Introduction
Welcome to the comprehensive course on Quantitative Data Management, Analysis, and Visualization with Python.
In an era driven by data, the ability to manage, analyze, and visualize quantitative information effectively is invaluable. Python’sextensive suite of tools makes it an ideal choice for professionals tackling data challenges. This course is designed to guide youthrough Python’s powerful ecosystem, providing hands-on expertise in data management, statistical analysis, and compelling visualization techniques.
Whether you’re a seasoned analyst or a beginner, this course will equip you with skills to transform raw data into actionable insights.
What You’ll Learn
- Build a solid foundation in Python programming, including data types, syntax, and libraries.
- Master data cleaning and preparation techniques for diverse datasets.
- Perform exploratory data analysis (EDA) and uncover patterns using visualization.
- Develop proficiency in statistical analysis, machine learning, and forecasting.
- Create visually compelling and interactive dashboards for data presentation.
Course Objectives
- Python Programming Basics: Learn syntax, data types, control flow, and error handling.
- Data Handling: Import, clean, and validate data efficiently using Python.
- Data Analysis: Conduct statistical analyses, including regression, clustering, and dimensionality reduction.
- Visualization: Create advanced visualizations using Matplotlib, Seaborn, and Plotly.
- Machine Learning: Explore foundational algorithms and predictive modeling techniques.
- Real-World Applications: Apply skills to case studies and industry-specific problems.
Target Participants
This course is designed for professionals, researchers, analysts, and decision-makers in industries such as finance, healthcare, marketing,
and academia. Participants should have a basic understanding of data analysis concepts and a desire to deepen their technical expertise.
Course Outline
- Foundations of Python Programming: Syntax, functions, error handling, libraries like NumPy, and Jupyter Notebooks.
- Data Import and Cleaning: Techniques for handling missing data, reshaping datasets, and automation strategies.
- Data Manipulation with Pandas: Filtering, grouping, merging, and time-series analysis.
- Exploratory Data Analysis (EDA): Visualizing data trends, correlations, and statistical summaries.
- Statistical Inference: Conducting hypothesis tests, ANOVA, and Bayesian inference.
- Linear Regression and Modeling: Implementing regression analysis and diagnostics using Statsmodels.
- Machine Learning Basics: Algorithms like decision trees, SVMs, and model optimization techniques.
- Data Visualization: Advanced plotting and creating interactive dashboards with tools like Dash and Streamlit.
- Advanced Techniques: Web scraping, text mining, and handling big data with PySpark.
General Information
- All training sessions are customizable to meet the specific needs of participants.
- Courses are offered both in-person and online at Stepsure Training And Research Institute.
- Participants should have a good command of the English language.
- Training includes presentations, hands-on exercises, and group discussions led by experienced facilitators.
- Participants will receive a certificate of completion from Stepsure Training And Research Institute.
- Contact us for more details:
- Email: info@stepsureresearchinstitute.org
- Phone: +254 723 482 495
- Website: www.stepsureresearchinstitute.org
