Programming Skills are no Longer an Essential Requirement for Data Science Beginners

Focus on using existing libraries and packages. Have some background on the math behind each package or library

Jan 2·4 min read

Image for post — Photo by Christopher Robin Ebbinghaus on Unsplash

There are so many good packages and libraries that can be used for building predictive models or for producing data visualizations. Some of the most common packages for descriptive and predictive analytics include:

Ggplot2
Pandas
Numpy
Matplotlib
Seaborn
Scikit-learn
Caret
TensorFlow
PyTorch
Keras

Thanks to these packages, anyone can build a model or produce a data visualization. With the availability of packages and libraries, programming skills are no longer an essential requirement for beginners in data science. However, to be successful in data science, it is important to have some basic programming knowledge in order to be able to use the available packages and libraries efficiently for building reliable and accurate models. In this article, we discuss some basic programming skills required for successful data science practice. We assume python as the default programming language, but the skills discussed here applies to any other programming language.

1. Basic Programming Skills

Data Types: Understand basic data types such as arrays, lists, tuples, dictionaries, and data frames.
Assignment statements
Function definition
Flow and Control: For example, be familiar with for and while loops.
Object-Oriented Aspects of Python: Understand python objects, parameters, methods, and attributes. Most machine learning libraries in Python are built using the object-oriented feature of python, for e.g. LogisticRegression() classifier.

2. Standard Libraries and Packages

You should be familiar with the following standard libraries and packages:

Pandas: For importing and exporting data into Python

Numpy: For working with arrays and matrices

Matplotlib, Seaborn, PyTorch: For producing various types of data visualization such as line graph, scatter plot, heat map, density plot, barplot, boxplot, etc.

Scikit-learn: For machine learning applications. Be familiar with the following estimators:

train_test_split() — for splitting dataset into train and test sets
StandardScalar() — for scaling features
SimpleImputer() — for performing simple data imputation using mean or median inputation
LinearRegression() — for building a model to predict a continuous target feature from predictor features in the dataset
LogisticRegression() — for building a model to predict a discrete target variable
SVC() Support Vector Classifier — for building a model to predict a discrete target variable
KNeighborsClassifier() — for building a model to predict a discrete target variable
Pipeline() — use to combine several estimators together

These estimators can be accessed using the code below:

from sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import StandardScaler
 
from sklearn.impute import SimpleImputerfrom sklearn.linear_model import LinearRegressionfrom sklearn.linear_model import LogisticRegressionfrom sklearn.neighbors import KNeighborsClassifierfrom sklearn.svm import SVCfrom sklearn.pipeline import Pipelinepipe_lr = Pipeline([('scl', StandardScaler()),('lr',    
                     LinearRegression())])

Other important regressors and classifiers included in the scikit-learn package include:

KNeighborsRegressor
Support Vector Regressor
Naive Bayes
Decision Tree

Scikit-learn also contains estimators for deep learning applications:

Keras
TensorFlow

3. Learning from the Python Documentation

The Python documentation is a very useful tool for learning Python commands. The documentation can be accessed using a question sign before a given command.

For example, to find out more about the pd.read_csv() method, we could use the code below.

import pandas as pd?pd.read_csv

This would take you to the Python documentation where you can learn more about the pd.read_csv() method, including its various adjustable parameters.

To find out more about the logistic regression classifier, one could use the following code to access the Python documentation file:

from sklearn.linear_model import LogisticRegression?LogisticRegression

This would open documentation that contains more information about the LogisticRegression classifier including a detailed explanation of all parameters and attributes.

It is important that when building a model using the scikit-learn library, you understand the various adjustable parameters for each estimator. Using default parameters will not always produce the optimal results. For example, logistic regression has the following parameters:

LogisticRegression(penalty ='l2',dual=False, tol=0.0001, C=1.0,    
                   fit_intercept=True, intercept_scaling=1, 
                   class_weight=None, random_state=None, 
                   solver='liblinear', max_iter=100, 
                   multi_class='ovr', verbose=0,
                   warm_start=False, n_jobs=1)

For logistic regression, the C parameter (regularization parameter) is very crucial, thus instead of using the default value, it’s good to tune your model performance for different C values before choosing the C value that yields optimal performance.

Real Case Studies with Code Included

The two case studies below illustrate how to focus on using available libraries and packages for data science and machine projects, instead of writing your own code from scratch:

Linear Regression Basics for Absolute Beginners

Machine Learning Process Tutorial

Summary and Conclusion

In summary, we’ve discussed the essential programming skills needed for data science practice. Thanks to the availability of libraries and packages such as numpy, pandas, matplotlib, scikit-learn, etc., programming skills are not longer an essential requirement for beginners in data science. Instead of focusing on hardcore coding skills, it is important to focus on using available libraries and packages for data visualization and machine learning. Anyone with some basic programming background can successful in data science. Becoming familiar with various packages and libraries using the python documentation is crucial. Without a thorough understanding, you’ll be using these packages as a blackbox, and this is very dangerous as it could lead to inefficient and inaccurate models.