PYTHON FOR DATA SCIENCE CERTIFICATION Training

 >>  PYTHON FOR DATA SCIENCE CERTIFICATION Training

PYTHON FOR DATA SCIENCE CERTIFICATION Training


 (5) | 450 Ratings


Introduction


PYTHON FOR DATA SCIENCE CERTIFICATION Training Details
Track Regular Track Weekend Track Fast Track
Course Duration 30 Hrs 8 Weekends 5 Days
Hours 1hr/day 2 Hours a day 6 Hours a day
Training Mode Online Classroom Online Classroom Online Classroom
Delivery Instructor Led-Live Instructor Led-Live Instructor Led-Live


Course Curriculum

Python for Data Science Certification Training

This course will enable you to:

  • Gain an in-depth understanding of data science process, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics.
  • Install the required Python environment and other auxiliary tools and libraries
  • Understand the essential concepts of Python programming like data types, tuples, lists, dicts, basic operators, and functions.
  • Perform high-level mathematical computing using NumPy package and its large library of mathematical functions
  • Perform scientific and technical computing using SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave.
  • Perform data analysis and manipulation using data structures and tools provided in Pandas package
  • Gain expertise in machine learning using the Scikit-Learn package
  • Gain an in-depth understanding of supervised learning and unsupervised learning models like linear regression, logistic regression, clustering, dimensionality reduction, K-NN, and pipeline
  • Use Scikit-Learn package for natural language processing
  • Use matplotlib library of Python for data visualization
  • Extract useful data from websites by performing web scrapping using Python
  • Integrate Python with Hadoop, Spark, and MapReduce

Exam & Certification

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Course Curriculum

Python for Data Science Certification Training

This course will enable you to:

  • Gain an in-depth understanding of data science process, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics.
  • Install the required Python environment and other auxiliary tools and libraries
  • Understand the essential concepts of Python programming like data types, tuples, lists, dicts, basic operators, and functions.
  • Perform high-level mathematical computing using NumPy package and its large library of mathematical functions
  • Perform scientific and technical computing using SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave.
  • Perform data analysis and manipulation using data structures and tools provided in Pandas package
  • Gain expertise in machine learning using the Scikit-Learn package
  • Gain an in-depth understanding of supervised learning and unsupervised learning models like linear regression, logistic regression, clustering, dimensionality reduction, K-NN, and pipeline
  • Use Scikit-Learn package for natural language processing
  • Use matplotlib library of Python for data visualization
  • Extract useful data from websites by performing web scrapping using Python
  • Integrate Python with Hadoop, Spark, and MapReduce

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