Statistics & Data Analytics

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. 

• 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

Day 1: 

-  Central  Tendency Measurements 

-  Scatter Tendency Measurements  

Day 2:

- Central Limit Theorem

- Estimations and Sampling

Day 3:    

- Descriptive Statistics with Excel, and Python

- Colored five stars PPT Booklet

- 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

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! 

• 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.

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, ...

- 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 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.

• 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.

Day 1:

Dependent Samples Analytics

Day 2:

Non-Parametric Analytics

Day 3:

Power Analysis

- 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