Pandas is a popular Python library used for data manipulation and analysis.

It provides easy-to-use data structures and functions to work with structured data.

Here are some basics: 1. Data Structures:

DataFrame: This is the primary data structure in pandas. It represents a two-dimensional, labeled data structure with columns of potentially different types, similar to a spreadsheet or SQL table.

Series: A Series is a one-dimensional labeled array capable of holding any data type. It's like a column in a data frame.

Optimizing your SQL queries is essential for efficiency and performance

It can easily save 2-3 times the time and computing power 1/ Indexing

A well-designed index structure can significantly boost query performance.

Identify columns that are frequently used in WHERE clauses and index them appropriately.

They significantly enhance query performance, but their usage requires careful planning to strike the right balance between read and write operations.

Too many indexes or improper use can lead to unintended consequences.

Here's the Syntax:

Bank Customer Churn Prediction Deployed using @streamlit

Let me explain how it works: The Customer Churn Prediction (CCP) Tool is a machine learning application designed to predict the likelihood of a customer churning from the bank.

It utilizes data from the bank's customer database to train models that can accurately predict customer churn.

It's deployed here:

churnpredicting.streamlit.app

Stream Processing VS Batch Processing

in Cloud Ecosystems: Before processing let's look how data is ingested

Data ingestion is the obtaining and importing of data from various sources.

Then it’s processed for immediate use or storage in an analytical data stored

Data processing is simply converting raw data to meaningful information through a process.

Types of Analytics based on the Question it can Answer

Types of Data Analytics explained: Descriptive analytics

What happened?

Descriptive analytics answers questions about what happened.

Descriptive analytics techniques summarize large datasets to present insights to stakeholders.

The presentation of data related to those KPIs is descriptive analytics.

Power BI 101

How to create a Calculated Measure? Measures are dynamic calculations that help you to derive meaningful insights from your datasets.

Measures are formula-driven Power BI calculations that enable you to aggregate, summarize, or compute data based on specific criteria.

They are dynamic, responding in real-time as you interact with your reports, making them indispensable for data-driven decision-making.

Power BI Interview Question

Whats the difference between SUM and SUMX in DAX? SUM totals (for example) a column in a table, based on context.

So if the context is a particular employee, then SUM could total all of the expenses for that employee.

The following example adds all the numbers that are contained in the column, Amt, from the table, Sales.

= SUM(Sales[Amt])

Dont be misled by how Violin charts look

It's one of the most powerful visualizations in Statistics

Let's learn more

/🧵/ Violin charts are a great way to visualize the distribution of numeric data for one or more groups using density curves.

A violin plot is a hybrid of a box plot and a kernel density plot, which shows peaks in the data.

The "window" in "window function" refers to a set of rows.

SQL windows function clearly explained: What are Window functions?

Window functions are a powerful tool in SQL that allows you to perform calculations over a set of rows, called a window, that is related to the current row.

Window functions are similar to aggregate functions, but they do not collapse the result of the rows into a single value.

Instead, all the rows maintain their original identity, and the calculated result is returned for every row.

Some common use cases for window functions include calculating running totals, rankings, and moving averages.

SQL Stored Procedures Clearly Explained: What are Stored Procedures?

Pre-defined sets of SQL statements or procedural code, stored right within your database.

Stored procedures are stored and managed within the database itself, making them available for repeated use by multiple applications or users.

Plus, they amp up your database's security

Here's an example of a simple stored procedure written in SQL Server:

Black-box models are ML models that cannot be explained.

Learn these 3 Black-box models: Black-box models are complex models that require external techniques to explain how they arrived at a prediction.

They are also called "explainable models”.

These models make predictions based on input data, but the relationships and features they use to make those predictions are not readily apparent.

Triggers are an integral part of any DataBase

Let me explain: SQL triggers are database objects that are automatically executed or fired in response to specific events on a particular table or view.

These events can include INSERT, UPDATE, DELETE, or even specific SQL operations.

Syntax of Creating a Trigger:

BEFORE or AFTER specifies when the trigger should be executed.

INSERT, UPDATE, or DELETE indicates the triggering event.

FOR EACH ROW specifies that the trigger should be fired once for each affected row.

SQL Indexes are quite useful but can also cause issues!

The Truth about SQL Index An SQL index is a data structure that enhances the speed of data retrieval operations on a database table.

SQL indexes achieve this by creating a separate data structure that stores a subset of the table's columns, organized for optimized query performance.

It's like having a map to navigate the data quickly.

Optimizing your SQL queries is essential for efficiency and performance

It can easily save 2-3 times the time and computing power 1/ Indexing

A well-designed index structure can significantly boost query performance.

Identify columns that are frequently used in WHERE clauses and index them appropriately.

They significantly enhance query performance, but their usage requires careful planning to strike the right balance between read and write operations.

Too many indexes or improper use can lead to unintended consequences.

Here's the Syntax:

Joins in SQL is a crucial topic where a lot of beginners struggle.

Joins explained with Example + Diagram 1/ INNER JOIN

This type of join returns only the matching rows from both tables.

It's like finding the intersection of two sets.

Perfect for retrieving data that share common values in specified columns!

Example: Inner join t1 and t2

SELECT c1, c2 FROM t1
INNER JOIN t2 ON condition;

Complete SQL Crash Course Study Plan

This is for those who can dedicate at least 1.5 hours a day for 28 days! Week 1: Fundamentals of SQL

Day 1-3: Introduction to SQL syntax, SELECT statements, filtering, and sorting.Resource: Khan Academy's "Intro to SQL" course on YouTube.


Day 4-5: Working with multiple tables using JOIN operations.Resource: DataCamp's "Joining Data in SQL" course.


Day 6-7: Aggregating data with GROUP BY, HAVING clauses, and understanding subqueries.Resource: Coursera's "SQL for Data Science" specialization.

app.datacamp.com/learn/courses/…
coursera.org/learn/sql-for-…

My classification model has a ROC AUC of 0.8266

What does it mean? The graph between the True Positive Rate on the y-axis and the False Positive Rate on the x-axis is called the ROC curve and is used in binary classification.

The graph shows the performance of a classification model at all classification thresholds.

A/B testing is used to make Data-Driven Decisions and eliminate guesswork.

/🧵/ A/B testing is a randomized control experiment that compares the performance of two versions.

Aim to find which version of content appeals more to visitors/viewers.

Are you bored with long Data Science video lectures?

Do you want to increase the pace of your learning?

5 Kaggle FREE micro-courses to accelerate your learning 1/ Machine Learning

kaggle.com/learn/intermed…

Tableau for Data Science Complete Study Plan 📊

Timeline of 4 weeks and you have to dedicate at least 1 hour a day Week 1: Introduction to Tableau

Day 1-3: Begin with the official Tableau Training Videos

Complete all the tutorials under “Creator”

Link:

Day 4-7: Watch the "Tableau Tutorials for Beginners" series

Link: tableau.com/learn/training

Machine Learning: Interpretability VS Explainability

What's the difference? In ML, interpretability and explainability are key for understanding and making sense of how models arrive at certain decisions.

Let's break down the concepts and highlight their differences: