IBM

Data Analysis with Python

This course is part of multiple programs.

Taught in English

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417,685 already enrolled

Course

Gain insight into a topic and learn the fundamentals

4.7

(17,676 reviews)

|

94%

Beginner level

Recommended experience

15 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Develop Python code for cleaning and preparing data for analysis - including handling missing values, formatting, normalizing, and binning data

  • Perform exploratory data analysis and apply analytical techniques to real-word datasets using libraries such as Pandas, Numpy and Scipy

  • Manipulate data using dataframes, summarize data, understand data distribution, perform correlation and create data pipelines

  • Build and evaluate regression models using machine learning scikit-learn library and use them for prediction and decision making

Details to know

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Assessments

11 assignments

Course

Gain insight into a topic and learn the fundamentals

4.7

(17,676 reviews)

|

94%

Beginner level

Recommended experience

15 hours (approximately)
Flexible schedule
Learn at your own pace

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There are 6 modules in this course

In this module, you will learn how to understand data and learn about how to use the libraries in Python to help you import data from multiple sources. You will then learn how to perform some basic tasks to start exploring and analyzing the imported data set.

What's included

6 videos1 reading2 assignments2 app items2 plugins

In this module, you will learn how to perform some fundamental data wrangling tasks that, together, form the pre-processing phase of data analysis. These tasks include handling missing values in data, formatting data to standardize it and make it consistent, normalizing data, grouping data values into bins, and converting categorical variables into numerical quantitative variables.

What's included

6 videos1 reading2 assignments2 app items1 plugin

In this module, you will learn what is meant by exploratory data analysis, and you will learn how to perform computations on the data to calculate basic descriptive statistical information, such as mean, median, mode, and quartile values, and use that information to better understand the distribution of the data. You will learn about putting your data into groups to help you visualize the data better, you will learn how to use the Pearson correlation method to compare two continuous numerical variables, and you will learn how to use the Chi-square test to find the association between two categorical variables and how to interpret them.

What's included

5 videos1 reading2 assignments2 app items2 plugins

In this module, you will learn how to define the explanatory variable and the response variable and understand the differences between the simple linear regression and multiple linear regression models. You will learn how to evaluate a model using visualization and learn about polynomial regression and pipelines. You will also learn how to interpret and use the R-squared and the mean square error measures to perform in-sample evaluations to numerically evaluate our model. And lastly, you will learn about prediction and decision making when determining if our model is correct.

What's included

6 videos1 reading2 assignments2 app items1 plugin

In this module, you will learn about the importance of model evaluation and discuss different data model refinement techniques. You will learn about model selection and how to identify overfitting and underfitting in a predictive model. You will also learn about using Ridge Regression to regularize and reduce standard errors to prevent overfitting a regression model and how to use the Grid Search method to tune the hyperparameters of an estimator.

What's included

4 videos1 reading2 assignments2 app items2 plugins

Congratulations! You have now completed all the modules for this course. In this last module, you will complete the final assignment that will be graded by your peers. In this final assignment, you will assume the role of a Data Analyst working at a real estate investment trust organization who wants to start investing in residential real estate. You will be given a dataset containing detailed information about house prices in the region based on a number of property features, and it will be your job to analyze and predict the market price of houses given that information.

What's included

5 readings1 assignment1 peer review2 app items1 plugin

Instructor

Instructor ratings
4.6 (2,786 ratings)
Joseph Santarcangelo
IBM
25 Courses1,310,188 learners

Offered by

IBM

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4.7

17,676 reviews

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