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Full Stack Data Science With Python, Numpy And R Programming
Last updated 5/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 6.23 GB | Duration: 20h 11m
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 ly 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
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