Mastering Data Analytics Course training programme demonstrates how to build on the technique covered in this training programme to produce a range of potent modelling, simulation, and predictive analytical methods. It has been demonstrated that the statistical analysis of numerical data is an effective tool for providing practical insight into issues like corporate finance, production methods, and quality control.

However, a lot of the analytical opportunities and needs of a contemporary, high-performing company cannot be satisfied using traditional statistical methods alone due to the emergence of the Internet of Things, the resulting growth in Big Data, and the ongoing need to model and predict.

You will learn the basics of data analytics, including data gathering and mining and the data ecosystem. The soft skills needed to communicate your data to stakeholders effectively will then be covered, along with how mastering these skills can enable you to make decisions based on data.

To optimise production systems, maximise performance efficiency, cut operating costs, combat risk, find fraud, and predict future behaviour and outcomes, more and more businesses are battling complex modelling and simulation issues.

You will know the principles of data analysis, such as data gathering and mining, and the data ecosystem. The soft skills needed to communicate your data to stakeholders effectively will then be covered, along with how mastering these skills can enable you to make decisions based on data.

In this Leadpoint Development course, you will learn how to distinguish between the responsibilities of a Data Scientist, Data Engineer, and Data Analyst. You will gain an understanding of what data analysis entails, as well as the duties of a data analyst. You will be able to enumerate the databases and data warehouses that make up the data ecosystem. The next step is identifying the key players in the data ecosystem and investigating the various tools available locally and online. Explore more of this thrilling journey and learn about Hadoop, Hive, and Spark, three popular big data platforms.

You will discover the essential elements of data analysis throughout this course. First, you’ll start looking into the fundamentals of data collection and learning how to recognise your data sources. The use of visualisations and dashboard tools will then be covered, along with data cleaning, analysis, and sharing techniques. Everything comes together in the final project, which includes a test of your understanding of the course material, an examination of what it means to be a data analyst, and a scenario for applying data analysis in the real world.


By the end of this Mastering Data Analytics Course, you will be able to picture a typical day in the life of a data analyst, comprehend the various career options in data analytics, and recognise the numerous tools at your disposal to become an expert in this field.

  • Processes for Data Analysis and Report Writing.
  • Describe data analytics and the main steps involved in the process.
  • Establish distinctions between data roles like data engineers, analysts, scientists, business analysts, and business intelligence analysts.
  • Describe the various file formats, data sources, data repositories, and data structure types.
  • By examining a business case study and its data set, you can pinpoint crucial components in the Data Analytics process.
  • To instruct participants on handling various business issues that call for modelling, simulation, and predictive analytical techniques.
  • Demonstrate to delegates how to use Microsoft Excel 2010 (or higher) and the Solver tool to implement a variety of the more popular modelling, simulation, and predictive analytical methods.
  • To give participants a conceptual understanding of various more popular modelling, simulation, and predictive analytical techniques.
  • To enable delegates to identify which modelling, simulation, and predictive analysis techniques are most appropriate for which categories of problems.
  • To equip delegates with the knowledge and experience, they need to recognise the situations in which a particular technique will probably result in erroneous conclusions.
  • To make it clear why the best businesses in the world believe that modelling, simulation, and predictive analytics are crucial to delivering the ideal products and services at the most reasonable prices.


Organisational Benefits.

Organisations that can make the best decisions and accurately predict future trends and behaviours can significantly improve their capacity to compete on the global stage. As a result of sending their employees on this training course, organisations can anticipate the following benefits:

  • Adjusting decision-making from intuition to information
  • Gives precise answers to challenging issues
  • Better behaviour prediction and forecasting.
  • Contemporary business process modelling and simulation.
  • Better ability to assess risks and make decisions based on that information.
  • Improved exploitation of the abundance of data found in big data.

Personal Benefits.

Participants will each gain a thorough understanding of a variety of the more popular modelling, simulation, and predictive analytical techniques, as well as plenty of hands-on experience, all of which will be able to solve various business challenges. More specifically, delegates will acquire:

  • New information regarding using Microsoft Excel for modelling, prediction, and optimisation.
  • Familiarity with linear programming.
  • Knowledge of the application of genetic and Newtonian optimisation techniques.
  • Understanding of Markov models, scenario analysis, and Monte Carlo simulation.
  • The capacity to recognise which categories of analysis apply to particular problems.
  • Sufficient situational awareness to recognise when a method will result in false conclusions.


For Analysts, managers, and all business professionals, professionals whose jobs require the manipulation, representation, interpretation, and analysis of data, this Leadpoint Development Mastering Data Analytics course has been created. Delegates must not only be numerate for this training course, which involves extensive modelling and analysis using Excel 2010 (or higher), but they must also enjoy carefully working with numerical data to solve challenging problems.

Attending this Mastering Data Analytics Course requires a basic understanding of Microsoft Excel (version 2007 or higher) and the ability to analyse data using standard statistical methods.


A problem-based learning approach is used in this Mastering Data Analytics Course training. Delegates are presented with real problems from the broadest possible range of applications, including insurance, supply chain logistics, chemistry, engineering, production optimisation, and financial risk assessment. Each issue illustrates the need for a unique modelling or analytical strategy.

This Leadpoint Development training course is entirely applications-oriented, which means that it spends the least time discussing the theory and mathematics of analysis and the most time using and comprehending practical Excel methods.

As technology advances, the tools and resources available to data analysts and engineers change. However, the core duty of an analyst remains the same. As a data analyst, your primary responsibility will always be to produce significant, data-driven insights that assist your company or organisation meet its objectives.
You may build an excellent foundation as an analyst by concentrating on these essential abilities. You can then improve your technical skills to broaden your capabilities. Whatever tools you choose, remembering these fundamental concepts will help you develop as an analyst and add more excellent value to your organisation.
1. Establish a Specific Data Analytics Process
2. Avoid Burying the Lede
3. Peer Review of Data Analytics
4. Check Your Data Three Times
5. Understand When to Stop Analysing
Data analytics (DA) is the act of studying data sets to discover trends and develop conclusions about the information contained within them. Data analytics is increasingly being performed with the assistance of specialist tools and software. Data analytics technology and methodologies are widely employed in commercial industries to help businesses make better business choices. Researchers also use analytics tools to validate or reject scientific models, ideas, and hypotheses.
Data analytics refers to a wide range of applications, from essential business intelligence (BI), reporting, and online analytical processing (OLAP) to various types of advanced analytics. In this sense, it is similar to business analytics, another umbrella term for data analysis techniques. The difference is that the latter is focused on commercial applications, whereas data analytics is more broad in scope. However, this broad interpretation is not universal: in certain circumstances, individuals use data analytics expressly to refer to advanced analytics, seeing BI as a different category.
Data analytics projects may assist firms in increasing revenue, improving operational efficiency, optimising marketing campaigns, and improving customer service. Depending on the application, the data evaluated might be either historical records or new data that has been processed for real-time analytics. It may also originate from a combination of internal and external data sources.

Having a defined procedure for your assignments is one of the most important aspects of becoming a great data analyst. A simple data analytics process is as follows:

State the Question: Clearly define the question you're attempting to answer and the objectives of your data analytics project.

Collect Data: Collect relevant data for your project with the help of data engineers or other data specialists.

Data Cleaning: Standardise your gathered information and delete any inaccurate or irrelevant entries.

Examine the Data: Use data analysis techniques to comprehend the data and generate answers to your questions. Depending on the question you're attempting to answer, this phase might take various shapes.

Share Your Findings: Create data visualisations and tools to assist others in understanding the insights you've generated.

With this basic approach, you'll have a clear road map for designing and executing data analytics initiatives. Following this fundamental procedure will also prevent you from being distracted while conducting your investigation.

Be the first to add a review.

Please, login to leave a review
Get course
Enrolled: 0 students
Duration: 3 DAYS
Lectures: 0
Video: 10 hours
Level: Intermediate


Working hours

Monday 9:30 am - 6.00 pm
Tuesday 9:30 am - 6.00 pm
Wednesday 9:30 am - 6.00 pm
Thursday 9:30 am - 6.00 pm
Friday 9:30 am - 5.00 pm
Saturday Closed
Sunday Closed
× How can I be of assistance?