3 mins to read Data Analysis has always been a very important field and a highly demanded skill. Until recently, it has been practiced using mostly closed, expensive, and limited tools like Excel or Tableau. Python, pandas, and other open-source libraries have changed Data Analysis forever and have become must-have tools for anyone looking to build a career as a Data Analyst.
LEARN TO ANALYZE DATA WITH PYTHON
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 lectures, hands-on labs, and assignments. It includes the following parts:
- Data Analysis libraries: will learn to use Pandas DataFrames, Numpy multi-dimensional arrays, and SciPy libraries to work with 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.
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
- 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
GENERAL INFORMATION
- This course is self-paced.
- It can be taken at any time.
- It can be audited as many times as you wish.
- Python programming, Statistics
REQUIREMENTS
- Some Python experience is expected
- Python for Data Science
How to Enroll:
- Choose your desired certificate program on the IBM website.
- Create an Amazon if you don’t have one.
- Select specific courses within your chosen program.
- Enroll in courses, and pay if necessary.
- Access course materials and complete requirements.
- Prepare for and take certification exams if required.
- Earn your certificate upon successful completion.
- Be aware of maintenance or renewal requirements, if applicable.
