À propos de ce cours : 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.
Les destinataires de ce cours : this course is geared for anyone with basic programming skills and has a knowledge of general mathematics
Enseigné par : Joseph Santarcangelo, Ph.D., Data Scientist at IBM
IBM Developer Skills Network
| Informations de base | Cours 6 de 9 dans IBM Data Science Professional Certificate Specialization |
| Niveau | Débutant |
| Engagement | This course requires approximately two hours a week for six weeks |
| Langue | Anglais |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
- Vidéo: Understanding the Data
- Vidéo: Python Packages for Data Science
- Vidéo: Importing and Exporting Data in Python
- Vidéo: Getting Started Analyzing Data in Python
- LTI Item: Lab 1:Importing Datasets
- Vidéo: Dealing with Missing Values in Python
- Vidéo: Data Formatting in Python
- Vidéo: Data Normalization in Python
- Vidéo: Binning in Python
- Vidéo: Turning categorical variables into quantitative variables in Python
- LTI Item: Data Wrangling
- Vidéo: Descriptive Statistics
- Vidéo: GroupBy in Python
- Vidéo: Analysis of Variance ANOVA
- Vidéo: Correlation
- Vidéo: Correlation - Statistics
- LTI Item: Exploratory Data Analysis
- Vidéo: Linear Regression and Multiple Linear Regression
- Vidéo: Model Evaluation using Visualization
- Vidéo: Polynomial Regression and Pipelines
- Vidéo: Measures for In-Sample Evaluation
- Vidéo: Prediction and Decision Making
- LTI Item: Model Development
- Vidéo: Overfitting, Underfitting and Model Selection
- Vidéo: Ridge Regression
- Vidéo: Grid Search
- LTI Item: Model Evaluation and Refinement
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| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
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.
This module is loaded with concepts. Even though they are introduced in a logical sequence, it gets a little overbearing and tend to lose the relevance in the context of car price prediction. At least, now I am aware of the techniques, methods and python's capability.
There are some errors in the labs