1. Descriptive Statistics KPIs
Workshop Overview
Workshop Overview
Workshop Overview
It all begins with effective data collection and/or selection. This requires a good understanding of various data types and their sources. Proper organization makes it easier to describe results using appropriate and efficient descriptive statistical measures. This workshop focuses on important aspects of creating a smart data collection process, selecting the best sampling approach, validating the quality of stored information for analysis, and identifying corresponding descriptive statistical KPIs.
Learning Outcomes
Workshop Overview
Workshop Overview
- Plan the lifecycle of a successful data analysis project.
- Translate any business into a comprehensive database.
- Assess data quality for analysis and reporting purposes.
- Explore various sampling methods and potential pitfalls.
- Describe and interpret data using descriptive statistics.
- Interpret estimates based on sample results.
- Differentiate between fluctuation and confidence intervals
Duration 3 days
What will it be about?
What will it be about?
Day 1:
- Central Tendency Measurements
- Scatter Tendency Measurements
Day 2:
- Central Limit Theorem
- Estimations and Sampling
Day 3:
- Descriptive Statistics with Excel, and Python
What will it be about?
What will it be about?
What will it be about?
- Colored PPT Booklet/ Videos
- Case studies from A to Z
- Group exercises for live practices
- Proprietary vs. Open source tools
- Report design 101
- Average, Median and Mode
- Variance and Standard Deviation
- Probabilistic vs. Non-Probabilistic sampling
Program Excerpts
2. Statistical Data Analysis for "Professionals"
Workshop Overview
Workshop Overview
Workshop Overview
For all machine learning solutions, data analytics is a must to kick off a career in the world of data. One cannot claim to apply AI analytics without a deep knowledge of data analytics.
This course is designed to give participants a clear and complete understanding of data structuring for efficient analysis, scientifically profiling different groups by analyzing data smartly and efficiently, and appropriately manipulating several technology tools in the market.
For those who believe that statistical tests are key to success ... fasten your seat belts!
Learning Outcomes
Workshop Overview
Workshop Overview
- Introducing the logic of hypothesis tests.
- Differentiating between prior and posterior errors.
- Profiling a sample against standard references.
- Distinguishing between profiling and describing groups.
- Identifying variables that most differentiate groups.
- Identifying variables that most distinctly profile groups.
- Fully exploring the narrative behind simple regression.
- Presenting all analytics in a single comprehensive chart.
- Comparing solutions using Excel, STATISTICA, and Python.
Duration 5 days
What will it be about?
What will it be about?
Day 1:
- One Group Statistical Tests
Day 2:
- Two Group Profiling with statistical tests
Day 3:
- Multiple Gr. Profiling with statistical tests
Day 4:
- Simple Linear Regression
Day 5:
- Applications with Excel, Python, ...
What will it be about?
What will it be about?
What will it be about?
- Colored PPT Booklet/ Videos
- Statistical tests concepts
- Statistical indicators: t, chi-square, F
- Proprietary tools solutions
- ANOVA table / R-Square
- Profiling Techniques
- The All-In-One chart
- The one and unique P-Value
- Data Analysis, before Machine Learning.
Program Excerpts
3. Statistical Data Analysis for "Experts"
Workshop Overview
Workshop Overview
Workshop Overview
Data scientists often encounter non-conforming data when analyzing multiple groups and when tracking the same group over various periods. It's also expected to have insufficient data for analysis. In such cases, data analysis techniques should focus on statistical science related to "dependent" samples and "non-parametric" tests as alternatives to parametric ones (Data Analysis for Professionals). Determining the sample size is a common question when designing an analytical project. Should we consider the Big Data solution where all the data is included? This course will address these questions, discussing their advantages and disadvantages.
Learning Outcomes
Workshop Overview
Workshop Overview
- Compare dependent samples with independent ones.
- Compare non-parametric analytics with parametric ones.
- Face the two types of errors in statistical tests with « power analysis».
- Calculate sample size with scientific methods.
- Understand factors that influence sample size.
- Explore the different errors while defining sample size.
- Manipulate multiple software solutions with the correct interpretation of results.
Duration 3 days
What will it be about?
What will it be about?
Day 1:
- Dependent Samples Analytics
Day 2:
- Non-Parametric Analytics
Day 3:
- Power Analysis
What will it be about?
What will it be about?
What will it be about?
- Colored PPT Booklet/ Videos
- Statistical tests: Man Whitney, Correlation Rank test, t-paired, ...
- Proprietary tools solutions
- Two Way ANOVA
- Profiling Techniques
- The All-In-One chart
- The one and unique P-Value
Program Excerpts
