Data science Beginners Guide
Hi Dataaspirant,
Looking for a decent start in data science and thinking the same question again and again
Where to start?
Then this page is especially for you. Learn the basic concepts to get into the data science world. We were continuously updating this page with our fresh content.
We organized all our posts by keeping in the mind to make the beginner’s learning journey happy. If you were looking for a particular data science concept, do let us know in the comments section. So we will write a post on the concept you suggested.
We wish you a happy learning.
If you want to share your learning through a blog article, feel free to check out our Join us page to become part of the dataaspirant family.
Learn Data Science concepts from dataaspirant data science beginners guide. Share on X
Quick Start Concepts
Projects
Introductory Concepts
- Introduction to data mining techniques
- Supervised and Unsupervised learning algorithms
- Difference between classification and Prediction techniques
- Classification and Clustering Algorithms
- Feature selection techniques in R
- How to handle imbalanced datasets in machine learning
Online Courses
- Four most popular Coursera data science specializations
- Most popular and high rated data science courses in udemy
- Online courses list
Libraries/Packages Installation
- List of python machine learning packages and installation commands
- Python machine learning libraries virtual environment setup
Regression Techniques
- Introduction to Regression Analysis
- Simple linear regression implementation in python without using any machine learning libraries
- Linear Regression implementation in Python
- Difference Between R-Squared and Adjusted R-Squared Methods
Classification Techniques
- Introduction to Knn Classifier
- Knn classifier implementation in python without any machine learning library
- Knn classifier implementation in R without any machine learning library
- Knn classifier implementation in python with Scikit-Learn
- Knn classifier implementation in R with Caret package
- How the logistic regression model works in machine learning
- Difference between Softmax and Sigmoid functions
- How multinomial logistic regression works in machine learning
- Implementing Logistic regression model in Python for binary classification
- Building Multinomial logistic regression model in Python
- Introduction to Support vector machine classifier
- Svm classifier implementation in R with Caret package
- Svm classifier implementation in python with Scikit-Learn
- Introduction to Decision Tree
- Decision Tree classifier implementation in python with Scikit Learn
- Decision Tree classifier implementation in R with Caret package
- Visualizing the trained Decision tree classifier in Python
- Introduction to Naive Bayes Classifier
- Gaussian Naive Bayes classifier implementation in Python
- How to perform principal component analysis in R
- Introduction to Random Forest Algorithm
- Random Forest classifier implementation in python
Classification Models Evaluation Metrics
Clustering Techniques
Reinforcement Learning Techniques
- Introduction to Reinforcement Learning
- How to use Q learning in Reinforcement learning
- Markov Chain Monte Carlo for airport network Simulation
Recommendation Engine Concepts
- Introduction to Recommendation Engine
- Most popular similarity measures implementation in Python
- Collaborative Filtering Recommendation engine implementation python
Extra Concepts
- How to save the trained Scikit-Learn Models with Python Pickle
- How to create histograms in R
- How to select the best features to use in the model with R
- How to become the better R programmer
Deep Learning Concepts
- Handwritten Digits recognization with Google Tensorflow
- How to handle overfitting in while building deep learning models
- Popular Data Augmentation Technique in Deep Learning
- Basics Of Neural Networks
- Popular Activation Functions in Deep Learning models
Natural Language Processing Concepts
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Continuously we will update this page. If you have any questions then feel free to comment below. If you want me to write on one specific topic then do let me know in the comments below.
Hi, I am trying to see the courses that u have suggested . But the link is broken..
Can u help
Thanks will check.
https://dataaspirant.com/data-mining – first lesson in the learning page itself is not working. 404 Error. Please check ASAP
Hi Viswa,
Thanks a lot, Issue with the link, Now it’s working please have a look.
Thanks and happy learning!
should non-programmer go for data science course?
Hi Atul,
My personal suggestion is to try with learning python first.
Hiii,
I’m interested in learning datascience in R.From where i have to start? Suggest some materials and resources.
Hi Arumugaraj,
If you are interested in learning data science in R, first spend some time on learning the R programming language. Then you start fine tuning the machine learning model in R. You can learn the R programming language from any online tutorials or any MOOC courses.
Hi Saimadhu Polamuri,
Could you please tell me ,how can we practice this data mining techniques. As a beginner to data science I’m asking what type of setup we should have like which programming language is best,which tool etc.
Thanks,
Shafi.
Hi Shafi,
We are glad for your interest in data mining.
First, spend some time on the basics of Python programming language. Once you are comfortable with Python, You can start to learn the basic machine learning concepts.
pls provide detail data scince course
Hi Shyam Sundar,
Thank your suggestion. We had added most popular data science courses and in details about each course. Please have look and leave your suggestion.
https://dataaspirant.com/data-science-courses/