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Self-paced Intermediate

Python For Data Analysis

A practical, hands-on course covering how to use Python's powerful data ecosystem to ingest, clean, transform, analyze, and visualize complex datasets.

8 weeks Verified certificate

What you'll learn

  • Data Cleaning & Wrangling: Handle missing data, filter, merge, join, and reshape disparate datasets into analysis-ready formats.
  • Numerical Computing: Execute high-performance mathematical operations and multi-dimensional array manipulations using NumPy.
  • Exploratory Data Analysis (EDA): Summarize large datasets using descriptive statistics, group-by aggregations, and pivot tables.
  • Data Visualization: Build professional, clear charts and plots (scatter plots, histograms, time-series lines, and heatmaps) to communicate insights effectively.
  • Multi-Source Integration: Seamlessly ingest and export data from various file formats and databases, including CSVs, Excel sheets, JSON, and SQL databases.

Course content

The full curriculum is being finalised. Contact us for the syllabus.

About this course

Turn raw, unstructured data into actionable insights using Python. This course guides you through the core tools of modern data science and analysis. You will learn how to clean messy datasets, handle missing values, perform complex data aggregations, reshape data frames, and conduct thorough exploratory data analysis (EDA). Using industry-standard libraries like Pandas and NumPy, alongside visualization tools like Matplotlib and Seaborn, you will uncover patterns, trends, and correlations to drive data-informed decision-making.

Requirements

  • •Foundation in python programming

Tools you'll use

PythonPandasNumpyMatplotlibJupyter Notbook

Learn anywhere

Phone, tablet or laptop. Pick up exactly where you left off.

Hands-on practice

Quizzes, coding exercises and projects marked by instructors.

Verified certificate

Each certificate has a code employers can verify online.

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