Data science is quickly becoming one of the most promising careers in the twenty-first century. It’s automated, program-driven, and analytical. As a result, it’s no surprise that the demand for data scientists has been increasing in the job market over the last few years.
This course begins with a quick refresher on Python fundamentals; however, if you’re already familiar with Python, you can skip to the next chapter.
The next three sections dive deep into data science, starting with the essential Python libraries for data science, progressing toward fundamental NumPy properties, mathematics, and its applications in data science.
Once you’ve gained insights into data science, you’ll learn about Python Pandas DataFrames and series, followed bydata cleaning. Next, you'll learn how to use Python to visualize data and leverage Python for data analysis on some sample datasets. Finally, you’ll cover time series in Python and learn how to work with and convert datasets to time series.
By the end of this course, you’ll be able to execute data manipulation for data science and analytics in Python with ease.
A quick refresher to Python fundamentals
Learn to use Pandas for data analysis
Learn to work with numerical data in Python
Learn statistics and math with Python
Learn how to code in Jupyter Notebook
Learn how to install packages in Python