Ementorhub

What you'll learn

Develop Python code for cleaning and preparing data for analysis - including handling missing values, formatting, normalizing, and binning data

Perform exploratory data analysis and apply analytical techniques to real-word datasets using libraries such as Pandas, Numpy and Scipy

Manipulate data using dataframes, summarize data, understand data distribution, perform correlation and create data pipelines

Build and evaluate regression models using machine learning scikit-learn library and use them for prediction and decision making

Skills you'll gain

Data Science

Data Wrangling

Statistical Analysis

Data Pipelines

Descriptive Statistics

Predictive Modeling

Data Import/Export

Exploratory Data Analysis

Scikit Learn (Machine Learning Library)

Data Manipulation

Regression Analysis

Machine Learning Methods

Data Cleansing

Pandas (Python Package)

Data Analysis

NumPy

There are 6 modules in this course

Topics covered include: - collecting and importing data - cleaning, preparing & formatting data - data frame manipulation - summarizing data - building machine learning regression models - model refinement - creating data pipelines You will learn how to import data from multiple sources, clean and wrangle data, perform exploratory data analysis (EDA), and create meaningful data visualizations. You will then predict future trends from data by developing linear, multiple, polynomial regression models & pipelines and learn how to evaluate them. In addition to video lectures you will learn and practice using hands-on labs and projects. You will work with several open source Python libraries, including Pandas and Numpy to load, manipulate, analyze, and visualize cool datasets. You will also work with scipy and scikit-learn, to build machine learning models and make predictions. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge.

Data Wrangling

Exploratory Data Analysis

Model Development

Model Evaluation and Refinement

Final Assignment