Staff Augmentation

Recruitment SLA

  • After getting the requirement we make sure that we submit quality profiles within 2 working days. If niche skill max 3 days
  • For every query from the client we reply within same day or max next day
  • We focus on getting only Malaysian first. If very difficult to find then we try for other nationalities.
  • Hence it reduces hiring time and cost
  • Result of the process we have clients from almost all types of industry from small to medium to large companies

Recent Hiring

Software Development Company

  • Java Developers (10)
  • System Engineer & System Analyst (8)
  • IOS & Android Developers (5)
  • PowerBuilder developer (3)
  • Senior Software Engineer – ETL (2)

IT Solutions Provider (both Software & Hardware).

  • Net developers & .Net lead (7)
  • Application Developer (5)
  • Software Testing Engineer (2)

Banking & Financial Services :

  • RPG Programmer (5)

The e-commerce Company

  • Software QA Engineer (3)
  • Senior software Engineer (5)

IT, Science, Engineering & Technical Company

  • J2EE programmers (2)
  • IT Helpdesk, call desk agent & incident Control Agents ( 30)

Recruitment Case Study

Client: We will give you our toughest requirement which is urgent. We prefer Malaysian only. If you succeed we will sign the contract.
Insistent: Yes we will do it

Within two days submitted two profiles. One got shortlisted, interviewed and offered.

PS: Requirement was to search Malaysian oil and gas downstream expert. Client is MNC technology firm.




  • Analytics is one of the top strategic technology identified by Gartner in 2018#
  • 47% respondents in Gartner’s 2017 survey mentioned BI & Analytics to be the most demanding field
  • According to Harvard Business Review (October 2012 edition), job of a data scientist is the sexiest job of 21st century.
  • According to the McKinsey Global Institute (In a May 2011 report): “By 2018, the United States alone could face a shortage of 140,000 + people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.”

Imagine what would be the number across the globe…


Hours of focused learning
Analytical cases execution
Years+ Industry experience for mentor
Advanced Analytical practice projects
Statistical Concepts with10 algorithms
Analytical Tools Training (R, Tableau & Excel)
  • To ensure your profile & candidature stand out in analytics world
  • Help build profiles on analytics portals
  • Participate in Hackathons
  • Subscribe for Alerts & industry updates
  • Get Youtube video links
  • Project sessions with Industry experts
  • Focused projects designed to ensuring industry expertise


Advanced Excel

  • Excel Basics – Pre-requisite
  • Data handling and extraction
  • Advanced excel functions (Advanced filters, nested functions)
  • Pivot tables, pivot charts, calculated fields in pivot
  • Slicer & Slicer connections
  • Charting techniques, Sparklines
  • What if Analysis
  • Simulation / Solver
  • Introduction to Macros
  • Dashboarding
  • Data Protection
  • File sharing and tracking


  • Introduction to Statistics
  • Univariate/Bivariate Analysis
  • Variance, Standard deviation, covariance, correlation
  • F-test, Anova
  • Data Distributions
  • Hypothesis Testing
  • Linear Regression
  • Logistic Regression
  • Market Basket Analysis
  • Clustering
  • Classification (Decision Trees)
  • Time Series Analysis

R Programming

  • R Studio - Introduction
  • Basic Operations
  • Data Handling
  • Data visualization using R
  • Custom function creation
  • Conditionals Statements
  • Looping
  • Execution of all statistical techniques
    • Linear Regression
    • Logistic Regression
    • Market Basket Analysis
    • Clustering
    • Classification (Decision Trees)
    • Time Series Analysis


  • Tableau Introduction
  • Data connection to Tableau
  • Calculated fields, hierarchy, parameters, sets, groups in
  • Tableau
  • Visualizations Techniques
  • Map based visualization
  • Reference Lines
  • Advanced Formatting
  • Table Calculations
  • Tableau Dashboard
  • Action Filters
  • Creating Story using Tableau
  • Analytics using Tableau
  • Clustering, Time series analysis & Regression
  • R integration in Tableau

why r?

R has been one of the highly ranked and fastest growing programming languages of the last decade, one key aspect being its open source nature which helps to adopt latest techniques available in market.

Important Attributers with reference to Date Science:

Above trend is captured from a market survey from TIOBE & IEEE which is updated on monthly basis and popular search engines like Google, Wikipedia, Amazon, YouTube and Baidu are used to calculate the ratings.

WHY tableau?

Tableau has been marked as leader in Business Intelligence & Analytics Platforms by Gartner consecutively for last many years

Course Details


  • Excel Basics – Pre-requisite (Online material will be provided to attain required proficiency)
  • Data handling and extraction
    • Connecting to various data sources (txt files, Access database, other data base connections)
    • Web data extraction overview
  • Advanced excel functions
  • Advanced filters | Data sorting | De-duplication | Conditional formatting | Table formatting
  • Grouping | Data Validation | If else | Vlookup/Hlookup | Index-match | Sumif/countif | Cell referencing
  • Pivot Functions
  • Pivot tables | Pivot charts | Calculated fields in pivot | Pivot summaries | Get pivot data
  • Data Visualization using Excel
  • Charting techniques | Sparklines | Slicer | What if Analysis | Dashboarding
  • Data Analytics using Excel
  • What if Analysis | Goal Seek | Data Tables | Simulation / Solver | Regression using Excel
  • Introduction to Macros
  • Data Protection
  • File sharing and tracking


  • Introduction to Business Analytics
  • Understanding Business Applications
  • Data types and data Models
  • Types of Business Analytics
  • Evolution of Analytics
  • Data Science Components
  • Data Scientist Skillset
  • Univariate Data Analysis
  • Introduction to Sampling


  • Introduction to Statistics
  • Basic Statistics
    • Measure of central tendency
    • Types of variables
    • Types of Distributions
  • Concept of Quantiles, Quartiles, percentile
  • Standard Deviation
  • Variance
  • Covariance
  • Correlation
  • Kurtosis
  • Skewness
  • Central Limit Theorem & applications
  • Hypothesis Testing
  • F-Test
  • Anova


Basic Operations in R

  • Introduction to R programming
  • Types of Objects in R
  • Naming standards in R
  • Creating Objects in R
  • Data Structure in R
  • Matrix, Data Frame, String, Vectors
  • Understanding Vectors & Data input in R
  • Lists, Data Elements
  • Creating Data Files using R
  • Importing Data Files from other sources

Basic Operations in R

  • Basic Operations in R – Expressions, Constant Values, Arithmetic, Function Calls, Symbols
  • Sub-setting Data
  • Variable Manipulations | Logical functions
  • If else selection, sampling
  • Merging Data | Sorting, Ordering & Ranking Data
  • Data Type Conversion
  • Built-In Numeric Functions | Built-In Character Functions
  • User Built Functions
  • Looping Functions
  • Visualization using R


Linear Regression

  • Concept of Regression
  • Best Fitting line
  • Building regression models using excel
  • Coefficient of determination (R- Squared)
  • Multiple Linear Regression
  • Assumptions of Linear Regression
  • Reading coefficients in MLR
  • Multicollinearity | VIF
  • Methods of building MLR model in R
  • Model validation techniques
  • Cooks Distance, Q-Q Plot, Homoskedasticity
  • Durbin- Watson Test, Kolmogorov-Smirnof Test

Linear Regression

  • Concept of odds
  • Concept of Odds Ratio
  • Derivation of logistic regression equation
  • Interpretation of logistic regression
  • output
  • Applications of logistic regression
  • Model building for logistic regression
  • Model validations
  • Confusion Matrix
  • Concept of ROC/AOC Curve
  • KS Test

2 Projects on Regression


Market Basket Analysis

  • Applications of Market Basket Analysis
  • What is association Rule
  • Overview of Apriori algorithm
  • Key terminologies in MBA
  • Support
  • Confidence
  • Lift
  • Model building for MBA
  • Transforming sales data to suit MBA
  • MBA Rule selection
  • Ensemble modelling applications using MBA

Decision Trees using R

  • What are Decision Trees?
  • Tree induction: Construction of the tree
  • Terminologies : Internal decision nodes |
  • Terminal leaves
  • Concept of Entropy
  • Information Gain
  • Overfitting, Causes for overfitting
  • Overfitting Prevention (Pruning) Methods
  • Reduced Error Pruning
  • Decision trees - Advantages &
  • Drawbacks

One Project each on MBA & Decision Tree



  • Clustering Overview
  • Direct Clustering Method
  • Hierarchical Clustering
  • Dendogram interpretation
  • K-Means
  • Distance Metrics
  • K-Means Algorithm
  • Scree Plot / Elbow Chart
  • K-Means Clustering using R

Time Series Analysis (Forecasting)

  • Concept of Time series data
  • Prerequisites for time series analysis
  • Components of time series
  • Time series analysis techniques
  • Model building using simple moving average
  • Model building using exponential smoothing
  • Concept of stationarity
  • Dickey Fuller Test | KPSS Test
  • Data De-trending & data differencing
  • Model building using ARIMA, ARIMAX,
  • Model validation

One Project each on Clustering & Time Series Analysis


Tableau Visualization

  • Data connection to Tableau
  • Calculated fields, hierarchy, parameters,
  • sets, groups in Tableau
  • Various visualizations Techniques in Tableau
  • Map based visualization using Tableau
  • Adding Totals, sub totals, Captions
  • Advanced Formatting Options
  • Show Filter & Use various filter options
  • Data Sorting, Create Combined Field
  • Reference Lines
  • Table Calculations
  • Creating Tableau Dashboard & Story
  • Action Filters

Analytics using Tableau

  • Clustering using Tableau
  • Time series analysis using Tableau
  • Simple Linear Regression using Tableau

R integration in Tableau

  • Integrating R code with Tableau
  • Creating statistical model with
  • dynamic inputs
  • Visualizing R output in Tableau

One Project each on Clustering & Time Series Analysis




We provide an array of analytical services covering business analytics, business intelligence & reporting for various domains. Our primary exposure has been retail, sales & Marketing, HR and manufacturing analytics. Below are the few areas of work –

  • Local & Global competition analysis (online only)
  • Product & market competitive analytics
  • Product bundling, catalogue optimization
  • Customer segmentation & target optimization
  • Empowering sales using data analytics
  • Customer conversion improvement projects
  • Demand forecasting
  • Pricing optimization
  • Channel pricing
  • Employee attrition prediction
  • AI based task allocation system
  • & many more custom solution


What is Exploratory Analysis?

- Exploratory Data Analysis (EDA) is the first step in your data analysis process. Here, you make sense of the data you have and then figure out what questions you want to ask and how to frame them, as well as how best to manipulate your available data sources to get the answers you need.

Business Applications:

- All businesses have transaction level data containing Revenue, Margin, Units sold, etc. and there are multiple dimensions for this data (e.g. Region, product category, sales people, etc.)

- Analyzing this data to create visual summary, key contributors to the deviation and hence identifying key areas where business should focus.

Analytical Solutions:

- Self Explanatory dashboards summarizing situations (using BI tools like Tableau, Power BI, Excel)

Expected Impact:

- Helps businesses to align their focus

- Take corrective actions or continue the actions to have positive impact obsered


Case I :

- Customer was experiencing continuous misses on margin targets, this was because of violations by sales representatives on margin targets set

- Designed an interactive tableau dashboard to quick analyze the root causes of violations by category & region

- Empowered leaders to act on particular individuals and cases where finance approvals were misused

- Observed significant correction (+573 bps improvement in margins) post deployment of robust monitoring system

Case II :

- Customer was dealing with ~10k products from 70 sub categories, making it difficult to monitor overall Revenue & margin performance and key business performance indicators (KPIs)

- Designed data warehouse which enabled easy availability of financial data

- Built Tableau dashboard connected to the data warehouse enabling ease in navigation of data

- Subject matter expert deployed to read the key movements in data in detail to deliver detailed explanation of deviations at weekly cadence

- Quick access to insights on KPI movement allowed faster actions and better control on overall business performance


What is Predictive Analysis?

- Predictive analysis defines statistical equation around available business data to help predict/simulate the business situation for various possible scenarios. Helping businesses pull correct levers to achieve expected results.

Business Applications:

- Business situations are impacted by multiple internal and external factors

- Quantifying this impact is difficult through simple exploratory analysis or business intuitions and cost of making incorrect guesses could run in billions

- Hence deployment of sophisticated statistical methods to analyze the situation is preferred

Analytical Solutions:

- Predictive analysis uses multiple statistical concepts (Regression, Forecasting, Association rules, Classification, Text mining, etc.)

- Provides an accuracy number associated with providing confidence in prediction

Expected Impact:

- Provides a data backed benchmark/predictions to make decisions with higher confidence

- Allows proactive actions for potential business threats


Case I : Price & Demand Modelling

- Customer was experiencing huge competitive pricing pressure leading to misses in sales targets, hence wanted to establish a statistical model for pricing and demand relationship

- Leveraged historic own pricing, promotion, sales information along with competitor pricing, and market sales indicators to build a linear regression model

- Statistical model provided a mathematical relationship between own price, competitor price, promotions on own product demand enabling business power to simulate various competitive pricing situations to predict product demand

- Also provided summary of price elasticity of products to ensure selective price movements to maximize profits

Case II : Personalized Product Recommendation

- Retail client wanted to define upsell path for group of customers and hence wanted recommendations on products that those customers might tend to purchase

- Leveraged historic sales information at transaction level with statistical techniques like market basket analysis and collaborative filtering to conclude item-item association and user-user similarities

- Same was leveraged to design personalized product recommendations for customer focus group

- This analytical exercise allowed the client to drive 37% improvement in revenue for focus group without any addition in marketing & sales team

Pricing Analytics