Data Science with R and Python | R Programming

Welcome to Data Science with R and Python | R Programming course.

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,

Python and R programming! Learn data science with R & Python all in one course. You'll learn NumPy, Pandas, and more

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, Udemy has a course for you.

Ready 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.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 as

• Datatypes in Python,

• Lots of datatype operators, methods, and how to use them,

• Conditional concept, if statements

• The logic of Loops and control statements

• Functions and how to use them

• How to use modules and create your own modules

• Data science and Data literacy concepts

• Fundamentals of Numpy for Data manipulation such as

• Numpy arrays and their features

• How to do indexing and slicing on Arrays

• Lots of stuff about Pandas for data manipulation such as

• Pandas series and their features

• Dataframes and their features

• Hierarchical indexing concept and theory

• Groupby operations

• The logic of Data Munging

• How to deal effectively with missing data effectively

• Combining the Data Frames

• How to work with Dataset files

• And also you will learn fundamentals thing about the Matplotlib library such as

• Pyplot, Pylab and Matplotlb concepts

• What Figure, Subplot, and Axes are

• How to do figure and plot customization

• Examining and Managing Data Structures in R

• Atomic vectors

• Lists

• Arrays

• Matrices

• Data frames

• Tibbles

• Factors

• Data Transformation in R

• Transform and manipulate a deal data

• Tidyverse and more

And we will do many exercises. Finally, we will also have 4 different final projects covering all of Python subjects.

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 Quality

All our content is created/produced as high-quality video/audio to provide you the best learning experience.

You will be,

• Seeing clearly

• Hearing clearly

• Moving through the course without distractions

  You'll also get:

• Lifetime Access to The Course

• Fast & Friendly Support in the Q&A section

Reed courses certificate of completion

Digital certificate - Included

• Data Science: Python is Easy To Learn 05:51

  • 1: Be Smart and Use Data But How: Answer is Data Science with Python 04:51
  • 2: Project Files and Course Documents for Data Science with Python and R 01:00

• Setting Up Python for Mac and Windows : Python, Data science, R programming 17:47

  • 3: Installing Anaconda for Windows 06:16
  • 4: Installing Anaconda for Mac 06:42
  • 5: Let's Meet Jupyter Notebook for Windows 02:21
  • 6: Basics of Jupyter Notebook for Mac 02:28

• Fundamentals of Python 1:42:16

  • 7: Data Types in Python 12:42
  • 8: Operators in Python 10:31
  • 9: Conditionals 09:49
  • 10: Loops 13:07
  • 11: Lists, Tuples, Dictionaries and Sets 17:54
  • 12: Data Type Operators and Methods 11:21
  • 13: Modules in Python 05:15
  • 14: Functions in Python 08:05
  • 15: Exercise Analyse 01:46
  • 16: Exercise Solution 10:46
  • 17: quiz 01:00

• Python For Data Science: Data Science 09:47

  • 18: What Is Data Science? 05:39
  • 19: Data Literacy 03:08
  • 20: quiz 01:00

• Using Numpy for Data Manipulation 3:25:35

  • 21: INTRODUCTION TO NUMPY LIBRARY 06:24
  • 22: THE POWER OF NUMPY 16:04
  • 23: CREATE A NUMPY ARRAY WITH THE ARRAY() FUNCTION 08:16
  • 24: CREATE A NUMPY ARRAY WITH THE ZEROS() FUNCTION 05:05
  • 25: CREATE A NUMPY ARRAY WITH THE ONES() FUNCTION 03:06
  • 26: CREATE A NUMPY ARRAY WITH THE FULL() FUNCTION 02:49
  • 27: CREATE A NUMPY ARRAY WITH THE ARANGE() FUNCTION 02:55
  • 28: CREATE A NUMPY ARRAY WITH THE EYE() FUNCTION 03:08
  • 29: CREATE A NUMPY ARRAY WITH THE LINSPACE() FUNCTION 01:31
  • 30: CREATE A NUMPY ARRAY WITH THE RANDOM() FUNCTION 08:29
  • 31: Properties of NumPy Array 05:24
  • 32: RESHAPING A NUMPY ARRAY RESHAPE() FUNCTION 05:57
  • 33: Identifying the Largest Element of a Numpy Array Max(), Argmax() Functions 03:45
  • 34: Detecting Least Element of Numpy Array Min(), Argmin() Functions 02:35
  • 35: Concatenating Numpy Arrays Concatenate() Function 09:40
  • 36: Splitting One-Dimensional Numpy Arrays The Split() Function 05:46
  • 37: Splitting Two-Dimensional Numpy Arrays Split(), Vsplit, Hsplit() Function 09:33
  • 38: Sorting Numpy Arrays Sort() Function 04:16
  • 39: Indexing Numpy Arrays 07:39
  • 40: Slicing One-Dimensional Numpy Arrays 06:08
  • 41: Slicing Two-Dimensional Numpy Arrays 09:30
  • 42: Assigning Value to One-Dimensional Arrays 05:02
  • 43: Assigning Value to two-Dimensional Arrays 09:57
  • 44: Fancy Indexing of One-Dimensional Arrrays 06:09
  • 45: Fancy Indexing of Two-Dimensional Arrrays 12:32
  • 46: Combining Fancy Index with Normal Indexing 03:25
  • 47: Combining Fancy Index with Normal Slicing 04:36
  • 48: Operations with Comparison Operators 06:09
  • 49: Arithmetic Operations in Numpy 15:10
  • 50: Statistical Operations in Numpy 06:35
  • 51: Solving second-degree equations with NumPy 07:00
  • 52: Quiz 01:00

• (Optional) Recap, Exercises, and Bonus Info from the Numpy Library 51:08

  • 53: What is Numpy? 06:49
  • 54: Array and Features 12:08
  • 55: Array Operators 04:53
  • 56: Indexing and Slicing 10:15
  • 57: Numpy Exercises 16:03
  • 58: Quiz 01:00

• Pandas: Using Pandas for Data Manipulation 6:57:21

  • 59: Introduction to Pandas Library 06:38
  • 60: Creating a Pandas Series with a List 10:21
  • 61: Creating a Pandas Series with a Dictionary 04:53
  • 62: Creating Pandas Series with NumPy Array 03:10
  • 63: Object Types in Series 05:14
  • 64: Examining the Primary Features of the Pandas Series 04:55
  • 65: Most Applied Methods on Pandas Series 12:53
  • 66: Indexing and Slicing Pandas Series 07:13
  • 67: Creating Pandas DataFrame with List 05:33
  • 68: Creating Pandas DataFrame with NumPy Array 03:03
  • 69: Creating Pandas DataFrame with Dictionary 04:01
  • 70: Examining the Properties of Pandas DataFrames 06:32
  • 71: Element Selection Operations in Pandas DataFrames Lesson 1 07:41
  • 72: Element Selection Operations in Pandas Data Frames Lesson 2 06:04
  • 73: Top Level Element Selection in Pandas DataFrames Structure of loc and iloc Le 08:42
  • 74: Top Level Element Selection in Pandas DataFrames Structure of loc and iloc Le 07:33
  • 75: Top Level Element Selection in Pandas DataFrames Structure of loc and iloc Le 05:35
  • 76: Element Selection with Conditional Operations in Pandas Data Frames 11:23
  • 77: Adding Columns to Pandas Data Frames 08:16
  • 78: Removing Rows and Columns from Pandas Data frames 04:00
  • 79: Null Values in Pandas Dataframes 14:42
  • 80: Dropping Null Values Dropna() Function 07:14
  • 81: Filling Null Values0 Fillna() Function 11:36
  • 82: Setting Index in Pandas DataFrames 07:03
  • 83: Multi-Index and Index Hierarchy in Pandas DataFrames 09:17
  • 84: Element Selection in Multi-Indexed DataFrames 05:12
  • 85: Selecting Elements Using the xs() Function in Multi-Indexed DataFrames 07:03
  • 86: 28 Concatenating Pandas Dataframes Concat() Function 12:40
  • 87: Merge Pandas Dataframes Merge() Function Lesson 1 10:45
  • 88: Merge Pandas Dataframes Merge() Function Lesson 2 05:37
  • 89: Merge Pandas Dataframes Merge() Function Lesson 3 09:44
  • 90: Merge Pandas Dataframes Merge() Function Lesson 4 07:34
  • 91: Joining Pandas Dataframes Join() Function 11:41
  • 92: Loading a Dataset from the Seaborn Library 06:41
  • 93: Examining the Data Set 07:29
  • 94: Aggregation Functions in Pandas DataFrames 21:45
  • 95: Examining the Dataset 10:38
  • 96: Coordinated Use of Grouping and Aggregation Functions in Pandas Dataframes 18:14
  • 97: Advanced Aggregation Functions Aggregate() Function 07:40
  • 98: Advanced Aggregation Functions Filter() Function 06:30
  • 99: Advanced Aggregation Functions The Transform() Function 11:38
  • 100: Advanced Aggregation Functions The Apply() Function 10:06
  • 101: Examining the Dataset 08:14
  • 102: Pivot Tables in Pandas Library 10:35
  • 103: Accessing and Making Files Available 05:11
  • 104: Data Entry with Csv and Txt Files 13:35
  • 105: Data Entry with Excel Files 04:25
  • 106: Output of File with CSV Extension 07:09
  • 107: Outputting as an Excel File 03:43
  • 108: quiz 10:00

• (Optional) Recap, Exercises, and Bonus Info from the Pandas Library 3:15:45

  • 109: What is Pandas? 05:48
  • 110: Series and Features 20:06
  • 111: Data Frame Attributes and Methods 18:14
  • 112: Data Frame Attributes and Methods Part – II 13:04
  • 113: Data Frame Attributes and Methods Part – III 11:38
  • 114: Multi Index 11:59
  • 115: Groupby Operations 13:30
  • 116: Missing Data and Data Munging 21:08
  • 117: Missing Data and Data Munging Part II 10:37
  • 118: How We Deal with Missing Data? 17:19
  • 119: Combining Data Frames 20:25
  • 120: Combining Data Frames Part – II 19:28
  • 121: Work with Dataset Files 11:29
  • 122: quiz 01:00

• Python For Data Science: Data Visualization 57:50

  • 123: What is Matplotlib? 03:02
  • 124: Using Matplotlib 07:30
  • 125: Pyplot – Pylab - Matplotlib 07:19
  • 126: Figure, Subplot and Axes 17:28
  • 127: Figure Customization 14:47
  • 128: Plot Customization 06:44
  • 129: quiz 01:00

• Data Science: Hands-On Projects 2:03:12

  • 130: Analyse Data With Different Data Sets: Titanic Project 03:42
  • 131: Titanic Project Answers 19:54
  • 132: Project II: Bike Sharing 04:24
  • 133: Bike Sharing Project Answers 27:45
  • 134: Project III: Housing and Property Sales 03:18
  • 135: Answer for Housing and Property Sales Project 30:06
  • 136: Project IV : English Premier League 04:22
  • 137: Answers for English Premier League Project 29:41

• Environment Installation for R 08:04

  • 138: Downloading and Installing R _ RStudio 03:27
  • 139: R Console Versus R Studio 04:37

• Data Management in R 34:58

  • 140: Getting Data into R 06:45
  • 141: Data Manipulation 08:47
  • 142: Graphs and Charts 18:26
  • 143: quiz 01:00

• Examining and Managing Data Structures in R 30:46

  • 144: Vector Basics 06:05
  • 145: Atomic Vector Types 03:50
  • 146: Converting Data Types of Atomic Vectors 04:03
  • 147: Test Functions 01:32
  • 148: Vector Recycling and Iterations 04:53
  • 149: Naming Vectors 04:30
  • 150: Subsetting Vectors 05:53

• Lists in R Programming 05:54

  • 151: Lists 05:54

• Arrays in Python R Programming 13:34

  • 152: Arrays 04:37
  • 153: Subsections of an Array 08:57

• Matrices in Python R Programming 19:02

  • 154: Matrices 06:54
  • 155: Naming Matrix Row and Columns 05:33
  • 156: Calculating With Matrices 06:35

• Data Frames in Python R Programming 36:30

  • 157: Introduction to Data Frames 07:19
  • 158: Naming Variables and Observations in DF 02:29
  • 159: Manipulating Values in DF 13:56
  • 160: Adding and Removing Variables 03:58
  • 161: Tibbles in R 08:48

• Factors in Python R Programming 16:41

  • 162: Introduction to Factors 04:32
  • 163: Manipulating Categorical Data with Forcats 12:09

• Data Transformation in R 1:06:56

  • 164: Introduction to Data Transformation 08:06
  • 165: Select Columns with Select Function 07:06
  • 166: Filtering Rows with Filter Function 16:21
  • 167: Arranging Rows with Arrange Function 11:36
  • 168: Adding New Variables with Mutate Function 06:51
  • 169: Grouped Summaries with Summarize Function 16:56

Welcome to Data Science with R and Python | R Programming course.

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,

Python and R programming! Learn data science with R & Python all in one course. You'll learn NumPy, Pandas, and more

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, Udemy has a course for you.

Ready 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.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 as

• Datatypes in Python,

• Lots of datatype operators, methods, and how to use them,

• Conditional concept, if statements

• The logic of Loops and control statements

• Functions and how to use them

• How to use modules and create your own modules

• Data science and Data literacy concepts

• Fundamentals of Numpy for Data manipulation such as

• Numpy arrays and their features

• How to do indexing and slicing on Arrays

• Lots of stuff about Pandas for data manipulation such as

• Pandas series and their features

• Dataframes and their features

• Hierarchical indexing concept and theory

• Groupby operations

• The logic of Data Munging

• How to deal effectively with missing data effectively

• Combining the Data Frames

• How to work with Dataset files

• And also you will learn fundamentals thing about the Matplotlib library such as

• Pyplot, Pylab and Matplotlb concepts

• What Figure, Subplot, and Axes are

• How to do figure and plot customization

• Examining and Managing Data Structures in R

• Atomic vectors

• Lists

• Arrays

• Matrices

• Data frames

• Tibbles

• Factors

• Data Transformation in R

• Transform and manipulate a deal data

• Tidyverse and more

And we will do many exercises. Finally, we will also have 4 different final projects covering all of Python subjects.

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 Quality

All our content is created/produced as high-quality video/audio to provide you the best learning experience.

You will be,

• Seeing clearly

• Hearing clearly

• Moving through the course without distractions

  You'll also get:

• Lifetime Access to The Course

• Fast & Friendly Support in the Q&A section

• Anyone interested in data sciences
• Anyone who plans a career in data scientist,
• Software developer whom want to learn python,
• Anyone eager to learn python and r with no coding background
• Statisticians, academic researchers, economists, analysts and business people
• Professionals working in analytics or related fields
• Anyone who is particularly interested in big data, machine learning and data intelligence
• Anyone eager to learn Python with no coding background
• Anyone who wants to learn Pandas
• Anyone who wants to learn Numpy
• Anyone who wants to work on real r and python projects
• Anyone who wants to learn data visualization projects.
• People who want to learn R programming, r studio

• No prior python and r knowledge is required

• Free software and tools used during the course

• Basic computer knowledge

• Desire to learn data science

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

• Curiosity for r programming

• Desire to learn Python

• Desire to work on r and python

• Desire to learn full stack data science with python, python and r, r programming, data science with r, r python,

• Desire to learn r and python

• Desire to data science r and python

• Desire to learn python r data science

Currently there are no Q&As for this course. Be the first to ask a question.