Online Course – Certified Professional Internship in Data Science with Python by Google, University of Michigan

Discover new insights into your data. Learn to apply data science methods and techniques and acquire analytical skills.

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קורס אונליין - התמחות מקצועית מוסמכת במדעי הנתונים המיועדים עם Python של Google

Professional Certificate

Intermediate level

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

  • Application of statistical techniques
  • Machine learning
  • Information visualization
  • Text analysis
  • Social network analysis
  • Using popular Python tools such as pandas, matplotlib, scikit-learn, nltk, networkx
  • Deep understanding of data science in Python
  • Ability to create graphs and charts to represent data
  • Developing Python programming skills

What you will learn in the course

Courses for which the course is suitable

  • Data Analyst
  • Data Scientist
  • Python developer
  • Machine Learning Expert
  • Business Analyst
  • Application developer with data
  • Data visualization developer
  • Text analyzer
  • Social Network Analyst
  • Data Science Teacher

Internship – 5-part course series

The 5 courses in the University of Michigan specialization series introduce teachers to data science through the Python programming language. The specialization is designed for teachers with a basic background in Python or programming who are interested in applying statistical techniques, machine learning, data visualization, text analysis, and social network analysis.

The courses include the use of popular Python tools such as:

  • panda
  • matplotlib
  • scikit-learn
  • nltk
  • networkx

The courses must be taken in the following order:

  1. Introduction to Data Science in Python (Course 1)
  2. Graphs, Charts, and Data Representation in Python (Course 2)
  3. Practical Machine Learning in Python (Course 3)

After completing these courses, courses 4 and 5 can be taken in any order. All 5 courses are required to receive a certificate.

Details of the courses that make up the specialization

Introduction to Data Science in Python

Course 1

34 hours
4.5 (27,048 ratings)

What will you learn?

  • Understand techniques like lambda functions and working with CSV files
  • Describe common Python functions and concepts used in data science
  • Query DataFrame structures for data cleaning and processing
  • Explain about distributions, sampling, and T-tests

Skills you will acquire

  • Natural Language Processing Toolkit (NLTK)
  • Text search
  • Programming in Python
  • Natural language processing

Applied Assumptions, Graphs, and Data Representation in Python

Course 2

24 hours
4.5 (6,246 ratings)

What will you learn?

  • Describe what makes a visualization good or bad.
  • Understand the best principles for creating basic graphs
  • Identify the appropriate functions for certain problems
  • Create a visualization using Matplotlib

Skills you will acquire

  • Graph theory
  • Network analysis
  • Programming in Python
  • Social network analysis

Machine learning applied in Python

Course 3

31 hours
4.6 (8,507 ratings)

What will you learn?

  • Describe how machine learning differs from descriptive statistics
  • Create and evaluate data clusters
  • Explain different approaches to creating predictive models.
  • Build content that meets the needs of the analysis

Skills you will acquire

  • Programming in Python
  • Machine learning algorithms
  • Machine learning
  • Scikit-Learn

Applied Text Data Exploration in Python

Course 4

25 hours
4.2 (3,807 ratings)

What will you learn?

  • Understand how text is handled in Python
  • Use basic natural language processing methods
  • Write code that groups documents by topic
  • Describe the nltk framework for text processing

Skills you will acquire

  • Programming in Python
  • Numpy
  • Pandas
  • Data Cleanup

Applied social network analysis in Python

Course 5

26 hours
4.6 (2,699 ratings)

What will you learn?

  • Represent and manipulate data in a network structure using the NetworkX library
  • Analyze the connectivity of a network
  • Measure the importance or centrality of a node in a network

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