Analysing data with Python is an essential skill for Data Scientists and Data Analysts. This course will take you from the basics of data analysis with Python to building and evaluating data models.

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.

You will 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.

You should have a working knowledge of Python and Jupyter Notebooks.

Part 1: Importing Datasets

• The Problem
• Understanding the Data
• Python Packages for Data Science
• Importing and Exporting Data in Python
• Getting Started Analyzing Data in Python

Part 2: Data Wrangling

• Pre-processing Data in Python
• Dealing with Missing Values in Python
• Data Formatting in Python
• Data Normalization in Python
• Binning in Python
• Turning categorical variables into quantitative variables in Python

Part 3: Exploratory Data Analysis

• Exploratory Data Analysis
• Descriptive Statistics
• GroupBy in Python
• Correlation
• Correlation – Statistics
• Analysis of Variance ANOVA

Part 4: Model Development

• Model Development
• Linear Regression and Multiple Linear Regression
• Model Evaluation using Visualization
• Polynomial Regression and Pipelines
• Measures for In-Sample Evaluation
• Prediction and Decision Making

Part 5: Model Evaluation

• Model Evaluation and Refinement
• Overfitting, Underfitting and Model Selection
• Ridge Regression
• Grid Search

Python is the programming language that opens more doors than any other, and the more you understand Python, the more you can do in the 21st Century. With a solid knowledge of Python, you can work in a multitude of jobs and a multitude of industries.

Data Analysis with Python certification will be particularly valuable for:

• Aspiring programmers and learners interested in learning programming for fun and job-related tasks;
• Learners looking to gain fundamental skills and knowledge for an entry-level job role as a software developer, data analyst, or tester;
• Industry professionals wishing to explore technologies that are connected with Python, or that utilize it as a foundation;
• Team leaders, product managers, and project managers who want to understand the terminology and processes in the software development cycle to more effectively manage and communicate with production and development teams.