Introduction to Knowledge Science in Python

About this Course

This course will introduce the learner to the fundamentals of the python programming setting, together with elementary python programming methods reminiscent of lambdas, studying and manipulating csv information, and the numpy library. The course will introduce information manipulation and cleansing methods utilizing the favored python pandas information science library and introduce the abstraction of the Collection and DataFrame because the central information constructions for information evaluation, together with tutorials on the way to use capabilities reminiscent of groupby, merge, and pivot tables successfully. By the top of this course, college students will be capable to take tabular information, clear it, manipulate it, and run primary inferential statistical analyses.

This course needs to be taken earlier than any of the opposite Utilized Knowledge Science with Python programs: Utilized Plotting, Charting & Knowledge Illustration in Python, Utilized Machine Studying in Python, Utilized Textual content Mining in Python, Utilized Social Community Evaluation in Python.

WHAT YOU WILL LEARN

• Perceive methods reminiscent of lambdas and manipulating csv information

• Describe widespread Python performance and options used for information science

• Question DataFrame constructions for cleansing and processing

• Clarify distributions, sampling, and t-tests

SKILLS YOU WILL GAIN

• Python Programming
• Numpy
• Pandas
• Knowledge Cleaning

Syllabus – What you’ll be taught from this course

Content material Ranking92%(71,575 rankings)

WEEK

1

12 hours to finish

Fundamentals of Knowledge Manipulation with Python

On this week you’ll get an introduction to the sector of knowledge science, assessment widespread Python performance and options which information scientists use, and be launched to the Coursera Jupyter Pocket book for the lectures. The entire course info on grading, conditions, and expectations are on the course syllabus, and you will discover extra details about the Jupyter Notebooks on our Course Sources web page.

WEEK

2

6 hours to finish

Fundamental Knowledge Processing with Pandas

On this week of the course you’ll be taught the basics of some of the necessary toolkits Python has for information cleansing and processing — pandas. You’ll learn to learn in information into DataFrame constructions, the way to question these constructions, and the small print about such constructions are listed.

WEEK

3

7 hours to finish

Extra Knowledge Processing with Pandas

On this week you’ll deepen your understanding of the python pandas library by studying the way to merge DataFrames, generate abstract tables, group information into logical items, and manipulate dates. We’ll additionally refresh your understanding of scales of knowledge, and focus on points with creating metrics for evaluation. The week ends with a extra important programming project.

WEEK

4

6 hours to finish

Answering Questions with Messy Knowledge

On this week of the course you’ll be launched to quite a lot of statistical methods such a distributions, sampling and t-tests. The week ends with two discussions of science and the rise of the fourth paradigm — information pushed discovery.