Practicing Data science indeed a long term effort than a learning handful of skills. We ought to be academically good enough to take up this challenge. However, if you think you came a long way from your academic rebuilding, but you still have that zeal & passion to take the oil from the data and fill the skill gap of data science then here is the** warm-up** tips. Below points must **exercised **before jumping into any data science & data mining problems:

Not all datasets are in the form of a data matrix. For instance, more complex datasets can be in the form of sequences, text, time-series, images, audio, video, and so on, which may need special techniques for analysis. However, in many cases even if the raw data is not a data matrix it can usually be transformed into that form via feature extraction. A practical example of feature example is explained in my last post on scikit-learn library.

- Number of attributes defines the dimensionality of the data matrix. Save the dimensionality in mind when you think of any matrix operations.
- Each row may be considered as a d-dimensional column vector (all vectors are assumed to be column vectors by default). You must also understand the term row space and column space.
- Treating data instances and attributes as vectors, and the entire dataset as a matrix, enables one to apply both geometric and algebraic methods to aid in the data mining and analysis tasks. At least you must aware about unit vector, identity matrix etc..
- Clear dust from your school learning about matrix manipulation i.e. matrix addition, multiplication, transpose, inverse etc. Similar applies to some of the algebraic equation like distance between two points,
*Pythagorean theorem*—or*Pythagoras*‘*theorem etc..* - Through understanding on matrix manipulation will help you to implement multiplication and summation of elements.
- Leaving probability is probably not a good idea. Run through some short probability problems & exercise before you go in detail of any supervised learning models.
- You may need to practice on the topics that you mightily left during schools like:
*Orthogonal projection of vector*(projecting a vector to another vector),*Probabilistic view of the data, Probability density function*. (i admit to avoid these topics during graduations 🙂 ) - Relax yourself with all the formula of descriptive statistical analysis. From Mean, median, mode to normal distribution, standard deviation, skewness and most importantly don’t forget to cover-up Variance and standard deviation. You should be ready with basic statistical analysis of univariate & multivariate numeric data. Believe me distance finding methodologies change due to distribution of the data. (Using Euclidean distance score when data is normally distributed otherwise Pearson coefficient score)
- Generalization, Correlation & regression concepts are widely used across statistics and mathematical modeling. So this must be broadly rehearsed before you go into modeling techniques.
- You must do some exercise on how to normalize vector. Vector normalization is the must-to-know concept in prediction algorithms.

” In fact, data mining is part of a larger knowledge discovery process, which includes pre-processing tasks like data extraction, data cleaning, data fusion, data reduction and feature construction. As well as post-processing steps like pattern and model interpretation, hypothesis confirmation and generation, and so on. This knowledge discovery and data mining process tends to be highly iterative and interactive. “

**CRUX**: The algebraic, geometric & probabilistic viewpoints of data play a key role in data mining. You should exercise them beforehand. So you can easily sail though your boat in Data Science !