avatarNaina Chaturvedi

Summary

The website presents a comprehensive series of data analytics projects, resources, and tutorials aimed at building practical skills through hands-on learning and project-based experience.

Abstract

The website content outlines an extensive collection of data analytics projects, providing a structured approach for learners to enhance their skills in the field. It introduces a series of 30 days of Data Analytics with Projects, which includes daily lessons and practical exercises covering a wide range of topics from basic data analysis to advanced techniques like machine learning and data visualization. The projects are supplemented with pre-requisite tutorials, a GitHub repository for code maintenance, and the use of tools such as Google Colabs, Jupyter Notebooks, Tableau, and Power BI. Additionally, the website offers insights into system design, data structures, algorithms, and other tech-related topics, with a newsletter for ongoing updates and a community for discussion and support.

Opinions

  • The author emphasizes the importance of practical, project-based learning for developing intuition and a deep understanding of data analytics.
  • There is a strong suggestion to follow the series in sequence, starting with foundational concepts before progressing to more complex projects.
  • The content promotes the idea that consistent practice and engagement with the community can lead to significant improvements in one's technical skills.
  • The author believes in the value of a hands-on approach, as evidenced by the provision of a GitHub repository to maintain and share project code.
  • By providing a mix of free resources, paid courses, and a curated list of best practices, the author aims to democratize access to high-quality tech education.
  • The website positions itself as a one-stop destination for individuals looking to enhance their knowledge and career prospects in data analytics and related tech fields.

Implemented Data Analytics Projects

Vertical series ( One post that will house all the projects and only projects as we build/implement them)

Welcome back peeps! Holiday season has started and before I jet off to my holidays, here’s something new that I have started which will help you build your data analytics skills through projects.

As we have already completed 30 days of Data Analytics; now we are moving ahead with the projects.

Project 1

Project 2

Project 3

Project 4

Project 5

Project 6

Project 7

Project 8

Project 9

Project 10

Project 11

Projects Videos —

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).

Subscribe today!

Tech Newsletter —

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 :

Goal/objective —

Note : Everyday new data analytics projects will be uploaded/posted here. This is a vertical post. Check this post regularly for new projects.

This post will house all the projects that you can build to have a solid projects portfolio, foundation and skills in data analytics. The goal is to develop an intuition and understand (in the depth) the practical side of data analytics by building projects.

This post will not cover theory related to data analytics. That you can cover through the pre-requisite mentioned above.

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

Pre-requisite to Data Analytics projects —

Complete 30 days of Data Analytics series ( as detailed below) before jumping on the projects —

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

Tools

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

Through end to end projects, we will be covering —

1. Data

Data Collection — web scraping

Data Cleaning

Python

Pandas

Numpy

Summary Functions

Indexing

Grouping

Sorting

2. Analysis

Regression Analysis

Statistical Analysis

Least Square and inference

Missing Value Analysis

Fill the missing Values

Unique Value Analysis

Univariate Analysis

Bivariate Analysis

Multivariate Analysis

Correlation Analysis

3. Data Visualization

4. Data Modeling

5. Data Evaluation

6. Regression Models

7. Data Analysis using SQL

8. Ensemble Modeling

9. Feature Engineering

Coming soon! Project 1 to 50.

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!!

Read More —

11 most important System Design Base Concepts

1. System design basics

2. Horizontal and vertical scaling

3. Load balancing and Message queues

4. High level design and low level design, Consistent Hashing, Monolithic and Microservices architecture

5. Caching, Indexing, Proxies

6. Networking, How Browsers work, Content Network Delivery ( CDN)

7. Database Sharding, CAP Theorem, Database schema Design

8. Concurrency, API, Components + OOP + Abstraction

9. Estimation and Planning, Performance

10. Map Reduce, Patterns and Microservices

11. SQL vs NoSQL and Cloud

12. Most Popular System Design Questions

13. System Design Template — How to solve any System Design Question

14. Quick RoundUp : Solved System Design Case Studies

System Design Case Studies — In Depth

Design Instagram

Design Messenger App

Design Twitter

Design URL Shortener

Design Dropbox

Design Youtube

Design API Rate Limiter

Design Web Crawler

Design Facebook’s Newsfeed

Design Yelp

Design Uber

Design Tinder

Design Tiktok

Design Whatsapp

Most Popular System Design Questions

Mega Compilation : Solved System Design Case studies

Complete Data Structures and Algorithm Series

Complexity Analysis

Backtracking

Sliding Window

Greedy Technique

Two pointer Technique

Arrays

Linked List

Strings

Stack

Queues

Hash Table/Hashing

Binary Search

1- D Dynamic Programming

Divide and Conquer Technique

Recursion

Some of the other best Series —

60 days of Data Science and ML Series with projects

30 Days of Natural Language Processing ( NLP) Series

30 days of Machine Learning Ops

30 days of Data Structures and Algorithms and System Design Simplified

60 Days of Deep Learning with Projects Series

30 days of Data Engineering with projects Series

Data Science and Machine Learning Research ( papers) Simplified **

100 days : Your Data Science and Machine Learning Degree Series with projects

23 Data Science Techniques You Should Know

Tech Interview Series — Curated List of coding questions

Complete System Design with most popular Questions Series

Complete Data Visualization and Pre-processing Series with projects

Complete Python Series with Projects

Complete Advanced Python Series with Projects

Kaggle Best Notebooks that will teach you the most

Complete Developers Guide to Git

Exceptional Github Repos — Part 1

Exceptional Github Repos — Part 2

All the Data Science and Machine Learning Resources

210 Machine Learning Projects

Tech Newsletter —

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 :

For Python Projects —

For complete 60 days of Data Science and ML : Day 1 — Day 60 : Quick Recap of 60 days of Data Science and ML

Follow for more updates. Stay tuned and keep coding!

For other projects, tune to —

Build Machine Learning Pipelines( With Code)

Recurrent Neural Network with Keras

Clustering Geolocation Data in Python using DBSCAN and K-Means

Facial Expression Recognition using Keras

Hyperparameter Tuning with Keras Tuner

Custom Layers in Keras

Data Science
Tech
Programming
Machine Learning
Artificial Intelligence
Recommended from ReadMedium