Data Visualization and Statistical Analysis

"A picture is worth a thousand words and... data itself!

However, it's important to select illustrations carefully based on the types of variables and the researcher's objectives.

This workshop will cover various types of charts and how to structure them into an attractive dashboard. It will compare different possibilities using common proprietary tools such as Excel, Power BI, and Tableau, as well as open-source tools like Python and two of its charting libraries: Matplotlib and Seaborn."

• Translate any case study into a comprehensive database.
• Learn to create various types of tables including counts/frequencies and percentages.
• Understand the importance and effectiveness of using graphs.
• Present data in an innovative and efficient manner.
• Compare the usage of Excel, Power BI, and Tableau with Python open-source.
• Interpret chart outputs accurately.
• Gain knowledge of the basics of color harmony.
• Introduction to Creating Infographics.

Day 1:

- Designing Efficient Data Sets 

Day 2: 

- Pies / Doughnuts / Bars / Histograms / Waterfall / ...

Day 3:

- Illustrating data with professional tools

- Comprehensive colored PPT documents 

- The multiple ways of generating charts 

- Group applications with adequate software 

- Pivot Tables

- 360 exploration of data representation

- Case studies from A to Z complete

- Proprietary tools for data visualization 

- Open source tools for data visualization 

-  Infographics 

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 PPT documents / 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

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 documents / 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.

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 documents / 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

The foundation of AI solutions for decision-making, "Supervised" ML models, are now more accessible to practitioners due to rapid technological advancements. Mastering the most critical predictive models has become more accessible, especially with the improvement of automation tools. This workshop provides a comprehensive overview of "supervised" Machine Learning algorithms and their role in improving predictions across various industries. To ensure practical application, it also explores models using different technologies (SAS, Alteryx, STATISTICA, PYTHON, etc.), enabling participants to become professional practitioners and expert consultants by evaluating and selecting the most suitable solution with the right technical package.

• Explore the rise of AI with IoT and technology capacities.
• Understand the true meaning of Machine Learning (ML).
• Connect Data Analysis to Machine Learning.
• Make it all with f(X) = y.
• Differentiate between Regression and Classification models.
• Validate models with p-value and Accuracy metrics.
• Improve predictions by testing different ML models.
• Fine-tune models with the stepwise methods.
• Compare models using accuracy measures.
• Understand the utility of all cross-validation techniques.
• Design dashboards for comparative models.
• Overview Ensemble Models.

Day 1:

- Introduction to Machine Learning

- Multiple Linear Regression 

Day 2: 

- Simple & Multiple Logistic Regression 

- Models Evaluation Indicators

Day 3:

-  Linear and Quadratic Discriminant Analysis

Day 4: 

- Decision Trees / Random Forest Trees 

- Naive Bayes

Day 5: 

- Support Vector Machines

- K Nearest Neighbors

- Colored PPT documents / Videos.

- The multiple-way model generation.

- All-in-one predictive model solution.

- Quality model indicators.

- Team Competition for Best Model Finding.  

- Complete case studies from A to Z.

- Proprietary tools for data visualization.

- Methods for selecting the best model.

- ROC charts.

It is common to have data sets with multiple variables describing business topics. But how can we extract all the hidden patterns from such complex data sets? Reducing the number of variables into simple "maps" with PCA becomes essential to highlight invisible relations within the data, facilitating the correct actions to take. This workshop also explains the difference between scientific market "clustering" and simple common sense "filtering". It empowers the definition of market niches and profiles them using Data Analysis techniques. Additionally, the program covers illustrations that reveal associations between the components of multiple variables for efficient tracking of pattern evolution. The workshop also includes practical applications with two different technologies, allowing participants to become more like consultants than mere experts.

• Highlighting the role of statistical data analysis in Unsupervised Machine Learning.
• Discovering hidden patterns within data sets.
• Understanding the "Dimension Reducibility" concept.
• Mastering all "pattern finding" algorithms in AI applications.
• Mapping complex data sets of multiple variables in simple charts.
• Visualizing relationships between variables and categories.
• Evaluating the quality of reduced multidimensionality.
• Differentiating between clustering and filtering.
• Delving into all clustering techniques and their applications
• Running professional segmentation with clustering.
• Applying it all using specialized software.

Day 1:

- Matrix Factorization

- Principal Component Analysis  

Day 2: 

- t-SNE and Multi-Dimensional Scaling

- Simple Correspondence Analysis

Day 3:

- Agglomerative Clustering

- K Means and Medoids

Day 4: 

- Recommender Systems

Day 5: 

- Gaussian Mixture Models

- Colored PPT documents / Videos.

- Multidimensional Reducibility

- Hierarchical   vs. Divisive Clustering

- Proprietary vs. Open Source tools   solutions

- Eigenvalues and Eigenvectors

- Team Exercises

- The science behind mapping illustration.

- Quality   measurements of unsupervised models

- Demystifying the Curse of   Multidimensionality

There is often confusion between forecasting methodologies and predictive modeling using supervised machine learning algorithms. While the latter relies on external information for its predictions, forecasting uses its own data.

This workshop aims to provide a comprehensive understanding of all forecasting methods and how to apply them for near-future predictions. It will cover basic models and then explore the evolution of various methods, enabling participants to use them effectively. Understanding all quality indicators will help participants select the best forecasting model for their businesses. 

• Compare forecasting with supervised machine learning.
• Learn how to select between forecasting models
• Evaluate the relationship between the future and the past.
• Measure the impact of the past on the near future
• Analyze all forecasting methods and their evolution.
• Develop all analytical models for estimation.
• Master the precision measures of models’ quality.
• Select the best forecasting model.
• Apply models with specialized software.

Day 1:

- Linear and Polynomial trends

- Exponential, Power, and Logarithm trends.

Day 2: 

- Averaging and Moving Averages.

Day 3: Exponential Smoothing

- Simple, Double, and Triple models in exponential smoothing.

Day 4: 

- Time Series

- ARIMA and Box Jenkins method

- Comprehensive colored PPT documents.

- Supervised ML vs. Forecasting approach.

- Stationary, Additive, and Multiplicative models.

- Proprietary tools solutions.

- Quality measures of forecasting models.

- "White Noise” data.

- All-in-one solution method.

- Selecting the Fit model.

- Forecasting or Linear Regression?

Many factors influenced the rise of AI and the launch of the fourth technological revolution. However, one primary invention that accelerated the process was the ability to transform images into information. This breakthrough paved the way for transforming videos, texts, and audio into information, resulting in advancements such as driverless cars, bots, and automation that almost match human abilities. This workshop focuses on the algorithms behind this technological breakthrough, making AI a reality and allowing you to apply deep learning algorithms to solve new, challenging problems. 

• Learn the mathematics behind Deep Learning.
• Explore the logic of optimization with Gradient Descent.
• Dissect components of neural networks.
• Adjust hyperparameters of algorithms to optimize cost functions.
• Explore the architecture of main deep learning networks.
• Improve the accuracy of Classification and Estimation.
• Establish knowledge in:

                    - Image classification

                    - Face recognition, and

                    - Object detection. 

Day 1:

- Algebra and Calculus 

Day 2: 

- Gradient Descent

- Perceptron Algorithm

Day 3:

- Feedforward Neural Networks

Day 4: 

- Convolutional Neural Networks

- Comprehensive colored PPT documents.

- Neurons, Hidden layers, Synapsis, ...

- Weights, Scores, ...

- Activation functions: Sigmoid, TanH, ...

- SoftMax rule

- Feed Forward of information

- Backpropagation

- Convolution windows, MaxReLu, ...

- TensorFlow coding applications 

This hands-on workshop dives deep into the rapidly evolving field of Generative and Sequential Deep Learning, focusing on the theory, applications, and practical implementation of models that can autonomously create data. Participants will explore how generative models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models work, and how they transform industries, from art and media to healthcare and business analytics. It will also delve into Recurrent networks and their LSTM-empowered alternatives. This workshop is designed for data scientists, machine learning engineers, AI enthusiasts, and developers who have a basic understanding of deep learning and want to expand their expertise in generative models. Attendees should have experience with Python and be familiar with common machine learning frameworks like TensorFlow.

• Gain a solid understanding of generative deep learning models' fundamental concepts and theories, including GANs, VAEs, and RNN and LSTM sequential models.
• Learn how to build and utilize these generative models effectively.
• Master the techniques for training generative models, covering loss functions, optimization strategies, and stability issues.
• Explore practical applications of generative models, such as image synthesis and text generation.
• Develop the skills to identify and troubleshoot common issues encountered during the training of generative models.
• Stay informed about the latest advancements and trends in generative deep learning research.

Day 1:

- Recurrent Neural Networks 

- LSTM and GRU

Day 2:

-  Autoencoders.

Day 3: 

-  Variational Autoencoders (VAE).

Day 4:

- Generative Adversarial Networks (GAN).

- Comprehensive colored PPT documents.

- Cell state: forget, convey, ...

- Encoders and decoders

- Latent Space

- Auto Encoders Vs. Variational Auto Encoders

- Generators vs. Discriminators

- Objective Functions

- Mode Collapse

- Training approaches

This workshop provides an introduction to Natural Language Processing (NLP), which is the technology that enables machines to understand, interpret, and generate human language. NLP is used in various AI applications such as virtual assistants, chatbots, sentiment analysis, language translation, and text summarization. Participants will learn fundamental NLP techniques, explore deep learning models for text processing, and gain hands-on experience using industry-standard libraries and frameworks like spaCy, Hugging Face, and Transformers. This workshop is designed for data scientists, machine learning engineers, AI practitioners, and software developers specializing in NLP. While attendees should have a basic understanding of Python and machine learning, no prior NLP experience is required. 

• Understand the data preparation process.
• Learn how to vectorize data.
• Explore the evolution of NLP models.
• Create word embedding and text generation models.
• Conduct sentiment analysis and topic classification.
• Gain knowledge of probabilistic Markov Models.
• Understand multinomial and Gaussian generative models.
• Learn the process of translating and summarizing text.
• Understand the Transformer architecture behind ChatGPT.
• Using interactive LLM models.

Day 1:

- Text Preparation.

- Word Vectorization: BoW - TF – IDF.

Day 2: 

- Word2Vec and Glove   

Day 3:

- Topic modeling, and text generating with RNN and LSTM.

Day 4:

- Multi Head Attention and Transformers.

Day 5:

- LLMs and Chatbots

- Tokenizing sentences and Stop Word

- Stemming and Lemmatizing words

- Parts of Speech / Name Entity   Recognition

- Bag of Words and TF-IDF

- Word - Term matrix

- Multinomial Naive Bayes model

- Latent Dirichlet Allocation (LDA) model

- word2vec: CBOW and SKIPGRAM methods

- Lama and other Large Language Models

This Python workshop for Data Science and AI comprehensively introduces the key tools and techniques used in data analysis, machine learning, and artificial intelligence. Students will learn Python programming essentials, including data handling with libraries like Pandas and NumPy, and gain hands-on experience with data visualization using Matplotlib and Seaborn. The course also covers machine learning algorithms and model evaluation and introduces deep learning concepts and NLPs with frameworks like TensorFlow. By the end of the workshop, participants will have the skills to build and evaluate data-driven models, preparing them for real-world data science and AI applications.

• Gain proficiency in Python syntax, data types, functions, and object-oriented programming.
• Learn to efficiently manipulate and preprocess datasets.
• Develop the ability to create insightful data visualizations.
• Understand and apply core machine learning algorithms.
• Explore the basics of deep learning, including neural networks and frameworks like TensorFlow.
• Learn how to evaluate machine learning models using appropriate metrics.
• Apply Python skills to real-world data science and AI problems. 

Day 1:

- Visualize and Describe  

Day 2: 

- Statistical Data Analysis 

Day 3:

- Machine Learning: Supervised and Unsupervised  

Day 4:

-  Deep and Generative Deep Learning  

Day 5:  

-  Natural Language Processing 

- NumPy

- Pandas

- Matplotlib / Seaborn

- SciPy / Statsmodels

- Scikit Learn

- Keras

- TensorFlow

- ChatGPT 4o prompt engineering

This practical Excel workshop focuses on mastering the essential statistical concepts and their data analysis functions, which are crucial for data-driven decision-making. Participants will learn to efficiently manage and manipulate data using Excel’s powerful features, including advanced functions, pivot tables, and conditional formatting. The course covers key statistical concepts such as regression analysis, hypothesis testing, and data summarization through descriptive statistics. Participants will also explore Excel's data visualization tools, including charts and graphs, to present insights. By the end of the course, learners will be equipped to perform robust data analysis and solve complex business problems using Excel’s full analytical capabilities with its Data Analysis library.

• Gain knowledge in using key statistical functions.
• Learn to create and customize pivot tables for summarizing large datasets.
• Use Excel’s Descriptive Statistics tool to summarize datasets and understand key metrics.
• Understand how to use Excel’s regression data analysis model.
• Develop skills to create meaningful charts, graphs, and histograms.
• Apply t-test, Anova, regression and all other statistical concepts with function and Excel Data Analysis tool.
• Learn to use Solver and Goal Seek to solve complex business problems. 

Day 1:

- Pivot tables and Visualization  

Day 2: 

- Statistical functions 

Day 3:

- Data Analysis 1 

Day 4:

-  Data Analysis 2

To name few:

-  AVERAGE(),  MEDIAN(), 

-  STDEV() or STDEV.P() 

- Anova: Single Factor

- Descriptive Statistics

- Regression

- t-Test: Two-Sample Assuming Equal Variances