HTML
• Introduction to WEB
• HTML Basic Tags
• HTML Formatting Tags
• HTML Lists
• HTML Tables
• HTML Images
• HTML Anchors
• HTML Forms
• HTML Input Elements
• HTML Form Attributes
• HTML Frames
• HTML GET/POST
• HTML5 Drag/Drop
• HTML Canvas
• HTML Multimedia

CSS 
• CSS Basics
• CSS Introduction
• CSS Syntaxes
• CSS Styling,
• Styling Backgrounds
• Styling Fonts,
• Styling Links,
• Styling Lists
• Styling Tables
• CSS Borders
• CSS Selectors
• CSS Fonts
• CSS Text Effects
• CSS Backgrounds
• CSS keyframes Rule
• CSS Multiple Columns
• CSS 2D Transforms
• CSS 3D Transforms
• CSS Transitions
• CSS Animations
• CSS User Interface

Java Script

• What is Script? Types of Scripts?
• Introduction to JavaScript
• Comments and Types of Comments
• Popup Boxes
• Variables & Operators
• JavaScript Functions and Events
• Conditional Statements
• Looping Control Statements

Django Framework

I) INTRODUCTION

Core Python, Advanced Python  

Python For Data Science

Introduction to Data Science

• • Machine Learning Introduction
  • Datasets
  • Supervised /Unsupervised Learning
  • Statistical Analysis
  •  Data Analysis
  •  Uni-variate/multi-variate analysis
  •  Correlation Analysis
  •  Algorithm types
  •  Applications

  Python Matplotlib

  • Introduction to matplolib
  • Installing matplotlib
  • Generating graphs
  •  Generating Bargraphs
  •  Histograms
  •  Scatter plots
  •  Stack plots
  •  Pie plots

  PANDAS
  Pandas – Introduction
  Pandas – Environment Setup

  • Series
  •  Data Frame
  •  Data Type of Columns
  •  Panel

  Pandas — Series

  • Series
  •  Create an Empty Series
  •  Create a Series f
  •  from ndarray
  •  from dict
  •  Accessing Data from Series with Position
  •  Retrieve Data Using Label (Index)

  Pandas – DataFrame

  • DataFrame
  •  Create DataFrame
  •  Create an Empty DataFrame
  •  Create a DataFrame from Lists
  •  Create a DataFrame from Dict of ndarrays / Lists
  •  Create a DataFrame from List of Dicts
  •  Create a DataFrame from Dict of Series
  •  Column Selection
  •  Column Addition
  •  Column Deletion
  •  Row Selection, Addition, and Deletion
  •  Pandas – Panel

  Panel()

  •  Create Panel
  •  Selecting the Data from Panel
    Pandas – Working with Text Data
    Pandas – Indexing and Selecting Data
  • loc()
  • .iloc()
  •  Use of Notations

  Pandas – Aggregations

  • Applying Aggregations on DataFrame

  Pandas – Missing Data

  •  Cleaning / Filling Missing Data
  •  Replace NaN with a Scalar Value
  •  Fill NA Forward and Backward
  •  Drop Missing Values

  Replace Missing (or) Generic Values

  • Pandas – GroupBy
  •  Split Data into Groups
  •  View Groups
  •  Iterating through Groups
  •  Select a Group
  •  Aggregations
  •  Transformations
  •  Filtration

  Pandas – Merging/Joining

  • Merge Using ‘how’ Arguments
  • Pandas – Concatenation
  •  Concatenating Objects

   

  • Pandas – Merging/Joining
    • Merge Using ‘how’ Arguments
    • Pandas – Concatenation

    Pandas – Concatenation

    • Concatenating Objects
    • Time Series

    Pandas – Date Functionality

    Pandas – Timedelta

    Pandas – Categorical Data

    • Object Creation

    Pandas – Visualization

    • Bar Plot
    • Histograms
    • Box Plots
    • Area Plot
    • Scatter Plot
    • Pie Chart

    Pandas – IO Tools

    • csv

    Pandas – Comparison with SQL

     

  • NUMPY

    NUMPY − INTRODUCTION

    NUMPY − ENVIRONMENT

    NUMPY − NDARRAY OBJECT

    NUMPY − DATA TYPES

    • Data Type Objects (dtype)

    NUMPY − ARRAY ATTRIBUTES

    • shape
    • ndim
    • itemsize
    • flags

    NUMPY − ARRAY CREATION ROUTINES

    • empty
    • zeros
  • NUMPY − ARRAY FROM EXISTING DATA

    NUMPY − ARRAY FROM NUMERICAL RANGES

    • arange
    • linspace

    NUMPY − INDEXING & SLICING

    NUMPY − ADVANCED INDEXING

    • Integer Indexing
    • Boolean Array Indexing

    NUMPY − ITERATING OVER ARRAY

    • Iteration
    • Order
    • Modifying Array Values
    • External Loop

    NUMPY – ARRAY MANIPULATION

    • reshape
    • ndarray.flat
    • ndarray.flatten
    • ravel
    • transpose
    • ndarray.T
    • swapaxes
    • rollaxis
    • broadcast
    • broadcast_to
    • expand_dims
    • squeeze
    • concatenate
    • stack
    • hstack and numpy.vstack
    • split
    • hsplit and numpy.vsplit
    • resize
    • append
    • insert
    • delete
    • unique
  • NUMPY – BINARY OPERATORS
    • bitwise_and
    • bitwise_or
    • invert()
    • left_shift
    • right_shift

    NUMPY − STRING FUNCTIONS

    NUMPY − MATHEMATICAL FUNCTIONS

    • Trigonometric Functions
    • Functions for Rounding

    NUMPY − ARITHMETIC OPERATIONS

    • reciprocal()
    • power()
    • mod()

    NUMPY − STATISTICAL FUNCTIONS

    • amin() and numpy.amax()
    • ptp()
    • percentile()
    • median()
    • mean()
    • average()
    • Standard Deviation
    • Variance

    NUMPY − SORT, SEARCH & COUNTING FUNCTIONS

    • sort()
    • argsort()
    • lexsort()
    • argmax() and numpy.argmin()
    • nonzero()
    • where()
    • extract()

    NUMPY − BYTE SWAPPING

    • ndarray.byteswap()

    NUMPY − COPIES & VIEWS

  • No Copy
  • View or Shallow Copy
  • Deep Copy

  NUMPY − MATRIX LIBRARY

  • empty()
  • matlib.zeros()
  • matlib.ones()
  • matlib.eye()
  • matlib.identity()
  • matlib.rand()

  NUMPY − LINEAR ALGEBRA

  • dot()
  • vdot()
  • inner()
  • matmul()
  • Determinant
  • linalg.solve()

  NUMPY − MATPLOTLIB

  • Sine Wave Plot
  • subplot()
  • bar()

  NUMPY – HISTOGRAM USING MATPLOTLIB

  • histogram()
  • plt()

  NUMPY − I/O WITH NUMPY

  • save()
  • savetxt()

Introduction to Cloud Computing

Selenium

Designing a solution infrastructure 

• Business use cases and product strategy
• Cost optimization
• Supporting the application design
• Integration with external systems
• Movement of data
• Design decision trade-offs
• Build, buy, or modify
• Success measurements (e.g., key performance indicators [KPI], return on investment[ROI], metrics)
• Compliance and observability
• High availability and failover design
• Elasticity of cloud resources
• Scalability to meet growth requirements
• Performance and latency

Designing network, storage, and compute resources

• Integration with on-premises/multi-cloud environments
• Cloud-native networking (VPC, peering, firewalls, container networking)
• Choosing data processing technologies
• Choosing appropriate storage types (e.g., object, file, RDBMS, NoSQL, NewSQL)
• Choosing compute resources (e.g., preemptible, custom machine type, specialized workload)
• Mapping compute needs to platform products

Creating a migration plan

• Integrating solution with existing systems
• Migrating systems and data to support the solution
• Licensing mapping
• Network planning
• Testing and proof of concept
• Dependency management planning

Envisioning future solution improvements

• Cloud and technology improvements
• Business needs evolution
• Evangelism and advocacy

Managing and provisioning a solution Infrastructure

Configuring network topologies

• Extending to on-premises (hybrid networking)
• Extending to a multi-cloud environment that may include GCP to GCP communication
• Security and data protection

Configuring individual storage systems

• Data storage allocation
• Data processing/compute provisioning
• Security and access management
• Network configuration for data transfer and latency
• Data retention and data life cycle management
• Data growth management

Configuring compute systems

• Compute system provisioning
• Compute volatility configuration (preemptible vs. standard)
• Network configuration for compute nodes
• Infrastructure provisioning technology configuration (e.g. Chef/Puppet/Ansible/Terraform/Deployment Manager)
• Container orchestration with Kubernetes

Analyzing and Optimizing Technical and Business Processes

Analyzing and defining technical processes

• Software development life cycle plan (SDLC)
• Continuous integration / continuous deployment
• Troubleshooting / post mortem analysis culture
• Testing and validation
• Service catalog and provisioning
• Business continuity and disaster recovery

Analyzing and Defining Business Processes

• Stakeholder management (e.g. influencing and facilitation)
• Change management
• Team assessment / skills readiness
• Decision-making process
• Customer success management
• Cost optimization / resource optimization (capex / opex)

Developing procedures to ensure resilience of solution in production

Managing implementation

Advising development/operation team(s) to ensure successful deployment of the solution

• Application development
• API best practices
• Testing frameworks (load/unit/integration)
• Data and system migration tooling

Interacting with Google Cloud using GCP SDK (gcloud, gsutil, and bq)

• Local installation
• Google Cloud Shell

Ensuring solution and operations reliability

• Monitoring/logging/profiling/alerting solution
• Deployment and release management
• Assisting with the support of solutions in operation
• Evaluating quality control measures