Hi, Welcome,

To my online home

I'm Allen and I am Data Analyst. I want to work for a UK based company that wants to analyse data.

That is why I made this website. Data analysis is the process in which raw data is ordered and organized, to be used in methods that help to explain the past and predict the future. Data analysis is not about the numbers, it is about making/asking questions, developing explanations, and testing hypotheses. Data Analysis is a multidisciplinary field, which combines Computer Science, Artificial Intelligence & Machine Learning, Statistics & Mathematics, and Knowledge Domain.

Data represents a fact or statement of an event without relation to other things. Data comes in many forms, such as web pages, sensors, devices, audio, video, networks, log files, social media, transactional applications, and much more. Most of these data are generated in real time and on a very large-scale. Although it is generally alphanumeric (text, numbers, and symbols), it can consist of images or sound. Data consists of raw facts and figures. It does not have any meaning until it is processed. For example, financial transactions, age, temperature, and the number of steps from my house to my office are simply numbers. The information appears when we work with those numbers and we can find value and meaning.

 

 

Information can be considered as an aggregation of data. Information has usually got some meaning and purpose. The information can help us to make decisions easier. After processing the data, we can get the information within a context in order to give proper meaning. In computer jargon, a relational database makes information from the data stored within it.

 

Knowledge is information with meaning. Knowledge happens only when human experience and insight is applied to data and information. We can talk about knowledge when the data and the information turn into a set of rules to assist the decisions. In fact, we can't store knowledge because it implies the theoretical or practical understanding of a subject. The ultimate purpose of knowledge is for value creation..

The image on the left is an example of how to turn data into knowledge:

The data analysis process is composed of following steps:

  • The statement of problem
  • Collecting, Cleaning, Normalizing, Transforming Data
  • Exploratory statistics
  • Predictive modelling
  • Visualizing and interpreting your results
  • Artificial intelligence has made such advancements in data analysis that businesses are realizing the benefits of AI and using it to analyse their data for fine-grained insights, automate processes, and make data-based decisions. Some of the common cases where AI is significantly used for data analysis are:

  • Handling vast amounts of data efficiently in a short time and at a fast speed
  • Can help reduce hardware cost required to manage the vast data.
  • Reduces the human work and effort to handle everything manually and give inputs to carry out the operations.
  • AI can produce better forecasts with more data and less human supervision. AI can calculate a churn likelihood for every customer in your database, based on recent data. With this information, we not only can tell which customers will probably churn next month, but also optimize our decision making. For example, we can determine, of all customers who will churn next month, which should be targeted with a marketing campaign. Combining ML with BI creates a potentially huge value proposition for an organization. And with the advance of novel techniques such as automated machine learning (AutoML) and AI as a service (AIaaS), organizations can reduce the bottlenecks caused by not having enough data scientists or ML practitioners to leverage these AI potentials.
  • AI can calculate predictions at scale for optimized decision making.
  • Performing those activities that require intelligence, like inference, simulation, similarity search, regression, time-series analysis, clustering or unsupervised classification. Fields like deep learning rely on artificial intelligence algorithms; some of its current uses are chatbots, recommendation engines, image classification, and so on.
  • Leveraging Unstructured Data. Unstructured data such as raw text files, PDF documents, images, and audio files can be turned into structured formats that match a given schema, such as a table or CSV file, and can then be consumed and analysed through a BI system.
  • AI can be thought of as the “ability of a machine to perform cognitive functions that we associate with human minds, such as perceiving, reasoning, learning, interacting with the environment, problem-solving, decision-making, and even demonstrating creativity. Recent reports and academic publications show that we are clearly on the path towards pervasive AI and business is one of the macro areas in which AI is showcasing the wider range of applications and implications.

    This is what, I can do for you; Help you maintain a competitive advantage, enter new businesses, identify new trends, cope with the threat of new entrants adopting AI as well as existing competitors using AI, pressure to reduce costs, suppliers offering – and customers asking for – AI-driven products and services. Using AI we can also offer business/ organization unprecedented innovative opportunities. For instance, AI technologies are critical in bringing about innovation, provide new business models, reshape the way organizations implement business intelligent systems, rethink collaborative strategies, design intelligent products, devise novel service offerings, using different existing business tools and invent new forms of governance, imagining and fostering new human-machine interactions to change the way you work, and stay competitive on the market.

    Use Tableau to visualise of results In an explanatory data analysis process, simple visualization techniques are very useful for discovering patterns, since the human eye plays an important role. Sometimes, we have to generate a three-dimensional plot for finding the visual pattern. But, for getting better visual patterns, we can also use a scatter plot matrix, instead of a three-dimensional plot. In practice, the hypothesis of the study, dimensionality of the feature space, and data all play important roles in ensuring a good visualization technique. Please see some simple Tableau charts in download section. They can be adapted to your needs.

    Importance of data visualization The goal of data visualization is to expose something new about the underlying patterns and relationships contained within the data. The visualization not only needs to be beautiful but also meaningful in order to help organizations make better decisions.

    Visualization is an easy way to jump into a complex dataset to describe and explore the data efficiently. Many kinds of data visualization are available, such as bar charts, histograms, line charts, pie charts, heat maps, frequency Wordles, and so on, for one variable, two variables, many variables in one, and even two or three dimensions.

    Data visualization is an important part of data analysis process because it is a fast and easy way to perform exploratory data analysis through summarizing their main characteristics with a visual graph.

    Marketing Dashboards provide all the up-to-the-minute information necessary to run the business operations for a company—such as sales versus forecast, distribution channel effectiveness, brand equity evolution, and human capital development. An effective dashboard will focus thinking, improve internal communications, and reveal where marketing investments are paying off and where they aren’t. The customer metrics pathway looks at how prospects become customers, from awareness to preference to trial to repeat purchase, or some less linear model. This area also examines how the customer experience contributes to the perception of value and competitive advantage.

    The aim of marketing is to make selling superfluous. To know and understand the customer so well that the product or service fits him and sells itself. Ideally, marketing should result in a customer who is ready to buy. All that should be needed then is to make the product or service available.

    To Summarise. I can help Improve competitiveness of your business by improving customer profiling, simpler detection value for customer and create customer relations and measure how satisfied customers are with the business. Provide information on what's going on in the business, identify bottle necks, pinpointing details on the cause of why metrics are not on target. Financial indicators (revenue, margin, cost to serve, profitability etc.) can be improved if you know where they are at particular moments.

    If you would like me to work part-time, contract, permanent, in-house/from home, I'm more than willing to travel almost anywhere in the UK. Either way, if you have a project, you need help with, pleasecontact me below.