This course shows you how to work on an end-to-end data science project including processing data, building & evaluating machine learning model, and exposing the model as an API in a standardized approach using various Python libraries.
Do you want to become a Data Scientist? If so, this course will equip you with concepts and tools that can bring you to speed and you can utilize the skills acquired in this course to work on any data science project in a standardized approach. This course, Doing Data Science with Python, follows a pragmatic approach to tackle end-to-end data science project cycle right from extracting data from different types of sources to exposing your machine learning model as API endpoints that can be consumed in a real-world data solution. This course will not only help you to understand various data science related concepts, but also help you to implement the concepts in an industry standard approach by utilizing Python and related libraries.
First, you will be introduced to the various stages of a typical data science project cycle and a standardized project template to work on any data science project. Then, you will learn to use various standard libraries in the Python ecosystem such as Pandas, NumPy, Matplotlib, Scikit-Learn, Pickle, Flask to tackle different stages of a data science project such as extracting data, cleaning and processing data, building and evaluating machine learning model. Finally you'll dive into exposing the machine learning model as APIs. You will also go through a case study that will encompass the whole course to learn end-to-end execution of a data science project. By the end of this course, you will have a solid foundation to handle any data science project and have the knowledge to apply various Python libraries to create your own data science solutions.
Is Python good for data science?
Yes! Python's robust libraries are ideal for manipulating data and it is a relatively easy language to learn for data analyst beginners!
Is Python better than R for data science?
Python and R are both great programming languages geared towards data science. However, Python is often easier for beginners, and is a more general purpose language with easy to read syntax. Python is better for raw data scraping, while R is more useful in analyzing already scrubbed data.
Will we be using Python libraries?
Yes. We will go over various standard Python libraries such as NumPy, Scikit-Learn, Pandas, Pickle, Matplotlib, and Flask to help with extracting, cleaning, and processing data, and building machine learning models.
What is data science with Python?
Simply put, it is a combination of statistical and machine learning techniques through the use of Python programming to help analyze and interpret data.
Are there prerequisites to this course?
Some previous exposure to Python or its libraries may come in handy, but is not required. Just come with an interest in data science.
Why learn data science?
Data science is a super popular field these days. Through data science we can find meaningful and valuable insights, and provide data-driven evidence to help organizations be more efficient and successful.
Course Overview Hi everyone. My name is Abhishek Kumar with Pluralsight, and welcome to my course on Doing Data Science with Python. Data science is one of the hottest fields these days, and no wonder data scientist has been termed as the sexiest job of the century, because with the help of data science you can unravel meaningful insights, and generate data-drive evidences that can benefit organizations in a significant way, and provide them a competitive edge. So if you also want to make a jump-start in this fascinating field of data science, or if you are already in this field and want to learn the standardized way of tackling end-to-end data science project cycle using Python, then this course is for you. In this course, we will dive into various phases of a data science project such as data extraction, data processing and visualization, building, evaluating, and fine-tuning predictive models, and finally, exposing your predictive models as APIs for real-time integration. The only prerequisite to this course is that you should be familiar with the basics of programming with Python. This course will not only help you to build the foundations of data science, but to also help you learn to implement the concepts through lots and lots of demos. by the end of this course, you'll be in a position of knowledge and skills necessary to kick start your data science journey in the Python world. So please join me in this very exciting course on Doing Data Science with Python, at Pluralsight.