Python for Data Analysts: Students and Professionals

What you’ll learn:
Installing Python and necessary libraries for a seamless coding environment setup.
Mastering data type conversion and formatting techniques for consistent data representation.
Utilizing Pandas functions for efficient data manipulation tasks.
Implementing various types of join operations to merge datasets effectively.
Aggregating data and engineering new features for insightful analysis.
Handling date and time data effectively using Python libraries.
Creating customizable visualizations with libraries like Matplotlib and Seaborn for effective data communication.
Completing a capstone project: E-commerce data using concepts and skills learned from this course to create effective visualizations and communicate findings.

Requirements:
Operating Systems: 64-bit versions of Microsoft Windows 7, 8.1 and 10 or Mac
Installation of Python and necessary libraries using Anaconda
No prior experience in Python but highly desirable to know some basic analytics with Excel

Description:
Interested in becoming a Data Analyst? Want to gain practical skills and solve real-world business problems? Then this is the perfect course for you! This course is created by a Senior Data Analyst with 10 years of experience in Insurance and Health Care sectors. It will equip you with foundational knowledge and help you learn key concepts of loading data, data manipulation, data aggregation, and how to use libraries/packages in a simple manner.I will guide you step-by-step into the World of Data Analysis. With every lecture and lab exercise, you will gain and develop an understanding of these concepts to tackle real data problems! This course primarily uses Python to solve labs and capstone project(s).This course will be super useful and exciting. I’ve designed the course curriculum in the most natural, logical flow:· Module 0 – Intro to Python: set up the Python environment and understand the basics of Python packages/libraries· Module 1 – Load and Write Data: learn how to load and write data from flat files (e.g., .csv or Excel format)· Module 2 – Data Types and Formatting: master the data types and learn how to convert data types for proper operations· Module 3 – Data Manipulation: clean and preprocess data, perform sorting, ordering, and subsetting records· Module 4 – Join Operations: learn how to perform joins using Python packages (e.g., pandas and SQL)· Module 5 – Data Aggregation: learn how to aggregate data using summary statistics and perform feature engineering· Module 6 – Time Intelligence: learn how to calculate business days and perform time dimension analysis· Module 7 – Data Visualization: learn the basics of exploratory data analysis (EDA) and uni-variate/bi-variate visualizationsEach module contains independent content. Technically, you can take the course from start to end or jump into any specific topics of interest. However, I highly recommend students to take the course from Module 1 to 7 in order to complete the capstone project challenge!This course is packed with real-world data/business problems that I solved during my career as a senior data analyst. You will learn not just concepts but also gain practical, hands-on experience from the course. Enroll today and take the first step towards mastering the art of data analysis using Python.

Who this course is for:
This course is designed for individuals with no prior experience in tools (e.g., R or Python).
For new graduates considering a data analytics career
For career switchers aiming to become data analysts or upgrade their skills beyond Excel spreadsheets.