Data Analytics with Python

The Google Data Analysis with Python Certificate helps learners build strong Python skills for data analytics in today’s data-driven world. Designed for beginners, this program guides learners from foundational Python concepts to confidently analyzing real-world datasets using industry-standard tools.

Through hands-on labs and practical examples inspired by Google’s day-to-day analytics work, learners gain experience working with Python, Jupyter Notebooks, NumPy, and pandas to clean, analyze, and interpret data. By the end of the program, learners will be able to solve complex data problems and clearly communicate insights to stakeholders

Estimated Time to Complete

Approximately 3–4 months, with flexible, self-paced learning

Why Take This Course?

  • Build In-Demand Python Skills – Learn Python from the ground up for data analysis.
  • Hands-On Practice – Work with real datasets using Jupyter Notebooks and Coursera Labs.
  • Analyze & Interpret Data – Apply statistics and structured thinking to support business decisions.
  • Problem-Solving Focus – Learn how to frame data questions using SMART and structured analysis.
  • Fully Online & Flexible – Learn at your own pace, anytime and anywhere.

What You’ll Learn

  • How to write clean, efficient Python code using variables, functions, and control structures.
  • How to organize, clean, and prepare real-world data for analysis.
  • How to manipulate and analyze data using pandas and NumPy.
  • How to perform exploratory data analysis (EDA) to uncover patterns and insights.
  • How to validate data quality, handle missing values, and identify outliers ethically.
  • How to summarize and communicate findings using descriptive statistics.

Course Modules & Skills Gained

  • Write conditional statements using if, else, and elif.
  • Use comparators and logical operators to evaluate data.
  • Define reusable Python functions using def and return.
  • Apply best practices for clean, modular, and well-documented code.
  • Work with core Python data structures: lists, tuples, dictionaries, and sets.
  • Manipulate pandas DataFrames using filtering, grouping, aggregating, and joining.
  • Use NumPy arrays for efficient numerical analysis.
  • Understand Python libraries, packages, modules, and global variables.
  • Apply Python tools to discover and structure raw data.
  • Merge, sort, and filter datasets using Python.
  • Clean raw data using relevant Python libraries.
  • Identify ethical considerations in data exploration.
  • Use the PACE workflow to assess data readiness and relevance.
  • Clean and validate datasets using Python.
  • Identify and handle missing values responsibly.
  • Transform categorical data into numerical formats.
  • Detect and analyze outliers in datasets.
  • Understand when to collaborate with stakeholders or engineers on data issues.
  • Learn core Python syntax, data types, and variable assignment.
  • Understand object-oriented programming concepts such as objects, classes, and methods.
  • Use Jupyter Notebooks as an interactive environment for data work.
  • Perform mathematical operations and explore data using built-in Python functions.
  • Manage Python packages and interpreter options.
  • Use for loops and while loops to automate repetitive tasks.
  • Work with strings through indexing, slicing, concatenation, and formatting.
  • Understand the syntax and use of the range() function.

Who Should Enroll?

This course is ideal for:

  • Beginners looking to learn Python for data analytics.
  • Students and professionals transitioning into data-related roles.
  • Analysts seeking to strengthen technical and programming skills.
  • Anyone interested in using Python to analyze and interpret data effectively.

No prior experience is required.