Full Stack Data Science With Python, Numpy And R Programming

Learn data science with R programming and Python. Use NumPy, Pandas to manipulate the data and produce outcomes | R

What you'll learn
Learn R programming without any programming or data science experience. R programming, full stack data science, full stack data science with python numpy and r
If you are with a computer science or software development background you might feel more comfortable using Python for data science. R programming, full stack
In this course you will learn R programming, Python and Numpy from the beginning. R programming, full stack data science, full stack data science with python
Learn Fundamentals of Python for effectively using Data Science
Fundamentals of Numpy Library and a little bit more. R programming, full stack data science, full stack data science with python numpy and r programming
Data Manipulation with python, python data science, python machine learning, python pandas, data analysis, machine learning a-z
Learn how to handle with big data, python machine learning, python data science, r programming and python
Learn how to manipulate the data, data science, python machine learning, numpy python, numpy, python numpy,
Learn how to produce meaningful outcomes, r programming, data science, r python, python r, python and r programming, data science, python r
Learn Fundamentals of Python for effectively using Data Science
Learn Fundamentals of Python for effectively using Numpy Library
Numpy arrays with python
Numpy functions
Linear Algebra
Combining Dataframes, Data Munging and how to deal with Missing Data
How to use Matplotlib library and start to journey in Data Visualization
Also, why you should learn Python and Pandas Library
Learn Data Science with Python
Examine and manage data structures
Handle wide variety of data science challenges
Create, subset, convert or change any element within a vector or data frame
Most importantly you will learn the Mathematics beyond the Neural Network
The most important aspect of Numpy arrays is that they are optimized for speed. We’re going to do a demo where I prove to you that using a Numpy.
You will learn how to use the Python in Linear Algebra, and Neural Network concept, and use powerful machine learning algorithms
Use the “tidyverse” package, which involves “dplyr”, and other necessary data analysis package
OAK 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.
Data science is everywhere. Better data science practices are allowing corporations to cut unnecessary costs, automate computing, and analyze markets.
Data science is the key to getting ahead in a competitive global climate.
Data science uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction.
Data Scientists use machine learning to discover hidden patterns in large amounts of raw data to shed light on real problems.
Python is the most popular programming language for data science. It is a universal language that has a lot of libraries available.
Data science requires lifelong learning, so you will never really finish learning.
It is possible to learn data science on your own, as long as you stay focused and motivated. Luckily, there are a lot of online courses and boot camps available
Some people believe that it is possible to become a data scientist without knowing how to code, but others disagree.
A data scientist requires many skills. They need a strong understanding of statistical analysis and mathematics, which are essential pillars of data science.
The demand for data scientists is growing. We do not just have data scientists; we have data engineers, data administrators, and analytics managers.
The R programming language was created specifically for statistical programming. Many find it useful for data handling, cleaning, analysis, and representation.
R is a popular programming language for data science, business intelligence, and financial analysis. Academic, scientific, and non-profit researchers use the R
Whether R is hard to learn depends on your experience. After all, R is a programming language designed for mathematicians, statisticians, and business analysts
What is Python? Python is a general-purpose, object-oriented, high-level programming language.
Python vs. R: what is the Difference? Python and R are two of today's most popular programming tools.
What does it mean that Python is object-oriented? Python is a multi-paradigm language, which means that it supports many programming approaches.
What are the limitations of Python? Python is a widely used, general-purpose programming language, but it has some limitations.
How is Python used? Python is a general programming language used widely across many industries and platforms.
What jobs use Python? Python is a popular language that is used across many industries and in many programming disciplines.
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.
What is machine learning? Machine learning describes systems that make predictions using a model trained on real-world data.
Requirements
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 machine learning a-z, numpy python, data analysis, python pandas, pandas
Description
Hello Dear,Welcome to Full Stack Data Science with Python, Numpy, and R Programming course.R programming, r process automation, r programming language, python, machine learning python, python programming, python django, machine learning a-zLearn data science with R programming and Python. Use NumPy, Pandas to  manipulate the data and produce outcomes | ROAK 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.It’s hard to imagine our lives without machine learning. Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the iPhone’s Siri, are all technologies that function based on machine learning algorithms and mathematical models.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 instructors on 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.Do you want to learn Python from scratch?Do you think the transition from other popular programming languages like Java or C++ to Python for data science?Do you want to be able to make data analysis without any programming or data science experience?Why not see for yourself what you prefer? It may be hard to know whether to use Python or R for data analysis, both are great options. One language isn’t better than the other—it all depends on your use case and the questions you’re trying to answer.In this course, we offer R Programming, Python, and Numpy!  So you will decide which one you will learn.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, manipulate it, and produce meaningful outcomes.In the 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 course.In this course, you will also learn Numpy which is one of the most useful scientific libraries in Python programming.Throughout the course, we will teach you how to use the Python in Linear Algebra, and Neural Network concept, and use powerful machine learning algorithms and we will also do a variety of exercises to reinforce what we have learned in this Full Stack Data Science with Python, Numpy and R Programming course.At the end of the course, you will be able to select columns, filter rows, arrange the order, create new variables, 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 featuresNumpy functionsNumexpr moduleHow to do indexing and slicing on ArraysLinear AlgebraUsing NumPy in Neural NetworkHow 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 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 moreMachine learning, machine learning python, python, data science, python for data science and machine learning bootcamp, r, machine learning a-z, python data science, deep learningAnd we will do some exercises.  Finally, we will also have hands-on projects covering all of the Python subjects.What is machine learning?Machine learning describes systems that make predictions using a model trained on real-world data. For example, let's say we want to build a system that can identify if a cat is in a picture. We first assemble many pictures to train our machine learning model. During this training phase, we feed pictures into the model, along with information around whether they contain a cat. While training, the model learns patterns in the images that are the most closely associated with cats. This model can then use the patterns learned during training to predict whether the new images that it's fed contain a cat. In this particular example, we might use a neural network to learn these patterns, but machine learning can be much simpler than that. Even fitting a line to a set of observed data points, and using that line to make new predictions, counts as a machine learning model.What is machine learning used for?Machine learning is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing, and more. In any situation in which pattern recognition, prediction, and analysis are critical, machine learning can be of use. Machine learning is often a disruptive technology when applied to new industries and niches. Machine learning engineers can find new ways to apply machine learning technology to optimize and automate existing processes. With the right data, you can use machine learning technology to identify extremely complex patterns and yield highly accurate predictions.Does machine learning require coding?It's possible to use machine learning without coding, but building new systems generally requires code. For example, Amazon’s Rekognition service allows you to upload an image via a web browser, which then identifies objects in the image. This uses a pre-trained model, with no coding required. However, developing machine learning systems involves writing some Python code to train, tune, and deploy your models. It's hard to avoid writing code to pre-process the data feeding into your model. Most of the work done by a machine learning practitioner involves cleaning the data used to train the machine. They also perform “feature engineering” to find what data to use and how to prepare it for use in a machine learning model. Tools like AutoML and SageMaker automate the tuning of models. Often only a few lines of code can train a model and make predictions from it. An introductory understanding of Python will make you more effective in using machine learning systems.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.How long does it take to become a data scientist?This answer, of course, varies. The more time you devote to learning new skills, the faster you will learn. It will also depend on your starting place. If you already have a strong base in mathematics and statistics, you will have less to learn. If you have no background in statistics or advanced mathematics, you can still become a data scientist; it will just take a bit longer. Data science requires lifelong learning, so you will never really finish learning. A better question might be, "How can I gauge whether I know enough to become a data scientist?" Challenge yourself to complete data science projects using open data. The more you practice, the more you will learn, and the more confident you will become. Once you have several projects that you can point to as good examples of your skillset as a data scientist, you are ready to enter the field.How can ı learn data science on my own?It is possible to learn data science projects on your own, as long as you stay focused and motivated. Luckily, there are a lot of online courses and boot camps available. Start by determining what interests you about data science. If you gravitate to visualizations, begin learning about them. Starting with something that excites you will motivate you to take that first step. If you are not sure where you want to start, try starting with learning Python. It is an excellent introduction to programming languages and will be useful as a data scientist. Begin by working through tutorials or Udemy courses on the topic of your choice. Once you have developed a base in the skills that interest you, it can help to talk with someone in the field. Find out what skills employers are looking for and continue to learn those skills. When learning on your own, setting practical learning goals can keep you motivated.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.Is data science a good career?The demand for data scientists is growing. We do not just have data scientists; we have data engineers, data administrators, and analytics managers. The jobs also generally pay well. This might make you wonder if it would be a promising career for you. A better understanding of the type of work a data scientist does can help you understand if it might be the path for you. First and foremost, you must think analytically. Data science from scratch is about gaining a more in-depth understanding of info through data. Do you fact-check information and enjoy diving into the statistics? Although the actual work may be quite technical, the findings still need to be communicated. Can you explain complex findings to someone who does not have a technical background? Many data scientists work in cross-functional teams and must share their results with people with very different backgrounds.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.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 in data science , 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.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.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 trends.Video and Audio Production QualityAll our content are 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!Full Stack Data Science with Python, Numpy and R ProgrammingWe offer full support, answering any questions.See you in the course!

Overview

Section 1: Data Science: Python Setup

Lecture 1 Installing Anaconda Distribution For MAC: R programming, Numpy, Full stack

Lecture 2 Installing Anaconda Distribution For Windows: Numpy Python

Lecture 3 Installing Python and PyCharm For MAC: Full stack data science

Lecture 4 Installing Python and PyCharm For Windows: R programming, Numpy python

Lecture 5 Installing Jupyter Notebook For MAC

Lecture 6 Installing Jupyter Notebook For Windows: Full Stack Data Science, R programming

Lecture 7 Project Files and Course Documents: R programming, Data science, Numpy

Lecture 8 FAQ regarding Data Science: R and Python

Lecture 9 FAQ regarding Python and R

Section 2: If there are variables there is Python 3

Lecture 10 What is a variable: Numpy python, R Programming, Data Science

Section 3: Math is not so confusing with Python

Lecture 11 Numbers and Math Operators with example: Python data science

Section 4: Strings in Python Programming

Lecture 12 Strings and Operations

Lecture 13 Data type Conversion in Python

Lecture 14 Python: Exercise

Section 5: Conditionals in Python

Lecture 15 Conditionals in Python Programming

Lecture 16 Bool() Function in Python

Lecture 17 Comparison and logical Operators in Python

Lecture 18 If Statements in Python

Lecture 19 Exercise: Calculator in Python

Lecture 20 Exercise: User Login in Python

Section 6: Loops in Python

Lecture 21 Loops in Python

Lecture 22 While Loops in Python

Lecture 23 For Loops in Python

Lecture 24 Range Function in Python

Lecture 25 Control Statements in Python

Lecture 26 Exercise : Perfect Numbers in Python

Lecture 27 Exercise : User Login with Loops in Python

Section 7: Functions in Python Bootcamp

Lecture 28 Functions in Python Programming for Python Numpy

Lecture 29 Create A New Function and Function Calls for Python Numpy

Lecture 30 Return Statement in Python

Lecture 31 Lambda Functions in Python

Lecture 32 Exercise 9: Finding Prime Number in Python

Section 8: Modules in Python 3

Lecture 33 Logic of Using Modules in Python

Lecture 34 How It is Work in Python

Lecture 35 Create A New Module in Python

Lecture 36 Python Exercise: Number Game

Section 9: Lists in Python

Lecture 37 Lists and List Operations in Python

Lecture 38 List Methods in Python

Lecture 39 List Comprehensions in Python

Lecture 40 Exercise: Fibonacci Numbers in Python

Lecture 41 Exercise: Merging Name and Surname in Python

Section 10: Tuples in Python

Lecture 42 Tuples in Python

Section 11: Dictionaries in Python

Lecture 43 Dictionaries in Python

Lecture 44 Dictionary Comprehensions in Python

Lecture 45 Exercise : Letter Counter in Python

Lecture 46 Exercise : Word Counter in Python

Section 12: Exceptions in Numpy Python

Lecture 47 What is exception?

Lecture 48 Exception Handling in Python and R programming

Lecture 49 Python Exercise : if Number

Section 13: Files in Python

Lecture 50 Files in Python

Lecture 51 File Operations in Python

Lecture 52 Exercise : Team Building in Python

Lecture 53 Exercise : Overlap in Python

Section 14: Sets in Python

Lecture 54 Sets and Set Operations and Methods

Lecture 55 Set Comprehensions in Python

Section 15: Object Oriented Programming (OOP)

Lecture 56 Logic of OOP

Lecture 57 Constructer in Object Oriented Programming (OOP)

Lecture 58 Methods in Object Oriented Programming (OOP)

Lecture 59 Inheritance in Object Oriented Programming (OOP)

Lecture 60 Overriding and Overloading in Object Oriented Programming (OOP)

Section 16: Project Python

Lecture 61 Final Project: Remote Controller Application

Section 17: In Foreign Lands: Data Science

Lecture 62 What Is Data Science?

Lecture 63 Data literacy in Data Science

Section 18: Using Numpy for Data Manipulation

Lecture 64 What is Numpy?

Lecture 65 Why Numpy?

Lecture 66 Array and features in Numpy

Lecture 67 Array’s Operators in Numpy

Lecture 68 Numpy Functions in Numpy

Lecture 69 Indexing and Slicing in Numpy

Lecture 70 Numpy Exercises in Numpy

Lecture 71 Using Numpy in Linear Algebra in Numpy

Lecture 72 NumExpr Guide in Numpy

Lecture 73 Using Numpy with Creating Neural Network in Numpy

Section 19: Using Pandas for Data Manipulation

Lecture 74 What is Pandas?

Lecture 75 Series and Features

Section 20: Data Frame with Pandas

Lecture 76 Data Frame attributes and Methods Part – I in Pandas

Lecture 77 Data Frame attributes and Methods Part – II in Pandas

Lecture 78 Data Frame attributes and Methods Part – III in Pandas

Lecture 79 Multi index in Pandas Python

Lecture 80 Groupby Operations in Pandas Python

Lecture 81 Missing Data and Data Munging Part I in Pandas Python

Lecture 82 Missing Data and Data Munging Part II in Pandas Python

Lecture 83 Dealing with Missing Data in Pandas Python

Lecture 84 Combining Data Frames Part – I in Pandas Python

Lecture 85 Combining Data Frames Part – II in Pandas Python

Lecture 86 Work with Dataset Files in Pandas Python

Section 21: Python For Data Science: Data Visualization

Lecture 87 What is Matplotlib

Lecture 88 Using Matplotlib

Lecture 89 Pyplot – Pylab - Matplotlib

Lecture 90 Figure, Subplot and Axes in Python Matplotlib

Lecture 91 Figure Customization in Python Matplotlib

Lecture 92 Plot Customization in Python Matplotlib

Section 22: Data Science: Hands on Projects

Lecture 93 Analyse Data With Different Data Sets: Titanic Project

Lecture 94 Titanic Project Answers in Numpy Python

Lecture 95 Project II: Bike Sharing in Numpy Python

Lecture 96 Bike Sharing Project Answers in Numpy Python

Lecture 97 Project III: Housing and Property Sales in Numpy Python

Lecture 98 Answer for Housing and Property Sales Project in Numpy Python

Lecture 99 Project IV : English Premier League in Numpy Python

Lecture 100 Answers for English Premier League Project in Numpy Python

Section 23: Why You Should Learn R Programming Language

Lecture 101 Introduction to R

Section 24: Environment Installation for R

Lecture 102 R and R Studio Installation

Lecture 103 Installation and Hands-On Experience

Lecture 104 R Console Versus R Studio

Section 25: Basic Syntax in Python

Lecture 105 Basic Syntax and Hands On Experience

Section 26: Data Types in R

Lecture 106 Variables in R programming

Lecture 107 Vectors Basics in R programming

Lecture 108 Lists in R programming

Lecture 109 Matrices in r programming

Lecture 110 Arrays in R programming

Lecture 111 Factors in R programming

Lecture 112 Introduction to Data Frames in R programming

Section 27: Operators and Functions in R Programming

Lecture 113 Operators in R

Lecture 114 Flowcharts in R Programming

Lecture 115 Loops and Strings in R Programming

Lecture 116 Functions in R Programming

Section 28: R Packages in R Programming

Lecture 117 Managing R Packages

Section 29: Data Management in R

Lecture 118 Getting Data into R

Lecture 119 Data Manipulation in R

Lecture 120 Graphs and Charts in R

Section 30: Computation and Statistics in R

Lecture 121 Simple Math Functions in R programming

Lecture 122 Normal Probability Distribution in R

Lecture 123 Correlation in R programming

Lecture 124 Paired T-Test in R programming

Lecture 125 Linear Regression in R programming

Lecture 126 Multiple Regression in R programming

Lecture 127 Decision Trees in R programming

Lecture 128 Chi Square tests in R programming

Section 31: Experiential Learning in Python

Lecture 129 Learn with Real Examples - Experiential learning 1

Lecture 130 Learn with Real Examples - Experiential learning 2

Lecture 131 Learn with Real Examples - Experiential learning 3

Section 32: Examining and Managing Data Structures in R

Lecture 132 Atomic Vector Types in Python R Programming

Lecture 133 Converting Data Types of Atomic Vectors in Python R Programming

Lecture 134 Test Functions in Python R Programming

Lecture 135 Vector Recycling and Iterations in Python R Programming

Lecture 136 Naming Vectors in Python R Programming

Lecture 137 Subsetting Vectors in Python R Programming

Lecture 138 Subsections of an Array in Python R Programming

Section 33: Matrices in Python R Programming

Lecture 139 Naming Matrix Row and Columns in Python R Programming

Lecture 140 Calculating With Matrices in Python R Programming

Section 34: Data Frames in Python R Programming

Lecture 141 Naming Variables and Observations in DF in Python R Programming

Lecture 142 Manipulating Values in DF

Lecture 143 Adding and Removing Variables in Python R Programming

Lecture 144 Tibbles in R

Section 35: Factors in Python R Programming

Lecture 145 Introduction to Factors

Lecture 146 Manipulating Categorical Data with Forcats

Section 36: Data Transformation in R

Lecture 147 Introduction to Data Transformation

Lecture 148 Select Columns with Select Function in r

Lecture 149 Filtering Rows with Filter Function in python and r

Lecture 150 Arranging Rows with Arrange Function in R programming

Lecture 151 Adding New Variables with Mutate Function in R programming

Lecture 152 Grouped Summaries with Summarize Function in R programming

Section 37: DATA SCIENCE BONUS

Lecture 153 Full Stack Data Science with Python, Numpy and R Programming Bonus

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, numpy, data science, r abd python, machine learning,People who want to learn r programming, python, numpy, numpy python, full stack data science, data science