Overview of Analytics Course in Miami

If you have an interest in data analysis and are looking for courses to enhance your skills, Miami is a great place to consider. Analytic courses in Miami are highly sought-after courses as they offer comprehensive knowledge in data analysis, visualization, and business intelligence. The city of Miami is located in the southeastern state of Florida and is home to several universities and educational institutions offering analytic courses. These courses cater to students interested in fields such as business intelligence, data analytics, data visualization, and more. Miami is home to several top universities with renowned business, computer science and engineering programs. These universities offer a range of analytic courses designed to provide students with the in-depth knowledge and skills necessary to succeed in the industry. Some of the universities that offer analytic courses in Miami include the University of Miami, Florida International University, and the Miami Dade College. The University of Miami’s Master of Science in Analytics program uses a hands-on approach to teach students the fundamentals of data analysis. The program provides students with skills in statistical techniques, data mining, predictive modeling, and more. The program is designed to prepare students for careers in data analysis and business intelligence in different industries, including healthcare, information technology, and finance. Another option is the Florida International University's Business Analytics program that offers a master's degree in business analytics. The program is ideal for professionals who want to gain advanced knowledge in analytics, data mining, decision science, and predictive modeling. The program provides students with a blend of technical and business skills and also offers them an opportunity to work on real-world projects. Miami Dade College offers an Associate in Science in Data Analytics degree, which focuses on preparing students with practical skills in data analysis using tools such as SAS and programming languages such as Python. Students learn about data querying, data modeling, and basic data analysis concepts. Besides universities, there are also several analytic courses offered by private entities and training centers in Miami. These courses cater to both beginners and professionals looking to enhance their skills or switch into a career path in data science or analytics. Courses usually cover topics including data mining, machine learning, data visualization, and more. In conclusion, studying analytics in Miami is an excellent opportunity for students who want to gain in-depth knowledge and skills in the field of data analysis. With the city’s wealth of universities and training centers, students are sure to find a course that suits their needs and career goals. Whether you are a beginner or a professional in the industry, Miami has analytic courses that cater to all levels of experience and provide a solid foundation in data analysis and business intelligence.

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Course Duration

2

Internship

Yes

Live Training

Yes

Career Assistance

Yes
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Skillfloor Course Training Process
Skillfloor Course Training Process

Internship Certificate

internship certificate

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 Miami?

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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