Data Science

Mastering Machine Learning with Python  

Program Objective:

By the end of this course, the trainee will be able to:
  1. Understand the key concepts and tools in the field of Machine Learning and determine where they might be used effectively.
  2. Recognize the fundamental ideas of programming.
  3. Establish hands-on proficiency in Python programming.
  4. Acquire Python skills to manage, wrangle, and manipulate data.
  5. Implement popular data analysis techniques using Python.
  6. Develop supervised and unsupervised Machine Learning models using Python.
  7. Apply them to resolve real-world practical problems in a capstone project.

Program Module:

Upon completion of all the following modules, participants will receive the Professional Certificate in Machine Learning with Python.

 

1. Programming Fundamentals in Python

1.1. Programming Fundamentals.
1.2. Algorithms and Flowcharts.
1.3. Python Installation and Usage.
1.4. Variables and Expressions.
1.5. Data Structures in Python.
1.6. Working with Python Libraries.
1.7. Conditional Statements.
1.8. Python Functions.
1.9. Working with Loops and Iterations.

 

2. Fundamentals of Data Analysis in Python

2.1. Fundamentals of Pandas and Numpy.
2.2. Data Wrangling and Manipulation.
2.3. Feature Engineering.
2.4. Data Visualization in Python.
2.5. Descriptive Statistics.
2.6. Exploratory Data Analysis.
2.7. Probability Distributions.
2.8. Hypothesis Testing.
2.9. T-Test, Chi-Squared Test, ana ANOVA.

 

3. Standard Algorithms of Machine Learning

3.1. Introduction to Machine Learning.
3.2. Fundamentals of Scikit-Learn as Python library for Machine Learning.
3.3. Supervised Learning.
3.3.1. Regression Models.
3.3.2. Evaluation Metrics.
3.3.3. Cross-Validation and Parameter Tuning.
3.3.4. Class-Imbalance Problem and Python Piplines.
3.3.5. K-Nearest Neighbours.
3.3.6. Decision Trees.
3.3.7. Naïve Bayes.
3.3.8. Support Vector Machines.
3.4. Unsupervised Learning.
3.4.1. K-Means.
3.4.2. Hierarchical Clustering.
3.4.3. Evaluation of Clustering Algorithms.

 

4. Ensemble Learning and Hybrid Models

4.1. Ensemble Learning and Hybrid Models.
4.1.1. Bagging.
4.1.2. Random Forest.
4.1.3. Boosting.
4.1.4. Stacking.
4.2. Capstone project.
4.2.1. How to conduct a capstone project in Machine Learning.
4.2.2. Project Follow-up and Discussions.
4.2.3. Final Presentation and Assessment.

 

Program Duration:

100 hrs 

Assessments:

  • Classroom exercises.
  • Group assignments.
  • Individual assignments.
  • Capstone project.

Certification:

After successfully completing all four of the aforementioned modules, participants will be awarded a professional certificate in Machine Learning with Python.

Intended Learners:

A professional wishing to boost his/her profile with highly sought-after abilities, a business owner seeking to acquire an advantage in the marketplace, or someone looking to start a career in data science would all benefit from taking this diploma.

Prerequisites:

  • Basic computer skills.
  • An ability to install software from online websites.

 

Express Interest : cce@guc.edu.eg 

For registration click here

Data Analysis with Python  

Program Objective:

By the end of this course, the trainee will be able to:
  1. Understand the key concepts and tools of statistical data analysis.
  2. Use different statistical tools for working with data sets.
  3. Establish hands-on proficiency in Python programming.
  4. Acquire Python skills to manage, wrangle, and manipulate data.
  5. Implement popular data analysis techniques using Python

Program Module:

Upon completion of all the following modules, participants will receive a certificate in Data Analysis with Python.

 

1. Programming Fundamentals in Python

1.1 Programming Fundamentals.
1.2 Python Installation and Usage.
1.3 Variables and Expressions.
1.4 Data Structures in Python.
1.5 Working with Python Libraries.
1.6 Conditional Statements.
1.7 Python Functions.
1.8 Working with Loops and Iterations.

 

2. Exploratory Data Analysis in Python

2.1 Fundamentals of Pandas and Numpy.
2.2 Data Wrangling and Manipulation.
2.3 Feature Engineering.
2.4 Data Visualization in Python.
2.5 Cross-Tabulation.
2.6 Pivot Tables and Pivot Charts.
2.7 Relationships in Data.

 

3. Hypothesis Testing and Statistical Modelling in Python

3.1 Hypothesis Testing and Statistical Modelling
3.1.1 Introduction to Hypothesis Testing.
3.1.2 Parametric Tests.
3.1.3 Non-Parametric Tests.
3.1.4 Linear Regression.
3.1.5 Logistic Regression.
3.1.6 Evaluation Metrics.
3.2 Capstone Project
3.2.1 How to conduct a capstone project in Data Analysis.
3.2.2 Final Presentation and Assessment.

 

4. Ensemble Learning and Hybrid Models

4.1. Ensemble Learning and Hybrid Models.
4.1.1. Bagging.
4.1.2. Random Forest.
4.1.3. Boosting.
4.1.4. Stacking.
4.2. Capstone project.
4.2.1. How to conduct a capstone project in Machine Learning.
4.2.2. Project Follow-up and Discussions.
4.2.3. Final Presentation and Assessment.

 

Program Duration:

60 hrs

Assessments:

  • Classroom exercises.
  • Group assignments.
  • Individual assignments.
  • Capstone project.

Certification:

After successfully completing all of the aforementioned modules, participants will be awarded a professional certificate in Data Analysis with Python.

Intended Learners:

A program for data analysis is designed for people who wish to learn Python completely from scratch as well as get started in the field of Statistical Data Analysis.

Prerequisites:

  • Basic computer skills.
  • An ability to install software from online websites.
  • Prior awareness of Excel sheets.

 

Express Interest : cce@guc.edu.eg 

For registration click here

Data Analysis with Excel and Power BI  

Program Objective:

By the end of this course, the trainee will be able to:
  1. Apply fundamental Excel functions and formatting techniques for effective data analysis.
  2. Utilize Named Ranges, Lookup Functions, and Conditional Functions proficiently in data analysis tasks.
  3. Demonstrate proficiency in creating Pivot Tables, Slicers, and Pivot Charts to analyze data effectively.
  4. Employ methods for data analysis, ensuring comprehensive understanding and application.
  5. Recognize the advantages of leveraging Power BI for data analysis purposes.
  6. Design interactive visualizations using Power BI to enrich and streamline data analysis processes.
  7. Develop sophisticated calculations and measures utilizing the DAX language within Power BI for enhanced analysis capabilities.

Program Module:

Upon completion of all the following modules, participants will receive the Professional Certificate in Data Analysis with Excel and Power BI.
 
 
1. An Overview of Excel Fundamentals
  • Introduction to Excel interface.
  • Data entry and manipulation.
  • Basic Excel Formulas and functions.
  • Advanced formulas (VLOOKUP, INDEX-MATCH, IF statements, etc.)
  • Data Validation vs. Data Cons
2. Introduction to Statistical Data Analysis using Excel
  • Descriptive Statistics.
  • Exploratory Data Analysis (tabular presentation and data visualization).
  • Inferential Data Analysis.
  • Regression Analysis.
3. Introduction to Power BI
  • Overview of Power BI interface.
  • Importing data into Power BI.
  • Building visualizations and dashboards.
  • Data modeling in Power BI.
4. Data Analysis with Power BI
  • Using DAX (Data Analysis Expressions) for calculations.
  • Advanced visualization techniques in Power BI.
  • Creating interactive reports and dashboards.
  • Sharing and collaborating on Power BI reports.

Program Duration:

60 hrs

Prerequisites:

  • Basic computer skills.
  • An ability to install software from online websites.

 

Express Interest : cce@guc.edu.eg 

For registration click here

 

 

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