Training on Python for Data Analysis and Visualization

Data analysis is the process of evaluating data using analytical and statistical tools to discover useful information and aid in decision making. There are several data analysis methods including data mining, text analytics, business intelligence and data visualization.

Data is everywhere. That means more companies are tracking, analyzing, and using the insights they find to make better decisions. In this course, you’ll learn the fundamentals of data analysis and visualization while building Python skills

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 analysis, create meaningful data visualizations, predict future trends from data, and more.

Who Should Attend?

• Data analysis’s and Business analysts
• People looking to learn a more powerful software for data analysis
• Anyone interested in learning more about python, data science, or data visualizations.
• Anyone interested about the rapidly expanding world of data science.

Requirements

• Have a computer (either Mac, Windows, or Linux)
• Basic to intermediate knowledge in data management and analysis
• Desire to learn

Learning Outcomes

By the end of this course participants will be able to:

• Import data sets
• Clean and prepare data for analysis
• Know how to use pandas to create and analyze data sets.
• Manipulate pandas DataFrame
• Analyze, Visualize and present data
• Build data pipelines

Course Content

Module I

Importing Datasets

• Understanding the Domain
• Understanding the Dataset
• Python package for data science
• Importing and Exporting Data in Python
• Basic Insights from Datasets
• Getting Started Analyzing Data in Python

Module II

Data Wrangling - Cleaning and Preparing the Data

• Pre-processing data in Python
• Identify and Handle Missing Values in Python
• Data Formatting
• Data Normalization Sets
• Binning in Python
• Indicator variables
• Turning categorical variables into quantitative variables in Python

Module III

Exploratory Data Analysis

• Descriptive Statistics
• GroupBy in Python
• Analysis of Variance - ANOVA
• Correlation
• Correlation - Statistics

Module IV

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 V

Working with Data in Python

• Model Evaluation
• Over Fitting, Under fitting and Model Selection
• Ridge Regression
• Grid Search
• Model Refinement

Methodology

The instructor led trainings are delivered using a blended learning approach and comprise of presentations, guided sessions of practical exercise, web-based tutorials and group work. Our facilitators are seasoned industry experts with years of experience, working as professionals and trainers in Data Science and programming.

All facilitation and course materials will be offered in English. The participants should be reasonably proficient in English.

Tailor-Made Training

This training course can also be tailored for your organization. This way, your organization will benefit by:

• Using own datasets and tools during the training.
• Being able to choose areas of interest you wish the trainer to put more emphasis on.
• Taking the course in-house or at a venue of choice.
• Cutting on the cost of transport and accommodation.