About this course: 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 analyses, create meaningful data visualizations, predict future trends from data, and more! Topics covered: 1) Importing Data sets 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.
Who is this class for: this course is geared for anyone with basic programming skills and has a knowledge of general mathematics
Created by: IBM
Taught by: Joseph Santarcangelo, Ph.D., Data Scientist at IBM
IBM Developer Skills Network
| Basic Info | Course 6 of 9 in the IBM Data Science Professional Certificate Specialization |
| Level | Beginner |
| Commitment | This course requires approximately two hours a week for six weeks |
| Language | English, Subtitles: Arabic |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
Importing Datasets
5 videos
- Video: Understanding the Data
- Video: Python Packages for Data Science
- Video: Importing and Exporting Data in Python
- Video: Getting Started Analyzing Data in Python
- LTI Item: Lab 1:Importing Datasets
Graded: Understanding the Data
Graded: Python Packages for Data Science
Graded: Importing and Exporting Data in Python
Graded: Getting Started Analyzing Data in Python
Graded: Importing Datasets
WEEK 2
Data Wrangling
6 videos
- Video: Dealing with Missing Values in Python
- Video: Data Formatting in Python
- Video: Data Normalization in Python
- Video: Binning in Python
- Video: Turning categorical variables into quantitative variables in Python
- LTI Item: Data Wrangling
Graded: Dealing with Missing Values in Python
Graded: Data Formatting in Python
Graded: Data Normalization in Python
Graded: Turning categorical variables into quantitative variables in Python
Graded: Data Wrangling
WEEK 3
Exploratory Data Analysis
6 videos
- Video: Descriptive Statistics
- Video: GroupBy in Python
- Video: Analysis of Variance ANOVA
- Video: Correlation
- Video: Correlation - Statistics
- LTI Item: Exploratory Data Analysis
Graded: Descriptive Statistics
Graded: GroupBy in Python
Graded: Correlation
Graded: Correlation - Statistics
Graded: Exploratory Data Analysis
WEEK 4
Model Development
6 videos
- Video: Linear Regression and Multiple Linear Regression
- Video: Model Evaluation using Visualization
- Video: Polynomial Regression and Pipelines
- Video: Measures for In-Sample Evaluation
- Video: Prediction and Decision Making
- LTI Item: Model Development
Graded: Linear Regression and Multiple Linear Regression
Graded: Model Evaluation using Visualization
Graded: Polynomial Regression and Pipelines
Graded: Measures for In-Sample Evaluation
Graded: Model Development
WEEK 5
Model Evaluation
4 videos
- Video: Overfitting, Underfitting and Model Selection
- Video: Ridge Regression
- Video: Grid Search
- LTI Item: Model Evaluation and Refinement
Graded: Model Evaluation
Graded: Overfitting, Underfitting and Model Selection
Graded: Ridge Regression
Graded: Quizz: Model Refinement
FAQs
How It Works
Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
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Creators
IBM
IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame.
Pricing
| Purchase Course | |
|---|---|
| Access to course materials | Available |
| Access to graded materials | Available |
| Receive a final grade | Available |
| Earn a shareable Course Certificate | Available |
Ratings and Reviews
There are some errors in the labs
this course finally gets to some key Python functions for data analysis. Some of this may be difficult without a basic stats background. Only knock is there are MULTIPLE typos in the slides and labs. Needs to get fixed.
Very systematic and clear.
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SM
would be better if there were more exercises.