Top 10 Data Science Websites to learn More

Being a data scientist means constantly growing, enabling businesses to become more data-propelled, and learning newer trends and tools. There are various excellent resources in data science that can help you to develop your skillset. According to International Data Corporation (IDC), organizations are turning towards digitalization completely. This will help to create more investments, technology development and open various new jobs.

Currently, numerous resources are being created on the internet consisting of data science websites, data analytics websites, data science portfolio websites, data scientist portfolio websites and so on. So, having the right knowledge of tools and technology is important for handling such data. The easiest way to get started is by taking an online data science bootcamp program. Most aspirants either spend too much time in search of the right course or the right technology.

Steps to Learn and Master Data Science

Learning a Language – Python

Choosing and learning a new programming language is not an easy thing, in terms of learning data science, Python comes out first. Python is a high-level, interpreted, general-purpose, object-oriented programming language. Python provides great functionality to deal with mathematics, statistics and scientific function. The best Website to learn Python: w3schools.com. 

Get to know more about data science for business. 

Learning Data Analysis in Excel

Data analysis is a process of inspecting, cleaning, transforming and modelling data with an objective of uncover the useful knowledge, results and supporting decision. Best Website for excel: excelexposure.com. 

Learning Data Visualization

To discover hidden truth or information on business problem, data needs to be viewed properly. Appropriate data visualization tool selection is important, to know what to expect from data is also important. Various data visualizations are used in Exploratory data analysis (EDA). EDA is used by data scientists to analyze and explore data sets and summarize their properties, often using data visualization techniques. 

Best website for data visualization learning: geeksforgeeks.org 

Start learning Inferential Statistics and Hypothesis Testing

Exploratory data analysis helps you to know patterns and trends in the data using many methods and approaches. In data analysis, EDA performs an important role. Hence, data analyst utilizes most of their time doing EDA. Sometimes, due to time constraints and resource constraints you can handle large-size data. Time like this asks for sampling methods and approaches. Instead of using whole data in analysis, data analyst tends to find defect or abnormality in the sample. Then, based on this information from the sample, defect or abnormality the rate for whole dataset is considered. This process of inferring the information from sample data is known as ‘inferential statistics.’ 

Hypothesis testing is a part of inferential statistics which uses data from a sample to analyze results about whole dataset or population. Hypothesis testing is done to know the null hypothesis can be rejected or fail to reject. If the null hypothesis is rejected, then the research hypothesis can be accepted. If the null hypothesis is accepted, then the research hypothesis is rejected. 

Learning inferential statistics website: wallstreetmojo.com, kdnuggets.com 

Learning Hypothesis testing website: stattrek.com 

Start learning database design and SQL.

A database is a structured data collection that is stored and accessed electronically. File systems can store small datasets, while computer clusters or cloud storage keeps larger datasets. According to a database model, the organization of data is known as database design. The designer must decide and understand the data storage, and inter-relation of data elements. Considering this information database model is fitted with data. 

SQL stands for Structured Query Language. It is created for the recovery and control of data in a relational database. 

Database design basics with example: blog.devart.com 

SQL learning: w3schools.com 

Start Machine Learning

Machine learning is a part of artificial intelligence that concentrate on the utilization of data knowledge and algorithms to follow methods that human learns and moderately improves its accuracy. Machine learning has four key types as follows: 

• Supervised learning: In supervised learning, we introduce the algorithm’s product into the structure so that the system notices the patterns and trends before working on them. 
• Semi-Supervised learning: Semi-supervised learning utilizes an arrangement of a small quantity of labelled data and a large quantity of unlabelled data to develop models. 
• Unsupervised learning: The unsupervised learning technique is impressive for displaying correlation and understanding in unlabelled datasets. Models introduce input data with unspecified useful outcomes. 
• Reinforcement learning: The reinforcement learning technique in machine learning controls the best track or choice to select in condition to maximize the profit. Leading machine learning examples in daily life-like video games, utilize this approach. 

Machine learning website: machinelearningmastery.com 

You may also be interested in exploring data science online training. 

Learn by Working on Projects

Working on hands-on projects gives you a real understanding and learning of the topic. Hence it is always good to work on the project. Implementation of various tools and methods to gain more knowledge on data, find insights and convert insights into useful decisions. Hands-on projects also teach collaboration, workflow of process and different experiences with the problem statement.

Data science project cycle is composed of six phases: 

• Business understanding 
• Data understanding
• Data preparation 
• Modelling 
• Evaluation 
• Deployment 

This is the greater abstraction level of the Crisp-DM methodology, meaning one that can apply, with no exception, to all data problems. Know more about Kaggle for data science. 

Website for Projects: Kaggle 

Working on live projects gives you understanding how things work in industry. Know more in KnowledgeHut data science bootcamp training. 

Top 10 Data Science Websites to Follow

Now, as we have little understanding of data science, we will have a look at following topics to know more about data science and newer developments in it. 

Blogs

KDnuggets: It is one of the compelling and regularly updated sites for blogs on analytics, Data Science, Big Data and machine learning. It offers various blogs based on above mentioned technology in alphabetical order.

Datasets 

Amazon Datasets: All the dataset on Amazon is kept in AWS S3 which is an object storage service on the cloud platform. While using Amazon SageMaker datasets are quick to access and load.

Kaggle Datasets: It is an online community platform for data science enthusiasts. You can find the image dataset, time-series dataset, reviews, etc. All these datasets are totally free to download off Kaggle. 

Research Papers

Papers With Code: It is a great platform and free website for research papers on data science, machine learning, big data, etc. 

Repository

GitHub: It is a place to find detailed codes, architecture design. With GitHub, not only you can store your code but also use code from another user for your projects. Sharing your codes for everyone to access is also an integral part of GitHub. 

Projects

PyImageSearch: This is one of the best websites for computer vision projects. It also covers OpenCV and deep learning topics for computer vision projects. If you are a fan of computer vision projects and want to continue building more projects in this domain, it is highly recommended checking out the website for further study material, knowledge, and resources. 

Videos

YouTube: It is one of the best platforms to learn more about machine learning and Data science through videos. You can browse through various channels and binge watch some amazing videos that will inspire you and teach you practical knowledge and implementations in these fields. 

Channels

2 min papers: This YouTube channel explains newer technology research papers in easy context.

FreeCodeCamp: It is a YouTube channel for design, planning and implementation tutorials for projects in various languages.

Software 

TensorFlow: TensorFlow is go-o library for machine learning and artificial intelligence. It can be utilized across a range of problem but has a particular distinct on training and inference of deep neural networks. 

Keras: It is a library that helps with a python interface to learn, utilize artificial neural networks. Keres works as a configuration for the TensorFlow library.

Troubleshooting

Reddit, Quora: Resources mentioned in this point tend to serve a similar objective, i.e., interaction and involvement, and answers to several questions for clarifications. 

Publication medium

Towards Data Science: one of the biggest publications on Medium that is one of the best websites for the viewers to acknowledge thoughts, ideas, codes, and information related to Data Science, Machine Learning, Visualizations, computer vision, and so much more. It requires $5 a month, but still using various IDs you can access content for Free.

Code Snippets & explanations

Stack Overflow: Stack Overflow is a question-and-answer website for professional and enthusiast programmers. 

Stack Exchange: Stack Exchange is a network of question-and-answer websites on topics in diverse fields, each site covering a specific topic, where questions, answers, and users are subject to a reputation award process. 

Conclusion

This article covers various topics and their related websites for machine learning and data science. Every one of the resources mentioned here contains tons of content and valuable information that you can utilize for becoming better at the subject as well as benefitting from them by gaining further knowledge.

The resources for them are limitless, and in a future article, we will try to cover at least ten more such amazing and useful websites that can help you out on your Data Science journey!