Python with Data Science

Become a Data Science  Expert & learn How to Apply Them in Real-world Applications from Training  Basket. Enroll Now! 

Course Duration - 3 Months

Course Content

Python With Data Science Course

Module 1: Python

  •  Environment set-up
  • Jupyter overview
  • Python Numpy
  •  Python Pandas
  •  Python
  • Matplotlib

Module 2: INTRODUCING DATA SCIENCE

  •  What is Data science..?
  •  Explore the Data science workflow
  •  Python Packages for data science
  •  Installing Jupyter

Module 3: R

  •  An introduction to R
  •  Data structures in R
  •  Data visualization with R
  •  Data analysis with R

Module 4: Statistics

  •  Types of variables
  •  Measures of central tendency
  •  Measures of variability
  •  Coefficient of variance
  •  Skewness and Kurtosis

Module 5: Inferential statistics

  •  Normal distribution
  •  Test hypotheses
  •  Central limit theorem
  •  Confidence interval
  •  T-test
  •  Type I and II errors
  •  Student’s T distribution

Module 6: visualization with matplotlib

  •  Simple Line Plots
  • Scatter Plots
  • Legends and annotations
  •  Heatmaps
  •  Subplots
  •  Plotting in Pandas

Module 7: Exploratory data analysis

  •  Data visualization
  •  Missing value analysis
  •  The correction matrix
  •  Outlier detection analysis

Module 8: Supervised machine learning

  •  Python Scikit tool
  •  Neural networks
  •  Support vector machine
  •  Logistic and linear regression
  •  Decision tee classifier

Module 9: Tableau

  •  Working with Tableau
  •  Deep diving with data and connection
  •  Creating charts
  • Mapping data in Tableau
  •  Dashboards and stories

 Module 9: Machine learning on cloud

  •  In this lesson, you will learn –
  •  ML on cloud platform
  •  ML on AWS
  •  ML on Microsoft Azure

Module 10: machine learning

  •  What is Machine Learning..?
  •  Introducing Scikit-learn
  •  Types of ML
  •  Basic steps of ML
  •  Data preprocessing
  •  Dealing with missing data
  •  Handling with categorical data
  •  Features scaling
  •  Splitting data
  •  Linear Regression
  •  Naive Bayes Classification
  •  Logistic Regression
  • Support Vector Machine
  •  Evaluate Classificaion model Performance
  •  Principal Component Analysis
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