SEO Certification: What It Is and How It Is Beneficial
October 23, 2024
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This course is part of multiple programs.
Instructor: Joseph Santarcangelo
504,669 already enrolled
Included with
(18,739 reviews)
Recommended experience
Intermediate level
You should have a working knowledge of Python and Jupyter Notebooks.
(18,739 reviews)
Recommended experience
Intermediate level
You should have a working knowledge of Python and Jupyter Notebooks.
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
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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.
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.
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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.
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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.
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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.
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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.
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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.
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We asked all learners to give feedback on our instructors based on the quality of their teaching style.
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University of Illinois Urbana-Champaign
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Reviewed on Sep 5, 2023
Wonderful course, explained everything in the most easiest way possible. BUt hte module 4 and 5 ffeels kinda hard for a person who is not familiar with data visualization and machine learning.
Reviewed on Mar 9, 2020
Very good course that goes straight to the main topics needed to work on data analysis using Python. This will kick start my learning process which will be followed with a lot of coding practices.
Reviewed on Jul 16, 2020
Although good to learn the know-how of basic data analysis techniques, the quizzes are predictable and you don't end up coding as much as you should. A good starter course to wet your feet in DA!
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