Welcome to Full Stack Data Science R and Python with Hands-on Projects course.
Ready for the Data Science career?
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.
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 to 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, manipulate it, and produce meaningful outcomes.
Throughout the course's 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 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.
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
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
Data Transformation in R
Transform and manipulate a deal data
Tidyverse and more
And we will do many exercises. Finally, we will also have 4 different final projects covering all of 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.
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,
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!
- 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
What you will learn
- R and Python in the same course. You decide which one you would go for!
- R was built as a statistical language, it suits much better to do statistical learning and R is a statistical programming software favoured by many academia
- If you have some programming experience, Python might be the language for you
- Since R was built as a statistical language, it suits much better to do statistical learning.
- You will learn R and Python from scratch
- Learn Fundamentals of Python for effectively using Data Science
- 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
- Numpy arrays
- Series and Features
- 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
- Select columns and filter rows
- Arrange the order and create new variables
- Create, subset, convert or change any element within a vector or data frame
- Transform and manipulate an existing and real data.
- 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
TAKE THIS COURSE
Who should attend
- Anyone interested in data sciences
- Anyone who plans a career in data scientist,
- Software developer whom want to learn python,
- Anyone eager to learn python and r 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
- Anyone eager to learn Python with no coding background
- Anyone who wants to learn Pandas
- Anyone who wants to learn Numpy
- Anyone who wants to work on real r and python projects
- Anyone who wants to learn data visualization projects.