Day 1 of 30 days of Data Analytics with Projects Series

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15 days of Advanced SQL Series

30 days of Data Structures and Algorithms Series

14 System Design Case Studies Series

60 Days of Data Science and Machine Learning with projects Series

Complete System Design with most popular Questions Series

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All the projects, data structures, SQL, algorithms, system design, Data Science and ML , Data Analytics, Data Engineering, , Implemented Data Science and ML projects, Implemented Data Engineering Projects, Implemented Deep Learning Projects, Implemented Machine Learning Ops Projects, Implemented Time Series Analysis and Forecasting Projects, Implemented Applied Machine Learning Projects, Implemented Tensorflow and Keras Projects, Implemented PyTorch Projects, Implemented Scikit Learn Projects, Implemented Big Data Projects, Implemented Cloud Machine Learning Projects, Implemented Neural Networks Projects, Implemented OpenCV Projects,Complete ML Research Papers Summarized, Implemented Data Analytics projects, Implemented Data Visualization Projects, Implemented Data Mining Projects, Implemented Natural Leaning Processing Projects, MLOps and Deep Learning, Applied Machine Learning with Projects Series, PyTorch with Projects Series, Tensorflow and Keras with Projects Series, Scikit Learn Series with Projects, Time Series Analysis and Forecasting with Projects Series, ML System Design Case Studies Series videos will be published on our youtube channel ( just launched).

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We are now starting a new series — 30 days of Data Analytics with Projects. This series would run in parallel with—

Ongoing Series —

30 days of Data Engineering Series

30 days of MLOps

30 days of Deep Learning Series

ML Research ( papers) Simplified

For Data Analytics Projects —

What’s covered till now —

Day 1 : Data Analytics basics and kickstart of Data analytics with projects series

Day 2: Business Understanding — Data Driven Decision Making, Descriptive Analysis, Predictive Analysis, Diagnostic Analysis, Prescriptive Analysis

Day 3 : Data Analytics Ecosystem — Data Life Cycle, Data Analysis complete process ( most important things)

Day 4 : Probability, Conditional Probability, Binomial Distribution, Probability Density Function, Sampling Distribution

Day 5 : Statistics

Day 6 : Basic and Advanced SQL

Day 7 : Data Collection, Data Cleaning and Python

Day 8 : Pandas and Numpy

Day 9 : Data Manipulation

Day 10 : Data Visualization — Part 1

Day 11 : Project 1 : Data Visualization — Part 2

Day 12 : Data Visualization — Part 3

Day 13: Tableau — Part 1

Day 14: Tableau — Part 2

Day 15: Tableau — Part 3

Day 16 : Data Analysis Project 2

Day 17 : Data Analysis Project 3

Day 18: Data Analysis Project 4

Day 19: Data Analysis Project 5

Day 20 : Data Analysis Project 6 — Part 1

Categorical and Numerical Features

Missing Value Analysis

Fill the missing Values

Unique Value Analysis

Univariate Analysis

Bivariate Analysis

Multivariate Analysis

Correlation Analysis

Day 21 : Data Analysis Project 7

Data Profiling

Feature Engineering

GroupBy Features

Categorical and Numerical Features

Missing Value Analysis

Fill the missing Values

Unique Value Analysis

Univariate Analysis

Bivariate Analysis

Multivariate Analysis

Correlation Analysis

Day 22 : Data analysis Project 8

Linear Regression

Data Profiling

Feature Engineering

Sort Values

Categorical and Numerical Features

Missing Value Analysis

Unique Value Analysis

Univariate Analysis

Bivariate Analysis

Multivariate Analysis

Correlation Analysis

Correlation Coefficients

Day 23: Data Analytics Project 9

Linear Regression

Data Profiling

Correlation Coefficients

Spearman’s ρ

Pearson’s r

Kendall’s τ

Cramér’s V (φc)

Phik (φk)

Day 24: Data Analytics Project 10

Standardization

Encoding

Linear Regression

Data Profiling

Categorical and Numerical Features

Missing Value Analysis

Unique Value Analysis

Univariate Analysis

Bivariate Analysis

Multivariate Analysis

Day 25: Data Analytics Project 11

Summary Functions

Indexing

Grouping

Sorting

Data Profiling

Categorical and Numerical Features

Missing Value Analysis

Unique Value Analysis

Data Visualization

Correlation Coefficients

Day 26: Power BI

Day 27: Performance Metrics

Day 28: Regression

Linear Regression

Multi Linear Regression

Polynomial Regression

Day 29: Regression

Support Vector Regression

Decision Tree Regression

Random Forest Regression

Day 30: Classification

Naive Bayes

Random Forest

Missing Value Analysis

Unique Value Analysis

Take Complete Hands On Tableau Course : Link

What is Data Analytics?

In layman terms, Data Analytics is about three things —

Business + Data + Statistics = Data Driven Decision Making

Data Analytics is a process in which data is collected/extracted into raw format, cleaned and processed and then utilized to make data driven business decisions using data visualizations and Statistics.

The most important question one should ask as a data analyst is — How to get better analysis?

Goal

Let’s set a clear objective.

The goal is to develop an intuition and understand (in the depth) the practical side of Data Analysis and build projects.

I have created a GitHub repo for this series where we will be maintaining our code.

Tools

We will be using Google Colabs, Jupyter Notebooks and Tableau( based on our requirement).

Let’s talk about what are we going to cover in this series —

Let me be very straightforward. Data Analytics is a vast field and to be able to cover everything isn’t the aim of this series; instead, it will be more hands on than digging down the theory rabbit hole.

We will be covering —

1. Business Understanding

Data Driven Decision Making

How to formulate solutions to business problems?

Descriptive Analysis

Predictive Analysis

Diagnostic Analysis

Prescriptive Analysis

2. Data Analytics Ecosystem

Data Life Cycle

Data Analysis complete process — Most important things

3. Probability

Basic Probability

Conditional Probability

Binomial Distribution

Probability Density Function

Sampling Distribution

4. Statistics

Must know Statistics topics

5. Basic and Advanced SQL

Must know SQL basics

SQL Basics and Kick start of Advanced SQL Series

SQL Basics, Query Structure, Built In functions Conditions

Most Important Commands, Joins and Filters

Set Theory Operations, Stored Procedures and CASE statements in SQL

Wildcards, Aggregation and Sequences in SQL

Subqueries, Group by, order by and Having clauses in SQL and Analytical Functions

Window Functions, Grouping Sets and Constraints in SQL

BigQuery Basics, SELECT, FROM, WHERE and Date and Extract in BigQuery

Common Expression Table, UNNEST Clause, SQL vs NoSQL Databases

Triggers, Pivot and Cursors in SQL

Views, Indexes and Auto Increment in SQL

Query optimizations, Performance tuning in SQL

Introduction to MySQL, PostgreSQL and Mongo DB, Comparison between MySQL and PostgreSQL and Mongo DB, Introduction to SQL and NoSQL Databases

MySQL in Depth

PostgreSQL inDepth

Data Collection and Data Cleaning

Data Collection

Data Cleaning

Python

Pandas

Numpy

9. Data Manipulation

Join

Melt

Cut

Transform

Clean

Slicing

Reshaping

Filter

Group by

Pivot and Merge

Concatenate

MultiIndexing

Stacking

Hierarchical indexing

Aggregate

Summarize data

10. Data Visualization

Data Visualization basics

Which chart to choose and when?

Data Visualization using Matplotlib and Seaborn with project

Data Visualization using Plotly

Data Visualization using Bokeh

13. Tableau

Tableau Basics

Work with Data Blending in Tableau

Create Table Calculations

Create Dual Axis Charts

Create Calculated Fields

Create Visualizations using Calculated Fields

Tableau String Functions

Tableau Date Functions

Tableau Type Conversion

Implement Aggregation

Create and add Filters and Quick Filters

Implement Filters, including the context filter

Implement Clustering

Create trend lines and understand the relevant statistical metrics such as p-value and R-squared

Create forecasts, Barcharts, Area Charts, Box and Whisker

Create Histogram, Bullet Chart, Bubbles Chart, Funnel Charts, Advanced Charts

Create Scatterplots , Piecharts, Treemaps

Create Maps — Detailed Maps, Symbol Maps, Density Maps

Create Advanced Maps

Create Interactive Dashboards

Projects

Data Analysis Project 1

Data Analysis Project 2

Data Analysis Project 3

Data Analysis Project 4

Data Analysis Project 5

Data Analysis Project 6 — Part 1

Categorical and Numerical Features

Missing Value Analysis

Fill the missing Values

Unique Value Analysis

Univariate Analysis

Bivariate Analysis

Multivariate Analysis

Correlation Analysis

14. Data Preparation

15. Data Modeling

16. Data Evaluation

17. Statistical Analysis

18. Regression analysis

19. Least squares and inference

20. Regression models

21. Big Data Analytics

21. Classification Trees

22. Projects

That’s it for now. Day 2 :

Let me know if you have questions in the comment section below. Subscribe/ Follow, Like/Clap as it would encourage me to write more in my free time

Stay Tuned!!

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If you are interested, you can join my newsletter through which I send tech interview tips, techniques, patterns, hacks — Software Development, ML, Data Science, Startups and Technology projects to more than 30K readers. You can subscribe to Tech Brew :

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