Learn how to research data using Python. This course will take you from the fundamentals of Python to exploring many various sorts of data. you'll find out how to organize data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more!
You will find out 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 figure with various datasets. CETPA provinding Data Analytics Using Python Onlin Training where you get introduce you to pandas, an open-source library, and that we will use it to load, manipulate, analyze, and visualize cool datasets. Then we'll introduce you to a different open-source library, scikit-learn, and that we will use a number of its machine learning algorithms to create 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 info
Identify and Handle Missing Values
Data Formatting
Data Normalization Sets
Binning
Indicator variables
Module 3 - Summarizing the info Frame
Descriptive Statistics
Basic of Grouping
ANOVA
Correlation
More on Correlation
Module 4 - Model Development
Simple and Multiple rectilinear regression
Model Evaluation Using Visualization
Polynomial Regression and Pipelines
R-squared and MSE for In-Sample Evaluation
Prediction and deciding
Module 5 - Model Evaluation
Model Evaluation
Over-fitting, Under-fitting, and Model Selection
Ridge Regression
Grid Search
Model Refinement
GENERAL INFORMATION
This course is free.
It is self-paced.
It is often taken at any time.
They are often audited as repeatedly as you would like.
Python programming, Statistics
REQUIREMENTS
Some Python experience is predicted
Python for Data Science
