- Foundation Course Module 1: Introduction of data analytics and analytical thinking
- Foundation Course Module 2 : The wonderful world of data
- Foundation Course Module 3 : Setup of data analytics toolbox
- Foundation Course Module 4: Becoming a fair and impactful data professional
- Foundation Course: Glossary
- Course 2: Ask questions to make data driven decisions, Module 1: Ask effective questions
- Course 2: Ask questions to make data driven decisions, Module 2: Make data-driven decisions
- Course 2: Ask questions to make data driven decisions, Module 3: Spreadsheet magic
- Course 2: Ask questions to make data driven decisions, Module 4: Always remember the stakeholder
- Course 3: Prepare Data For Exploration: Learning objectives and overviews
- Course 3: Prepare Data For Exploration, Module 1: Data types and structures
- Course 3: Prepare Data For Exploration, Module 2: Data responsibility
- Course 3: Prepare Data For Exploration, Module 3: Database Essentials
- Course 3: Prepare Data For Exploration, Module 4: Organise and Secure Data
- Course 4: Process Data from Dirty to Clean: Overview
- Course 4: Process Data from Dirty to Clean, Module 1: The importance of integrity
- Course 4: Process Data from Dirty to Clean, Module 2: Clean it up
- Course 4: Process Data from Dirty to Clean, Module 3: SQL
- Course 4: Process Data from Dirty to Clean, Module 4: Verify and Report Results
- Course 5: Analyse Data to Answer Questions, Module 1: Organise data for more effective analysis
- Course 5: Analyse Data to Answer Questions, Module 2: Format and adjust data
- Course 5: Analyse Data to Answer Questions, Module 3: Aggregate data for analysis
- Course 5: Analyse Data to Answer Questions, Module 4: Perform Data Calculations
- Course 6: Share Data Through the Art of Visualisation, Course Overview plus Module 1: Visualise Data
- Course 6: Share Data Through the Art of Visualisation, Course Overview plus Module 2: Create Data Visualisation with Tableau
Concept
Even though I already have some knowledge and experience in Data Analytics using Python, Pandas, Seaborn etc I wanted to take this certification out of curiosity and to learn something new. I am not going to go into the debate of Python vs R and which one is better. Rather, I will only focus on this course and its materials.
This course is based on spreadsheets, SQL, R programming (not Python), Tableau.
Why this course and ultimate Goal
At first glance, I liked this course because it is a combination of 8 courses and gives a solid foundation on the fundamental understanding of Data Analytics, what is it and why. And then then this course dives deep into learning spreadsheets, R programming Language, visualisation with Tableu etc. I think these are very powerful concepts to understand moving forward.
So the 8 subjects in this course are:
- Foundations: Data, Data, Everywhere
- Ask Questions to Make Data-Driven Decisions
- Prepare Data for Exploration
- Process Data from Dirt to Clean
- Analyse Data to Answer Questions
- Share Data Through the Art of Visualisation
- Data Analysis with R Programming
- Data Analytics Capstone Project: Complete a Case Study
My ultimate goal from this course is to learn theoretical and practical knowledge and get better at data analysis. At the end I will post my Capstone Project Case Study here and in GitHub with all the documentation.
I will mainly take notes and details from the course directly and paste them here. I also decided to write them as a series to make it easy to jump in and out in different posts and course modules. The series will grow and evolve as I move forward with the course. Might seem a bit of unclear time to time, but I will try my outmost to build a comprehensive preparation guide.
I will go module by module notes for now..
Welcome to the journey and Happy Learning 🙂
Course 1: Foundations: Data, Data, Everywhere
Module 1 Introducing data analytics and analytical thinking
Learning Objectives
- Define key concepts involved in data analytics including data, data analysis, and data ecosystem
- Discuss the use of data in everyday life decisions
- Identify the key features of the learning environment and their uses
- Describe principles and practices that will help to increase one’s chances of success in this certificate
- Explain the use of data in organizational decision-making
- Describe the key concepts to be discussed in the program, including learning outcomes
Module 1 Key Concepts Recap
- Data-Driven Decision-Making: Businesses use data analysis to identify patterns, make predictions, and inform strategic decisions. This involves collecting, transforming, and organizing data to gain actionable insights.
- Data Analyst Role: Data analysts are in high demand due to their ability to drive business innovation and success. They follow a six-step process: Ask, Prepare, Process, Analyse, Share, and Act.
- Data Analysis Process: The course introduces the six-step data analysis process, emphasizing the importance of analytical thinking beyond computing skills. It includes activities to simulate a data analyst’s mindset and provides insights from professionals.
- Data Ecosystem: Data analysts utilize the data ecosystem, which includes elements for producing, managing, storing, organizing, analysing, and sharing data. Cloud computing plays a significant role in providing virtual storage and access.
- Data Science vs. Data Analytics: Data science focuses on creating new ways of understanding data, while data analysis answers existing questions with data. Data analytics is the broader science encompassing data management, analysis, tools, and methods.
Data Analyst skills:
Description: The qualities and characteristics associated with solving problems using facts
Skill required: Analytical skills
Description: The analytical skill that involves breaking processes down into smaller steps and working with them in an orderly, logical way
Skill required: A technical mindset
Description: The analytical skill that involves how you organize information
Skill required: Data design
Description: The analytical skill that has to do with how you group things into categories
Skill required: Understanding context
Description: The analytical skill that involves managing the processes and tools used in data analysis
Skill required: Data strategy
Glossary terms from module 1
Terms and definitions for Course 1, Module 1
Analytical skills: Qualities and characteristics associated with using facts to solve problems
Analytical thinking: The process of identifying and defining a problem, then solving it by using data in an organized, step-by-step manner
Context: The condition in which something exists or happens
Data: A collection of facts
Data analysis: The collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision-making
Data analyst: Someone who collects, transforms, and organizes data in order to draw conclusions, make predictions, and drive informed decision-making
Data analytics: The science of data
Data design: How information is organized
Data-driven decision-making: Using facts to guide business strategy
Data ecosystem: The various elements that interact with one another in order to produce, manage, store, organize, analyse, and share data
Data science: A field of study that uses raw data to create new ways of modelling and understanding the unknown
Data strategy: The management of the people, processes, and tools used in data analysis
Data visualization: The graphical representation of data
Dataset: A collection of data that can be manipulated or analysed as one unit
Gap analysis: A method for examining and evaluating the current state of a process in order to identify opportunities for improvement in the future
Root cause: The reason why a problem occurs
Technical mindset: The ability to break things down into smaller steps or pieces and work with them in an orderly and logical way
Visualization: (Refer to data visualization)
