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 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 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 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
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:
- Lifetime Access to The Course
- Fast & Friendly Support in the Q&A section
- Udemy Certificate of Completion Ready for Download
- 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, 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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