Managed Analytic Services, Data Science and Statistics

B. Sc (Hons): Statistics (University of Cape Town)

We offer competencies that cover advanced data analysis on traditional to big datasets. We provide our customers with regular and adhoc reports and predictions that have a significant impact on the bottom line of a business. This helps businesses to plan accurately and make changes to their business going forward.

Cleansing Data

Dirty data gives poor forecasts. It is important that data is cleansed and prepped well before analysis is done.

Visual Overview

Scatter plots, histograms, bar charts, frequency tables are initially used to give an overview of data.

Summary Statistics and Observations

Before doing indepth analysis, basic statistics and simple tests are done.

Linear Regression and Modelling

A model is set up and is tested on sample data and predictions tested on subset.

Maximum Impact

Analysis helps businesses to save on costs, improve their performance and maximise future growth.

Why Outsource?

It allows us to focus on what we are good at and allows the company to avoid technicalities and to prioritise and focus on planning.

It's commonly said that data scientists spend 80% of their time cleaning and manipulating data and only 20% of their time actually analyzing it.

Contact

Reference

A Data Scientist’s Guide to Acquiring, Cleaning, and Managing Data in R (Wiley), A Modern Introduction to Probability and Statistics (F.M. Dekking, C. Kraaikamp, H.P. Lopuhaa, L.E. Meester), All of Statistics_ A Concise Course in Statistical Inference (Larry Wasserman), Basic Statistics for Business & Economics (Douglas A. Lind, William G.Marchal, Samuel A. Wathen), Comprehensive sampling and sample preparation _ analytical techniques for scientists (Professor J. Pawliszyn), Course in Probability Theory (Kai Lai Chung), Introduction to Statistical Learning_ with Applications in R (Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani), Introduction to Time Series Analysis and Forecasting (Wiley), Large Sample Techniques for Statistics (Springer Texts in Statistics), Regression Modeling Strategies_ With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis (Frank E. Harrel, Jr.), Statistical Data Cleaning with Applications in R (Wiley)