datascience training

Data science course content

 

Module 1: Python Language

Module 2: R Programming

Module 3: Advanced Python for DS, Numpy, Scipy, Scikit-learn,            

                   Pandas, Theano, Tensor flow, Keras

Module 4: Machine Learning

Module 5: Speech Reorganization and Image Analytics

 

Module 6: Natural Language Processing with Sentiment Analysis

Module 7: Deep Learning

Module 8: Adv NLP with Deep Learning:

Module 9: Artificial Intelligence

Module 10: HADOOP with Spark

 

Module 1: Python Language

1 Python Programming Basics::

  • Demo: Evolution and Top Computer Languages, Why step into Python, Python Features,

Why Python is Year-on-Year Demand, Python Frameworks, Career Perspective in Python,

Job Opportunities in Python, Where Python is Used, Compare with Other Popular Languages

  • org, Python Installation & Introduction, What are Operators, Expressions, Data Types,

What are Variables and Rules, Statements, Python Shell, Program Execution, Python Editors?

Different types of Comments and Functions, Programming Rules

  • Boolean Values, Data type Conversions, Flow Controls: If-else, If-elif-else, Assignment,

Augmented Operators, Multiple Assignment,

Progs:   Biggest among 3 values, Given number is Even or Odd

 

  • While Loop, For Loop, Range() function, Break, Continue, Pass, exit(), Infinite Loops

Progs: Print Sum of the Given Range of values, Print Given Range of Tables,

  • Functions, Parameter types, Loops with Functions, Return statement, Optional and Named Parameters

Value and Reference Types, Local and Global Scope,

Progs:  Find Given number is Prime or not

  • LIST, Tuple, Type, Index, Slicing, Sub lists, Concatenation, Replication, in and not in, append,

index(), insert(), remove(), sort(), like, Mutable, Immutable, Converting types

Dictionary Type: keys(), values(), items(),  get(), setdefault(),pprint(), pformat()

 

2 Strings :: 2 Hours

in String Literals, Escape Sequences, Raw strings, Triple Quotes, String Concatenation and Replica,  Multiline Strings, Indexing and Slicing, in and not in Operators, String Methods, Regular Expressions, Pattern Matching, matching Multiple groups

            Methods: lower(), upper(), title(), isalpha(), isdecimal(), isspace(), join(), split(), rjust(), ljust(), strip(), lstrip(),startswith(), endswith(), isdigit(), maketrans(), replace(), max(), min(), replace(), index(),  rindex(), find(), rfind(), swapcase(), zfill() Modules: re, re.compile(), re.search(), format(),

Programs: User need to input until correct age and pwd, Program to find given string is Palindrome, To

Remove special Characters, Sort Words alphabetical order,

Print list of Unicode chars, Common Characters in strings,

Substring in a string, to check Phone Number string

3 Files: 3 Hours

            Reading and Writing Files, Absolute and Relative path, file methods, Open files, Reading file, Writing or Appending data, Current Working directory, File path, checking path directory, store list of variables onto a file, Savings variables using Shelve module, Shutil module, Moving and Renaming Files and Folders, Permanently Deleting Folders and files, Command Line Arguments

Methods: open(), read(), Write(), close(), readLine(), seek(), tell(), makedirs(), getcwd(), getsize(), listdir(), isfile(), isdir(), pprint.pformat(), unlink(),  rmdir(), rmtree(), os.walk(), shutil.copy(), copytree(), move(), read(n), readlines(), truncate(), argv

Programs: Reading data from a file character wise, Line by Line,

Accept String and write onto a file until user presses Enter key

Find Given String in a File, Program to Merge Name and body,

Display list of Folders, Filenames and File size, Restore data from drive

 

4 Debugging: 2 Hours

Types of Errors, Syntax and Logical Errors, Runtime Error, Exception Handling, Try-Except, Types of Exception, Multiple Exception blocks, Raise Exception, Handle Multiple Exceptions, IDLE’s Debugger,

Go, Step, Over, Out, Quit, Debugger Mode, Global, and Local Variables, Debugging Mutable and Immutable Objects, Treace outs, Breakpoints,

 

Programs: How to handle Runtime Error, How to Handle Multiple Exceptions, Raising Exceptions

Program using Mutable and Immutable Objects

5 Functions: 4 Hours

Passing Parameter to a function, Default Parameters, Keyword Arguments, Recursive Functions, Inner Functions, Nested Functions, Typeless Functions, Returning Multiple Parameters, Mixed List and Dictionary Parameter Passing, Recursive Functions, ASCII and Unicode, Enumerate, Generator Function

Lambda Function, Collecting lambda functions in LIST,

            Methods: ord(), chr(), Enumerate(), yield(), zip(), dir(), help(),

Programs: Difference between Normal and Lambda Function, Return List of elements which are divisible

by 2 using Lambda, collecting List of values in  Lambda functions.

6 Collections: 5 Hours

 

List, Set, Tuple and Dictionary Data types, Mutable and Immutable Data Types, Working with References, List Concatenation and Replica, Nested List : List of Lists, Stack and Queue DS, List Slicing, Two and Three  Dimensional Matrix,  List Comprehension, Multiple Assignment, dictionary methods,  pretty Printing, set, union, intersection, difference, Swapping values using or, xor

            Methods: append(), extend(), insert(), remove(), clear(), index(), count(), sort(), copy(), reverse(), len(), list(), enumerate(), max(), min(), sum(), append and pop(), items(), values(), keys(), setdefault(),

Programs: Search a key in a List, Difference between Extend and Append in a List,

Intersect strings and List, Sort and Reverse using Lists and Tuples,

Using Nested Lists, Stack and Queue, Slice Insert and Delete,

Addition of two Matrices, Multiplication of Matrix, Transpose of a Matrix,

Fibonacci Series, Find number of Primes in a List, Building List Comprehension,

 

8 Regular Expressions

Jupyter (iPython) Notebooks, Regular Expression Basics , Regex Groups and the Pipe Character,  Repetition in Regex Patterns and Greedy / Non greedy Matching, Regex Character Classes and the findall() Method , Regex Dot-Star and the Caret/Dollar Characters, Regex sub() Method and Verbose Mode, Regex

Program: A Phone and Email Scraper

 

9 Python Decorators and Generators

Generator Syntax, Iteration, and Generators, Creating Generators, Decorators Purpose, Simple Function Decorators, Classes as Decorators, Decorator Arguments

           

                Module 2:  Advanced Python

                Duration: 30 Hours

 

OOPs:

Procedure and Object Oriented, Code Reusability, Object-Oriented Concepts, What is Class and Object, OOPS Features: Encapsulation, Polymorphism, Abstraction, Inheritance, Types of Inheritance, what is a constructor, Passing parameters to Constructors, Initializing Objects, class method, static variable, and methods

 

Programs: What is Class and Object, Declaring self, Multiple methods call thru object,

Fibonacci Series, Find a number of Primes in a List, Building List Comprehension,

Example using Overloading and Overriding, Object Overloading

 

Python with Web Scraping

Web browser Module, Downloading from the Web with the Requests Module, Parsing HTML with the

Beautifulsoup Module, Controlling with the browser, Real Webpage Extraction, Walking the tree

Logging Levels, Logging setup, File and Rotating Logger

 

Serialization: Csv Module, XML Parsing, JSON Parsing, universal conversion

 

Python with Multithreading

Multithreading, Subprocess, Multiprocessing, Multithreading, Concurrency and Parallelism, Creating and Joining Thread, Daemon and Canceling Threads, Asynchronous Programming, Critical Section and deadlock

 

Python with Database (SQLite )

SQLite Introduction, Creating Database and Table, Inserting Data, Inserting Dynamic Data, Reading Data,

Limit, MySQL Basics, Nohup, Crontab, SQLAlchemy, Querying, Creating Tables, Updating Records, Deletion

cursors

 

Python with GUI (Graphical User Interface) Automation

            Controlling the Mouse from Python, Controlling the Keyboard from Python, Screenshots and Image

Recognition, checkbox, Multi-section, a Dialogue box

 

Python with Networking

TCP/IP Basics, Simple TCP Client, Simple UDP Client, Socket Programming, Emailing, FTP, SSL Context, Creating a ClientFactory, Twisted Protocol Support, Using Twisted SMTP Protocol, Creating Network Servers

Unit Testing with unit test (PyUnit)

About unit test (PyUnit), The Test Case Class, TestCase Methods, Checking & Reporting Failures, Setting up Simple Tests, Compiling Test Suites, Working with Test Result Objects, Using Test Loader Objects

Introduction to Pandas, Django, Flask, Numpy, ticket

 


 

              Module 3: Machine Learning

              Duration: 30 Hours

 

 R-Language Essential for Data Science: 2 Hours

Data Types, Importing Data from a Comma delimited file, from Excel, from SPSS

from SAS, from Stata and From RDBMS

Exporting data:

               To a text file, to Excel,  to SAS, to Stata,  to RDBMS

  • R operators, R decision Making, R loops, viewing data
  • Variable labels [using Hmisc package], value labels
  • Handling missed values. Handling Strings, Handling data values
  • Handling Matrices and Multi-Dimensional Arrays
  • Handling data frames, Handling vectors. Handling lists
  • Working with XML files. working with JSON files
  • Statistical Models, you will be learning in Section III of ds-part-1 course.

Statistics and Mathematical Essentials for Data Science.

  • The measure of Central Tendency,
  • Mean: Arithmetic Mean, Geometric Mean, Harmonic Mean
  • Mode: Median
  • Dispersion techniques:
  • Range, Inter-Quartile Range
  • Variance, Standard Deviation
  • Correlational Analysis
  • Introduction to Machine Learning.
  • Approaches of Machine Learning.

* supervised learning

* unsupervised learning

* semi-supervised learning

* reinforcement learning

  • Predictive models & Introduction.

* Regression problems [Introduction]

* Classification Problems [Introduction]

  • Regression Models [Deeper Study]
  • Linear Regression
  • Non Linear Regression
  • Accuracy Measurement
  • choosing Models
  • Lasso Regression
  • Ridge Regression
  • Gradient Descent Algorithm
  • Stochastic Gradient descent Algorithm.
  • More Models in Remaining Modules.

[Above all will be implemented in R, Python]

Constructing a Classifier [Classification Models]

  • Building a simple classifier
  • Building a logistic regression classifier
  • Building a Naive Bayes classifier
  • Building a Decision Tree
  • Building a Random Forest
  • Splitting the dataset for training and testing
  • Evaluating the accuracy using cross-validation
  • Visualizing the confusion matrix
  • Extracting the performance report
  • Evaluating cars based on their characteristics (task)
  • Extracting validation curves
  • Extracting learning curves
  • Estimating the income bracket (task)

Machine Learning Using SVM Models:

  • What is the goal of the Support Vector Machine (SVM)?
  • How to compute the margin?
  • How to find the optimal hyperplane?
  • Unconstrained minimization
  • Convex functions
  • Duality and Lagrange multipliers
  • Support Vector Machines (SVM) Overview and Demo using R
  • SVM implementation By Python
  • Building a linear classifier using Support Vector Machines(SVMs)
  • Building a nonlinear classifier using SVMs
  • Tackling class imbalance
  • Extracting confidence measurements
  • Finding optimal hyperparameters
  • Building an event predictor
  • Estimating traffic

Working With Time Series and Sequential Data:

  • Transforming data into the time series format
  • Slicing time series data
  • Operating on time series data
  • Extracting statistics from time series data
  • Building Hidden Markov Models for sequential data
  • Building Conditional Random Fields for sequential text data
  • Analyzing stock market data using Hidden Markov Models

 Machine Learning with Clustering Modeles[ unsupervised Learning]

  • what is clustering? types of clusters.
  • Clustering data using the k-means algorithm
  • Compressing an image using vector quantization
  • Building a Mean Shift clustering model
  • Grouping data using agglomerative clustering
  • Evaluating the performance of clustering algorithms
  • Automatically estimating the number of clusters using DBSCAN algorithm
  • Finding patterns in stock market data
  • Building a customer segmentation model
  • Python Examples
  • R Examples

Text Mining With Natural Language Processing with NLTK(Python):

  • Introduction to NLP and NLTK
  • Preprocessing data using tokenization
  • Stemming text data
  • Converting text to its base form using lemmatization
  • Dividing text using chunking
  • Building a bag-of-words model
  • Building a text classifier
  • Identifying the gender
  • Analyzing the sentiment of a sentence
  • Identifying patterns in text using topic modeling

  Speech Recognition :

  • Reading and plotting audio data
  • Transforming audio signals into the frequency domain
  • Generating audio signals with custom parameters
  • Synthesizing music
  • Extracting frequency domain features
  • Building Hidden Markov Models
  • Building a speech recognizer

Building Recommendation Engines:

  • What is recommendation Engine
  • Types of Recommendation Engines
  • Pattern-Based Recommendations Using Apriori and FPGrowth.
  • Collaborative Filtering
    • IBCF (Item Based)
  • UBCF (User Based)
  • Graph-Based Recommendations.
  • Building function compositions for data processing
  • Building machine learning pipelines
  • Finding the nearest neighbors
  • Constructing a k-nearest neighbors classifier
  • Constructing a k-nearest neighbors regressor
  • Computing the Euclidean distance score
  • Computing the Pearson correlation score
  • Finding similar users in the dataset
  • Generating movie recommendations

Free WorkShop On 

  • Image Content Analysis
  • Biometric Face Recognition.

 

Free BigData Stuff:

  • Hadoop
  • Spark with scala and Python.
  • Data Science part-II

Module 7: Deep Learning

   Duration: 30 Hours

 

Deep Learning 

  • What is Deep Learning?
  • Introduction to Artificial Neural Networks
  • The Neuron
  • The Activation Function
  • How do Neural Networks work?
  • How do Neural Networks learn?
  • Gradient Descent
  • Stochastic Gradient Descent
  • Back propagation
  • Business Problem Description
  • Building an ANN – Step 1, Step 2, Step 3, Step 4 & Step 5

Convolutional neural networks introduction:

  • Convolution Operation
  • step1(b)- ReLU Layer
  • Different Activation functions and when to use them.
  • step2 — Pooling
  • step3 — Flattening
  • step4 — Full Connection
  • Summary
  • Softmax & cross entropy
  • Building a CNN – step1, step2, step3, step4, step5, step7, step8, step9, step10
  • Case study: what the pet is ?

 Introduction to Recurrent Neural Networks:

  • The idea behind RNN
  • The vanishing Gradient Problem
  • LSTMs
  • LSTM variations
  • Building a RNN – step1, step2, step3, step4, step5, step6, step7, step8, step9, step11& step12
  • Summary & Next steps
  • case Study: Google stock price prediction.
  • Evaluating the RNN
  • Improving and Tuning the RNN

Self Organizing Maps:

  • Introduction to SOMs
  • . How do SOM works?
  • why revisit K-Means ?
  • K-means Clustering(Refresher)
  • How do SOMs Learn?(part 1)
  • How do SOMs Learn?(part 2)
  • Live SOM example
  • Reading an Advanced SOM
  • Extra: K-means clustering(part 2)
  • Extra: K-means Clustering(part 3)
  • Building a SOM – step 1, step 2, step 3, step 4
  • Mega Case Study – step1 , step2, step3, step4

Introduction to Boltzmann Machines:

  • Energy Based Models (EBM)
  • Restricted Boltzmann Machine
  • Contrastive Divergence
  • Deep Belief Networks
  • Deep Boltzmann Machines
  • Building a Boltzmann Machine – Introduction
  • Building a Boltzmann Machine – step 1, step 2,step 3, step 4,step 5,step 6,step 7,step 8,step 9,step 10,step 11,step 12, step 13 & step 14

Introduction to AutoEncoders:

  • A note on biases
  • Training an Auto Encoder
  • overcomplete hidden layers
  • Sparse Autoencoders
  • Denoising Autoencoders
  • Contractive Autoencoders
  • Stacked Autoencoders
  • Deep AutoEncoders.
  • Building an AutoEncoder – step1, step2, step3, step4, step5, step6, step7, step8, step9, step10 & step11

Unsupervised Deep Learning:

  • Introduction to Unsupervised Deep Learning.
  • Introduction to Reducing Dimensionality
  • What does PCA do?
  • PCA derivation
  • MNIST visualization , finding the optimal number of principal components
  • PCA objective function
  • t-sne(t-distributed stochastic Neighbor Embedding)
  • t-SNE Theory-SNE on the Donut,t-SNE on XOR,t-SNE on MNIST
  • Autoencoders
  • Denoising Autoencoders
  • Stacked Autoencoders
  • writing the autoencoder class code (Theano)
  • Testing our Autoencoder(Theano)
  • Writing the deep neural network class code(Theano)
  • Autoencoder in Code( Tensorflow)
  • Testing greedy layer-wise autoencoder training vs. pure backpropagation
  • Cross-Entropy vs. KL Divergence
  • Deep AutoEncoder Visualization Description
  • Deep Autoencoder Visualization in Code
  • Restricted Boltzmann Machine Theory
  • Deriving Conditional Probabilities from Joint Probability
  • Contrastive Divergence for RBM training
  • RBM in Code(Theano) with Greedy Layer-Wise training on MNIST
  • RBM in Code(Tensorflow)
  • The Vanishing Gradient Problem Description
  • The Vanishing Gradient Problem Demo in Code