Data Analysis

KITAU work closely with business partners to understand their goals and determine how data can be used to achieve those goals. We design data modeling processes, create algorithms and predictive models to extract the data the business needs, and help analyse data to share insights with with the team. Good data analysis will refine and improve the business strategy for better customer engagement. We pend time with each company to fully understand your challange, before starting any data extraction or analysis process. Contact today to begin your journey in data analysis for your company. 

KITAU Data analysis services


An essential aspect of data science is the use of statistical analysis to detect anomalies in data sets. We can put data into clusters or groups and then identify outliers when working with small amounts of data. This task becomes significantly more difficult for organizations that must analyse petabytes or exabytes of data. Financial services companies, for example, are increasingly challenged to detect fraudulent spending in transaction data. KITAUs anomoly detection analysis is a counter measure to this. 


Identifying patterns in data sets is a primary objective in data analysis. For example, pattern recognition helps retailers and e-commerce companies uncover trends in customer purchasing behavior. Making product offerings relevant and ensuring supply chains are reliable is essential for organizations that want to keep their customers happy. KITAU has strng experience is custom pattern recognition algorithms to give you more insight into your customers needs. 


In addition to spotting patterns and outliers, data science focuses on refining predictive modeling. Data science applies machine learning and other algorithmic approaches to large data sets to improve decision-making capabilities.Predictive models can be used to create models that better predict customer behavior, financial risk, market trends. More. Predictive analytics applications are used across a wide range of industries, including financial services, retail, manufacturing, healthcare, travel and government.


Based on the data analysis capabilities of machine learning and deep learning systems, KITAU can dig through reams of data to understand customer or user sentiment and behavior. Through the application of sentiment analysis and behavioral analysis, we can effectively identify purchasing and usage patterns. These patterns give insight to what people think about products and services and how satisfied they are with their experiences. We can also categorise customer sentiment and behavior and track how it changes over time.


User and customer satisfaction is usually highest when products and services are tailored to an individual’s needs or interests. When users can can get the right product at the right time, you’ve got a sale. With  the combination of data science, machine learning and big data now enables organisations to create a detailed profile of individual customers. Over time, thethe  systems can learn preferences and match individuals with others who have similar preferences.

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SQL:   Structured Query Language (SQL) is used to communicate with relational database management systems (RDBMS) and retrieve data from large databases. This is useful in reports and data analysis.

Apache Spark:  Spark is used to write parallel programs that run in clusters (a network of computers in a data center). Its powerful machine learning library, mllib, can be used with R to efficiently solve a variety of problems.

Hadoop:   Hadoop is a whole set of technologies designed to manage data and run programs in a cluster (on a network of computers in a data center). This includes a file system designed for big data, MapReduce for running programs in parallel.

R:   R is a standard programming language used in data science for statistical problems. Thanks to its libraries, R can be an efficient way to perform mathematical data science procedures. 

Python :   Python is a general programming language used by data scientists for algorithms, automation and more. It can also be used for some web development tasks. It is one of the most beginner-friendly programming languages and offers a huge community that has created useful libraries and frameworks for data science.

Machine Learning :  Machine learning refers to the practice of using algorithms to teach a machine how to improve and analyse large data sets. This can be used to also automate some data science practices. As machine learning grows in popularity, it allows data scientists to predict future events far beyond the capabilities of traditional statistics. KITAU used machine learnig in complex data science analysis where data is unstructured or require high future insight.