4.7
stars
16,210 ratings

Joseph Santarcangelo

299,593 already enrolled

Offered By

Part of the Bachelor of Applied Arts and Sciences degree
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IBM Skills Network

About this Course

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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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Beginner Level

You should have a working knowledge of Python and Jupyter Notebooks.

Approx. 14 hours to complete

English

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What you will 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

Skills you will gain

Predictive Modelling
Python Programming
Data Analysis
Data Visualization (DataViz)
Model Selection

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Beginner Level

You should have a working knowledge of Python and Jupyter Notebooks.

Approx. 14 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Instructor rating

4.61/5 (2,394 Ratings)

Joseph Santarcangelo

Ph.D., Data Scientist at IBM

IBM Developer Skills Network

885,133 Learners

26 Courses

Offered by

IBM Skills Network

IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.

Start working towards your Bachelor's degree

This course is part of the 100% online Bachelor of Applied Arts and Sciences from IBM Skills Network. If you are admitted to the full program, your courses count towards your degree learning.

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Syllabus - What you will learn from this course

Content Rating93%(31,812 ratings)

Week

Week 1

2 hours to complete

Importing Datasets

2 hours to complete

6 videos (Total 20 min), 1 reading, 6 quizzes

6 videos

The Problem1m

Understanding the Data2m

Python Packages for Data Science2m

Importing and Exporting Data in Python4m

Getting Started Analyzing Data in Python4m

Accessing Databases with Python4m

1 reading

Lesson Summary1m

5 practice exercises

Practice Quiz: Understanding the Data6m

Practice Quiz: Python Packages for Data Science2m

Practice Quiz: Importing and Exporting Data in Python3m

Practice Quiz: Getting Started Analyzing Data in Python3m

Graded Quiz: Importing Datasets15m

Week

Week 2

2 hours to complete

Data Wrangling

2 hours to complete

6 videos (Total 19 min), 1 reading, 6 quizzes

6 videos

Pre-processing Data in Python2m

Dealing with Missing Values in Python6m

Data Formatting in Python3m

Data Normalization in Python3m

Binning in Python1m

Turning categorical variables into quantitative variables in Python2m

1 reading

Lesson Summary10m

5 practice exercises

Practice Quiz: Dealing with Missing Values in Python6m

Practice Quiz: Data Formatting in Python3m

Practice Quiz: Data Normalization in Python3m

Practice Quiz: Turning categorical variables into quantitative variables in Python3m

Graded Quiz: Data Wrangling15m

Week

Week 3

2 hours to complete

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.

2 hours to complete

6 videos (Total 20 min), 1 reading, 6 quizzes

Week

Week 4

2 hours to complete

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.

2 hours to complete

6 videos (Total 27 min), 1 reading, 6 quizzes

6 videos

Model Development1m

Linear Regression and Multiple Linear Regression6m

Model Evaluation using Visualization4m

Polynomial Regression and Pipelines4m

Measures for In-Sample Evaluation3m

Prediction and Decision Making5m

1 reading

Lesson Summary1m

5 practice exercises

Practice Quiz: Linear Regression and Multiple Linear Regression6m

Practice Quiz: Model Evaluation using Visualization3m

Practice Quiz: Polynomial Regression and Pipelines3m

Practice Quiz: Measures for In-Sample Evaluation3m

Graded Quiz: Model Development15m

Reviews

4.7

2473 reviews

5 stars
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4 stars
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3 stars
3.81%
2 stars
0.91%
1 star
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TOP REVIEWS FROM DATA ANALYSIS WITH PYTHON

by BDNov 22, 2019

Really interesting course, if one wants learn programming language. Well designed and structured. Only suggestion is, if the small videos contains example that be really great to understand it well

by WZNov 7, 2020

A great introduction to fitting data in python.

The core principles and measures are well explained.

The only slight minus point: No information about the interpretation of fitting parameters.

by HSJul 29, 2020

perfect for beginner level. all the concepts with code and parameter wise have been explained excellently.overall best course in making anyone eager to learn from basics to handle advances with ease.

by VSJan 30, 2022

This is totally one of the hardest course I've ever taken on Coursera. It's packed with knowledge I did not know before. Definitely recommended for people who want to learn data analysis with Python.

View all reviews

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When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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Data Analysis with Python

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