Introduction to Data Analytics and AI

In this lecture, you will know where the definition of analytics and that is a vast domain with many methods.

Analytics started thousands of years ago and has evolved quickly in the last decades.

Big Data, AI, NLP; You heard these nearly everyday but what do they actually mean?

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Take this optional 16-question graded productivity assessment (0–100 score) to benchmark your current work habits and support your progress as you learn data analytics and AI.

This lesson is about the different kind of disciplines that are present in the Data and analytics landscape, in 2020

An effective Business Intelligence system serves as a mechanism to identify key patterns and trends producing actionable Information

Data analysis is important in a company to understand problems confronting an organisation, and to examine data in meaningful ways

Market research can identify how consumers and potential clients view your business. It answers the why customers do what they do

Statistics for business allows managers to analyze past performance, predict future outcomes and respond to changes in consumer demand.

Econometrics is the integration of statistics and economic events to provide numerical values to the parameters of economic relationships.

Predictive Analytics offers an unprecedented opportunity to identify future trends and allows businesses to act upon them

Data mining is used to discover patterns and relationships in large amounts of data in order to help make facilitate analysis and decision making

The purpose of Text Analytics is to create structured data out of free text content (unstructured)

Ever wondered how autonomous vehicles work? The base of the magic is computer vision algorithms.

There are operational problems that are common to many businesses. Providing with solutions based on that is the role of Operations Research

Digital Signal Processing has many daily yet unknown applications.

Image processing is efficiently used in computer vision, medical imaging, meteorology, astronomy and other related fields.

Natural language processing helps computers communicate with humans. For example, NLP makes it possible for machines to read a text, hear speech, understand it, and conclude which parts are important.

Some problems are too complex to solve, even using lots of data. An informed decision could work faster than a very accurate but slow one. This is the field of Metaheuristics.

Data architecture is a fundamental part to align business goals with the data strategy.

Data quality drives better decision-making across a company. The more high-quality data you have, the more faith you can have in your decisions.

Master data is the core data that is essential to operate a business

Data Privacy enables us to create limits and protect ourselves from unwarranted intrusion in our lives, it allows us to decide how we want to interact with the society around us

The Data Analytics Life cycle defines stages, analytics methods and best practices. From data generation to data-driven decisions and actions.

By the end of this year 2020, every human on the planet will be creating 1.7 megabytes of information each second. In this lesson, we cover how data is generated.

Source systems or systems of record are those powering the applications of a company.

In this lecture, you will discover how they work.

With the amount of data that is generated every day by a business, a place to store it and access it for reporting and intelligence becomes essential. This lecture is about Data Warehouses.

A few years ago building a data warehouse was a long and costly exercise. Today you can build one in hours.

Extracting data from source systems and transforming it so that it can be analyzed is the role of ELT

Databases are the epicentre of business analytics. Connecting them to improve data coverage is called Data Modelling.

SQL is used to communicate with a database. Analysts can update or retrieve data from a database.

Python is a flexible programming language. In this chapter, we cover how is it used for data analytics.

With so much data to analyze the scientific community put together R, a programming language specifically designed to do analytics

By using visual components like charts, diagrams, and maps, data visualization tools provide an easy way to see and understand trends, outliers, and patterns in data.

An ad-hoc analysis is a business report or data analysis created when they need it, to solve a specific business problem.

Executive reporting is a primary monitoring tool for a company's performance, tailored to the needs of executives.

A quick intro to what we will see in the AI module.

From Lean Square to Chat GPT. 200 Years of AI.

Understand the AI landscape in minutes: what AI is, the difference between machine learning, deep learning, generative AI, and AI agents, plus practical examples you already use every day.

Understand the AI landscape in minutes: what AI is, the difference between machine learning, deep learning, generative AI, and AI agents, plus practical examples you already use every day.

Spot real AI vs. hype! Learn the litmus test, generative AI strengths/weaknesses, and when to trust AI outputs.

This lesson shows how AI supercharges predictive and prescriptive analytics through machine learning to generate insights such as churn prediction, forecasting, and decision-making science.

This lesson explains why AI ethics matters in real-world analytics, introduces the TRUST framework for evaluating AI systems, and shows how to manage risks such as bias and hallucinations through human oversight and critical judgment.

This lesson explores the path from today’s narrow AI toward potential general and super intelligence, highlighting the paradox that as AI becomes more capable and autonomous, humans gain efficiency but risk losing oversight and control.​

It is time to say "Hasta la vista".

I hope that you have learned something new.

Please leave a review if you liked the course and also if you did not. It will help me get better.

Thanks a million

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