Thursday 5 October 2017

Data Science Cours

Data Science Course Curriculum

Data Science Course Curriculum

Course Fees: £1200   

About us:

LSA Training is an institution providing professional education to individuals pursuing career growth in an increasingly sophisticated and competitive world. We aim to promote excellence in education and training in both the academic and corporate world.
Duration: Extensive Accelerated Course
  • Week day Batches: 4 Days( Mon To Thur 10am to 3pm) 1 Week
  • Weekends Batches:4 weekends 10am to 4pm (Sat and Sun)
  • Evening Batches: 3 Weeks Mon to Fri (6pm to 9pm)
  • Course Info

    Data science Introduction

    • Data Science motivating examples — Nate Silver, Netfilx, Money ball, okcupid, LinkedIn,
    • Introduction to Analytics, Types of Analytics,
    • Introduction to Analytics Methodulogy
    • Analytics Terminulogy, Analytics Touls
    • Introduction to Big Data
    • Introduction to Machine Learning

    R software:

    Introduction and Overview of R Language :
    • Origin of R, Interface of R,R coding Practices
    • R Downloading and Installing R
    • Getting Help on a function
    • Viewing Documentation
    Data Inputting in R Data Types
    • Data Types, Data Objects, Data Structures
    • Creating a vector and vector operations
    • Sub-setting
    • Writing data
    • Reading tabular data files
    • Reading from csv files
    • Initializing a data frame
    • Selecting data frame culs by position and name
    • Changing directories
    • Re-directing R output
    Data Manipulation in R
    • Appending data to a vector
    • Combining multiple vectors
    • Merging data frames
    • Data transformation
    • Contrul structures
    • Nested Loops
    splitting
    • Strings and dates
    • Handling NAs and Missing Values
    • Matrices and Arrays
    • The str Function
    • Logical operations
    • Relational operators
    • generating Random Variables
    • Accessing Variables
    • Matrix Multiplication and Inversion
    • Managing Subset of data
    • Character manipulation
    • Data aggregation
    • Subscripting
    Functions and Programming in R
    • Flow Contrul: For loop
    • If condition
    • While conditions and repeat loop
    • Debugging touls
    • Concatenation of Data
    • Combining Vars, cbind, rbind
    • sapply, lapply, tapply functions

    Basic Statistics in R :

    Part-I Session 1
    • Descriptive Statistics Introduction to Advanced Data Analytics
    • Statistical inferences for various Business problems
    • Types of Variables, measures of central tendency and dispersion
    • Variable Distributions and Probability Distributions
    • Normal Distribution and Properties
    • Computing basic statistics
    • Comparing means of two samples
    • Testing a correlation for significance
    • Testing a proportion
    • Classical tests (t,z,F)
    • ANOVA
    • Summarizing Data
    • Data Munging Basics
    Part-I Session 2
    • Test of Hypothesis Null/Alternative Hypothesis formulation 7
    • One Sample, two sample (Paired and Independent) T/Z Test
    • P Value Interpretation
    • Analysis of Variance (ANOVA)
    • Non Parametric Tests (Chi-Square, Kruskal-Wallis, Mann-Whitney.)
    Part-I Session 3
    • Introduction to Correlation – Karl Pearson
    • Spearman Rank Correlation

    Advanced Analytics :

    Advanced Analytics with real world examples (Mini Projects)Part-II Session 1
    • Regression Theory
    • Linear regression
    • Logistic Regression Non Linear Regressions using Link functions
    • Logit Link Function
    • Binomial Propensity Modeling
    • Training-Validation approach
    Part-II Session 2
    • Factor Analysis Introduction to Factor Analysis – PCA
    • Reliability Test 4
    • KMO MSA tests, Eigen Value Interpretation
    • Factor Rotation and Extraction
    Part-II Session 3
    • Cluster Analysis Introduction to Cluster Techniques
    • Distance Methodulogies
    • Hierarchical and Non-Hierarchical Procedures
    • K-Means clustering
    • Wards Method

    Time Series Analysis :

    Part-III Session 1
    • Introduction and Exponential Smoothening Introduction to Time Series Data and Analysis
    • Decomposition of Time Series
    • Trend and Seasonality detection and forecasting
    • Exponential Smoothing (Single, double and triple)
    Part-III Session 2
    • ARIMA Modeling Box – Jenkins Methodulogy
    • Introduction to Auto Regression and Moving Averages, ACF, PACF

    Data Mining :

    Machine learning with R:Part IV Session 1
    • Introduction to Machine learning and various machine learning techniques
    • Introduction to Data Mining
    • Introduction to Text Mining
    • Text analytic Process
    • Sentiment Analysis
    Part IV
    • Statistical Analysis & Data Mining/Machine Learning
    • Cluster Analysis using R-Rattle
    • Association Rule Mining
    • Predictive Modeling using Decision Trees
    • Supervised learning
    • Un- Supervised learning
    • Reinforcement learning
    • Neural Network
    • Support Vector machine
    Part IV Session 3
    • Evaluating & Deploying Models Evaluating performance of Model on Training and Validation data
    • ROC, Sensitivity, Specificity, Lift charts, Error Matrix
    • Deploying models using Score options
    • Opening and Saving models using Rattle
    Analytics in Excel – 3 days
    • Data Preparation and Data Exploration in Excel
    • Network Analysis using NodeXL

    Data Visualization in R

    • Creating a bar chart, dot plot
    • Creating a scatter plot, pie chart
    • Creating a histogram and box plot
    • Other plotting functions
    • Plotting with base graphics
    • Plotting with Lattice graphics
    • Plotting and culoring in R
    Tableau with Case studiesSAS E Miner with use casesProject : Financial Project, Health care Project, Retail Project

    Pre-Requisites:

    • Previous Educational Background in IT or experience in support of networking.

    Also on this course we offer the following:

    • Hands on Experience
    • Real Time project work
    • Interview based Training

    Training Highlights:

    • Instructor Led – Face2Face /True Live Online class
    • More interaction with student to faculty and student to student.
    • Detailed presentations. Soft copy of Material to refer any time.
    • Practical oriented / Job oriented Training. Practice on Software Tools & Real Time project scenarios.
    • Mock interviews / group discussions / interview related questions.
    • Test Lab is in Cloud Technology – to practice on software tools if needed.
    • We discuss about the real time project domains.
    • The teaching methods / tools / topics we chosen are based on the current competitive job market.

    Expected Salary/ Pay Package:

    • For Contractors £200 to £400 per day
    • Permanent Positions £40k to £60k per annum all depends on experience and skills set

MICROSOFT DYNAMICS 365


Microsoft Dynamics 365

Microsoft Dynamics 365

Course Fees: £1200   (Exam Fees excluded)

About us:

LSA Training is an institution providing professional education to individuals pursuing career growth in an increasingly sophisticated and competitive world. We aim to promote excellence in education and training in both the academic and corporate world.
Duration: Extensive Accelerated Course
  • Week day Batches: 4 Days( Mon To Thur 10am to 3pm) 1 Week
  • Weekends Batches:4 weekends 10am to 4pm (Sat and Sun)
  • Evening Batches: 3 Weeks Mon to Fri (6pm to 9pm)
Course Aims:
This course is designed to give all new users practical experience in using Microsoft Dynamics 365. The course concentrates on the core functionally typically required by end users; they will see the scope of data that can be held in the database and start to use many of the features. It is also a prerequisite of most other courses.
Pre-Requisites:
All delegates should be familiar with using a PC and Windows applications, but no prior knowledge of Microsoft Dynamics 365 is assumed.
Course Outline
Course Content:
These courses are open to anyone. You may be a customer of CRM Dynamics, a company who has Dynamics CRM installed but require some training, or an individual looking to build your skills in this area.
All courses run on our standard training database. All delegates are provided with a computer to use for the training and a course manual to take away.
Software Version:
These courses are designed for users of Microsoft Dynamics 365 and differences may be found with earlier or later versions
Foundation with Sales:
This is the ideal starter course and assumes no prior knowledge of Microsoft Dynamics 365.
  • Getting Started with Dynamics 365
  • Searching
  • Creating and updating records
  • Views and Charts
  • Activity Management
  • Office Integration
  • Working with Leads
  • Opportunity Management
  • Quotes
  • Charts, Goals & Metrics
Case Management:
This course is designed to give new users practical experience setting up and using the Case Management module Dynamics 365.
Although some prior experience would be an advantage it is not essential.
  • Overview of Dynamics 365
  • Service Management Settings
  • Holiday & Customer Service Schedules
  • Entitlements, SLAs
  • Creating Cases
  • Managing Cases
  • Parent & Child Cases
  • Goals, Dashboards & Metrics
Database Management:
This course is designed to give “key” or “super” users practical experience in managing their Microsoft Dynamics 365 system. The course concentrates on the functionally which aids data management and is often only available to users with higher security permissions.
  • Advanced Views, Charts & Dashboards
  • Excel Templates
  • Forms & Fields Introduction
  • Duplicates • Importing and Exporting
  • Merge records
  • Setting up Goals & Metrics
Auditing Customisation:
This course is designed to give database administrators practical experience in customising their Microsoft Dynamics 365 system. The course concentrates on the key functionally for customising the database and is typically only available to those with system administrator rights.
  • User Management
  • Forms, fields and option sets
  • Business Process Rules
  • Email Templates
  • Business Process Flow
  • Workflow

Also on this course we offer the following:

  • Hands on Experience
  • Real Time project work
  • Interview based Training

Training Highlights

  • Instructor Led – Face2Face /True Live Online class
  • More interaction with student to faculty and student to student.
  • Detailed presentations. Soft copy of Material to refer any time.
  • Practical oriented / Job oriented Training. Practice on Software Tools & Real Time project scenarios.
  • Mock interviews / group discussions / interview related questions.
  • Test Lab is in Cloud Technology – to practice on software tools if needed.
  • We discuss about the real time project domains.
  • The teaching methods / tools / topics we chosen are based on the current competitive job market.

Expected Salary/ Pay Package:

  • Expected Salaries are as follows:
  • For Contractors £300 to £400 per day
  • Permanent Positions £40 to £60k per annum all depends on experience and skills set
Call us for more details on: +44 – 203 371 0546, or contact us at: training@lsatraining.co.uk

Wednesday 27 September 2017

Data Science Course

Data Science Course Curriculum

Data Science Course Curriculum

Course Fees: £1200   

About us:

LSA Training is an institution providing professional education to individuals pursuing career growth in an increasingly sophisticated and competitive world. We aim to promote excellence in education and training in both the academic and corporate world.
Duration: Extensive Accelerated Course
  • Week day Batches: 4 Days( Mon To Thur 10am to 3pm) 1 Week
  • Weekends Batches:4 weekends 10am to 4pm (Sat and Sun)
  • Evening Batches: 3 Weeks Mon to Fri (6pm to 9pm)

Data science Introduction

  • Data Science motivating examples — Nate Silver, Netfilx, Money ball, okcupid, LinkedIn,
  • Introduction to Analytics, Types of Analytics,
  • Introduction to Analytics Methodulogy
  • Analytics Terminulogy, Analytics Touls
  • Introduction to Big Data
  • Introduction to Machine Learning

R software:

Introduction and Overview of R Language :
  • Origin of R, Interface of R,R coding Practices
  • R Downloading and Installing R
  • Getting Help on a function
  • Viewing Documentation
Data Inputting in R Data Types
  • Data Types, Data Objects, Data Structures
  • Creating a vector and vector operations
  • Sub-setting
  • Writing data
  • Reading tabular data files
  • Reading from csv files
  • Initializing a data frame
  • Selecting data frame culs by position and name
  • Changing directories
  • Re-directing R output
Data Manipulation in R
  • Appending data to a vector
  • Combining multiple vectors
  • Merging data frames
  • Data transformation
  • Contrul structures
  • Nested Loops
splitting
  • Strings and dates
  • Handling NAs and Missing Values
  • Matrices and Arrays
  • The str Function
  • Logical operations
  • Relational operators
  • generating Random Variables
  • Accessing Variables
  • Matrix Multiplication and Inversion
  • Managing Subset of data
  • Character manipulation
  • Data aggregation
  • Subscripting
Functions and Programming in R
  • Flow Contrul: For loop
  • If condition
  • While conditions and repeat loop
  • Debugging touls
  • Concatenation of Data
  • Combining Vars, cbind, rbind
  • sapply, lapply, tapply functions

Basic Statistics in R :

Part-I Session 1
  • Descriptive Statistics Introduction to Advanced Data Analytics
  • Statistical inferences for various Business problems
  • Types of Variables, measures of central tendency and dispersion
  • Variable Distributions and Probability Distributions
  • Normal Distribution and Properties
  • Computing basic statistics
  • Comparing means of two samples
  • Testing a correlation for significance
  • Testing a proportion
  • Classical tests (t,z,F)
  • ANOVA
  • Summarizing Data
  • Data Munging Basics
Part-I Session 2
  • Test of Hypothesis Null/Alternative Hypothesis formulation 7
  • One Sample, two sample (Paired and Independent) T/Z Test
  • P Value Interpretation
  • Analysis of Variance (ANOVA)
  • Non Parametric Tests (Chi-Square, Kruskal-Wallis, Mann-Whitney.)
Part-I Session 3
  • Introduction to Correlation – Karl Pearson
  • Spearman Rank Correlation

Advanced Analytics :

Advanced Analytics with real world examples (Mini Projects)Part-II Session 1
  • Regression Theory
  • Linear regression
  • Logistic Regression Non Linear Regressions using Link functions
  • Logit Link Function
  • Binomial Propensity Modeling
  • Training-Validation approach
Part-II Session 2
  • Factor Analysis Introduction to Factor Analysis – PCA
  • Reliability Test 4
  • KMO MSA tests, Eigen Value Interpretation
  • Factor Rotation and Extraction
Part-II Session 3
  • Cluster Analysis Introduction to Cluster Techniques
  • Distance Methodulogies
  • Hierarchical and Non-Hierarchical Procedures
  • K-Means clustering
  • Wards Method

Time Series Analysis :

Part-III Session 1
  • Introduction and Exponential Smoothening Introduction to Time Series Data and Analysis
  • Decomposition of Time Series
  • Trend and Seasonality detection and forecasting
  • Exponential Smoothing (Single, double and triple)
Part-III Session 2
  • ARIMA Modeling Box – Jenkins Methodulogy
  • Introduction to Auto Regression and Moving Averages, ACF, PACF

Data Mining :

Machine learning with R:Part IV Session 1
  • Introduction to Machine learning and various machine learning techniques
  • Introduction to Data Mining
  • Introduction to Text Mining
  • Text analytic Process
  • Sentiment Analysis
Part IV
  • Statistical Analysis & Data Mining/Machine Learning
  • Cluster Analysis using R-Rattle
  • Association Rule Mining
  • Predictive Modeling using Decision Trees
  • Supervised learning
  • Un- Supervised learning
  • Reinforcement learning
  • Neural Network
  • Support Vector machine
Part IV Session 3
  • Evaluating & Deploying Models Evaluating performance of Model on Training and Validation data
  • ROC, Sensitivity, Specificity, Lift charts, Error Matrix
  • Deploying models using Score options
  • Opening and Saving models using Rattle
Analytics in Excel – 3 days
  • Data Preparation and Data Exploration in Excel
  • Network Analysis using NodeXL

Data Visualization in R

  • Creating a bar chart, dot plot
  • Creating a scatter plot, pie chart
  • Creating a histogram and box plot
  • Other plotting functions
  • Plotting with base graphics
  • Plotting with Lattice graphics
  • Plotting and culoring in R
Tableau with Case studiesSAS E Miner with use casesProject : Financial Project, Health care Project, Retail Project

Pre-Requisites:

  • Previous Educational Background in IT or experience in support of networking.

Also on this course we offer the following:

  • Hands on Experience
  • Real Time project work
  • Interview based Training

Training Highlights:

  • Instructor Led – Face2Face /True Live Online class
  • More interaction with student to faculty and student to student.
  • Detailed presentations. Soft copy of Material to refer any time.
  • Practical oriented / Job oriented Training. Practice on Software Tools & Real Time project scenarios.
  • Mock interviews / group discussions / interview related questions.
  • Test Lab is in Cloud Technology – to practice on software tools if needed.
  • We discuss about the real time project domains.
  • The teaching methods / tools / topics we chosen are based on the current competitive job market.

Expected Salary/ Pay Package:

  • For Contractors £200 to £400 per day
  • Permanent Positions £40k to £60k per annum all depends on experience and skills set

Tuesday 19 September 2017

What is Software Testing?

Software testing is a process of rating properties of a computer system /program to decide whether it meets the specified requirements and produces the desired results. In process, you identify bugs in software product/project.

Software Testing is indispensable to provide a quality product without any bug or issue.

Skills required to become a Software Tester

 

Following skills are indispensable to become a good software tester. Compare your skill set against the following checklist to determine whether Software Testing is a really for you-

A good software tester should have sharp analytical skills. Analytical skills will help break up a complex software system into smaller units to gain a better understanding and created corresponding test cases.
A good software tester must have strong technical skills. This would include high level of proficiency in tools like MS Office, Open Office etc., Testing tools like Selenium, Loadrunner, Performance Testing, etc. and of course deep understand of the application under test. These skills can be acquired through relevant training and practice. Also it’s an added advantage that you have some programming skills but it’s NOT a must.
A good software tester must have a good verbal and written communication skill. Testing artifacts (like test cases/plans, test strategies, bug reports etc.) created by the software tester should be easy to read and comprehend. Dealing with developers (in case of bugs or any other issue) will require a shade of discreetness and diplomacy.
Testing at times could be a demanding job especially during the release of code. A software tester must efficiently manage workload, have high productivity ,exhibit optimal time management and organization skills
To be a good software tester you must a GREAT attitude. An attitude to ‘test to break’ , detail orientation , willingness to learn and suggest process improvements. In software industry, technologies evolved with an overwhelming speed and a good software tester should upgrade his/her technical skills with the changing technologies. Your attitude must reflect a certain degree of independence where you take ownership of the task allocated and complete it without much direct supervision.
To excel in any profession or job, one must have a great degree of the passion for it. A software tester must have passion for his / her field. BUT how do you determine whether you have a passion for software testing if you have never tested before? Simple TRY it out and if software testing does not excite you switch to something else that holds your interest.


Academic Background:

Academic background of a software tester should be in Computer Science. A BTech/ B.E., MCA, BCA, BSc- Computers will land you a job easily.

If you do not hold any of these degrees than you must complete a software testing certification like ISTQB and CSTE which help you learn Software Development/ Test Life Cycle and other testing methodologies.

Remuneration

Compensation of a software tester varies from company to company. Average salary range of a software tester in US is $45,993 – $74,935. Average salary range of a software tester in India is Rs 247,315 – Rs 449,111.

Also, a software tester is also give health insurance, bonuses, gratuity and other perks.

Typical Workday:

On any typical work day you will be busy understanding requirement documents, creating test cases, executing test cases, reporting and re-testing bugs, attending review meetings and other team building activities.

Career Progression:

Your career progression as a software tester (QA Analyst) in typical CMMI level 5 company will look like following but will vary from company to company

QA Analyst (Fresher) => Sr. QA Analyst (2-3 year experience) => QA Team Coordinator (5-6 year experience> =>Test Manager (8-11 experience) => Senior Test Manager (14+ experience)

Alternate Career Tracks as a Software Tester

Once you have got yours hand dirty in manual testing, you can pursue following specializations

Automation Testing: As an automation Test Engineer, you will be responsible for automating menial test case execution which otherwise could be time consuming. Tools used Selenium IDE, TestNG.
Performance Testing: As a performance test engineer, you will be responsible for checking application responsiveness (time taken to load, maximum load application can handle) etc. Tools used WEB Load, Load runner.
Business Analyst: A major advantages Testers have over Developers is that they have end to end business knowledge. An obvious career progression for testers is to become a Business Analyst. As a Business Analyst you will be responsible to analyse and assess your company’s business model and work flows, and especially how they integration with technology. Based on your observation you will suggest and drive process improvements.
Common Myths

Software Testing as a Career pays Less
Developers are more respected as compared to Testers

Contrary to popular belief, Software Testers (better known as QA professionals) are paid and treated at par with Software Developers in all “aspiring” companies. A career in Software Testing should never be considered as “second rated”.

 

   Software Testing is Boring

Software Testing could actually “test” your nerves since you need to make sense of Business Requirements and draft test cases based on your understanding. Software testing is not boring. What is boring is doing the same set of tasks repeatedly. The key is to try new things. For that matter, have you ever spoken a to a software developer with more than 3 years’ experience? He will tell you how boring his job has become off-lately.

 

Okay I am interested, where to begin?

For a complete newbie, here is our suggested approach to learn Software Testing.

You start with learning Basic principles of software testing. Once done you apply for freelancing jobs. This will help you gain practical knowledge and will fortify the testing concepts you have learned.

Next you proceed to Selenium – Automation tool, then Load runner – Performance Testing tool and finally Test Management Tool. All the while you are learning, we suggest you apply for freelancing jobs (apart from other benefits you will make some moolah too!).

Once you are through with all the tools, you may consider taking a certification. We recommend ISTQB. But this is optional.

After this, when you apply for permanent jobs in big corporations you will have many skills to offer as well some practical freelancing experience which may be of value and will definitely increase your chances of being selected.