Codeliq

Data Science Fundamentals (NumPy, Pandas, Matplotlib)

Build a strong foundation in data analysis and visualization using industry-standard Python tools.

Duration
8 weeks (16 classes)
Founding price
$2,200

Course Overview

Build the analytical foundation to turn raw data into meaningful decisions.

Across eight weeks, you will use industry-standard Python tools to clean, explore, visualize, and communicate insights from real-world data.

Analyze
Manipulate structured data with NumPy and Pandas.
Visualize
Create clear, professional charts with Matplotlib.
Apply
Build repeatable workflows from raw data to insight.

What You’ll Learn

Build practical data-analysis skills with Python, NumPy, Pandas, and Matplotlib.

AI icon representing numerical analysis

NumPy Foundations

Work confidently with arrays, numerical operations, and vectorized computation.

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Pandas Analysis

Clean, transform, join, and analyze tabular datasets with practical workflows.

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Data Visualization

Create clear charts that reveal trends, comparisons, relationships, and anomalies.

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Data Pipelines

Move from raw data to repeatable analysis and actionable insight.

Curriculum

8 weeks (16 classes). Live instructor-led classes.

  1. Week 01

    Python and NumPy Foundations

    Set up practical numerical-analysis workflows.

    1. NumPy arrays, indexing, and vectorized operations
    2. Data types, broadcasting, and numerical computation
  2. Week 02

    Pandas Fundamentals

    Work with structured datasets and tabular data.

    1. Series, DataFrames, loading data, and inspection
    2. Filtering, sorting, grouping, and aggregation
  3. Week 03

    Data Cleaning and Transformation

    Prepare dependable data for analysis.

    1. Missing values, duplicates, and type conversion
    2. Merging, reshaping, and feature creation
  4. Week 04

    Exploratory Data Analysis

    Find patterns, relationships, and anomalies.

    1. Summary statistics and distributions
    2. Correlation, segmentation, and analytical questions
  5. Week 05

    Matplotlib Visualization

    Communicate findings through effective charts.

    1. Line, bar, scatter, and histogram charts
    2. Labels, scales, layouts, and visual storytelling
  6. Week 06

    Applied Analysis

    Use data workflows on realistic scenarios.

    1. Analyzing a real-world dataset
    2. Interpreting results and validating assumptions
  7. Week 07

    Data Pipelines

    Organize analysis into reusable processes.

    1. From raw files to analysis-ready datasets
    2. Documentation and reproducible notebooks
  8. Week 08

    Capstone Project

    Present an end-to-end data analysis.

    1. Build and refine the final analysis
    2. Present findings and receive feedback
8 weeks16 live classes1 capstone project

Frequently Asked Questions

Everything you need to know before joining.

This course is for beginners, university students, analysts, and technical professionals who want a practical foundation in Python-based data analysis.

Data Science Fundamentals (NumPy, Pandas, Matplotlib) Course

Turn data into insight

Join the cohort and build the practical data-analysis skills that underpin machine learning and AI.