PYTHON  PROGRAMMING for Data Analytics

Introduction

          Course Introduction

          Course Curriculum Overview & python3

          Installation Notes

          Quick Note on Jupyter Notebook

          Python Installation --- Windows, ubuntu

          IDE Selection

          Jupyter (iPython) Notebooks

Basic Command

          Numbers

          Strings

          Print Formatting

          Lists

          Dictionaries

          Tuples

          Files

          Sets and Booleans

          Resources for More Basic Practice

Condition, Expression and Loop

          Introduction to Python Statements

          if, elif, and elif Statements

          for Loops

          while Loops

          range()

          Exception Handling: try, except, finally

          Errors and Exceptions Homework

          Errors and Exceptions – Solutions

Function and Importing & Exporting

          Introduction to functional programming

          Function recipe

          Function parameters and resuse

          Recursive functions

          Creating modules

          Lamda

          Import & Export

          Text file

          CSV

          Excel

          JSON

          HTML 

PLOT

          Matplotlib

          seaborn

Library Numpy (array matrix)

          Numpy

Library Panda (complete)

          Panda

Regular Expression

          Pattern Matching And replacement

Date and Time and Missing Treatment

          Date and Time

          Data wrangling and cleaning

          Missing treatment

Python & MySQL Connection

Web Scraping using Python

Predictive Analytics and Machine Learning using Python

Introduction to Analytics

•      Evolution of Analytics

•      Definition of Analytics

•      Scope of analytics in different industries

Types of Analytics

•      Descriptive Analysis

•      Predictive Analysis

•      Prescriptive Analysis

Concepts of Analytics

 •     Confirmatory & Exploratory Analysis

•      Different Scale of Measurement-Nominal, Ordinal, Interval

•      Ratio Attribute and Variable concept

•      Graphical Representation of Data

•      Measures of Central Tendency-Mean, Median, Mode

•      Measures of Dispersion-Range, Variance, Standard Deviation

•      Measures Of Location-Quartiles, Interquartile Range

•      Outliers & Box Plot Graphs

Probability

•      Concept of Probability

•      Probability mass function

•      Random Variables-Discrete and Continuous

•      Binomial Distribution

•      Poisson Distribution

•      Normal Distribution

Sampling Theory

•      Concept of sampling: Population and Sample

•      Types of Sampling

•      Probability  sampling-Simple,  Stratified, Systematic

•      Non probability Sampling-Convenience, Judgmental

•      Testing Of Hypothesis-Null and Alternative

•      Type I error and Type II error

•      Significance level

•      Confidence Interval

Parametric Test

•      Concept of Parametric test

•      Z test

•      T test

•      Two independent sample T test

•      Paired sample T test

Association between Variables

•      Chi square Test for Independence

•      Scatter Plot

•      Correlation

•      Partial Correlation

Analysis Of Variance (ANOVA)

•      One-Way & Two-Way ANOVA

•      Concept of Eigen Value and Eigen Vector

Introduction of Machine Learning

Introduction to Data Science and Artificial Intelligence

Introduction to Machine Learning

How Artificial Intelligence relates to Machine Learning

History of Machine Learning

Introduction of Basic Mathematical concepts used in Machine Learning

Vectors

•      Vectors Operations ( Addition , subtract, multiplication )

•      Sparsh Vector

•      Dense Vector

•      Eigen Values

•      Eigen Vectors

Machine Learning using Python Programming

Machine Learning Algorithms in Theory

Type of Machine Learning Algorithms

•      Supervised

•      Unsupervised

•      Recommendation Systems

Supervised Learning

•      Classification

Decision Trees

Naïve Bayes

Gaussian Naïve Bayes

Logistic Regression

Linear Discriminant Analysis

K- Nearest Neighbour ( KNN )

Support Vector Machine (SVM)

•      Regression

Linear Regression

Ridge Regression

LASSO Regression

ElasticNet Regression

Decision Tree Regressor

KNN Regressor

Support Vector Regressor

          •        Ensemble Algorithm

Boosting

Random Forest

Extra Trees

AdaBoost

Gradient Boosting Machine

Unsupervised Learning

Dimension Reduction using PCA

K Mean Clustering

Recommendation Systems

• Collaborative filtering

• Content based filtering

-Singular value decomposition

Text Mining and Introduction to NLP

Regular Expression

Bag of Word Model

Projects and Case Study

Recommender System (recommend movies to watch)

Titanic (EDA and ML)

SMS spam classification

Yelp reviews classification

Boston House price prediction

Irish data classification

Sonar vs Rock data classification

Election data analysis

Cancer classification

Time series Analysis

    • Fundamentals

What is Time Series Forecasting

Time Series as Supervised Learning

Load and Explore Time Series Data

Feature Engineering for Time Series

Time Series Visualization

Resampling and Interpolation

Power Transforms

Moving Average Smoothing

White Noise

Introduction to the Random Walk

Decompose Time Series Data

Stationarity in Time Series Data

Backtest Forecast Models

Persistence Model for Forecasting

Residual Forecast Errors

Introduction to the Box-Jenkins Method

Autoregression Models for Forecasting

Moving Average Models for Forecasting

ARIMA Model for Forecasting

Autocorrelation and Partial Autocorrelation

Grid Search ARIMA Model Hyperparameters

Save Models and Make Predictions

Projects

Project: Monthly Armed Robberies in Boston

Project: Annual Water Usage in Baltimore

Project: Monthly Sales of French Champagne

Project: Stock market analysis and Risk analysis

Project: Stock market prediction

*******  Capstone Project --- Choosen  by candidate  that  will be monitored by instructor to complete the project.

 Gradient Boosting Trees with XGBoost

XGBoost Basics

- Introduction to Gradient Boosting

- AdaBoost the First Boosting Algorithm

- Introduction to XGBoost

-Algorithm  XGBoost

-XGBoost Model in Python with scikit-learn

-Problem Description: Predict Onset of Diabetes

-Predictions with XGBoost Model

-Data Preparation for Gradient Boosting

-Evaluate Models With k-Fold Cross-Validation

-Plot  XGBoost Decision Tree

  2.    XGBoost Advanced

           - Save and Load Trained XGBoost Models

           - XGBoost and Feature Selection

           - Monitor Training Performance and Early Stopping

           -Tune Multithreading Support for XGBoost

           - Train XGBoost Models in the Cloud with Amazon Web Services

  3.   XGBoost Tuning 

           -  Tune the Number and Size of Decision Trees with

  - Tune Learning Rate and Number of Trees with XGBoost

           - Tuning Stochastic Gradient Boosting with XGBoost

Deep Learning

For

Natural Language Processing

1- Foundations

1.1 Natural Language Processing

2- Deep Learning

2.1 - Deep Learning for Natural Language

3- Develop Deep Learning Models with Keras

3.1 Keras Model Life-Cycle

3.2 Keras Functional Models

4- Data Preparation

4.1 How to Clean Text Manually and with NLTK

4.2 Metamorphosis by Franz Kafka

4.3 Tokenization and Cleaning with NLTK

4.4 Additional Text Cleaning

5- Prepare Text Data with scikit-learn

5.1 The Bag-of-Words Model

5.2 Word Counts with CountVectorizer

5.3 Word Frequencies with TfidfVectorizer

5.4 Hashing with HashingVectorizer

6- Prepare Text Data with Keras

6.1 Split Words with text to word sequence

6.2 Encoding with one hot

6.3 Hash Encoding with hashing trick

6.4 Tokenizer API

7- Bag-of-Words

8 The Bag-of-Words Model

8.4 Example of the Bag-of-Words Model

8.5 Managing Vocabulary

8.6 Scoring Words

9 - Prepare Movie Review Data for Sentiment Analysis

10- Project: Develop a Neural Bag-of-Words Model for Sentiment Analysis

11- Word Embeddings

11.1 How to Develop Word Embedding with Genism

11.2 Genism Python Library

11.3 Develop Word2Vec Embedding

11.4 Visualize Word Embedding

11.5 Load Google's Word2Vec Embedding

11.6 Load Stanford's GloVe Embedding

11.7 Learn and Load Word Embeddings in Keras

12 - Text Classification

12.1 Neural Models for Document Classification

12.2 Word Embeddings + CNN = Text Classification

13- Project: Develop an Embedding + CNN Model for Sentiment Analysis

14- Project: Develop an n-gram CNN Model for Sentiment Analysis

15- Language Modeling

15.1 Neural Language Modeling

15.2 Statistical Language Modeling

15.3 Neural Language Models

16- Develop a Character-Based Neural Language Model

16.1 Sing a Song of Sixpence

16.2 Generate Text

16.3 Develop a Word-Based Neural Language Model

16.4 Jack and Jill Nursery Rhyme

16.5 Model 1: One-Word-In, One-Word-Out Sequences

16.6 Model 2: Line-by-Line Sequence

16.7 Model 3: Two-Words-In, One-Word-Out Sequence

17- Project: Develop a Neural Language Model for Text Generation

17.1 The Republic by Plato

18- Image Captioning

18.1 Neural Image Caption Generation

18.2 Describing an Image with Text

18.3 Encoder-Decoder Architecture

19- Neural Network Models for Caption Generation

19.1 Image Caption Generation

19.2 Load and Use a Pre-Trained Object Recognition Model

19.3 ImageNet

19.4 The Oxford VGG Models

19.5 Load the VGG Model in Keras

19.5 Develop a Simple Photo Classifier

20-   BLEU Score

20.1 Bilingual Evaluation Understudy Score

20.2 Calculate BLEU Scores

20.3 Cumulative and Individual BLEU Scores

20.4 Prepare a Photo Caption Dataset for Modeling

21- Project: Develop a Neural Image Caption Generation Model

21.1- Machine Translation

21.2 Statistical Machine Translation

21.3 Neural Machine Translation

21.4 Encoder-Decoder Models for Neural Machine Translation

21.5 Encoder-Decoder Architecture for NMT

21.6  Sutskever NMT Model

21.7 Cho NMT Model

22- Configure Encoder-Decoder Models for Machine Translation

22.1 Encoder-Decoder Model for Neural Machine Translation

23- Project: Develop a Neural Machine Translation Model

German to English Translation Dataset

Projects ----

Sentiments Analysis

Twitter Data Analysis

Fake News Classifier

Topic Modelling

Major Projects

1-Chatbot using Deep Learning

2-Recommender System

3-Language Translation

4-Credit Risk Modelling

 **Note:**

The syllabus is subject to adjustments based on the pace of the class and emerging developments in the field of data engineering with PySpark, Scala, and Shell scripting.