Python Training in Hyderabad Classroom, Online

Python Training in Hyderabad Classroom, Online

Python Training in Hyderabad

Python is a general-purpose interpreted, interactive, object-oriented and high-level programming language. Currently Python is the most popular Language in IT. Python adopted as a language of choice for almost all the domain in IT including Web Development, Cloud Computing (AWS, OpenStack, VMware, Google Cloud, etc.. ), Infrastructure Automations , Software Testing, Mobile Testing, Big Data and Hadoop, Data Science, etc. This course to set you on a journey in python by playing with data, creating your own application, and also testing the same. We call this course as Python for Everyone. myTectra offers Python Training in Hyderabad, Bangalore,Chennai, Pune using Class Room. We offers Live Online Python Training Globally.

Course Content:

Chaper 1: An Introduction to Python
Chapter 2: Beginning Python Basics 
2.1. The print statement
2.2. Comments
2.3. Python Data Structures & Data Types
2.4. String Operations in Python
2.5. Simple Input & Output
2.6. Simple Output Formatting
Chapter 3: Python Program Flow 
3.1. Indentation
3.2. The If statement and its' related statement
3.3. An example with if and it's related statement
3.4. The while loop
3.5. The for loop
3.6. The range statement
3.7. Break & Continue
3.8. Assert
3.9. Examples for looping
Chapter 4: Functions & Modules
4.1. Create your own functions
4.2. Functions Parameters
4.3. Variable Arguments
4.4. Scope of a Function
4.5. Function Documentation/Docstrings
4.6. Lambda Functions & map
4.7. An Exercise with functions
4.8. Create a Module
4.9. Standard Modules
Chapter 5: Exceptions
5.1. Errors
5.2. Exception Handling with try
5.3. Handling Multiple Exceptions
5.4. Writing your own Exceptions
Chapter 6: File Handling 
6.1. File Handling Modes
6.2. Reading Files
6.3. Writing & Appending to Files
6.4. Handling File Exceptions
6.5. The with statement
Chapter 7: Classes In Python 
7.1. New Style Classes
7.2. Creating Classes
7.3. Instance Methods
7.4. Inheritance
7.5.Polymorphism
7.6. Exception Classes & Custom Exceptions
Chapter 8: Regular Expressions 
8.1 Simple Character Matches
8.2 Special Characters
8.3 Character Classes
8.4 Quantifiers
8.5 The Dot Character
8.6 Greedy Matches
8.7 Grouping
8.8 Matching at Beginning or End
8.9Match Objects
8.10 Substituting
8.11 Splitting a String
8.12 Compiling Regular Expressions
8.13 Flags
Chapter 9: Data Structures 
9.1 List Comprehensions
9.2 Nested List Comprehensions
9.3 Dictionary Comprehensions
9.4 Functions
9.5 Default Parameters
9.6 Variable Arguments
9.7 Specialized Sorts
9.8 Iterators
9.9 Generators
9.10 The Functions any and all
9.11 The with Statement
9.12 Data Compression
Chapter 10: Writing GUIs in Python 
10.1 Introduction
10.2 Components and Events
10.3 An Example GUI
10.4 The root Component
10.5 Adding a Button
10.6 Entry Widgets
10.7 Text Widgets
10.8 Checkbuttons
10.9 Radiobuttons
10.10Listboxes
10.11 Frames
10.12 Menus
10.13 Binding Events to Widgets
Chapter 11: Network Programming 
Introduction
11.1 A Daytime Server
11.2 Clients and Servers
11.3 The Client Program
11.4 The Server Program
11.5 Recap
11.6 An Evaluation Client and Server
11.7 The Server Portion
11.8 A Threaded Server
Chapter 12 : Python MySQL Database Access 
Introduction
12.1 Installation
12.2 DB Connection
12.3 Creating DB Table
12.4 INSERT, READ,UPDATE, DELETE operations
12.5 COMMIT & ROLLBACK operation
12.6 Handling Errors
To Learn Python Training in Hyderabad using Class Room and Live Online Python Training in Hyderabad

Angular 4 Training in Hyderabad India

Angular 4

angular 4 training

Duration: 25hrs

Angular4 Training Course Content
  • TypeScript
  • Real-GitHub Environment
  • In-Depth Routing
  • Nodejs Integration Restful API
  • Automation Tool Bower
  • Gulp
  • Test Cases with Karma
  • Mini Project with Real-Time Environment

React Native Training in hyderabad india

React Native Training in hyderabad india

React Native
 


React Native lets you build mobile apps using only JavaScript. It uses the same design as React, letting you compose a rich mobile UI from declarative components.

With React Native, you don't build a “mobile web app”, an “HTML5 app”, or a “hybrid app”. You build a real mobile app that's indistinguishable from an app built using Objective-C or Java. React Native uses the same fundamental UI building blocks as regular iOS and Android apps. You just put those building blocks together using JavaScript and React

The React Native framework is an ideal way for Web / JavaScript developers to get into developing mobile applications for iOS and Android devices, as well as reusing code for / from web apps too.

Duration: 25hrs

Prerequisites


  • Delegates should be comfortable coding JavaScript from scratch, and web fundamentals (HTML & CSS).
Course Content:

Intro to React Native
  • What it is, who is developing it, and why you should use it
Getting Started with React Native
  • Setting up your development and testing environment.
React Native Tools
  • Console + editor
Javascript ES6 Overview
  • The tricky bits from a native mobile divs perspective.
Create your first React Native app
  • Firing up the simulators on iOS and Android
  • Exploring Project Structure
Developing your UI with JSX
  • Adding controls to your UI
  • Buttons & Text Labels
  • Styling - in JavaScript
  • Interactive Design
  • Creating custom Components
  • Properties (props)
  • Managing State
  • Populating & Manipulating Lists
Using open source (NPM)

Going deeper with React Native
  • Dynamic properties
  • Dynamic styles
  • More on State and how it effects the rendering pipeline
  • Network requests.
  • Navigation
  • Storing data - Realm for React Native
  • Integrating with Map APIs
  • Creating native React components.
  • How to share code effectively between iOS & Android




All Indian trainings Reviews
4.8 rating, out of 5
based on 251 Professionals and Students.

Upcoming Batches:
  • 21 OCT Saturday 7:00 AM IST
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IBM InfoSphere Data Replication - InfoSphere Change Data Capture Training in Hyderabad India

IBM InfoSphere Data Replication - InfoSphere Change Data Capture Training in Hyderabad India

IBM InfoSphere Data Replication

This course will teach about the InfoSphere Change Data Capture (CDC) component of the IBM InfoSphere Data Replication family of solutions. This course will examine the architecture, components and capabilities of CDC, and discuss various ways to setup and implement the software. This course will explore how to operate and troubleshoot CDC and discuss "Best Practices" in maintaining the Environment. Lastly, use cases will be provided to help student understand how replication is used using InfoSphere Change Data Capture to a Business Environment.

Course Duration: 25hrs

Course Content:

  • 1:  An Introduction to InfoSphere Change Data Capture
  • 2:  InfoSphjere Change Data Capture Architecture
  • 3:  InfoSphere Change Data Capture Components
  • 4:  InfoSphere Change Data Capture Capabilities
  • 5:  InfoSphere Change Data Capture, Other Capabilities
  • 6:  InfoSphere Change Data Capture Apply Methods
  • 7:  InfoSphere Change Data Capture Replication Scenarios
  • 8:  Setting Up InfoSphere CDC Replication
  • 9:  Collision Detection and Resolution
  • 10: Monitoring Infosphere CDC
  • 11: InfoSphere CDC Utilities
  • 12: Troubleshooting InfoSphere CDC
  • 13: InfoSphere Use Cases
  • 14 InfoSphere CDC Best Practices
Data Science Training in Hyderabad | Best Data Scientist Training India

Data Science Training in Hyderabad | Best Data Scientist Training India

Data Science is considered as the new arena, which is the most emerging technology that can easily enhance the Organizational growth. Data Administration and Management is being the biggest challenges that can face real time challenges in the explosion of happening these days.

What is Data Science?


Data Science is the software library framework which allows for the distributing processing large sets of data across a cluster of computers by using simple programming tools. It can easily scale up from a single server to thousands of machines in an easy manner.

Prerequisites and Requirements of Data Scientist



There are no pre-requisites. No prior knowledge of Statistics, the language of R, Python or analytic techniques is required.
This course covers from basic to advanced Statistics and Machine Learning Techniques


Duration

40 to 50 Hours

Course Content:


Introduction to Data Science
• What is Data Science?
• Role of Data Science
• Scope of Data Science
1. Descriptive and Inferential Statistics
 Samples and Populations
• Sample Statistics
• Estimations of Population Parameters
• Random and Non-random Sampling
• Sampling Distributions
• The Central limit Theorem
• Degree of Freedom
 Percentiles and Quartiles
 Measures of Central Tendency
• Mean
• Median
• Mode
 Measures of Variability/Dispersions
• Range
• IQR
• Variance
• Standard Deviation
 Distributions
• Normal Distributions
• Binomial Distribution
 Probability Distribution
• Events, Sample Space and Probabilities
• Conditional Probabilities
• Independence of Events
• Bayes’ Theorem
 Random Variable
 Confidence Intervals
 Hypothesis Testing
• Null Hypothesis
• The Significance Level
• p-value
• Type I and Type II Errors
 Inferential Test Metrics
• t test
• f test
• Z test
• Chi square test
• Student test
 The Comparison of Two Populations
 Analysis of Variance
• ANOVA Computations
• Two-way ANOVA
 Similarity Metrics
• Euclidean Distance
• Jaccard Distance
• Cosine Similarity
 Graphical Representation and summaries
2. Data Exploration
 Variable Identification
 Uni-variate Analysis
 Bi-variate Analysis
 Missing Values Treatment
• Imputation
• Deletion
• Prediction
 Outlier Detection
• Deletion
• Binning and Transformation
 Feature Engineering
• Variable transformation
• Variable / Feature creation
 Dimensionality Reduction
• Missing Values
• Low Variance
• High Collinearity
• PCA
• Factor Analysis
 Principal Component Analysis
 Data Summaries Using Stats and plots
 Covariance, Correlation, and Distances
 Correlation vs Causation
3. Machine Learning: Introduction and Concepts
 Differentiating algorithmic and model based frameworks
 Supervised Learning with Regression and Classification
• Model Validation Approaches
• Training Set
• Validation Set
• Test Set
• Cross-Validation
• Regression Algorithms
• Linear Regression
• Ordinary Least Squares
• Ridge Regression
• Lasso Regression
 Unsupervised Learning
• Clustering
• Hierarchical (Agglomerative) Clustering
• Non-Hierarchical Clustering: The k-Means Algorithm
 Recommender Engines:
• Collaborative Filtering Recommenders
• Content Based Recommenders
4. R-Analytical Tool (Data Mining / Machine Learning)
 Basic Data Types
 R Data Structures
• Vectors
• Matrix
• Data Frames
• List
 R Functions
 Predictive Modeling Project based on R
 Classification Model Attention:ing Project based on R
 Clustering Project based on R
 Association Mining Project based on R
 R Visualization Packages
 Machine Learning Packages in R
5. Python Scientific Libraries for Machine Learning
 Scikit-Learn
 Numpy
 Scipy
 Pandas
 Matplotlib
• Rmsc
• R/Square
• K Nearest Neighbors Regression & Classification
• Classification
• Logistic Regression
• Naive Bayes
• Classifier Threshold And Interpretation
• Confusion Matrix-Error Measurement
• Roc Curve
• Accuracy, Precision, Recall
• Measuring Sensitivity And Specificity
• Regression And Classification Trees
• Decision Trees
• Recursive Portioning
• Impurity Measures (Entropy And Gini Index)
• Pruning The Tree
 Support Vector Machines
 Ensemble Methods
• Bagging (Parallel Ensemble) – Random Forest
• Boosting (Sequential Ensemble) – Gradient Boosting
 Neural Networks
• Structure Of Neural Network
• Hidden Layers And Neurons
• Weights And Transfer Function
 Deep Learning
 Forecasting (Time-Series Modeling )
• Trend And Seasonal Analysis
• Different Smoothing Techniques
• Arima Modeling
6. Spark Mllib (Scalable Machine Learning)
 Spark Vs Hadoop
 Spark Architecture
 Distributed Computing Advantages
 Rdd Concept
 Spark Mllib: Data Types, Algorithms, And Utilities

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