Python Data Analysis Certification Program

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About Course

Duration: 3 Months (12 Weeks)

Mode: Online/Blended Learning

Weekly Classes: 1 Classes / Week

Class Duration: 2 Hours / Per Class

Certification: Upon successful completion and passing of final project/exam

Program Overview

This intensive 3-month certification program is designed to equip aspiring data analysts, researchers, and professionals with the essential skills to perform robust data analysis using Python. From data collection and cleaning to visualization and statistical modeling, participants will gain hands-on experience with industry-standard libraries and best practices. The course emphasizes practical application through real-world datasets and projects, preparing students for immediate impact in data-driven roles.

Course Curriculum: Monthly Breakdown

Month 1: Foundations of Data Handling & Visualization

  • Week 1: Set up your Python environment (Anaconda, Jupyter). Review core Python data structures and begin with NumPy for numerical operations.

  • Weeks 2-3: Dive deep into Pandas, mastering DataFrames for data loading, inspection, cleaning (missing values, duplicates), transformation, and basic aggregation.

  • Week 4: Learn to create compelling static and statistical visualizations using Matplotlib and Seaborn to explore and present data insights.

Month 2: Statistical Analysis & Advanced Data Techniques

  • Week 5: Grasp fundamental statistical concepts (descriptive stats, probability) and perform basic hypothesis testing (t-tests) using SciPy.

  • Week 6: Work with diverse data formats: loading and processing data from Excel, JSON, and connecting Python to databases (SQL).

  • Week 7: Focus on Feature Engineering and data preprocessing techniques, including handling categorical variables (one-hot encoding) and feature scaling.

  • Week 8: Get an introduction to Machine Learning concepts, applying simple models like Linear and Logistic Regression using Scikit-learn for prediction and classification.

Month 3: Application, Storytelling & Capstone Project

  • Week 9: Explore basic time series analysis and learn to create simple interactive maps with geospatial data.

  • Week 10: Develop data storytelling skills, learning to effectively communicate insights through compelling narratives and professional reports.

  • Weeks 11-12: Dedicate to the Capstone Project. Apply all learned skills to a comprehensive real-world data analysis problem, from data acquisition to final presentation, building a valuable portfolio piece.

Program Features

  • Hands-on Learning: Practical exercises, coding challenges, and real datasets.

  • Expert Instructors: Learn from experienced data professionals.

  • Capstone Project: A key component to solidify skills and build your portfolio.

  • Dedicated Support: Access to instructors and teaching assistants.

Assessment & Certification

Certification is awarded upon successful completion and presentation of your Capstone Project, demonstrating your practical proficiency in Python data analysis.

Enroll Today!

Unlock the power of data with Python and transform your career. Join our certification program and become a skilled data analyst ready to tackle real-world challenges.

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What Will You Learn?

  • Here's a summary of what you will learn in the 3-Month Python Data Analysis Certification Program:
  • You will learn to:
  • Set up and navigate a Python environment for data analysis (Anaconda, Jupyter Notebooks).
  • Master data manipulation using Pandas for tasks like:
  • Loading and inspecting data from various sources (CSV, Excel, JSON, databases, APIs).
  • Cleaning data (handling missing values, duplicates, incorrect data types).
  • Transforming data (applying functions, creating new features).
  • Aggregating and combining datasets (grouping, merging, joining).
  • Create compelling data visualizations using Matplotlib and Seaborn to explore and present insights.
  • Understand fundamental statistical concepts and perform basic hypothesis testing using SciPy.
  • Prepare data for machine learning models through feature engineering and preprocessing techniques (e.g., encoding categorical variables, scaling features).
  • Apply basic machine learning models (Linear Regression, Logistic Regression) for prediction and classification using Scikit-learn.
  • Perform basic time series analysis and explore geospatial data visualization.
  • Communicate data insights effectively through data storytelling and professional reporting.
  • Complete a comprehensive Capstone Project, applying all learned skills to a real-world dataset, building a portfolio piece.

Course Content

Month 1: Foundations of Data Handling & Visualization

Month 2: Statistical Analysis & Advanced Data Techniques

Month 3: Application, Storytelling & Capstone Project

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