The purchase of a property is likely to be one of the largest financial decisions you will make in your life. Melbourne Buyers Agents is important to have a professional on your side to ensure that the process is conducted fairly and that you are making an educated decision. A buyers advocate can assist in providing unbiased advice, support and guidance throughout the entire purchasing process. They will help to ensure that you purchase the property that best meets your needs at an appropriate price.

Is it better to buy and sell with the same agent?

Whether you are a first home buyer, investor or self managed super fund purchaser, a Melbourne buyer’s advocate will act solely in your interests. They will source, inspect and assess properties for you, negotiate on your behalf and provide you with comprehensive property reports. They will also attend auctions and bid on your behalf if required, as well as arrange building and pest inspections and engineering reports.

Kim Easterbrook and David founded Elite Buyers Agents in 2011 to create a fresh approach to property advice that starts and ends with your best interest at the forefront of all that they do. They have an unwavering commitment to honesty and integrity, and aim to level the playing field with their superior knowledge of property in Melbourne.

Cate Bakos is a licensed buyer’s agent and Property Investment Advisor who started her business in 2014. She services Melbourne metropolitan and specialises in helping clients ‘buy smart’. She is an accredited member of REBAA, Australia’s leading professional body for buyer’s agents and advocates.

Business intelligence (BI) is a collection of strategies and technologies that enable companies to make informed business decisions. It encompasses data visualization software, dashboard tools, enterprise-level reporting, infrastructure and best practices to turn raw information into useful and understandable insights. BI tools help organizations collect, store, analyze, and present data from internal and external sources. These insights are then used to improve business processes and strategies, boost revenue, and increase profit.

While it may sound like a buzzword, business intelligence is hardly a new technology. In fact, its roots stretch back to the 1860s. However, the modern term dates from 1989, when consultant Howard Dresner coined it as an umbrella phrase for the methods and architectures of analytics systems to support decision-making.

A key function of business intelligence is to spot and highlight patterns and trends in a given market that could impact current or future business strategy. This can be done by gathering and analyzing data from internal sources, as well as using public information or field reports.

The data can be presented in the form of charts and graphs to help users identify issues and opportunities. It is also possible to mash up data from different sources and combine it for deeper analysis and more complete insights. For example, an airline might use BI to monitor flight capacity and occupancy rates for its airports, while a hotel might combine booking and guest feedback to improve customer service.

Traditionally, the responsibility for collecting, storing, and analyzing business data has fallen to IT teams. The IT department would extract, clean and structure the data before putting it into a database where business users can access and query it. This is called data preparation and can be a very time-consuming process. BI solutions automate this and streamline the whole process.

In addition, BI platforms enable users to create their own data and dashboards. This self-service functionality is becoming increasingly important as BI adoption expands to include users across all departments. However, it is important that BI users are equipped with the skills and knowledge to extract value from the data they are using. This is why many BI vendors are now taking a proactive approach to improving data literacy with initiatives such as the Qlik-led Data Literacy Project.

Businesses rely on business intelligence to support functions as diverse as hiring efforts, compliance issues, production and marketing. As a result, the demand for BI professionals is high.

To be successful in this field, it is important to have a background in business or IT, as well as a good understanding of data visualization, SQL and programming skills. Additional qualifications include an analytical mindset and strong problem-solving abilities. With the growth of the BI space, a wide range of career paths are now available. This includes the roles of business analysts, software developers and project managers. This is a great career choice for people who want to use their skills and passion to bring valuable insight into the way companies operate.

Data science is a domain that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. It uses complex machine learning algorithms to build predictive models and requires a deep understanding of the functioning of databases, as well as of programming languages like Python.

While a common misconception is that you need a science or math Ph.D to become a data scientist, that’s not entirely true. There are plenty of multidisciplinary data scientists out there who are able to combine a strong foundation in statistics, programming, and business, along with advanced skills in data visualization, machine learning, and other specialized disciplines.

The first step in data science is to gather structured and unstructured data from multiple disparate sources—enterprise systems, public domain data, and more. It’s important to recognize that raw data can be messy and chaotic, with mismatched or missing records, inconsistent formats, and more. This process is known as “data munging,” and it requires a good pattern-recognition sense and clever hacking skills to transform masses of database-level information into a form that’s ready for analysis.

From here, data scientists use a variety of methods to find new insights and opportunities in the data. Predictive analytics forecasts what will happen based on existing patterns in the data, while descriptive analytics examines the underlying factors that influence those outcomes. Advanced methodologies include ML algorithms, recommendation engines, neural networks, and simulation.

Lastly, data scientists often need to communicate their findings to non-technical audiences. This includes creating narratives about the problem and its solution, supporting them with data insights. The Oakland Athletics’ general manager in the movie Moneyball, for example, used data on player performance that other teams overlooked to assemble a successful team despite a limited budget.

Data science is a rapidly growing field that has applications in nearly every industry. Companies from Amazon to Google use it to predict customer behavior and identify new products or services they could offer. It’s also been used to improve customer service by identifying patterns in customer complaints and satisfaction surveys.

If you’re interested in becoming a data scientist, a comprehensive program like Great Learning’s Postgraduate Diploma in Data Science can give you the skills and knowledge you need to succeed. The program is developed in partnership with top-ranked universities, including MIT and Northwestern University. It teaches you the latest tools and techniques for working with big data, helping you build real-world applications through hands-on projects and case studies. Learn more about the program today.