Welcome to Complete Python Data Science, Deep Learning, R Programming course.
Python Data Science A-Z, Data Science with Machine Learning A-Z, Deep Learning A-Z, Pandas, Numpy and R Statistics

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Data Science A-Z, Python Data Science with Machine Learning, Deep Learning, Pandas, Numpy, Data visualization, and R

Ready for the 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.
Train up with a top-rated data science course on Udemy. Gain in-demand skills and help organizations forecast product and service demands for the future. From machine learning to data mining to data analysis, we’ve got a data science course to help you progress on your career path.
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 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, group by and summarize your data simultaneously.

Because data can mean an endless number of things, it’s important to choose the right visualization tools for the job. Whether you’re interested in learning Tableau, D3.js, After Effects, or Python, Udemy has a course for you.

In this course, we will learn what is data visualization and how does it work with python.

This course has suitable for everybody who is interested data visualization concept.

First of all, in this course, we will learn some fundamentals of pyhton, and object oriented programming ( OOP ). These are our first steps in our Data Visualisation journey. After then we take a our journey to Data Science world. Here we will take a look data literacy and data science concept. Then we will arrive at our next stop. Numpy library. Here we learn the what is numpy and how we can use it. After then we arrive at our next stop. Pandas library. And now our journey becomes an adventure. In this adventure we'll enter the Matplotlib world then we exit the Seaborn world. Then we'll try to understand how we can visualize our data, data viz. But our journey won’t be over. Then we will arrive our final destination. Geographical drawing or best known as Geoplotlib in tableau data visualization.

Learn python and how to use it to python data analysis and visualization, present data. Includes tons of code data vizualisation.

In this course, you will learn data analysis and visualization in detail.

Also during the course you will learn:

The Logic of Matplotlib

• What is Matplotlib

• Using Matplotlib

• Pyplot – Pylab - Matplotlib - Excel

• Figure, Subplot, Multiplot, Axes,

• Figure Customization

• Plot Customization

• Grid, Spines, Ticks

• Basic Plots in Matplotlib

• Overview of Jupyter Notebook and Google Colab

  
  

• Seaborn library with these topics

  • What is Seaborn

  • Controlling Figure Aesthetics

  • Color Palettes

  • Basic Plots in Seaborn

  • Multi-Plots in Seaborn

  • Regression Plots and Squarify

  
  

• Geoplotlib with these topics

  • What is Geoplotlib

  • Tile Providers and Custom Layers

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 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

• Python

• Python data science

• R, r programming

• r statistics

• machine learning

• deep learning

• numpy

• pandas

This course has suitable for everybody who interested in Machine Learning and Deep Learning concepts in Data Science.

First of all, in this course, we will learn some fundamental stuff of Python and the Numpy library. These are our first steps in our Deep Learning journey. After then we take a little trip to Machine Learning Python history. Then we will arrive at our next stop. Machine Learning in Python Programming. Here we learn the machine learning concepts, machine learning a-z workflow, models and algorithms, and what is neural network concept. After then we arrive at our next stop. Artificial Neural network. And now our journey becomes an adventure. In this adventure we'll enter the Keras world then we exit the Tensorflow world. Then we'll try to understand the Convolutional Neural Network concept. But our journey won't be over. Then we will arrive at Recurrent Neural Network and LTSM. We'll take a look at them. After a while, we'll trip to the Transfer Learning concept. And then we arrive at our final destination. Projects in Python Bootcamp. Our play garden. Here we'll make some interesting machine learning models with the information we've learned along our journey.

In this course, we will start from the very beginning and go all the way to the end of "Deep Learning" with examples.

The Logic of Machine Learning such as Machine Learning models and algorithms, Gathering data, Data pre-processing, Training and testing the model etc.

Before we start this course, we will learn which environments we can be used for developing deep learning projects.

• Artificial Neural Network with these topics

  • What is ANN

  • Anatomy of NN

  • Tensor Operations

  • The Engine of NN

  • Keras

  • Tensorflow

• Convolutional Neural Network

• Recurrent Neural Network and LTSM

• Transfer Learning

• Reinforcement Learning

And 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 uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction. 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 includes preparing, analyzing, and processing data. It draws from many scientific fields, and as a 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 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 Python very quickly.

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.

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 is Python?

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 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, 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 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. The concept of combining data with functionality in an object is called encapsulation, a core concept in the object-oriented programming paradigm.

I am glad that I took this course. There was always something to learn in every lesson. The Jupyter notebooks provided are very helpful. The two milestone projects and the final capstone project helped me gain a lot of confidence. Moreover, there were short challenges, assignments, and quizzes which also helped a lot.

This course's approach is very practical and easy to understand, especially for beginners. Thank you for the excellent course.

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

• Udemy Certificate of Completion Ready for Download

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Complete Python Data Science, Deep Learning, R Programming

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See you in the course!