Overview

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!You will learn how to:

Import data sets
Clean and prepare data for analysis
Manipulate pandas DataFrame
Summarize data
Build machine learning models using scikit-learn
Build data pipelines

Data Analysis with Python is delivered through lecture, hands-on labs, and assignments. It includes following parts:

Data Analysis libraries: will learn to use Pandas DataFrames, Numpy multi-dimentional arrays, and SciPy libraries to work with a various datasets. 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.

Syllabus

Module 1 - Importing Datasets

Learning Objectives
Understanding the Domain
Understanding the Dataset
Python package for data science
Importing and Exporting Data in Python
Basic Insights from Datasets

Module 2 - Cleaning and Preparing the Data

Identify and Handle Missing Values
Data Formatting
Data Normalization Sets
Binning
Indicator variables

Module 3 - Summarizing the Data Frame

Descriptive Statistics
Basic of Grouping
ANOVA
Correlation
More on Correlation

Module 4 - Model Development

Simple and Multiple Linear Regression
Model Evaluation Using Visualization
Polynomial Regression and Pipelines
R-squared and MSE for In-Sample Evaluation
Prediction and Decision Making

Module 5 - Model Evaluation

Model Evaluation
Over-fitting, Under-fitting and Model Selection
Ridge Regression
Grid Search
Model Refinement

Reviews

4.0 rating, based on 4 Class Central reviews

Start your review of Data Analysis with Python

Komal Badgujar @pythondeveloper

One of the best courses on data analysis in python. It was a deep dive into the basics. More than the course videos, i enjoyed the assignments more.
Shadaf Iqbal Sanadi

The course curriculum was well-structured and covered a comprehensive range of topics essential for aspiring data analysts. From data collection and cleansing to advanced analytics and visualization techniques, the course left no stone unturned. It…

The course curriculum was well-structured and covered a comprehensive range of topics essential for aspiring data analysts. From data collection and cleansing to advanced analytics and visualization techniques, the course left no stone unturned. It provided me with a solid foundation and practical skills that are invaluable in the field of data analysis.

What truly sets IBM's Data Analyst course apart is the caliber of the instructors. They demonstrated a deep understanding of the subject matter and a genuine passion for teaching. Their willingness to provide guidance, answer questions, and engage in discussions greatly enhanced the learning process. The course also featured a variety of real-world case studies and practical exercises, which allowed me to apply what I learned and gain hands-on experience.
Anonymous

I have started learning this courses which seeks more attention to learn something new on python, however the code that was shared for practice in lab session is completely wrong and unable to fetch the data. I request to check the proper URL link to import the csv file from URL in python. Would be really appreciated if anyone can take a glance of it and try to resolve.
Adam Khalid

The IBM Data Analysis with Python course is an excellent introduction to data analysis for beginners and intermediate learners. The curriculum is well-structured, starting with the basics of Python programming and gradually moving on to more complex data analysis concepts. The hands-on labs and projects are particularly beneficial, as they provide practical experience with real-world datasets. Additionally, the course covers essential libraries such as Pandas, Matplotlib, and Scikit-learn, which are crucial for any data analyst. The instructors are knowledgeable, and their explanations are clear and concise. Overall, this course is a valuable resource for anyone looking to enhance their data analysis skills using Python.

Module 1 - Importing Datasets

Module 3 - Summarizing the Data Frame

Module 4 - Model Development

Module 5 - Model Evaluation

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