Data Science Essentials Advanced Algorithms and Visualizations

Data Science Essentials Advanced Algorithms and Visualizations

Become an efficient data science practitioner by understanding Python's key concepts

15.87 9.52

About This Course

This course will make you look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. We start with SVM and random forest for classification and regression. We look at big data, deep learning, and language processing. Then we use graph analysis techniques for very interesting and trending social media analytics. Finally, we take a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment tools that make it easier to present your results to an audience of both data science experts and business users.

All the code and supporting files for this course are available on Github at

Other Information

  • Certificate will provided in this course on Completion
  • Full lifetime access
  • Available on Mobile & Laptop

What Students Will Learn In Your Course?

Set up an experimental pipeline to test your data science hypotheses
Choose the most effective and scalable learning algorithm for your data science tasks
Optimize your machine learning models to get the best performance
Explore and cluster graphs, taking advantage of interconnections and links in your data

Are There Any Course Requirements Or Prerequisites?

If you are an aspiring data scientist and have at least a working knowledge of data analysis and Python, this course will get you started with data science. Data analysts with experience of R or MATLAB will also find the course a comprehensive reference with which to enhance their data manipulation and machine learning skills.

Who Are Your Target Students?



Course Content

  • 13 lectures
  • 01:50:23
  • The Course Overview
  • Support Vector Machine
  • Ensemble Strategies
  • Dealing with Big Data
  • Approaching Deep Learning
  • A Peek at Natural Language Processing (NLP)
  • Introduction to Graph Theory
  • Graph Algorithms
  • Graph Loading, Dumping, and Sampling
  • Introducing the Basics of Matplotlib
  • Wrapping Up Matplotlib's Commands
  • Interactive Visualizations with Bokeh
  • Advanced Data-learning Representations

Packt Publication

  • 4.48 (3)
  • 13 Reviews
  • 3 Students
  • 935 Courses