Free python for data analysis course

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Free Course

Data Analysis Using NumPy and Pandas

About this Course

This course will introduce you to the world of data analysis. You'll learn how to go through the entire data analysis process, which includes:

  • Posing a question
  • Wrangling your data into a format you can use and fixing any problems with it
  • Exploring the data, finding patterns in it, and building your intuition about it
  • Drawing conclusions and/or making predictions
  • Communicating your findings

You'll also learn how to use the Python libraries NumPy, Pandas, and Matplotlib to write code that's cleaner, more concise, and runs faster.

This course is part of the Data Analyst Nanodegree.

Course Cost

Approx. 6 weeks

Skill Level

Included in Product

Rich Learning Content

Interactive Quizzes

Taught by Industry Pros

Self-Paced Learning

Course Leads

Caroline Buckey


What You Will Learn

Prerequisites and Requirements

To take this course, you should be comfortable programming in Python and familiar with Python concepts like classes, objects, and modules. The Introduction to Python Programming course would be a good place to start learning that material.

See the Technology Requirements for using Udacity.

Why Take This Course

This course is a good first step towards understanding the data analysis process as a whole. Before delving into each individual phase, it is important to learn the difference between all phases of the process and how they relate to each other. After taking this course, you will be better positioned to succeed in other courses in the Data Analyst Nanodegree program. For example, a student who started with Data Analysis with R, which covers the exploratory data analysis phase, might not understand at that point the difference between data exploration and data wrangling. By taking this course first, you will learn what each phase accomplishes and how it fits into the larger process.

This course also covers the Python libraries NumPy, Pandas, and Matplotlib, which are indispensable tools for doing data analysis in Python. Their many convenient functions and high performance make writing data analysis code a lot easier!

What do I get?

  • Instructor videos
  • Learn by doing exercises
  • Taught by industry professionals

Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.


Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more!

Topics covered:

1) Importing Datasets
2) Cleaning the Data
3) Data frame manipulation
4) Summarizing the Data
5) Building machine learning Regression models
6) Building data pipelines

Data Analysis with Python will be delivered through lecture, lab, and assignments. It includes following parts:

Data Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with a sample dataset. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions.

If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge.

LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.


  • Importing Datasets
    • 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.
  • Data Wrangling
    • 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.
  • Exploratory Data Analysis
    • 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.
  • Model Development
    • 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.
  • Model Evaluation
    • 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.
  • Final Assignment
    • 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.

Taught by

Joseph Santarcangelo


4.0 rating, based on 1 Class Central review

4.7 rating at Coursera based on 15444 ratings

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  • Berbelek completed this course, spending 5 hours a week on it and found the course difficulty to be medium.

    It takes a few hours to complete. The course provides some basic lessons on working with data in Python. I think there are some better introductions available.

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Can I do data analysis in Python?

In the Data Analysis with Python Certification, you'll learn the fundamentals of data analysis with Python. By the end of this certification, you'll know how to read data from sources like CSVs and SQL, and how to use libraries like Numpy, Pandas, Matplotlib, and Seaborn to process and visualize data.

Is there any free course for Python?

Learn Python - Full Course for Beginners In this freeCodeCamp YouTube Course, you will learn programming basics such as lists, conditionals, strings, tuples, functions, classes and more. You will also build several small projects like a basic calculator, mad libs game, a translator app, and a guessing game.