Data Analysis with Python
Self-paced course
Certification program
Price
Rating
Overview
Analyzing data with Python is an essential skill for Data Scientists and Data Analysts. This course will take you from the basics of data analysis with Python to building and evaluating data models.
Topics covered include: - collecting and importing data - cleaning, preparing & formatting data - data frame manipulation - summarizing data - building machine learning regression models - model refinement - creating data pipelines
You will learn how to import data from multiple sources, clean and wrangle data, perform exploratory data analysis (EDA), and create meaningful data visualizations. You will then predict future trends from data by developing linear, multiple, polynomial regression models & pipelines and learn how to evaluate them.
In addition to video lectures you will learn and practice using hands-on labs and projects. You will work with several open source Python libraries, including Pandas and Numpy to load, manipulate, analyze, and visualize cool datasets. You will also work with scipy and scikit-learn, to build machine learning models and make predictions.
If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge.
Thanks for course! I met some errors, described them in your forms. I liked every models, but the final assignment was not interesting. I think it can be done better, with decisions and conclusions.
Highly recommended...
This course not only teaches tactics of analysis but also intuitions behind them.
I would suggest to go over labs carefully because they have been very well designed.
Good course, sometimes moves a bit fast in the final modules and the labs are quite tough but great course and would recommend to broaden your knowledge of coding, data analysis and visualisation
You will learn
1
Develop Python code for cleaning and preparing data for analysis - including handling missing values, formatting, normalizing, and binning data
2
Perform exploratory data analysis and apply analytical techniques to real-word datasets using libraries such as Pandas, Numpy and Scipy
3
Manipulate data using dataframes, summarize data, understand data distribution, perform correlation and create data pipelines
4
Build and evaluate regression models using machine learning scikit-learn library and use them for prediction and decision making
Learning outcomes
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