Overview of Analytics Course in Kansas City

Kansas City is a bustling metropolis in the Midwest that is home to a growing business community that relies heavily on data-driven decision-making. As a result, analytics courses are becoming increasingly popular among professionals seeking to advance their careers in the field of data analytics. In this article, we will explore some of the most popular analytics courses in Kansas City and how they can help you enhance your skills in data analysis. 1. "Data Analysis Bootcamp" - This intensive course offered by the University of Missouri-Kansas City focuses on providing students with a comprehensive understanding of statistics, data visualization, and programming tools like SQL and Python. The course is designed for beginners as well as intermediate-level data professionals seeking to enhance their skills in data analysis. 2. "Data Science for Leaders" - This course is offered by the Kansas City Data Science and Analytics Meetup group, and it focuses on providing a high-level overview of data science concepts and techniques. The course is designed for executives, managers, and other non-technical professionals seeking to understand how data science can be used to drive business decisions. 3. "Applied Data Science with Python" - This course is offered by the University of Kansas City-Missouri, and it focuses on providing students with hands-on experience in using Python for data analysis. The course covers topics such as data cleaning, data manipulation, and data visualization, and it is designed for beginners with no programming experience. 4. "Data Analytics and Visualization" - This course is offered by Rockhurst University and it focuses on providing a comprehensive understanding of data analytics tools and techniques. The course covers topics such as data preparation and cleaning, data visualization, and statistical analysis. The course is designed for professionals seeking to enhance their skills in data analytics and visualization. 5. "Business Analytics Certification" - Offered by the University of Kansas City-Missouri, this course provides students with a comprehensive understanding of business analytics concepts and techniques. The course covers topics such as data mining, predictive modeling, and data visualization, and it is designed for professionals seeking to enhance their skills in business analytics. 6. "Data Science Professional Certificate" - This is an online course offered by IBM that provides students with hands-on experience in data science techniques. The course covers topics such as data analysis, machine learning, and data visualization, and it is designed for beginners with no programming experience. In conclusion, Kansas City offers a wide range of analytics courses that can help professionals advance their careers in the field of data analytics. Whether you are a beginner seeking to learn the basics of data analysis or an experienced data professional seeking to enhance your skills in predictive modeling or data visualization, there is a course in Kansas City that is right for you. So why not invest in your professional development by enrolling in one of these analytics courses today!

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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 Kansas City?

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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