Data Analysis with Python

LEARN TO ANALYZE DATA WITH PYTHON
Data Analysis has always been a very important field, a highly demanded skill and a well-paid occupation. Until recently, it has been practised using mostly closed, expensive, and limited tools like Excel or Tableau. Python, pandas, scikit-learn 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 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
Completing Python for Data Science course would be helpful

Data Analysis with Python

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