Overview of Analytics Course in Sarasota

Sarasota is a beautiful city located in Florida and has recently emerged as a hub for analytics courses. With the rapid growth of the data analytics industry, businesses, and individuals are constantly seeking ways to enhance their analytical skills to remain competitive. Sarasota has a plethora of options when it comes to analytics courses for all levels of experience. One of the frequently sought-after analytics courses in Sarasota is the Data Analytics Bootcamp. This course is designed for beginners and those who wish to begin their analytical journey. The course offers hands-on training on tools and technologies such as R and Python programming languages, data visualization tools like Tableau and Power BI, and data analysis techniques such as statistics, exploratory data analysis, and machine learning. For those who wish to upscale their proficiency in analytics, Sarasota offers more advanced courses such as the Data Science Immersive program. This program takes a deep dive into the world of data science, providing training on data wrangling, data storytelling, and data modeling, among other things. The course is widely accepted for industry-recognized certifications like the General Assembly Data Science certificate. In addition to these courses, there are also online courses available for aspiring analytics professionals in Sarasota. These programs offer flexible schedules, allowing students to work while studying. One such course is the Google Analytics certification course, which covers the principles of web analytics and provides training on Google Analytics tools and techniques for data-driven marketing. Sarasota's analytics courses offer more than just theoretical learning. Students are exposed to real-world scenarios and are given projects and assignments to work on. This approach allows students to apply their skills and knowledge to actual business cases, providing them with a comprehensive understanding of the industry. Another unique aspect of analytics courses in Sarasota is the opportunity to network. Many courses conduct workshops and events, providing students with a platform to meet and interact with analytics professionals from various industries. Networking is an essential skill in the analytics industry, and students are encouraged to build relationships with industry peers and experts. In conclusion, analytics courses in Sarasota equip students with the skills necessary to tackle the challenges of the data-driven world. These courses offer hands-on training on the latest tools and technologies and provide students with the opportunity to apply their skills to real-world scenarios. The flexible schedules and networking opportunities offered by these courses make them an appealing option for both beginners and professionals seeking to upskill. With Sarasota's many options for analytics courses, individuals and businesses alike can stay ahead of the competition.

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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 Sarasota?

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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