Course

Advanced Diploma in Data Science

The Python Data Science course teaches you to master the concepts of Python programming. Through this Data Science with Python certification training, you will learn Data Analysis, Machine Learning, Data Visualization, Web Scraping, & NLP. Upon course completion, you will master the essential tools of Data Science with Python.

Data Science is an evolving field and Python has become a required skill for 46-percent of jobs in Data Science. The demand for Data Science professionals will grow an estimated 1581-percent by 2022 and professionals with Python skills will have an additional advantage.

Data science is a concept to integrate Statistics, Analytics, informatics and analysis to convert an abstract from vast raw data. Mathematics, Computer science and information technology contribute to data science. Our Subject matter experts have designed the best curriculum which can make the students best among the best.

TOPICS TO BE COVERED IN THIS COURSE

C & C++ Programming

⦁ History
⦁ Features
⦁ Setting up path
⦁ Working with Python scripts
⦁ Basic Syntax
⦁ Variables and Data Types in scripting
⦁ Operators

⦁ If, If- else, Nested if-else statements
⦁ For, While loops
⦁ Nested loops
⦁ Control Statements

⦁ Accessing Strings
⦁ Basic Operations
⦁ String slices
⦁ Function and Methods

⦁ Accessing list
⦁ Operations
⦁ Working with lists
⦁ Function and Methods

⦁ Accessing tuples
⦁ Operations
⦁ Working
⦁ Functions and Methods

⦁ Accessing values in dictionaries
⦁ Working with dictionaries
⦁ Properties
⦁ Functions

⦁ Defining a function
⦁ Calling a function
⦁ Types of functions
⦁ Function Arguments
⦁ Anonymous functions
⦁ Global and local variables

⦁ Importing module
⦁ Math module
⦁ Random module
⦁ Packages
⦁ Composition

⦁ Printing on screen
⦁ Reading data from keyboard
⦁ Opening and closing file
⦁ Reading and writing files
⦁ Functions

⦁ Exception
⦁ Exception Handling
⦁ Except clause
⦁ Try ? finally clause
⦁ User Defined Exceptions

⦁ Class and object
⦁ Attributes
⦁ Inheritance
⦁ Overloading
⦁ Overriding
⦁ Data hiding

⦁ Match function
⦁ Search function
⦁ Matching VS Searching
⦁ Modifiers
⦁ Patterns

⦁ Architecture
⦁ CGI environment variable
⦁ GET and POST methods
⦁ Cookies
⦁ File upload

⦁ Connections
⦁ Executing queries
⦁ Transactions
⦁ Handling error

⦁ Socket
⦁ Socket Module
⦁ Methods
⦁ Client and server
⦁ Internet modules

⦁ Thread
⦁ Starting a thread
⦁ Threading module
⦁ Synchronizing threads
⦁ Multithreaded Priority Queue

⦁ Tkinter Programming
⦁ Tkinter widgets
⦁ Mail Communication in python scripts

PYTHON WEB APP DEVELOPMENT WITH DJANGO

⦁ What is Django?
⦁ DRY programming: Don’t Repeat Yourself
⦁ How to get and install Django

⦁ models.py
⦁ urls.py
⦁ views.py
⦁ Setting up database connections
⦁ Managing Users & the Django admin tool
⦁ Django URL Patterns and Views
⦁ Designing a good URL scheme
⦁ Generic Views
⦁ Django Forms
⦁ Form classes
⦁ Validation & Authentication
⦁ Advanced Forms processing techniques
⦁ Unit Testing with Django
⦁ Using Python’s unittest2 library
⦁ Test
⦁ Test Databases

⦁ Scrapy – Overview
⦁ Scrapy – Environment
⦁ Scrapy – Spiders
⦁ Scrapy – Item Pipelines
⦁ Scrapy – Link Extractors
⦁ Scrapy – various output consoles
⦁ Define item
⦁ First spider project
⦁ Crawling Content
⦁ Extracting item
⦁ Scraped data

⦁ What is Flask?
⦁ Flask – Overview
⦁ Flask – Environment
⦁ Flask – Application
⦁ Flask – Routing
⦁ Flask – Variable Rules
⦁ Flask – URL Building
⦁ Flask – SQLite
⦁ Flask – SQLAlchemy

⦁ The core: Image – load, convert, and save
⦁ Smoothing Filters A – Average, Gaussian
⦁ Smoothing Filters B – Median, Bilateral
⦁ OpenCV 3 with Python
⦁ Image – OpenCV BGR: MatplotLIB
⦁ Basic image operations – pixel access
⦁ iPython – Signal Processing with NumPy
⦁ Signal Processing with NumPy I – FFT and DFT for sine, square waves, unitpulse, and random signal
⦁ Signal Processing with NumPy II – Image Fourier Transform: FFT & DFT
⦁ Inverse Fourier Transform of an Image with low pass filter: cv2.idft()
⦁ Image Histogram
⦁ Video Capture and Switching colour spaces – RGB / HSV
⦁ Adaptive Thresholding – Otsu’s clustering-based image thresholding
⦁ Edge Detection – Sobel and Laplacian Kernels
⦁ Canny Edge Detection
⦁ Hough Transform – Circles
⦁ Watershed Algorithm: Marker-based Segmentation I
⦁ Watershed Algorithm: Marker-based Segmentation II
⦁ Image noise reduction: Non-local Means denoising algorithm
⦁ Image object detection: Face detection using Haar Cascade Classifiers
⦁ Image segmentation – Foreground extraction Grabcut algorithm based on graph cuts
⦁ Image Reconstruction – Inpainting (Interpolation) – Fast Marching Methods
⦁ Video: Mean shift object tracking

⦁ Installation
⦁ Features and feature extraction – iris dataset
⦁ Machine Learning Quick Preview
⦁ Data Preprocessing I – Missing / Categorical data
⦁ Data Preprocessing II – Partitioning a dataset / Feature Scaling / Feature Selection / Regularization
⦁ Data Preprocessing III – Dimensionality Reduction vs Sequential Feature Selection / Assessing Feature importance via random forests
⦁ Data Compression via Dimensionality Reduction I – Principal component analysis (PCA)
⦁ Data Compression via Dimensionality Reduction II – Linear Discriminant Analysis (LDA)
⦁ Data Compression via Dimensionality Reduction III – Nonlinear mappings via kernel principal component (KPCA) analysis
⦁ Logistic Regression, Overfitting & regularization
⦁ Supervised Learning & Unsupervised Learning – e.g. Unsupervised PCA dimensionality reduction with iris dataset
⦁ Unsupervised Learning – KMeans clustering with iris dataset
⦁ Linearly Separable Data – Linear Model & (Gaussian) radial basis function kernel (RBF kernel)
⦁ Decision Tree Learning I – Entropy, Gini, and Information Gain
⦁ Decision Tree Learning II – Constructing the Decision Tree
⦁ Random Decision Forests Classification
⦁ Support Vector Machines (SVM)

MACHINE LEARNING USING FLASK

⦁ Serializing with pickle & DB setup
⦁ Basic Flask App
⦁ Embedding Classifier
⦁ Deploy
⦁ Updating the Classifier

  • Batch Gradient Algorithm
  • Perceptron model on the Iris Dataset using Heaviside step Activation Function
  • Batch Gradient Descent Vs Stochastic Gradient Descent
  • Adaptive Linear Neuron using linear activation function with – batch gradient descent method
  • Adaptive Linear Neuron using linear activation function with – stochastic gradient descent (SGD)
  • Logistic Regression
  • VC (Vapnik-Chervonenkis) Dimension & Shatter
  • Bias – Variance trade-off
  • Maximum Likelihood Estimation (MLE)
  • Neural Networks with backpropagation for XOR using one hidden layer
  • minHash
  • tf-idf weight
  • Natural Language Processing (NLP)
    1. Sentiment Analysis
    2. IMDb & bag-of-words
    3. Tokenization, Stemming & stop words
    4. Training & Cross-Validation
    5. Out-of-Core
  • Locality-Sensitive Hashing (LSH) using Cosine Distance (Cosine Similarity)
  • Forward propagation
  • Gradient descent
  • Backpropagation of errors
  • Checking Gradient
  • Training via BFGS
  • Overfitting & Regularization
  • Deep Learning
  • Image Recognition (Image Uploading)
  • Image Recognition (Image Classification)
  • Theano, TensorFlow & Keras

DURATION

The duration of this course will be 2-3 Months, with 2-hour sessions each day.

CERTIFICATION POLICY

⦁ Certificate of Merit for all the participants.
⦁ At the end of this course, an assessment will be organized among the participating candidates, and front-runners will be awarded a ‘Certificate of Excellence’.