Data Analysis

About this learning track

Data Analysis involves examining, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making. This field focuses on applying statistical and computational techniques to large datasets to identify patterns, trends, and relationships.

Data Analysts use tools such as Python, R, SQL, and Excel, along with visualization tools like Tableau and Power BI, to analyze data effectively. They work closely with stakeholders across various departments to provide insights that drive business strategies and improve performance.

What you will learn

Data Cleaning and Preprocessing: Learn techniques to clean and prepare data for analysis.
Statistical Analysis: Understand the principles of statistical analysis to interpret data accurately.
Data Visualization: Master tools like Tableau and Power BI to create compelling data visualizations.
Programming for Data Analysis: Gain proficiency in Python for data manipulation and analysis.
SQL for Data Management: Learn to query and manage databases using SQL.

Requirements

A working laptop
Steady internet access
A working mobile phone
A learning attitude

Curriculum

Master statistical techniques, data visualisation and advanced analytics for real-world impact.

Week 1 - Introduction to Programming

This lesson provides an overview of the fundamental concepts of programming, including algorithms, control structures, data types, and syntax.

It aims to build a strong foundation in logical thinking and problem-solving skills, essential for writing efficient code.

Week 2 - Introduction to Data

This lesson introduces the core concepts of data, including types of data, data collection methods, and data representation. It covers the significance of data in various fields, emphasizing its role in decision-making and strategic planning.

Week 3 - Introduction to Data Analysis

In this lesson, students learn the basic principles and techniques of data analysis, focusing on extracting meaningful insights from data. It covers the data analysis process, including data exploration, visualization, statistical analysis, and interpretation of results.

The lesson emphasizes the importance of critical thinking and the use of analytical tools to solve real-world problems.

Week 4 - Introduction to Python Programming

This lesson covers the basics of Python programming, a versatile and widely-used language in data analysis. Students will learn Python syntax, variables, control structures, functions, and error handling.

Week 5 - Data Structures and Libraries

This lesson explores essential data structures such as lists, dictionaries, sets, and tuples, which are crucial for organising and storing data efficiently. It also introduces key Python libraries like NumPy, Pandas, and Matplotlib, which provide robust functionalities for data manipulation, analysis, and visualisation.

At Learnable, you're given the chance to turn data into discovery, and insights into impact!

Obinna Okamgba

Path Lead

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