Data Science With Python Corparate Trainning

✅ Introduction to Data Science

✅ Introduction to Python

✅ Basic Operations in Python

✅ Variable Assignment

✅ Functions: in-built functions, user defined functions

✅ Condition: if, if-else, nested if-else, else-if

✅ List: Different Data Types in a List, List in a List

✅ Operations on a list: Slicing, Splicing, Sub-

✅ Condition(true/false) on a List

✅ Applying functions on a List

✅ Dictionary: Index, Value

✅ Operation on a Dictionary: Slicing, Splicing, Sub-setting

✅ Condition(true/false) on a Dictionary

✅ Applying functions on a Dictionary

✅ Numpy Array: Data Types in an Array, Dimensions of an Array

✅ Operations on Array: Slicing, Splicing, Sub-setting

✅ Conditional(T/F) on an Array

✅ Loops: For, While

✅ Statistics & Plotting

✅ Seabourn & Matplotlib - Introduction

✅ Univariate Analysis on a Data

✅ Plot the Data - Histogram plot

✅ Find the distribution

✅ Find mean, median and mode of the Data

✅ Take multiple data with same mean but different sd, same mean and sd but different kurtosis: find mean, sd, plot

✅ Multiple data with different distributions

✅ Bootstrapping and sub-setting

✅ Making samples from the Data

✅ Making stratified samples - covered in bivariate analysis

✅ Find the mean of sample

✅ Central limit theorem

✅ Plotting

✅ Hypothesis testing + DOE

✅ Bivariate analysis

✅ Correlation

✅ Scatter plots

✅ Making stratified samples

✅ Categorical variables

✅ Class variable

✅ File I/O

✅ Series: Data Types in series, Index

✅ Data Frame

✅ Series to Data Frame

✅ Re-indexing

✅ Operations on Data Frame: Slicing, Splicing (also Alternate), Sub-setting

✅ Pandas

✅ Stat operations on Data Frame

✅ Reading from different sources

✅ Missing data treatment

✅ Merge, join

✅ Options for look and feel of data frame

✅ Writing to file

✅ db operations

✅ Data Aggregation, Filtering and Transforming

✅ Lamda Functions

✅ Apply, Group-by

✅ Map, Filter and Reduce

✅ Visualization

✅ Matplotlib, pyplot

✅ Seaborn

✅ Scatter plot, histogram, density,
heat-map, bar charts

✅ Regression - Introduction

✅ Linear Regression: Lasso, Ridge

✅ Variable Selection

✅ Forward & Backward Regression

✅ Logistic Regression: Lasso, Ridge

✅ Naive Bayes

✅ Unsupervised Learning

✅ Unsupervised Learning - Introduction

✅ Distance Concepts

✅ Classification

✅ k nearest

✅ Clustering

✅ k means

✅ Multidimensional Scaling

✅ PCA

✅ Random Forest

✅ Decision trees

✅ Cart C4.5

✅ Random Forest

✅ Boosted Trees

✅ Gradient Boosting

✅ Creating Contracts

✅ Visibility and Getter

✅ Function Modifiers

✅ Constant State Variables

✅ View Functions

✅ Pure Functions

✅ Fallback Functions

✅ Function Overloading

✅ Events

✅ Inheritance

✅ Libraries