DEV Community: Kipkosgei

90 Days Challenge : Learning Data Science 2024

Kipkosgei — Fri, 15 Mar 2024 17:43:55 +0000

Introduction

In your daily activities u've been waking up and scrolling the whole time, switching from one app to another. Spend a lot of time on social media meaning this media accounts have your basic data from name, when you frequently use there app. They collect and analysed that DATA Time Spent on certain social media and general over view of social media this has drained my thoughts...

Day_1

I have read through how Venn Diagram Data Science Venn Diagram yes, on my desk i have a lot.

I have understood Data science is a discipline,and a pivot domain of data. One is you cant wake up today and be a data scientist but maybe I can, even in 24 hours but is it even realistic?

Today I've covered what I anticipate to learn.

Data Science is Discipline of three Mix:

Mathematics Techniques

Algorithms

Visualization

"Understand the tools, libraries, framework,how modules work, and principals in it" Joel Grus

There is Data Cycle : that involves the daily or the track of a data scientist

On This Journey i will cover

Python

Basic Mathematics(Algebra, Statistics, and Probability)

The EDA (Collecting, explore, clean, munge, and manipulate data)

ML(machine learning)

Models - k-nearest neighbor

NLP, recommender system and Database

It's a cycle:

Understand, find insights, patterns and trends in datasets
Know and when to apply and use algorithms and data models
Get the know-how of machine learning techniques
Get your way-around with Python, and SQL
Read Articles

From simpli learn

I will be documenting My Learning and Challenge task on my Github https://github.com/kipkosgeii/90-Day-Challenge

Phone Addiction

Kipkosgei — Tue, 27 Feb 2024 00:52:11 +0000

Your own phone is your enemy!

Your own phone can be the best thing you have or be the biggest distraction you holding on your hand.

Unfortunately, the way our brain processes information is backwards. For example, if you reject an advertisement today, often you will continue to see it later on. What is the most effective approach to market ads? Using Phones(THE DRUG)!

It's challenging to reverse what you just saw on your phone, but after seeing it multiple times, your "perception" of it becomes less negative. Before you realize it, you've become another consumer of the information and guess who is in charge? "Your Phone"

Think about and Analyse how much of your everyday activities are influenced by the amount of time you spend on your phone ("Ads, videos, or blog post"). Your daily activities determine how much you need of it, and over time, that need will either grow or decrease. A phone is a Drug!

A phone is like a narcotic; before you realize it, you're "addicted to it," obsessed with the latest developments, and "switching from one application to another." And I wonder if those who make money from it are any different from those who struggle with addiction?

Data Engineering for Beginners: A Step-by-Step Guide

Kipkosgei — Wed, 08 Nov 2023 16:49:20 +0000

Time Series Models, Guide for Beginners.

Kipkosgei — Thu, 02 Nov 2023 09:36:00 +0000

Exploratory Data Analysis using Data Visualization Techniques.

Kipkosgei — Tue, 10 Oct 2023 14:24:10 +0000

Introduction

Think of what data is for a moment, from gathering it through sharing the final report with the domain or company to enable them make informed decision-making.

Data collection has its dos and don'ts, and in order to make sense of the data so that you may derive insights, a method known as exploratory data analysis (EDA) must be used.

EDA aids in learning more about the data set and discovering patterns, trends, and connections between them. In order to obtain high-quality data, it also helps to discover missing values, outliers, and anomalies.

We employ statistical functions and tools to complete all of this.

What is EDA

Exploratory data analysis (EDA) is an approach of analyzing data sets to summarize their main
characteristics, often using statistical graphics
and other data visualization methods this is according to wikipedia

With EDA, you may examine data, find patterns, anomalies, and outliers, as well as fill in missing values to get the proper data for insights and machine learning models (both supervised and unsupervised).

Steps of performing EDA

  1. Understanding Domain
  2. Get the right libraries and Read file
  3. Cleaning Data & Preparing data
  4. Feature Engineering 
  5. Data Visualization & Interpretation

Tools Used For EDA

There are a several tools used to do EDA, including Tableau, Power BI, and Python libraries, among many others.

Python Libraries

  - Numpy
  - Pandas
  - Matplotlib
  - Seaborn

Others

   Tableau
   PowerBI

Statistical Function & Techniques for EDA tools

 - Clustering and Dimension
 - Univariate Viausalization
 - Bivariate Visualization
 - Multivaraite
 - K-means Clustering
 - Predictive Models

To get more understanding of how this techniques read through IBM

Data Visualization in EDA

Graphical techniques:

  Box plot, Histogram, Multi-vari chart
  Run chart, Pareto chart, 
  Scatter plot (2D/3D),Heat map
  Bar chart

Dimensionality reduction:

Multidimensional scaling
Principal component analysis (PCA)
Multilinear PCA
Nonlinear dimensionality reduction (NLDR)
Iconography of correlations

Typical quantitative:

Median polish
Trimean
Ordination

Conclusion

This article may not include much information, but it will offer you a general idea of what to have and when to use certain tools and basic actions. EDA is essential for understanding data insights and obtaining clean data for modeling (ML). Google is your friend outside of this, though.

  REMEMBER: "A picture is worth a thousand words"

Road map for Complete Beginners : Data Science: 2023 - 2024

Kipkosgei — Sun, 01 Oct 2023 12:37:40 +0000

We connect with data every day in one way or another, whether directly or indirectly. Your phone book and even how you use daily computations contain data. According to wikipedia, data science is an interdisciplinary academic subject that employs statistics, scientific computers, and scientific methods, procedures, algorithms, and systems to deduce knowledge and insights from noisy, structured, and unstructured data.

Why Learn Data Science?

Data science is recognized as the "sexiest job of the 21st century" by Harvard Business review in 2021. Its capacity to pull, obtain, and derive actionable insights from various data sets

1) Demand for data scientists - As more organizations begin to value data, they will require someone who can extract insights from it.
2) It has Numerous endeavors in the fields of health,disaster relief,and even business use the data that data scientists collect.
3) Data scientists earn competitively high salaries.

What you should Learn to become a data scientist

        Programming
        Math
        Version Control
        Communication

1. Programming For Data Science

Learning is a journey with its own curve, so getting your hands dirty to grasp the fundamentals of what you are going into should be your first step as you enter the world of data science. Understanding programming techniques will assist you in performing fundamental tasks as well as efficiently processing massive datasets to get insightful conclusions.

Python

learn Python; it's simple to pick up, and it'll allow you investigate real-world data sets and provide you insights and solutions using its libraries and framework to Finnish tasks. Libraries & Modules like Pandas, a Python module used for data manipulation, will be available for you to utilize and interact with. To understand more visit https://www.codecademy.com/resources/blog/why-you-should-learn-python-for-data-science .

SQL

Yes, It refers back to data and teaches how to handle, analyze, and manage it all in order to gain insightful information for wise decision-making. In order to extract, compute, visualize, and explore
https://emeritus.org/blog/data-science-and-analytics-sql-for-data-science/

2.0 Maths For Data Science

You will learn and need to comprehend the crucial computation of data through math.

Statistics

plays a part in the evaluation of data to make decisions

 - Descriptive Statistics - Inferential Statistics

Probabilities

used for machine learning and deep learning that are.

 - Conditional Probability and Joint Probability

Calculus

 - Deferential and Integral Probability

Linear Algebra

 - Vectors, Linear regression, Matrices

3.0 Version control For Data Science

Get familiar with version control systems like Git. Your code can be kept or stored here. It makes peer cooperation and change tracking easier.
Learn how to start a project, commit, branch, push, rebase, merge, and resolve disputes and work around with open source

Work with version control like git and git providers like

    - Bitbucket, Github,AWS code commit, and Git Lab.

Also how to use CLI for work around on your pc

4. Data Visualization

Know to present your data

Present your data with Visuals like graph. Understand

    - Matplotlib and Seaborn

to share the details of your data.

5. Career Paths For Data Science

Data Scientist
Data Analyst
Data Engineer
Data Architect