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Programming with Python for Data Science

This course is created in association with Coding Dojo, which targets people who have initial level experience of Python programming.

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Microsoft

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Beginner

Difficulty

24 Hours

Course Duration

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This course is created in association with Coding Dojo, which targets people who have initial level experience of Python programming.

Course Information

About this course:

This course is created in association with Coding Dojo, which targets people who have initial level experience of Python programming. The course shows understudies how to begin taking a gander at information with the data scientist lens by applying effective, popular mining models so as to uncover helpful insight, utilizing Python, one of the well-known Data Scientists language. Subjects incorporate feature importance and selection, data visualization, clustering, classification, dimensionality reduction, and more! The entirety of the informational collections utilized in this course are included live-information or motivated by domains of the real-world that can advantage from machine learning.

Course Objective:

  • The most effective method to represent raw data in a way helpful for determining important data
  • Knowledge about machine learning and the kinds of issues it is adept to solving
  • How to utilize different techniques of data visualization.
  • The most effective method to apply administered learning calculations to your information, for example, support vector and random forest classifier
  • The most effective method to utilize principal component analysis and isomap brilliantly to improve your information
  • Concepts like model selection, cross-validation, and pipelining
  • The big picture of Data Science and Analysis, Machine Learning, and Dive Deeper
  • Exploring Data by Basic Plots, Visualizations, Lab – Visualizations, Higher Dimensionality, and Dive Deeper
  • Transforming Data with Principal Component Analysis (PCA), Lab – PCA, Isomap, Lab – Isomap, Data Cleansing, and Dive Deeper
  • Data Modeling with Clustering, Lab – Clustering, K-Nearest Neighbors, Supervised Learning, Neighbors, Regression, Lab - K-Nearest, Lab – Regression, and Dive Deeper
  • Evaluating Data with Confusion, Cross-Validation, Power Tuning, and Dive Deeper

Audience:

Data Scientist

Prerequisite:

No prerequisite required for this course

Outline

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BrandMicrosoft
SubjectsApp Development, Big Data
Lab AccessNo
TechnologyMicrosoft
Learning StyleSelf-Paced Learning
Learning TypeCourse
DifficultyBeginner
Course Duration24 Hours
LanguageEnglish

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Course Expert:

Author

Tom Robertson
(Data Science Enthusiast)

Tom is an innovator first, and then a Data Scientist & Software Architect. He has integrated expertise in business, product, technology and management. Tom has been involved in creating category defining new products in AI and big data for different industries, which generated more than hundred million revenue cumulatively, and served more than 10 million users.
As a Data Scientist and Software Architect Tom has extensive experience in data science, engineering, architecture and software development. To date Tom has accumulated over a decade of experience in R, Python & Linux Shell programming.

Tom has expertise on Python, SQL, and Spark. He has worked on several libraries including but not limited to Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, SciPy, NLTK, Keras, and Tensorflow.

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