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Certified Data Science Python Programming
(DSPP-802)

Target Students​

Data science professionals, analysts, and developers who seek to enhance their Python programming skills specifically for data science applications.

Exam Formats​
 

100 multiple-choice questions

Duration :  40 hours (5 days)
Learning Objectives

-Develop proficiency in Python programming tailored for data science.

-Master data manipulation, analysis, and visualization using Python.

-Implement machine learning algorithms and statistical models in Python.

-Understand best practices for writing efficient, maintainable, and scalable Python code.

-Prepare for data science certification exams and real-world applications.

Exam Options​
 

Online

In-Person

Exam Codes: DSPP-802
Exam Duration: 2 hours
Passing Score​​: 70% 

Course Outline

Day 1: Python Basics and Data Handling
​
Module 1: Introduction to Python Programming
​

  • Python Fundamentals

  • Overview of Python: Syntax, Variables, and Data Types

  • Python Development Environments: Jupyter Notebooks, PyCharm, VS Code

  • Writing and Executing Python Scripts

  • Control Structures

  • Conditional Statements: If-Else, Switch-Case

  • Loops: For, While

  • Error Handling: Try-Except Blocks

Module 2: Data Structures in Python
​

  • Introduction to Core Data Structures

  • Lists, Tuples, and Dictionaries: Creation, Manipulation, and Use Cases

  • Understanding Sets and Their Applications

  • Nested Data Structures: Handling Complex Data with Nested Lists and Dictionaries

  • Working with Strings and Files

  • String Manipulation Techniques

  • File Handling in Python: Reading, Writing, and Parsing Files

  • Best Practices for Managing Data Files

Day 2: Data Manipulation with Python
Day 3: Data Visualization with Python
Module 3: Data Manipulation with Pandas
​

  • Introduction to Pandas

  • Overview of the Pandas Library: DataFrames and Series

  • Importing Data: Reading CSV, Excel, and JSON Files

  • Data Cleaning: Handling Missing Values, Duplicates, and Outliers

  • Data Transformation

  • Filtering and Sorting Data

  • Aggregation and Grouping Operations

  • Merging and Joining DataFrames

Day 4: Machine Learning with Python
Module 4: Advanced Data Manipulation
​

  • Handling Time Series Data

  • Working with Dates and Times in Pandas

  • Time Series Operations: Resampling, Shifting, and Rolling Windows

  • Case Study: Analyzing Financial Time Series Data

  • Data Integration

  • Combining Data from Multiple Sources

  • Working with APIs to Import Data

  • Introduction to Web Scraping with Python: Extracting Data from Websites

Module 5: Data Visualization Fundamentals
​

  • Introduction to Data Visualization

  • Importance of Data Visualization in Data Science

  • Overview of Python Visualization Libraries: Matplotlib, Seaborn, Plotly

  • Creating Basic Visualizations

  • Line Plots, Bar Charts, and Histograms

  • Scatter Plots and Pair Plots for Exploring Relationships

  • Customizing Plots: Titles, Labels, Legends, and Styles

Module 6: Advanced Data Visualization Techniques
​

  • Interactive Visualizations with Plotly

  • Creating Interactive Charts: Hover, Zoom, and Filter Capabilities

  • Building Dashboards for Data Presentation

  • Case Study: Interactive Data Dashboards for Business Analytics

  • Geospatial Data Visualization

  • Mapping Data with Geopandas and Folium

  • Visualizing Geographic Data: Choropleth Maps, Heatmaps

  • Applications of Geospatial Data Visualization in Real-World Scenarios

Day 5: Real-World Applications and Project Work
Module 7: Introduction to Machine Learning
​

  • Overview of Machine Learning Concepts

  • Understanding Supervised vs. Unsupervised Learning

  • Introduction to Machine Learning Algorithms: Regression, Classification, Clustering

  • Setting Up a Machine Learning Environment in Python

  • Implementing Machine Learning Models with Scikit-Learn

  • Building Regression Models: Linear, Polynomial, Ridge, Lasso

  • Classification Techniques: Logistic Regression, Decision Trees, Random Forests

  • Model Evaluation: Accuracy, Precision, Recall, F1-Score

Module 8: Advanced Machine Learning Applications
​

  • Model Tuning and Optimization

  • Hyperparameter Tuning: Grid Search and Random Search

  • Cross-Validation Techniques

  • Feature Engineering: Creating New Features to Improve Model Performance

  • Unsupervised Learning Techniques

  • Clustering with K-Means and Hierarchical Clustering

  • Dimensionality Reduction: PCA and t-SNE

  • Case Study: Clustering Customer Data for Market Segmentation

Module 9: Practical Data Science Applications
​

  • Data Science Workflow

  • End-to-End Data Science Project Lifecycle

  • Case Study: Implementing a Data Science Project from Scratch

  • Best Practices for Code and Project Organization

  • Ethics and Data Privacy

  • Understanding the Ethical Implications of Data Science

  • Data Privacy Laws and Compliance: GDPR, CCPA

  • Techniques for Ensuring Data Security in Python Projects

Module 10: Capstone Project and Review
​

  • Capstone Project

  • Participants Work on a Comprehensive Data Science Project Using Python

  • Application of Skills Learned: Data Collection, Cleaning, Analysis, Visualization, and Machine Learning

  • Peer Review and Feedback on Project Work

  • Exam Preparation and Review

  • Review of Key Concepts Covered During the Course

  • Sample Exam Questions and Discussion

  • Final Q&A Session and Wrap-Up

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