Python is the most popular programming language used in data science (Statista). So, if you’re keen to kickstart your career in data analytics, get ready to dive into the world of data with Python! This course is your ticket to mastering data analysis and building data models using Python. It’s the perfect introduction to this in-demand language for aspiring data scientists and analysts.

During the course, which is also part of the IBM Data Analyst Professional Certificate, you’ll master importing data from various sources and learn how to clean and prepare it for exploratory data analysis (EDA) and eye-catching visualizations. You'll also predict future trends by creating linear, multiple, and polynomial regression models and pipelines and understand how to evaluate them like a pro.

As you progress through the modules, you’ll learn to:

Collect and import data
Clean, prep, and format data
Manipulate data frames
Summarize data
Build machine learning regression models
Refine your models
Create data pipelines.

You’ll also build your practical understanding of Python through hands-on labs where you'll explore open-source Python libraries like Pandas and NumPy for data manipulation and visualization. Plus, you'll gain proficiency in using SciPy and scikit-learn to build machine-learning models and make predictions.

Enroll today and build job-ready skills in one of the world’s most popular programming languages in just 5 weeks!

Course 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
Reading: Kernel Density Estimation (KDE) Plots for Model Evaluation, Completed
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

Learning Objectives

Import, clean, and prepare data for analysis.
Use Pandas, DataFrames, NumPy, and SciPy.
Load, manipulate, analyze, and visualize data.
Build machine learning models

Recommended Skills Prior to Taking this Course

This is a beginner-friendly introduction to data analysis, therefore no prior programming experience is necessary. However, basic knowledge of using a computer, navigating files and folders, and using basic software applications is recommended.

Python for Data Analysis

Language

Topic

Industries

Enrollment Count

Skills You Will Learn

Offered By

Estimated Effort

Platform

Last Update