L1- Introduction to Data Analytics

data analytics

a form of business intelligence, used to solve specific problems and challenges within an

organization

data analytics

It's all about finding patterns in a dataset which can tell you something useful and relevant about a particular area of the business

data analytics

Data analytics to make sense of the past and to predict future trends and behaviors; rather than basing your decisions and strategies on guesswork, you're making informed choices

based on what the data is telling you

data analytics

Focus: analyzing historical data to gain insights and improve decision-making

data analytics

Methods: descriptive and diagnostic (what happened? why did it happen?)

data analytics

Tools: excel, sql, tableau, power bi

data analytics

Techniques: data visualization, statistical analysis, reporting

data analytics

Application: business intelligence, process optimization, reporting

data science

Focus: extracting insights from data using machine learning, statistics, and ai

data science

Methods: predictive and prescriptive (what will happen? what should we do?)

data science

Tools: python, r, TensorFlow, Scikit-learn

data science

Techniques: machine learning, ai, deep learning, predictive modeling

data science

Application: forecasting, recommendation systems, innovation

Machine Learning

a branch of artificial intelligence

(AI) that enables computers to

learn patterns from data and make predictions or decisions without being explicitly programmed.

Machine Learning

Instead of following fixed rules, _______________ models improve their performance as they are exposed to more data

over time.

data analytics

tends to be more focused on analyzing data to understand past and current trends

data science

encompasses a broader range of activities including predictive modeling, machine learning, and developing algorithms to automate decision making processes

Descriptive Analysis

What happened?

Descriptive Analysis

It utilizes data aggregation, summarization, and visualization techniques to identify trends, patterns, and outliers in historical data.

Descriptive Analysis

This type of analysis focuses on summarizing past data to understand what has happened.

Diagnostic Analysis

Why did it happened?

Diagnostic Analysis

Aims to uncover the reason behind past events or behaviors.

Diagnostic Analysis

It involves hypothesis testing, root cause

analysis, and comparative analysis to identify patterns, correlations, and causal relationships within the data.

Predictive Analysis

What will happen?

Predictive Analysis

uses historical data to forecast future trends, behaviors, or events

Predictive Analysis

By applying statistical models, machine learning, business intelligence tools, it helps analyst anticipate potential outcomes and make informed decisions.

Prescriptive Analysis

What should be done?

Prescriptive Analysis

This analysis suggests actions to achieve desired outcomes or mitigate future risks.

Prescriptive Analysis

It uses data driven insights to recommend intervention strategies, optimizing potential future scenarios based on predicted trends.