Python Data Analysis Training

Hello,

I am İbrahim ÖZTÜRK, a software development specialist and a graduate of Tallinn University of Technology. I have been working in the software industry for over 10 years, and I develop my projects on a large scale using the Python programming language.

I have prepared a comprehensive training program to share my deep knowledge and experience in data analysis with Python. This training will last a total of 70 hours, spread over 4 months, and will cover 90 lessons.

Training Duration and Format:

• Total Class Duration: 70 hours
• Total Training Duration: 4 months
• Participation Options: Online or in-person
• Duration of Each Class: 45 minutes
• Weekly Class Hours: 2 days a week, 4 class hours in total per week
• Payment Options: Monthly installments or a one-time total payment for the package
• Training Fee: 972,24 €
• Package Training Prepayment Fee: 950 €

Training Content:

1. Introduction to Python and Basics:

   • Python installation and environment setup
   • Python data structures: Lists, tuples, dictionaries, and sets
   • Functions, modules, and packages
   • Debugging and testing techniques

2. Data Cleaning and Preparation:

   • Data preprocessing and cleaning methods
   • Handling missing data
   • Data types and transformations
   • Data standardization and normalization

3. Data Analysis and Visualization:

   • Mathematical operations and arrays with NumPy
   • Data frames and series with Pandas
   • Data manipulation and grouping operations
   • Data visualization with Matplotlib and Seaborn
   • Interactive visualization tools: Plotly and Bokeh

4. Statistical Analysis:

   • Basic statistical concepts: Mean, median, variance
   • Hypothesis testing and confidence intervals
   • Correlation and regression analysis
   • Time series analysis and forecasting

5. Data Mining and Machine Learning:

   • Fundamentals of machine learning and data mining
   • Modeling with Scikit-Learn: Classification, regression, and clustering
   • Model evaluation and cross-validation
   • Model selection and hyperparameter optimization
   • Introduction to deep learning: TensorFlow and Keras

6. Databases and SQL:

   • Basic data queries with SQL
   • Database connections and operations
   • Data retrieval, updating, and deletion
   • Pandas and SQL integration

7. Project Development and Application:

   • Developing projects with real-world data
   • Phases of data analysis projects: Planning, implementation, and evaluation
   • Presentation and reporting of results
   • Project management tools and methodologies

Tools and Libraries Used:

• Python: Core features and benefits of the Python language
• NumPy: Basic mathematical operations and multidimensional arrays
• Pandas: Essential library for data processing and analysis
• Matplotlib & Seaborn: Data visualization and chart creation
• Scikit-Learn: Machine learning and modeling
• TensorFlow & Keras: Deep learning and artificial intelligence
• SQL: Database management and querying
• PyCharm: Interactive environment for coding and reporting

Post-Training Benefits:

Upon completing this program, you will have a solid foundation in data analysis and machine learning. Your skills in interpreting, analyzing, and visualizing data will be enhanced. Additionally, with the experience gained from real-world projects, you will be well-equipped to build a strong career in data analytics and data science.

Shape your future with data and leverage the power of Python. This training will provide you with all the knowledge and skills needed to specialize in data analysis!