Data science

Data science is a multidisciplinary field that utilizes scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. This mind map aims to explore the various aspects of data science, including its applications, techniques, tools, and impact on different industries. By visually organizing these components, we can gain a comprehensive understanding of the intricacies of data science and its role in the modern world.

Edited at 2023-11-10 21:54:20

SHARAD KUMAR

Data science

SHARAD KUMAR

Recommended to you
Outline

40k classes
- 1.8k
- 2
- 1
Oliveettom
Cisco IOS interface
- 578
- 3
Roy Mustang
application development and deployment
- 785
- 8
- 2
Roy Mustang
Data Sanitization Tools
- 375
- 1
Roy Mustang
DATA COLLECTION MEASURES
- 634
- 2
Roy Mustang
Android
- 444
- 1
Roy Mustang
Java Training
- 1.7k
- 11
- 2
Roy Mustang
Active reading techniques
- 569
- 3
- 2
Study Smarter
Classification of visual languages
- 284
- 1
Study Smarter
Game Theory
- 1.0k
- 9
- 3
Fiona_

Data science road map with project

Introduction

Data science overview

Definition of data science

Importance of data science in various industries

Road map

Learn programming languages

Python

Basics of Python programming

Basic

Variable and data type

Conditional

type casting

Exception

functions andbuilt-in functions

List

Tuple

Sets

Dictionary

Sub Topic

decorators

Python lambdas

Data structure and algorithm

Array

String

Linked list

Stack and queues

Binary search tree

Recurssion

Sorting algorithm

Libraries for data science in Python

numpy

Pandas

Matplotlib

Scikit-learn

Basics of R programming

Libraries for data science in R

data.table, dplyr, ggplot2, caret

Understand statistics and mathematics

Descriptive statistics

Measures of central tendency

Measures of dispersion

Measures of association

Inferential statistics

Hypothesis testing

Confidence intervals

Analysis of variance (ANOVA)

Master machine learning algorithms

Supervised learning

Linear regression

Logistic regression

Decision trees and random forests

Support vector machines (SVM)

Unsupervised learning

Clustering

Principal component analysis (PCA)

Association rule learning

Work with big data technologies

Hadoop

Hadoop ecosystem components

Hadoop MapReduce

Spark

Spark architecture

Spark RDD

Spark SQL

Explore data visualization

Data visualization tools

Tableau

Power BI

Best practices for data visualization

Choosing the right chart type

Designing visually appealing visualizations

Dive into natural language processing (NLP)

Basics of NLP

Tokenization

Text cleaning and preprocessing

Word embeddings and word2vec

NLP techniques

Sentiment analysis

Named entity recognition

Topic modeling

Project

Identify a real-world problem

Select a domain of interest

Define the problem statement

Collect and preprocess data

Gather relevant datasets

Data cleaning and handling missing values

Perform exploratory data analysis (EDA)

Visualization of data

Statistical analysis of data

Model building and evaluation

Feature engineering

Model selection and training

Model evaluation using performance metrics

Deploy and communicate the results

Presenting the findings

Deploying the model in production

Monitoring and updating the model