Overview of Analytics Course in Raleigh

If you are looking to boost your career in analytics, consider taking an analytics course in Raleigh. The city is home to many reputable educational institutions and training centers that offer comprehensive courses in analytics using modern tools and technologies such as R. Raleigh is one of the fastest-growing cities in the US and is a hub for technology and innovation. The area attracts top talent and is an ideal place to learn cutting-edge data analytics skills. There are many courses available in Raleigh that cover various aspects of analytics, such as data visualization, predictive modeling, data mining, and machine learning. Some popular analytics courses available in Raleigh include: 1. Analytics Bootcamp: This course covers the fundamentals of data analytics, including data visualization, data cleaning, and analysis techniques. Students will learn how to use R to manipulate and analyze data, build predictive models, and create interactive visualizations. 2. Machine Learning: This course focuses on the application of machine learning algorithms and techniques in data analysis. Students will learn how to use popular machine learning libraries such as caret and tensorflow to build predictive models and analyze data. 3. Data Visualization: This course covers the principles of data visualization and the best practices for creating effective visualizations. Students will learn how to use R libraries such as ggplot2 and plotly to create interactive and engaging visualizations. 4. Big Data Analytics: This course focuses on the analysis of large datasets using popular big data technologies such as Hadoop and Spark. Students will learn how to use R and these technologies to analyze, clean, and visualize large datasets. 5. Data Mining: This course covers the techniques for discovering patterns and relationships in data. Students will learn how to use R to perform data mining tasks such as clustering, association rule mining, and anomaly detection. Many of these courses are offered by top universities and training centers in Raleigh, such as North Carolina State University and the Raleigh Durham Institute of Technology. These institutions provide a collaborative learning environment and access to state-of-the-art facilities and resources. In addition to coursework, many analytics courses in Raleigh offer hands-on projects and real-world case studies to help students apply what they have learned. These projects help students build a portfolio of work that can demonstrate their skills and knowledge to potential employers. Employment opportunities for skilled data analysts and scientists are growing rapidly, and Raleigh is an excellent place to start your career in analytics. According to Glassdoor, the average salary for a data analyst in Raleigh is around $70,000 per year, with senior analysts earning over $100,000 per year. In conclusion, if you are looking to launch or enhance your career in data analytics, taking an analytics course in Raleigh is an excellent choice. With a robust curriculum, access to top-notch resources, and a supportive community, you can learn the skills you need to succeed in this exciting and in-demand field.
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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 Raleigh?

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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