New York City is a hub for innovation and technology, and as such, it has become a destination for those seeking to advance their skills in analytics. Many courses are available in New York City, providing students with the opportunity to learn about the latest techniques and tools used in analytics. One popular analytics course in the city is the "Applied Data Science with Python" course offered by the University of Columbia. This course is designed to help students learn about the essentials of data science and machine learning using the Python programming language. The course is aimed at students with a strong background in computer science or mathematics, and covers topics such as data manipulation, data analysis, visualization, and machine learning. Another course available in New York City is the "Data Analytics Bootcamp" offered by the Flatiron School. This is a 15-week immersive program where students learn about data analysis and visualization, machine learning, and data modeling. The course also provides students with the opportunity to develop practical skills by working on real-world projects. Moreover, the New York Institute of Technology offers a master's degree in Data Analytics. This program is designed for professionals seeking to advance their careers in data analytics, big data, and business intelligence. It takes two years to complete the degree, and students learn about modern analytics tools, data mining techniques, and statistical analysis. The Columbia Business School also provides a "Venture Capital and Private Equity Analytics" program. This course is aimed at individuals who want to learn about the principles and practices of venture capital and private equity investing, and how analytics can be used to improve investment decisions. This program also includes case studies and guest lectures from industry experts. Overall, New York City offers a wide range of analytics courses designed to meet the needs of individuals with different skill levels and career goals. These courses provide students with a foundation in data analysis, visualization, and machine learning, and help them develop practical skills by working on real-world projects. It is no wonder that New York City has become a global hub for innovation, analytics, and technology, making it an ideal destination for those seeking to advance their career in analytics.
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- 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
