Data Science with Python Course

·      Introduction to the Statistics

·      About Statistical and Non-statistical Analysis

·      The Major Categories of Statistics

·      About the Statistical Analysis Considerations

·      The Population and Sample

·      What is the Statistical Analysis Process?

·      The Data Distribution

·      Dispersion

·      Practical Exercise

·         Introduction of Python

·         The Companies using Python

·         Different Applications where Python is used

·         Discuss Python Scripts on UNIX/Windows

·         Values, Types, Variables

·         Operands and Expressions

·         Conditional Statements

·         Loops

·         Command Line Arguments

·         Writing to the screen

·         Sequences and File Operations

·         Python files I/O Functions

·         Numbers

·         Strings and related operations

·         Tuples and related operations

·         Lists and related operations

·         Dictionaries and related operations

·         Sets and related operations

·         Functions, OOPs, Modules, Errors and Exceptions

·         Functions

·         Function Parameters

·         Global Variables

·         Variable Scope and Returning Values

·         Lambda Functions

·         Object-Oriented Concepts

·         Standard Libraries

·         Modules Used in Python

·         The Import Statements

·         Module Search Path

·         Package Installation Ways

·         Errors and Exception Handling

·         Handling Multiple Exceptions

·      Introduction To Statistic

·      Distributions and Hypothesis Tests

·      Distributions of One Variable

·      Hypothesis Testing

·      Typical Analysis Procedure

·      Data Screening and Outliers

·      Normality Check

·      Hypothesis Concept

·      Errors

·      p-Value

·     Sample Size

·      Chi Square

·      ANOVA

·      Practical Exercise              

·      Pandas Introduction

·      Series, Data Frames and CSVs

·      Data from URLs

·      Describing Data with Pandas

·      Selecting and Viewing Data with Pandas

·      Manipulating Data

·      Practical Exercise              

·      NumPy Introduction

·      NumPy DataTypes and Attributes

·      Creating NumPy Arrays

·      NumPy Random Seed

·      Viewing Arrays and Matrices

·      Manipulating Arrays

·      Standard Deviation and Variance

·      Reshape and Transpose

·      Dot Product vs Element Wise

·      Exercise: Store Sales

·      Comparison Operators

·      Sorting Arrays

·      Turn Images Into NumPy Arrays

·      Practical Exercise              

·      Introduction to the SciPy

·      SciPy Sub Package - Integration and Optimization

·      What is SciPy sub package?

·      SciPy Sub Package - Statistics, Weave and IO

·      Practical Exercise              

·      What Is Machine Learning?

·      AI/Machine Learning/Data Science

·      Practical Exercise              

·      Machine Learning Framework

·      Types of Machine Learning

·      Types of Data

·      Types of Evaluation

·      Features In Data

·      Modelling - Splitting Data

·      Modelling - Picking the Model

·      Modelling - Tuning

·      Modelling – Comparison

·      Practical Exercise              

·      Introducing Our Tools

·      Windows Environment Setup

·      Linux Environment Setup

·      Jupyter Notebook Walkthrough

·      Practical Exercise              

·      Matplotlib Introduction

·      Importing And Using Matplotlib

·      Anatomy Of A Matplotlib Figure

·      Scatter Plot And Bar Plot

·      Histograms And Subplots

·      Subplots Option 2

·      Plotting From Pandas DataFrames

·      Customizing Your Plots

·      Saving And Sharing Your Plots

·      Practical Exercise              

·      Scikit-learn Introduction

·      Scikit-learn Cheatsheet

·      Typical scikit-learn Workflow

·      Debugging Warnings In Jupyter

·      Splitting Your Data

·      Clean, Transform, Reduce

·      Convert Data To Numbers

·      Handling Missing Values With Pandas

·      Handling Missing Values With Scikit-learn

·      Choosing The Right Model For Your Data

·      Practical Exercise              

·      Types of Regression Algorithms

·      Simple Linear Regression

·      Multiple Linear Regression

·      Logistic Regression,

·      Polynomial Regression

·      Support Vector Regression

·      Ridge Regression

·      Lasso Regression

·      ElasticNet Regression

·      Bayesian Regression

·      Decision Tree Regression

·      Random Forest Regression

·      Case Studies

·      Practical Exercise        

·      Types of Classification Algorithms

·      Logistic Regression/Classification

·      K-Nearest Neighbours

·      Support Vector Machines

·      Kernel Support Vector Machines

·      Naive Bayes

·      Decision Tree Classification

·      Random Forest Classification

·      Case Studies

·      Practical Exercise              

·      K-Means Clustering

·      A Simple Example of Clustering

·      Clustering Categorical Data

·      How to Choose the Number of Clusters

·      Pros and Cons of K-Means Clustering

·      To Standardize or not to Standardize

·      Relationship between Clustering and Regression

·      Market Segmentation with Cluster Analysis

·      Species Segmentation with Cluster Analysis

·      Advanced Statistical Methods - Other Types of Clustering

·      Types of Clustering

·      Dendrogram

·      Heatmaps

·      Practical Exercise                    

·      The concepts of text-mining

·      Use cases

·      Text Mining Algorithms

·      Quantifying text

·      TF-IDF

·      Beyond TF-IDF

·      Practical Exercise              

·      What is time series?

·      Techniques and applications

·      Time series components

·      Moving average

·      Smoothing techniques

·      Exponential smoothing

·      Univariate time series models

·      Multivariate time series analysis

·      Sentiment analysis in Python (Twitter sentiment analysis)

·      Text analysis

·      Rolling Mean For Detecting Temporal Variation

·      Simple Exponential Smoothing (SES)

·      Holt extended simple exponential smoothing

·      Holt Winters

·      Auto Regression Model (AR): Consider Previous Time Steps

·      Implement a Basic ARIMA Model

·      Automated ARIMA & Account for Seasonality (SARIMA)

·      Practical Exercise              

·      Introduction to Natural Language Processing (NLP)

·      Introduction to text mining

·      Importance and applications of text mining

·      How NPL works with text mining

·      Writing and reading to word files

·      OS modules

·      Natural Language Toolkit (NLTK) environment & text mining

·      Principal Component Analysis (PCA)

·      Practical Exercise                    

·      Introduction to Deep Learning with neural networks

·      Biological neural network vs artificial neural network

·      Understanding perceptron learning algorithm

·      Introduction to Deep Learning frameworks

·      Tensor Flow constants, variables and place-holders

·      Practical Exercise