Data Science and Big Data Analytics

  • Data Science: Solving problems for questions we do not know we do not know.
  • Data Analytics: Tackling problems to questions we know we do not know.

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Big data has become an integral component in the tech world today. Thanks to the actionable insights and results businesses can glean. However, the creation of such large datasets also requires understanding and having the right tools on hand to parse through them to uncover the right information. To better comprehend big data, the fields of data science and analytics have gone from largely being relegated to academia, to instead becoming integral elements of Business Intelligence and big data analytics tools.

Explore Our Big Data Services

Our range of services are modelled around the Data Science Life Cycle. For us, this begins with understanding the business problem, then collecting relevant data, cleaning that data which may at times involve embellishing with external data to extract meaningful insights during data exploration and feature engineering. Once a suitable hypothesis is developed, we start to develop predictive models that can be used for forecasting. These models in their raw form will not make sense to your sales or marketing team hence the need to present them in a simple, eye-catching visual manner and that is where things begin to get interesting. Ideally every project should include these seven stages but we are flexible to plug-in at any stage and where need be, make suggestions for iteration.

Problem Understanding

Incidentally, not all business problems need Data Science to solve them. The notion of Data Science and Big Data Analytics is currently a fancy one and it is possible to lose ourselves in the hype of the moment. At the end of the day if it is not helping a business transform existing business operations, improve business processes, or identify the causes and possible solutions to bad customer reviews or poor sales, it is not meaningful. We will work closely with you to ensure the problem being tacked is well understood and interpreted.

Data Mining

This is the heart of Data Science and Big Data Analytics. It is very critical that the right data is collected in order to build a good model. We will employ a set of data mining tools including data scraping, to build datasets that make sense. Where necessary, we will augment your data with other existing datasets such as spatial data to embellish the final data product.

Data Cleaning

This is where we deal with duplicate and null values, inconsistent data types, missing data, invalid entries, improper formatting and such things. This is another major step in dealing with Big Data particularly where the sources of data are varied and striking harmony or common ground is vital. What deliver from this stage is a usable dataset that can be subjected to meaningful analysis.

Data Exploration

This is the stage where the proverbial Exploratory Data Analysis (EDA) in Data Science happens. Here we get technical with processes such as univariate analysis, bivariate analysis, missing values treatment, outlier treatment, variable transformation to better understand your data.

Feature Engineering

This is the one single process that distinguishes Data Science from statistics. We transform your data into a feature matrix of multi-dimensional data that allows deeper insights into hidden patterns that were not possible to see before.

Predictive Modelling

This is where the real magic of Data Science happens. At this stage we split your data into test and train sets to help with Machine Learning (ML) as we build towards a predictive model that can be able to forecast with a certain reasonable degree of accuracy.

Data Visualisation

This is where we demystify all the Deep Leaning and Machine Learning into simple, clear visually appealing summaries that tell you where you should be, what you should be doing better and how. Simply put, we hide all the algorithms behind great graphs that tell a story anyone can understand and take action on.

When is the right time to do analytics?

Now! Once you start, it should become part of your organisational culture and something you do continously.

What are the benefits?

Data analytics improves the decision-making process by eliminating guesswork in your operations. Data analytics also provides actionable insights into how your marketing campaigns. Such insights help you fine-tune those campaigns to generate more reliable outcomes. Once you have a better understanding of your customer, you stand a better chance of developing better customer service.

Where will the big data come from?

It will come from your day-to-day processes. It could be your call logs, social media or website engagement metrics, data from your accounting software or your CRM and so on.