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In this course, you will learn :
- The course equips you with all you need to know to become a data analyst.
- Fill your resume with in-demand data skills such as Python programming, NumPy, pandas, data preparation (data collecting, cleaning, preprocessing, visualisation), data analysis, and data analytics.
- Gain a broad grasp of the data analyst role.
- Python for beginners and advanced users.
- Python requires you to study maths.
- We will teach you the fundamentals and advanced features of NumPy and Pandas.
- You must be able to work with text files.
- Understand different data formats and how they use memory.
- Learn how to use a simple script to retrieve interesting, real-time data from an API.
- Data cleaning with pandas Series and DataFrames.
- Complete an absenteeism rate data cleansing exercise.
- Increase your understanding of NumPy statistics and preprocessing.
- Go through a complete loan data case study and apply your NumPy skills
- Master data visualization
- Learn how to create pie, bar, line, area, histogram, scatter, regression, and combo charts
- Engage with coding exercises that will prepare you for the job
- Practice with real-world data
- Solve a final capstone project
Syllabus ;
- Introduction to Data Analytics
- Setting up the Environment
- Python Basics
- Fundamentals for Coding in Python
- Mathematics for Python
- NumPy Basics
- Pandas - Basics
- Working with Text Files
- Working with Text Data
- Must-Know Python Tools
- Data Gathering/Data Collection
- APIs (POST requests are not needed for this course)
- Data Cleaning and Data Preprocessing
- pandas Series
- pandas DataFrames
- NumPy Fundamentals
- NumPy DataTypes
- Working with Arrays
- Generating Data with NumPy
- Statistics with NumPy
- NumPy - Preprocessing
- A Loan Data Example with NumPy
- The "Absenteeism" Exercise - Introduction
- Solution to the "Absenteeism" Exercise
- Data Visualization
