Full Stack Data Science with Python, Numpy and R Programming
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 19h 54m | 6.22 GB
Created by Oak Academy

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

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 beginning
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 Munging and how to deal with Missing Data
How to use MatDescriptionlib 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 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

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 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 MatDescriptionlib library such as
PyDescription, Pylab and MatDescriptionlb concepts
What Figure, SubDescription and Axes are
How to do figure and Description 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

Dive in now!
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, 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

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