Module 1: Introduction to Data Science
What is Data Science?
What is Machine Learning?
What is Deep Learning?
What is AI?
Data Analytics & it’s types
Module 2: Introduction to Python
What is Python?
Why Python?
Installing Python
Python IDEs
Jupyter Notebook Overview
Module 3: Python Basics
Python Basic Data types
Lists
Slicing
IF statements
Loops
Dictionaries
Tuples
Functions
Array
Selection by position & Labels
Module 4: Python Packages
Pandas
Numpy
Sci-kit Learn
Mat-plot library
Module 5: Importing data
Reading CSV files
Saving in Python data
Loading Python data objects
Writing data to csv file
Module 6: Manipulating Data
Selecting rows/observations
Rounding Number
Selecting columns/fields
Merging data
Data aggregation
Data munging techniques
Module 7: Statistics Basics
Central Tendency
Mean
Median
Mode
Skewness
Normal Distribution
Probability Basics
What does mean by probability?
Types of Probability
ODDS Ratio?
Standard Deviation
Data deviation & distribution
Variance
Bias variance Trade off
Underfitting
Overfitting
Distance metrics
Euclidean Distance
Manhattan Distance
Outlier analysis
What is an Outlier?
Inter Quartile Range
Box & whisker plot
Upper Whisker
Lower Whisker
catter plot
Cook’s Distance
Missing Value treatments
What is a NA?
Central Imputation
KNN imputation
Dummification
Correlation
Pearson correlation
Positive & Negative correlation
Error Metrics Duration-3hr
Classification
Confusion Matrix
Precision
Recall
Specificity
F1 Score
Regression
MSE
RMSE
MAPE
Module 8: Machine Learning
Module 9: Supervised Learning (Duration-6hrs)
Linear Regression
Linear Equation
Slope
Intercept
R square value
Logistic regression
ODDS ratio
Probability of success
Probability of failure
ROC curve
