Data Analysis with Python

Data Analysis Essentials with Python has been designed for anyone and everyone who wants to learn Python and modern programming techniques used for data analysis and data visualization purposes.
It will particularly appeal to:

• aspiring programmers, data science beginners, and learners interested in learning programming for the purposes of data manipulation and analysis for fun and job-related tasks
• learners looking to gain fundamental skills and knowledge for an entry-level job role as a software engineer, data analyst, data scientist, marketing analyst, machine learning engineer, and business intelligence analyst
• data science industry professionals who know other programming language(s) and tools, wishing to explore technologies connected with Python, or to utilize it as a foundation for data analytics purposes
• aspiring programmers, learners, and industry professionals looking to acquire essential skills in Python and data analytics for further self-development in the areas of AI and machine learning, data science, data visualization, marketing analysis, business analysis, data engineering, and Big Data
• team leaders, product managers, project managers, and product owners who want to understand the terminology and processes in data science to more effectively manage and communicate with software development and data analytics teams

Dive into data science, learn how to process, analyze, model, and visualize data using Python, and prepare for the PCAD™ – Certified Associate in Data Analytics with Python certification.

This intermediate course gives you an opportunity to dive into Python programming for data analysis purposes, and it will teach you the foundational skills required in the field of data analytics. The course will introduce you to the main toolkits, concepts, and methodologies employed by data analysts and data scientists, and will teach you how to program in Python in order to obtain, clean, analyze, summarize, and present data accurately and effectively.

Having completed the course, you will be prepared to attempt the qualification PCAD™ – Certified Associate in Data Analytics with Python certification, and to get your foot in the door to careers in data science, marketing analytics, machine learning, and software engineering.

Key skills you will learn

Sign up for Data Analysis Essentials with Python, and learn these core skills:

• Python for Data Analysis
• Computer Programming
• Algorithmic Thinking
• Analytical Thinking
• Data Mining, Manipulation, Modelling, and Visualization
• Best Practices in Programming
• Statistical Operations
• Data-Driven Decision-Making

Prerequisites

This course has been designed for learners who are already familiar with basic to intermediate Python programming concepts, such as: data types, containers, functions, conditions, loops, exceptions, modules, packages, and the basics of procedural, structural, functional, and object-oriented programming.

Experience required: completion of the Python Essentials PCAP course, or equivalent experience.

What you will know after the course

By the end of the course, you will be able to:

• design, develop, debug, execute, and refactor Python scripts;
• think algorithmically to analyze problems and implement them as computer processes;
• create and process arrays using mathematical operations from the NumPy library;
• manipulate and analyze data using the pandas library;
• perform data visualizations using the matplotlib plotting library;
• understand the role of a data scientist in data analysis projects;
• create and develop your own programming portfolio to stand out from the crowd in the job market;
• continue your professional development at an advanced level with Python for Data Analysis: Advanced (TBA).

Course Curriculum

Module 1 – Importing Datasets

• Learning Objectives
• Understanding the Domain
• Understanding the Dataset
• Python package for data science
• Importing and Exporting Data in Python
• Basic Insights from Datasets
• Lab

Module 2 – Cleaning and Preparing the Data

• Identify and Handle Missing Values
• Data Formatting
• Data Normalization Sets
• Binning
• Indicator variables
• Lab

Module 3 – Summarizing the Data Frame

• Descriptive Statistics
• Basic of Grouping
• ANOVA
• Correlation
• Hypothesis Tests
• Lab

Module 4 – Model Development

• Simple and Multiple Linear Regression
• Model Evaluation Using Visualization
• Polynomial Regression and Pipelines
• R-squared and MSE for In-Sample Evaluation
• Prediction and Decision Making
• Lab

Module 5 – Model Evaluation

• Model Evaluation
• Over-fitting, Under-fitting, and Model Selection
• Ridge Regression
• Grid Search
• Model Refinement