Data Analytics Using Python Visualizations

Description

If you are working on data science projects and want to create powerful visualization and insights as an outcome of your projects or are working on machine learning projects and want to find patterns and insights from your data on your way to building models, then this course is for you. This course exclusively focuses on explaining how to build fantastic visualizations using Python. It covers more than 20 types of visualizations using the most popular Python visualization libraries, such as Matplotlib, Seaborn, and Bokeh along with data analytics that leads to building these visualizations so that the learners understand the flow of analysis to insights.

If you are working on machine learning projects and want to find patterns and insights from your data on your way to building models, then this course is for you. This course takes a holistic approach to teach visualization techniques. We will be taking real-life business scenarios and raw data to go through detailed Exploratory Data Analysis (EDA) techniques to prepare the raw data to suit the appropriate visualization needs. You will learn about data analytics and exploratory data analysis techniques using multiple different data structures with NumPy and Pandas libraries. You will also learn various chart/graph types, customization/configuration, and vectorization techniques. We will look at advanced visualizations using business applications such as single and multiple bar charts, pie charts, and bubble charts with the vectorization of properties. We will further explore Seaborn Boxplot, Violin plot, Categorical Scatterplot, and how to create heat maps. By the end of the course, you will learn the foundational techniques of data analytics and deeper customizations on visualizations. You will be able to confidently use Python visualization libraries such as Matplotlib, Seaborn, and Bokeh in your future projects. All resources and code files are placed here: https://github.com/PacktPublishing/Data-Analytics-using-Python-Visualizations

Learn about the various visualization concepts
Learn to create simple plots using Matplotlib
Learn about marginal histograms and marginal boxplots
Learn handling images using pixel metrics
Learn about categorical variables and histograms (with EDA)
Learn various data generation techniques

This course is for Python and machine learning developers, data scientists, data analysts, and business analysts. This course will also be beneficial to leaders, managers, and anyone whose job involves presenting data in the form of visuals, which include developers, architects, and system analysts.

A basic understanding of Python will be helpful, but not mandatory.

This practical hands-on course has easy, step-by-step explanations with code to draw over 20 diverse kinds of charts and graphs using Python. Extensive quizzes are infused at logical points to validate the learning effectiveness.

The art of presenting data in the form of powerful, innovative, and intuitive visualizations * In-depth coverage of Matplotlib, Seaborn, and Bokeh visualization libraries * Use of data analytics techniques/Exploratory Data Analysis (EDA) using several data generations and manipulation methods

https://github.com/PacktPublishing/Data-Analytics-using-Python-Visualizations

Manas Dasgupta holds a master's degree (MSc) from the Liverpool John Moore's University (LJMU), the UK in Artificial Intelligence and Machine Learning (AI/ML). My specialization and research areas are Natural Language Processing (NLP) using Deep Learning Methods such as Siamese Networks, Encoder-Decoder techniques, various Language Embedding methods such as BERT, and areas such as Supervised Learning on Semantic Similarity and so on. His expertise area also encompasses an array of Machine Learning and Data Science / Predictive Analytics areas including various Supervised, Unsupervised, and Clustering methods. He has almost 20 Years of experience in the IT Industry, mostly in the Financial Services domain. Starting as a Developer to being an Architect for several years to a leadership position. His key focus and passion are to increase technical breadth and innovation.

1. Matplotlib and Seaborn - Libraries and Techniques

2. Advanced Visualizations Using Business Applications

3. Working with the Beautiful and Powerful Bokeh Library