Start Your Data Science Journey with these Books and Courses
Every type of research requires data. The data could be primary data (collected by you) or secondary data (collected by someone). In the research process, it is always recommended first to build your hypothesis (what are you expecting from the data?) and then plan and collect it. Once you have data, then the exciting part comes, which is the most vital one, i.e., preparing the data for analysis (cleaning and wrangling). It is typically called data engineering in data science. In addition, you require some techniques essential for extracting vital information from the datasets, which is called statistical analysis.
In the past, the whole process was carried out by researchers using traditional software programs. I'm a transportation researcher and I have used statistical analytics tools like Excel, SPSS, Stata, Minitab, and so on. These software programs are good, and I’m still using them, but they are expensive and not flexible to work with. Here come the open-source programming languages that bridged these gaps. Currently, one can utilize a data science-friendly programming language like R, Python, and Julia to analyze and retrieve meaningful results from data. They are absolutely free, fast, user-friendly, and, most importantly, reproducible, which is most important for research work.
As a transportation researcher, I have to perform numerous data analyses (statistical analysis) on collected datasets to retrieve meaningful results. I have been learning data science since 2017. Since then, I have read numerous books and completed numerous courses on various topics. I have gone through many ups and downs in the learning process. After almost four years of learning, I thought I should share the learning steps one should take if he/she starts it from scratch in the year 2023. Based on my experience here (through this blog), I will share the books and courses you need to go through that will give you a hands-on experience on the topic called "Data Science."
If I have to start it from the beginning, I will definitely start with “Python” (in my opinion).
Why…???? My answer will be, "it is easy to learn."
First, one needs to have a data-crunching tool like Python to dive into data science. Let's begin the journey……..
1. A Whirlwind Tour of Python
“A Whirlwind Tour of Python” is the best book I came across to learn and revise the basics of python programming language. The book contains the parts you require to be proficient in Python programming. This book will help you to grasp python’s basic concepts, which include variables & objects, operations, built-in data types & data structures, control flow (if, elif, else, loops, and so on), functions, and error & exception handling. It also covers some higher-level concepts that you will need later (during data analysis), like iterators, list comprehensions, generators, modules & packages, and string manipulation (regular expressions).
2. 100 Days of Code: The Complete Python Pro Bootcamp for 2022
Udemy Course Link: 100 Days of Code: The Complete Python Pro Bootcamp for 2022
Once you have a basic understanding of the python language and its syntax, then you can go for the following course on Udemy, “100 Days of Code: The complete Python Pro Bootcamp for 2022”. I am recommending this course because of Dr. Angela’s wonderful teaching style and carefully curated projects. This course offers python beginners advanced modules. It will give you confidence and help you learn how to build intuition for a problem to solve it structurally. This course also teaches you about python Object-Oriented Programming (OOP) and how to use it for solving large projects (💡 like building a game).
3. Data Structures & Algorithms — Python
Udemy Course Link: : Data Structures & Algorithms — Python
Initially, when I started learning Python and Data Science, I thought that Data Structures and Algorithms were for software engineers. But over the year, I realized that it is also important for Data Scientists and Analysts. Because it teaches how to think about a problem to solve it step by step using optimized codes.
I have gone through various courses, but I liked the most that teach DS and Algorithms using visual and animated graphics. The course “Data Structures & Algorithms — Python” will give you an excellent visual and code-based introduction to various Data Structures and Algorithms using Python.
4. The Git & Github Bootcamp
Udemy Course Link: The Git & Github Bootcamp
Before entering the world of data science, I will recommend learning git and GitHub (version control). This will give you an extensive understanding of how large projects are developed in a collaborative manner. The course “The Git & Github Bootcamp” by Colt Steele will help you learn how to track projects, monitor changes, and get back to a previous change. In one sentence, “git is a powerful tool”.
The course will teach you how Git works behind the scenes, Git workflow (adding & committing), git merging, and resolving merge conflicts. It also covers advanced topics like local and remote repositories, squash, clean up, rewrite history, writing Git aliases, hosting static websites, working with git branches, undo changes (using git restore, git revert, git reset), and workflows (pull requests, “fork & clone”, etc.).
5. Pandas 1.x Cookbook
Once you get familiar with the python programming language, its syntax, and git, the next step will be to enter the world of data science. It is recommended to start with the book called “Pandas 1.x Cookbook”. The book will teach you about the Pandas (a data manipulation library). It will teach you how to load datasets, clean data, impute missing values, and retrieve meaningful results from datasets. In a word, it is the book that you need to clean and prepare data.
This book covers the concept of DataFrame, exploratory analysis, data subset selection, filtering rows, groupby, aggregation, restructuring data into a Tidy form, and combining pandas objects. Further, it also covers the Date and Time data manipulation (for time series analysis), data visualization (using matplotlib, pandas, and seaborn libraries), and debugging and testing.
6. Google Sheets — The Comprehensive Masterclass
Udemy Course Link: Google Sheets — The Comprehensive Masterclass
Even though learning python libraries are good enough for data analysis, you should learn at least one spreadsheet, which will help you shape your skills further. I will recommend learning “google sheets”, which is free, and the cloud-based integration and sharing make it outstanding for collaborative work. The course “Google Sheets — The Comprehensive Masterclass” by Leila Gharani is one of the best courses available on the internet.
This course covers Sorting, Filtering, creating filter views and cleaning data. Additionally, it will give you a hands-on detailed overview of different functionalities such as VLOOKUP, INDEX & MATCH, FILTER & SORTN and aggregation functions (SUM, COUNT, SUMIFS, COUNTIFS). Further, it also teaches advanced topics like Pivot tables, Charts & Slicers, macros & Google Apps Script.
7. Complete Course on Data Visualization, Matplotlib and Python
Udemy Course Link: Complete Course on Data Visualization, Matplotlib and Python
Once you understand how to manipulate data (prepare data for analysis), the next step will be to get a command of the basic python visualization libraries. The “Complete Course on Data Visualization, Matplotlib and Python” course will give you an extensive overview of Matplotlib and Seaborn python visualization libraries. This course will teach you the intuition of how matplotlib built different types of plots from scratch.
The course covers basic to advance line plots, bar plots, heatmaps, box plots, violin plots, grid plots, pair plots, and faceted plots using catplot and linear model plots. Further, it teaches how to fine-tune different components of a plot to make it publication-ready. Once you learn these libraries, you can build almost any plot to present useful insights.
8. The Hitchhicker’s Guide to Plotnine
Once you get the idea of matplotlib and seaborn, the next step is to get familiar with a low-code standard library which will help you generate publication-ready statistical plots with few lines of code. The plotnine library is the python implementation of the popular library “ggplot2”. The ggplot2 is a plotting library created by Hadley Wickham for the “R programming language”. The plotting syntax is based on the concept of the grammar of graphics.
The book, “The Hitchhicker’s Guide to Plotnine”, provides a systematic step-by-step syntax for plotting a variety of plots. It covers line plot, area plot, bar plot, stacked bar plot, scatter plot, weighted scatter plot, histogram, density plot, function plot, box plot, linear regression plot, and lowess plot.
9. Map Academy: Get mapping quickly, with QGIS
Udemy Course Link: Map Academy: Get mapping quickly, with QGIS
Next, you should add the geospatial data visualization capabilities to your bucket list. It will help you present data on maps and help you understand the spatial variabilities across regions and countries, like transportation demand over various metro stations, COVID-19 spread and recovery, or the impact of natural disasters across states. For this, I will recommend the course “Map Academy: Get mapping quickly, with QGIS”, which will step by step teach you the open-source mapping and analytics software tool called QGIS. This course will give you hands-on experience using different real-world projects.
10. Master statistics & machine learning: intuition, math, code
Udemy Course Link: Master statistics & machine learning: intuition, math, code
Once you get the idea about data manipulation and visualization, then you are ready to jump into the world of statistics. This will help you retrieve meaningful results from data and pave the path for statistical modeling, as it is the backbone of most machine learning algorithms. The course “Master statistics & machine learning: intuition, math, code” by Mike X Cohen is one of the hidden gems which will give you the idea behind different statistical approaches used today for data analysis.
It will teach you about different visualizations, descriptive statistics, inferential statistics, data normalization and outliers, probability theories, hypothesis testing, T-tests, confidence intervals, correlation, ANOVA, regression, statistical power and sample size calculation, clustering, dimension reduction, all using Python.
In addition, I would recommend visiting these great YouTube channels to learn/revise statistical concepts.
11. Practical Statistics for Data Scientists
Once you get the fundamental ideas behind different statistical concepts, you are ready to know how real-life data scientists use statistics to solve real-world problems. What concepts are essential for data science problem-solving, and which ones do you need to know in detail? The book “Practical Statistics for Data Scientists” gives you a hands-on experience with practical statistics for solving real-world problems.
The book covers exploratory analysis, data sampling distributions, statistical elements, significance testing, regression, classification models, their functionalities behind the scenes, and various statistically supervised and unsupervised machine learning algorithms.
12. Bayesian Statistics the fun way
As you are familiar with the statistical concepts used in data science; now it is time to learn some advanced concepts. It is time to think and analyze data from a Bayesian perspective. I will 100% recommend the book “Bayesian Statistics the fun way” which will give you a taste of Bayesian statistics. This book starts with basic statistics and proceeds step by step toward the exciting part, like solving problems in a Bayesian way. This book would motivate you to think about different problems in a Bayesian way. It presents various Star Wars examples, making the learning process fun and fascinating. Finally, it will lay the foundation for Bayesian A/B testing for solving real-world problems.
13. Python Regular Expressions Complete Masterclass
Udemy Course Link: Python Regular Expressions Complete Masterclass
Next, I would recommend everyone learn the pattern-matching concept using regular expressions that help you identify patterns in your data. It is especially useful for detecting patterns in text data and will help you clean your messy data. Pandas support regular expression, which will give you an additional advantage.
The course “Python Regular Expressions Complete Masterclass” by Mihai Catalin Teodosiu is one of the best courses available on the internet that teach you almost everything about regular expressions. It covers methods and objects, metacharacters & special sequences, extension notation & assertion, projects on validating user account details, and how to use it for Excel files and for filtering data scrapped from HTML pages.
14. The Ultimate MySQL Bootcamp: Go from SQL Beginners to Expert
Udemy Course Link: The Ultimate MySQL Bootcamp: Go from SQL Beginner to Expert
Companies store their day-to-day generated data in databases. As a data analyst/scientist, you have to query data from databases to perform data analytics and retrieve meaningful results that will drive business decisions. Thus, you need to learn at least one database. My recommendation will be to start with MySQL, which is mature, stable, and very popular.
I would recommend going for the course “The Ultimate MySQL Bootcamp: Go from SQL Beginners to Expert” by Colt Steele and lan Schoonover. The course teaches generating databases and tables, data insertion, CRUD (Create, Read, Update, Delete) commands, string functions, selection, aggregation, data types, logical operations, and joining (one to many and many to many). It also contains projects that will give you a hands-on experience with MySQL.
15. Complete linear algebra: theory and implementation in code
Udemy Course Link: Complete linear algebra: theory and implementation in code
Before you enter the world of machine learning, you must know the basics of linear algebra if you really want to understand the underlying concepts and want to implement them from scratch. I would recommend the course “Complete linear algebra: theory and implementation in code” by Mike X Cohen.
The course covers a wide variety of topics like theoretical concepts in linear algebra, proofs, application of linear algebra on real-world datasets, applying and solving linear algebra problems using Python (vectors and matrix), and the maths underlying most of AI (artificial intelligence) concepts.
16. Python Feature Engineering
Before you jump into the machine learning part, you should have an idea about what format of data is acceptable for model building. How to extract relevant features from datasets to improve the model’s prediction accuracy. The book “Python Feature Engineering” by Soledad Galli teaches you the entire process of feature engineering.
The book covers different techniques of missing value imputation, encoding categorical variables, transforming numerical variables, performing variable discretization, handling outliers, and deriving features from dates and time variables. Further, it also covers feature scaling, applying mathematical computations to features, creating new features using transactional and time series data, and feature extraction from text data.
17. Machine Learning Bookcamp
Once you get the idea of feature engineering, then I would recommend getting familiar with different machine learning algorithms. The book “Machine Learning Bookcamp” will introduce you to the world of intelligent machines and systems using practical real-world projects.
This book will give you an intuitive overview of the concepts with minimal theory and more practical production-ready examples. You will be introduced to a wide variety of ML algorithms. It will introduce you to regression and classification algorithms (linear regression, logistic regression, decision trees, ensemble learning, and deep learning) using popular ML libraries, such as scikit-learn, and Keras.
18. Feature Selection for Machine Learning
Train in Data Course Link: Feature Selection for Machine Learning
Book: Feature Selection in Machine Learning with Python
The next step after learning feature engineering and ML algorithms will be to identify influential variables. The course “Feature Selection for Machine Learning” will help you select the most important predictive variables for your model from a set of features (if an enormous set of features exist in your dataset), as some of them will be identical or very similar, some of them are not predictive at all.
The course will cover how to remove features with low variance, identify redundant features, then features selection based on statistical tests, and select features based on changes in model performance.
19. A deep understanding of deep learning (with Python intro)
Udemy Course Link: A deep understanding of deep learning (with Python intro)
The next step will be to learn the most popular ML algorithm, deep learning. To start your journey with deep learning, first, you should learn the concepts from scratch. The course “A deep understanding of deep learning (with Python intro)” by Mike X Cohen on Udemy will give you a hands-on experience with the algorithm using the most popular library PyTorch.
The course starts with a short refresher course on python, then proceed to the artificial neural networks. The course will cover gradient descent, ANN, overfitting and cross-validation, regularization, activations and optimizers, feedforward networks, measurement of model performance, autoencoders, and running models using GPU. In addition, it also covers advanced concepts like Convolutional Neural Networks (CNN), style transfer, generative adversarial networks, Recurrent Neural Networks, and ethics of deep learning.
20. Deep Learning with Python
After learning the deep learning concepts and their implementation using PyTorch, I will recommend learning the low code implementation deep learning library called “Keras”. The book “Deep Learning with Python” written by Francois Chollet, is one of the best books out there that will give you hands-on experience on low code deep learning model implementation using python. Even though PyTorch is very popular, and learning that will be enough, I would still recommend you add this library to your knowledge bucket.
21. Hyperparameter Optimization for Machine Learning
Train in Data Course Link: Hyperparameter Optimization for Machine Learning
Most ML algorithms have a set of hyperparameters. Identifying the set of stable hyperparameters that increases model performance on unknown datasets is the objective of a predictive model. The course “Hyperparameter Optimization for Machine Learning” covers all the techniques required to identify the best set of hyperparameters.
This course will teach you the concept of cross-validation and nested cross-validation for optimization, Grid search and Random search, Bayesian Optimization, Tree-structured Parzen estimators, SMAC, Population-Based Optimization, and other SMBO algorithms. It also covers how to implement them with open-source libraries, including Hyperopt, Optuna, Scikit-optimize, Keras Turner, and others.
22. Machine Learning with Imbalanced Data
Train in Data Course Link: Machine Learning with Imbalanced Data
Typically, we use accuracy as an evaluation metric, but in practice, “accuracy” evaluation metric is useful when your data (the outcome variable) is balanced (contains an equal proportion of class labels). But in practice, you will mostly encounter an imbalanced data set.
This course, “Machine Learning with Imbalanced Data” by Soledad Galli, will help you to build an excellent predictive model with imbalanced data. The course will teach you different evaluation metrics and their application scenario. It also covers undersampling, oversampling, ensemble methods to handle imbalance datasets, cost-sensitive methods which penalize wrong decisions, and probability-based calibration.
23. Forecasting Time Series Data with Facebook Prophet
Now you are familiar with the Machine learning algorithms and codes. The next step is to learn Time Series forecasting. The book “Forecasting Time Series Data with Facebook Prophet” will guide you through basic to advanced time series forecasting using one of the wonderful libraries called “prophet” which was open-sourced by Meta.
This book will guide you through various concepts of time-series forecasting and its real-world use cases. It will cover seasonalities (hourly, daily, monthly, yearly, and custom), seasonality modes (additive and multiplicative), holidays (national, state, and custom), different growth modes (linear, logistic, and flat), trend change points, adding and analyzing regressors, handling outliers and special events, estimating uncertainty intervals, model diagnosis and evaluation, cross-validation, performance metrics, and productionizing prophet.
24. Feature Engineering for Time Series Forecasting
Train in Data Course Link: Feature Engineering for Time Series Forecasting
Once you get familiar with the concepts of time series forecasting the next step would be to get more expertise in this subject area. Feature engineering for time series data is another skill set that makes your forecasting models more robust.
The course “Feature Engineering for Time Series Forecasting” by Soledad Galli will guide you through converting time series into tabular data, providing a solid foundation for predictive analytics. Additionally, it will help you predict multiple future steps with confidence, whether through direct or recursive forecasting methods. This course will equip you with effective techniques to handle missing data through strategic imputation, ensuring the robustness of your models. It will also delve into the art of feature extraction from past data, leveraging lags and windows to enhance predictive accuracy. You will learn invaluable strategies to identify and manage outliers, encode categorical variables, and extract meaningful insights from date and time features.
25. Python OOP Course: Master Object-Oriented Programming
Udemy Course Link: Python OOP Course: Master Object-Oriented Programming
Once you are familiar with the details of python, different packages, and their modules, you can start writing wonderful custom codes and make them available to millions of Python users through PyPI or Conda. Here comes Python’s Object-Oriented Programming part. The course “Python OOP Course: Master Object-Oriented Programming” will help you learn the details of OOP concepts. You may use this knowledge to create your own python packages. It gives you the fundamental knowledge of how Python scripts, modules, and packages form complex projects like Pandas, Matplotlib, and Scikit-learn. This course covers the concept of classes and objects, class attributes vs instance attributes, class methods vs instance methods vs static methods, encapsulation, inheritance, and magic methods.
26. Applied Data Science using Pyspark
My next recommendation will be to learn big data handling frameworks like PySpark. The python libraries can handle a limited volume of data, which is enough for almost all fundamental data analysis. But think about big companies, at present, companies (small ones) generate terabytes of data every day. So, to handle 100 terabytes of data, you need something fast, reliable, and scalable where PySpark comes into action.
PySpark is the Python API for using Apache Spark, which is a parallel and distributed engine used to perform big data analytics. In the era of big data, PySpark is extensively used by Python users for performing data analytics on massive datasets and building applications using distributed clusters.
The book “Applied Data Science using Pyspark” will give you hands-on experience on how to perform data analysis on large volumes of data. The book teaches you the basics of PySpark, utility functions and visualization, variable selection, supervised machine learning algorithms, model evaluation, unsupervised ML and recommendation algorithms, machine learning flow and automated pipelines, and deployment of machine learning models.
Note 1: The above books or courses will show you the tools and sometimes show you the path for solving a problem, but it won’t make you a good data analyst or data scientist. The key to become best at data science is to apply and solve real-world projects/problems. This will give you the real taste and illustrate the extend of real-world problems. Solving them with the tools you have learned will make you proficient and extend your problem solving capabilities, and ultimately makes you good at solving data science problems.
Note 2: I am not promoting any book, course, author, or organization. The recommended list (books and courses) is entirely based on my personal experience.
Note 3: The above list is dynamic. If I encounter a better book or course in future then I will add/update it in the existing list.
I hope you learned something new!
