Data Science Curriculum for self-study

Are you asking the question, "how do I become a Data Scientist?" This list recommends the best essential topics to gain an introductory understanding for getting started in the field. After learning these basics, keep in mind that doing real data science projects through internships or competitions is crucial to acquiring the core skills necessary for the job.

By Benjamin Obi Tayo, Ph.D., KDnuggets on February 26, 2020 in Advice, Data Science, Data Science Education, Data Visualization, Mathematics, Probability, Programming, Statistics

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Photo by Kelly Sikkema on Unsplash.

As a data science educator, many people interested in getting into data science have contacted me for guidance on how to get into the field of data science. This article will discuss the recommended topics that one has to study to build essential skills in data science.

The topics presented here, if studied thoroughly, will provide the minimum background needed to start doing data science. This curriculum could also be used for designing an introductory college-level course in data science.

Keep in mind that knowledge acquired from courses alone will not make you a data scientist. Course work has to be accompanied by a capstone project or an internship. Kaggle competitions can be used for capstones, as they provide an opportunity to work on real-world data science projects.

The following list presents essential topics to learn introductory Data Science.

1. Math Basics

(I) Multivariable Calculus

Most machine learning models are built with a dataset having several features or predictors. Hence familiarity with multivariable calculus is extremely important for building a machine learning model. Here are the topics you need to be familiar with:

Functions of several variables
Derivatives and gradients
Step function, Sigmoid function, Logit function, ReLU (Rectified Linear Unit) function
Cost function
Plotting of functions
Minimum and Maximum values of a function

(II) Linear Algebra

Linear algebra is the most important math skill in machine learning. A dataset is represented as a matrix. Linear algebra is used in data preprocessing, data transformation, and model evaluation. Here are the topics you need to be familiar with:

Vectors
Matrices
Transpose of a matrix
The inverse of a matrix
The determinant of a matrix
Dot product
Eigenvalues
Eigenvectors

(III) Optimization Methods

Most machine learning algorithms perform predictive modeling by minimizing an objective function, thereby learning the weights that must be applied to the testing data in order to obtain the predicted labels. Here are the topics you need to be familiar with:

Cost function/Objective function
Likelihood function
Error function
Gradient Descent Algorithm and its variants (e.g., Stochastic Gradient Descent Algorithm)

2. Programming Basics

Python and R are considered the top programming languages for data science. You may decide to focus on just one language. Python is widely adopted by industries and academic training programs. As a beginner, it is recommended that you focus on one language only.

Here are some Python and R basics topics to master:

Basic R syntax
Foundational R programming concepts such as data types, vectors arithmetic, indexing, and data frames
How to perform operations in R including sorting, data wrangling using dplyr, and data visualization with ggplot2
R studio
Object-oriented programming aspects of Python
Jupyter notebooks
Be able to work with Python libraries such as NumPy, pylab, seaborn, matplotlib, pandas, scikit-learn, TensorFlow, PyTorch

3. Data Basics

Learn how to manipulate data in various formats, for example, CSV file, pdf file, text file, etc. Learn how to clean data, impute data, scale data, import and export data, and scrap data from the internet. Some packages of interest are pandas, NumPy, pdf tools, stringr, etc. Additionally, R and Python contain several inbuilt datasets that can be used for practice. Learn data transformation and dimensionality reduction techniques such as covariance matrix plot, principal component analysis (PCA), and linear discriminant analysis (LDA).

4. Probability and Statistics Basics

Statistics and Probability is used for visualization of features, data preprocessing, feature transformation, data imputation, dimensionality reduction, feature engineering, model evaluation, etc. Here are the topics you need to be familiar with:

Mean
Median
Mode
Standard deviation/variance
Correlation coefficient and the covariance matrix
Probability distributions (Binomial, Poisson, Normal)
p-value
Baye’s Theorem (Precision, Recall, Positive Predictive Value, Negative Predictive Value, Confusion Matrix, ROC Curve)
A/B Testing
Monte Carlo Simulation

5. Data Visualization Basics

Learn essential components of a good data visualization. A good data visualization is made up of several components that have to be pieced up together to produce an end product:

a) Data Component: An important first step in deciding how to visualize data is to know what type of data it is, e.g., categorical data, discrete data, continuous data, time-series data, etc.

b) Geometric Component:Here is where you decide what kind of visualization is suitable for your data, e.g., scatter plot, line graphs, bar plots, histograms, Q-Q plots, smooth densities, boxplots, pair plots, heatmaps, etc.

c) Mapping Component:Here, you need to decide what variable to use as your x-variable and what to use as your y-variable. This is important, especially when your dataset is multi-dimensional with several features.

d) Scale Component:Here, you decide what kind of scales to use, e.g., linear scale, log scale, etc.

e) Labels Component:This includes things like axes labels, titles, legends, font size to use, etc.

f) Ethical Component: Here, you want to make sure your visualization tells the true story. You need to be aware of your actions when cleaning, summarizing, manipulating, and producing a data visualization and ensure you aren’t using your visualization to mislead or manipulate your audience.

Important data visualization tools include Python’s matplotlib and seaborn packages, and R’s ggplot2 package.

6. Linear Regression Basics

Learn the fundamentals of simple and multiple linear regression analysis. Linear regression is used for supervised learning with continuous outcomes. Some tools for performing linear regression are given below:

Python: NumPy, pylab, sci-kit-learn

R: caret package

7. Machine Learning Basics

a) Supervised Learning (Continuous Variable Prediction)

Basic regression
Multi regression analysis
Regularized regression

b) Supervised Learning (Discrete Variable Prediction)

Logistic Regression Classifier
Support Vector Machine (SVM) Classifier
K-nearest neighbor (KNN) Classifier
Decision Tree Classifier
Random Forest Classifier
Naive Bayes

c) Unsupervised Learning

Kmeans clustering algorithm

Python tools for machine learning: Scikit-learn, Pytorch, TensorFlow.

8. Time Series Analysis Basics

Use for a predictive model in cases where the outcome is time-dependent, e.g., predicting stock prices. There are 3 basic methods for analyzing time-series data:

Exponential Smoothing
ARIMA (Auto-Regressive Integrated Moving Average), which is a generalization of exponential smoothing
GARCH (Generalized Auto Regressive Conditional Heteroskedasticity), which is an ARIMA-like model for analyzing variance.

These three techniques can be implemented in Python and R.

9. Productivity Tools Basics

Knowledge on how to use basic productivity tools such as R studio, Jupyter notebook, and GitHub, is essential. For Python, Anaconda Python is the best productivity tool to install. Advanced productivity tools such as AWS and Azure are also important tools to learn.

10. Data Science Project Planning Basics

Learn basics on how to plan a project. Before building any machine learning model, it is important to sit down carefully and plan what you want your model to accomplish. Before delving into writing code, it is important that you understand the problem to be solved, the nature of the dataset, the type of model to build, how the model will be trained, tested, and evaluated. Project planning and project organization are essential for increasing productivity when working on a data science project. Some resources for project planning and organization are provided below.