What you'll learn

Learn R programming without any programming or data science experience

If you are with a computer science or software development background you might feel more comfortable using Python for data science

In this course you will learn R programming, Python and Numpy from the bning

Learn Fundamentals of Python for effectively using Data Science

Fundamentals of Numpy Library and a little bit more

Data Manipulation

Learn how to handle with big data

Learn how to manipulate the data

Learn how to produce meaningful outcomes

Learn Fundamentals of Python for effectively using Data Science

Learn Fundamentals of Python for effectively using Numpy Library

Numpy arrays

Numpy functions

Linear Algebra

Combining Dataframes, Data Mug 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

We're going to do a demo where I prove to you that using a Numpy vectorized operation is faster than using a Python list.

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

Requirements

No prior 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

Description

Welcome to Full Stack Data Science with Python, Numpy, and R Programming course.

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 the 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 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

Numpy functions

Numexpr module

How to do indexing and slicing on Arrays

Linear Algebra

Using NumPy in Neural Network

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 Mug

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

And we will do many exercises. Finally, we will also have hands-on projects covering all of the 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 trends.

Video and Audio Production Quality

All our content are 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:

Life Access to The Course

Fast & Friendly Support in the Q&A section

Udemy Certificate of Completion Ready for

Dive in now!

We offer full support, answering any questions.

See you in the course!

Who this course is for:

Anyone interested in data sciences

Anyone who plans a career in data scientist,

Software developer whom want to learn data science,

Anyone eager to learn Data Science with no coding background

Statisticians, acad 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



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