Information Evaluation with Python

About this Course

Learn to analyze information utilizing Python. This course will take you from the fundamentals of Python to exploring many several types of information. You’ll discover ways to put together information for evaluation, carry out easy statistical evaluation, create significant information visualizations, predict future traits from information, and extra!

Subjects coated: 1) Importing Datasets 2) Cleansing the Information 3) Information body manipulation 4) Summarizing the Information 5) Constructing machine studying Regression fashions 6) Constructing information pipelines Information Evaluation with Python will likely be delivered by lecture, lab, and assignments. It consists of following elements: Information Evaluation libraries: will study to make use of Pandas, Numpy and Scipy libraries to work with a pattern dataset. We are going to introduce you to pandas, an open-source library, and we are going to use it to load, manipulate, analyze, and visualize cool datasets. Then we are going to introduce you to a different open-source library, scikit-learn, and we are going to use a few of its machine studying algorithms to construct sensible fashions and make cool predictions. Should you select to take this course and earn the Coursera course certificates, additionally, you will earn an IBM digital badge. LIMITED TIME OFFER: Subscription is simply $39 USD per thirty days for entry to graded supplies and a certificates.

This course is a part of a number of packages

This course will be utilized to a number of Specializations or Skilled Certificates packages. Finishing this course will depend in the direction of your studying in any of the next packages:

• IBM Information Science Skilled Certificates
• IBM Information Analyst Skilled Certificates

WHAT YOU WILL LEARN

• Describe Python information acquisition and evaluation methods.

• Analyze Python information utilizing a dataset.

• Determine three Python libraries and describe their makes use of.

• Learn information utilizing Python’s Pandas package deal.

SKILLS YOU WILL GAIN

• Predictive Modelling
• Python Programming
• Information Evaluation
• Information Visualization (DataViz)
• Mannequin Choice

Syllabus – What you’ll study from this course

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WEEK

1

2 hours to finish

Importing Datasets

On this module, you’ll discover ways to perceive information and study tips on how to use the libraries in Python that will help you import information from a number of sources. You’ll then discover ways to carry out some fundamental duties to start out exploring and analyzing the imported information set.

WEEK

2

2 hours to finish

Information Wrangling

On this module, you’ll discover ways to carry out some basic information wrangling duties that, collectively, type the pre-processing part of information evaluation. These duties embrace dealing with lacking values in information, formatting information to standardize it and make it constant, normalizing information, grouping information values into bins, and changing categorical variables into numerical quantitative variables.

WEEK

3

2 hours to finish

Exploratory Information Evaluation

On this module, you’ll study what is supposed by exploratory information evaluation, and you’ll discover ways to carry out computations on the info to calculate fundamental descriptive statistical info, resembling imply, median, mode, and quartile values, and use that info to higher perceive the distribution of the info. You’ll study placing your information into teams that will help you visualize the info higher, you’ll discover ways to use the Pearson correlation technique to check two steady numerical variables, and you’ll discover ways to use the Chi-square check to search out the affiliation between two categorical variables and tips on how to interpret them.

WEEK

4

2 hours to finish

Mannequin Improvement

On this module, you’ll discover ways to outline the explanatory variable and the response variable and perceive the variations between the easy linear regression and a number of linear regression fashions. You’ll discover ways to consider a mannequin utilizing visualization and study polynomial regression and pipelines. Additionally, you will discover ways to interpret and use the R-squared and the imply sq. error measures to carry out in-sample evaluations to numerically consider our mannequin. And lastly, you’ll study prediction and choice making when figuring out if our mannequin is appropriate.