in Python Matplotlib

Lecture 195 Figure Customization in Python Matplotlib

Lecture 196 Plot Customization in Python Matplotlib

Section 23: Data Science: Hands-On Projects

Lecture 197 Analyse Data With Different Data Sets: Titanic Project

Lecture 198 Titanic Project Answers in Data Analysis

Lecture 199 Project II: Bike Sharing in Data Analysis

Lecture 200 Bike Sharing Project Answers in Data Analysis

Lecture 201 Project III: Housing and Property Sales in Data Analysis

Lecture 202 Answer for Housing and Property Sales Project in Data Analysis

Lecture 203 Project IV: English Premier League in Data Analysis

Lecture 204 Answers for English Premier League Project in Data Analysis

Section 24: Environment Installation for R

Lecture 205 Downloading and Installing R & R Studio

Lecture 206 R Console Versus R Studio

Section 25: Data Management in R

Lecture 207 Getting Data into R

Lecture 208 Data Manipulation in R programming

Lecture 209 Graphs and Charts in R programming

Section 26: Examining and Managing Data Structures in R

Lecture 210 Vector Basics in R Programming

Lecture 211 Atomic Vector Types in R Programming

Lecture 212 Converting Data Types of Atomic Vectors in R Programming

Lecture 213 Test Functions in R Programming

Lecture 214 Vector Recycling and Iterations in R Programming

Lecture 215 Naming Vectors in R Programming

Lecture 216 Subsetting Vectors in R Programming

Section 27: Lists in R Programming

Lecture 217 Lists in R Programming

Section 28: Arrays in Python R Programming

Lecture 218 Arrays in Python R Programming

Lecture 219 Subsections of an Array in Python R Programming

Section 29: Matrices in Python R Programming

Lecture 220 Matrices in Python R Programming

Lecture 221 Naming Matrix Row and Columns in Python R Programming

Lecture 222 Calculating With Matrices in Python R Programming

Section 30: Data Frames in Python R Programming

Lecture 223 Introduction to Data Frames in Python R Programming

Lecture 224 Naming Variables and Observations in DF in Python R Programming

Lecture 225 Manipulating Values in DF

Lecture 226 Adding and Removing Variables in Python R Programming

Lecture 227 Tibbles in R

Section 31: Factors in Python R Programming

Lecture 228 Introduction to Factors

Lecture 229 Manipulating Categorical Data with Forcats

Section 32: Data Transformation in R

Lecture 230 Introduction to Data Transformation in R

Lecture 231 Select Columns with Select Function in R

Lecture 232 Filtering Rows with Filter Function in R

Lecture 233 Arranging Rows with Arrange Function in R

Lecture 234 Adding New Variables with Mutate Function in R

Lecture 235 Grouped Summaries with Summarize Function in R

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