Data Science course to learn online

By ridhigrg |Email | May 8, 2020 | 6510 Views

Applied Data Science with Python Specialization
Gain new insights into your data. Learn to apply data science methods and techniques, and acquire analytical skills.
Offered By University of Michigan
  • Conduct an inferential statistical analysis
  • Discern whether a data visualization is good or bad
  • Enhance a data analysis with applied machine learning
  • Analyze the connectivity of a social network

About this Specialization
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.

Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.

Introduction to Data Science in Python
Offered By University of Michigan
About this Course
This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating CSV files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as group by, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. 

This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.

  • Understand techniques such as lambdas and manipulating CSV files
  • Describe common Python functionality and features used for data science
  • Query DataFrame structures for cleaning and processing
  • Explain distributions, sampling, and t-tests

Be The Leader Your Data Team Needs. Learn to lead a data science team that generates first-rate analyses in four courses.
  • Become conversant in the field and understand your role as a leader.
  • Recruit, assemble, evaluate, and develop a team with complementary skill sets and roles.
  • Navigate the structure of the data science pipeline by understanding the goals of each stage and keeping your team on target throughout.
  • Overcome the common challenges that frequently derail data science projects.

Data Science: Foundations using R Specialization
Offered By Johns Hopkins University
  • Use R to clean, analyze, and visualize data.
  • Learn how to ask the right questions, obtain data, and perform reproducible research.
  • Use GitHub to manage data science projects.
  • Set up R, R-Studio, Github and other useful tools

Source: HOB