Python with Data Science

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