Analytical Tools

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

The "SPSS / STATISTICA for Data Science" training is designed to equip participants with practical skills in using SPSS and STATISTICA for data analysis, statistical modeling, and decision-making.

Over four days, attendees will learn how to manipulate data, perform statistical tests, visualize insights, and apply advanced analytics techniques essential for data science.

This hands-on workshop will bridge the gap between statistical tools and real-world applications. 

• Navigate SPSS / STATISTICA interfaces for efficient data analysis.
• Perform descriptive and inferential statistics.
• Conduct hypothesis testing and statistical significance analysis.
• Apply regression models (linear, logistic, multiple).
• Use ANOVA, MANOVA, and correlation analysis.
• Generate visualizations and interpret results effectively.
• Apply predictive analytics and "supervised" machine learning solutions.
• Explore data with "unsupervised" machine learning.

Day 1:

- Data Visualization and Statistical Analysis 

Day 2: 

- Statistical Analysis: t-test, Chi-Squares, and ANOVA

Day 3:

- Multiple Regressions, Discriminant Analysis, Decision Trees, Naive Bayes, etc.

Day 4:

-  Dimension Reducibility: PCA, t-SNE

- Clustering: Agglomerative, K-Means, etc.

- Exploring SPSS and STATISTICA for data analysis.

- Understanding core statistical concepts for data-driven decision-making.

- Data cleaning, transformation, and visualization techniques.

- Applying statistical models for business and research insights.

- Hands-on practice with real datasets to reinforce learning.

- Leveraging automation tools for efficient workflows.

- Interpreting results to make data-driven conclusions.