Overview of Analytics Course in Melbourne

Melbourne is a thriving hub for business and technology, making it an ideal location for individuals interested in pursuing an analytics course. Analytics is a field that focuses on using data to make informed decisions in different industries such as marketing, finance, healthcare, and many more. With an analytics course in Melbourne, students can gain the skills and knowledge they need to excel in this rapidly growing field. One of the popular courses in Melbourne is the Master of Business Analytics. This course is designed for individuals seeking to advance their careers in business analytics, data analysis, or data science. The course covers areas such as statistical analysis, data mining, machine learning, and programming languages, among others. Students can also expect to learn how to apply these skills to real-world business problems, preparing them for careers in different industries. Another popular analytics course in Melbourne is the Graduate Diploma in Data Science. This course focuses on the technical skills required to analyze and interpret large sets of data. Students learn about data mining, machine learning algorithms, and statistical analysis techniques, among many other topics. This analytics course also covers popular programming languages such as Python and R, which are widely used in the field of data science. For individuals looking for industry-specific analytics courses, Melbourne offers a range of options. For example, the Graduate Certificate in Healthcare Analytics focuses on using data analytics to improve healthcare outcomes. Students learn about healthcare data management, data visualization, and healthcare economics, among other topics. This course is suitable for professionals working in healthcare who want to improve their analytics skills to advance their careers. Another example of industry-specific analytics courses is the Master of Marketing Analytics. This course is designed to equip students with the skills needed to excel in marketing and customer analytics. Students learn about data-driven marketing strategies, predictive modeling techniques, and customer segmentation, among other topics. Graduates of this course are well-positioned to pursue careers in marketing research, marketing analytics, and other related fields. Overall, an analytics course in Melbourne can offer individuals a great opportunity to gain the skills they need to succeed in this rapidly growing field. With a range of courses available, individuals can choose one that suits their career aspirations and the industry they want to work in. Moreover, Melbourne's business and technology-oriented environment provides an excellent setting for students to learn, grow, and network with like-minded individuals. Graduates of analytics courses from Melbourne can expect to have not only strong technical skills but also the ability to apply these skills to solve real-world business problems.
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Course Duration

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Internship

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Live Training

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Career Assistance

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Skillfloor Course Training Process
Skillfloor Course Training Process

Internship Certificate

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Completing this Analytics Course certifies your proficiency in data analysis, statistical methods, and essential analytics tools. This certification shows you can transform raw data into actionable insights, improving decision-making in various business contexts. It indicates your understanding of data visualization, predictive modelling, and data-driven strategy development, providing you with the skills to excel in analytics. This credential reflects your dedication to continuous learning and commitment to leveraging data for strategic advantage.

Analytics Tools Covered

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Why Choose SKILLFLOOR for Analytics in Melbourne?

Why Course Training in Skillfloor

Syllabus

- Overview of data analysis and its importance in business

- Types of analytics: Descriptive, Predictive, Prescriptive

- Role of data in decision-making processes

- Introduction to common tools: Tableau, PowerBI, Excel

- Ethical considerations in data collection and analysis

- Data sources: Primary and secondary data

- Data collection methods (surveys, web scraping, databases)

- Data cleaning techniques (handling missing values, outliers)

- Data transformation and feature engineering

- Data storage concepts (structured vs. unstructured data)

- Descriptive statistics: Mean, median, mode

- Data visualization basics (histograms, scatter plots)

- Identifying data patterns and trends

- Outlier detection and handling methods

- Correlation and causation analysis

- Inferential statistics and probability theory

- Hypothesis testing (t-tests, chi-square tests, ANOVA)

- Measures of central tendency and variability

- Confidence intervals and margin of error

- Regression analysis: Linear and logistic regression

- Principles of effective data visualization

- Types of charts and their uses (bar, line, pie, heatmaps)

- Designing dashboards for different audiences

- Interactive visualization techniques

- Data storytelling for impactful presentations

- Time series analysis and forecasting methods

- Clustering and segmentation analysis

- Decision trees and classification techniques

- Introduction to machine learning in business analytics

- Model evaluation and selection

- Basics of SQL for data manipulation

- Creating databases and relationships

- Aggregating data with SQL (GROUP BY, JOIN)

- Data modeling for business intelligence (star and snowflake schemas)

- Case study: Building a business model with SQL

- Connecting and preparing data in Tableau

- Creating basic visualizations (charts, maps)

- Advanced Tableau functions (LOD calculations, table calculations)

- Building interactive dashboards and stories

- Publishing and sharing visualizations on Tableau Server/Online

- Introduction to PowerBI workspace and components

- Data import and transformation with Power Query

- Data modeling and relationships in PowerBI

- Creating and customizing visualizations

- Publishing and collaborating on PowerBI Service

- Selecting a real-world dataset for analysis

- Defining business questions and objectives

- Conducting data analysis and visualization

- Presenting findings in a comprehensive dashboard

- Peer review and feedback on project

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