Technical Business Analytics For Agile Decision Making 2023

For aspiring analysts and data-eager professionals to learn Excel, SQL, Tableau, and relevant communication skills

What you'll learn

Learn what happens to data as they flow through a typical pipeline

Learn how to use SQL to query databases and pull data for analysis

Learn how to use Excel functions and features to analyze data and implement machine learning methods

Learn how to use Tableau to generate appropriate visualizations to communicate your insights

Learn how to communicate analytical insights to various audiences

Requirements

No prior experience required

An interest in asking questions and digging deeper

A desire to apply classroom theory to practical case studies

Description

Welcome to my Business Analytics course! The data-driven business analyst role is a key entry point into working in technology / agile environments, and I'm beyond excited to teach you ALL of the necessary skills using free tools such as Google Sheets (Excel, Analysis ToolPak), MySQL, and Tableau (trial)! This is an introductory self-paced course with an MBA-level curriculum tailored to suit students from all backgrounds! Whether you're currently a student preparing for the job market, a seasoned professional looking to up-skill / pivot, or a business owner looking to accelerate your growth, you will finish this course with a fundamentally different understanding of how the world operates today. Our course will consist of theoretical videos, hands-on tutorials where we work through short assignments together, and mock cases to dive into a wide range of topics, including data pipelines, intro/advanced spreadsheets functions, descriptive/inferential statistics,  machine learning, time series, SQL, visualization, and nontechnical skills.I will be constantly updating this course with industry breakthroughs regularly, and adjusting materials based on student feedback to provide an optimized learning experience. By the end of this course, you'll have a comprehensive understanding of Business Analytics and how to use various tools and techniques to analyze data and make informed business decisions.So, what exactly will you be learning? Here's a brief breakdown of the course units:Introduction: Welcome to the course where you'll learn about software development and data analytics.Data Pipeline: You'll learn about big data and how data is collected, stored, and organized. Spreadsheets & Descriptive Statistics: You'll explore spreadsheets and descriptive statistics, understanding normal distributions and relationships between data points. Inferential Statistics: You'll dive into inferential statistics, learning about hypothesis testing and decision making based on expected value. Probability: You'll study random variables and probability distributions, calculating statistics and making decisions. Machine Learning 1: You'll be introduced to machine learning and artificial intelligence, including linear and non-linear regressions. Machine Learning 2: You'll learn about classification, clustering, and reinforcement learning. Time Series: You'll forecast with time series and predict future events. SQL: You'll understand databases, reading, changing and working with multiple tables. Visualization & Presentation: You'll study business intelligence, creating visualizations (Tableau) and presenting data effectively. Non-Technical Skills: You'll evaluate businesses and work within teams, prioritizing projects and understanding KPIs.Conclusion: Quick wrap up of everything that you have learnedAre you ready to dive into the world of Business Analytics? Let's get started!

Overview

Section 1: Introduction

Lecture 1 What will we learn?

Lecture 2 What do BAs do?

Lecture 3 What is agile software development?

Lecture 4 What are the different types of analytics?

Lecture 5 Why do we use living documents and mobile apps?

Lecture 6 What are no-code/low-code softwares?

Section 2: Data Pipeline

Lecture 7 What is big data?

Lecture 8 What are data pipelines?

Lecture 9 How is data collected?

Lecture 10 What kinds of data are there?

Lecture 11 How is data stored?

Section 3: Spreadsheets & Descriptive Statistics

Lecture 12 What are spreadsheets?

Lecture 13 What are spreadsheet functions?

Lecture 14 What are descriptive statiistics?

Lecture 15 What is a normal distribution?

Lecture 16 How do we filter and organize information?

Lecture 17 How do we characterize data point relationships?

Section 4: Inferential Statistics

Lecture 18 What are inferential statistics?

Lecture 19 What are hypotheses?

Lecture 20 What are error types?

Lecture 21 How do we know if our hypothesis is true (pt 1)?

Lecture 22 How do we know if our hypothesis is true (pt 2)?

Lecture 23 What types of hypothesis tests exist?

Lecture 24 What is A/B testing?

Section 5: Probability

Lecture 25 What are random variables and probability distributions?

Lecture 26 How are statistics calculated for random variables?

Lecture 27 What are probability distributions?

Lecture 28 How do we make decisions based on expected value?

Section 6: Machine Learning 1

Lecture 29 What is machine learning and artificial intelligence?

Lecture 30 Why do we need to clean our data?

Lecture 31 What is linear regression?

Lecture 32 What are multiple linear regressions?

Lecture 33 How do we compare models?

Lecture 34 What are non-linear regressions?

Section 7: Machine Learning 2

Lecture 35 What is classification?

Lecture 36 What is clustering?

Lecture 37 How do we make clusters?

Lecture 38 How do we optimize clusteriing?

Lecture 39 What is reinforcement learning?

Lecture 40 What is model fit?

Lecture 41 What is the role of AI in modern workflows?

Section 8: Time Series

Lecture 42 What is forecasting with time series?

Lecture 43 How do we predict with time series?

Section 9: Structure Query Language (SQL)

Lecture 44 How is data stored in databases?

Lecture 45 How do we read from a table?

Lecture 46 How do we change information in a table?

Lecture 47 How do we work with multiple tables?

Lecture 48 How do we work with other data structures?

Section 10: Visualization & Presentation

Lecture 49 What is business intelligence?

Lecture 50 What is Tableau?

Lecture 51 What types of information can be visualized?

Lecture 52 Why are dashboards useful?

Lecture 53 How do you present data?

Section 11: Non-Technical Skills

Lecture 54 What frameworks are used to evaluate businesses?

Lecture 55 How do I work within a team?

Lecture 56 How do I prioritize multiple projects?

Lecture 57 What are KPIs within SaaS?

Section 12: Conclusion

Lecture 58 Course Conclusion

University/College students who want to enter the workforce as a business analyst or prepare for a course,Experienced professionals who want to improve data literacy, implement agile practices, or do a career pivot,Small business owners who want to implement data-drive decision making in their operations,Businesses looking to enrol new business analysts in a data training bootcamp