Although you might not hear the term data science a lot these days (everything is about artificial intelligence), it is a very important skill. Data science is about extracting insights from data and making decisions through data analysis. It is the foundation for machine learning and has become increasingly important across many industries. Companies are collecting vast amounts of data, but they need professionals who can translate that data into meaningful insights and recommendations.
A strong foundation in data science is essential for many careers in different fields, including technology, finance, and healthcare. It takes great effort and study to master data science, but getting started is not hard.
The book Dive Into Data Science by Bradford Tuckfield aims to provide an accessible and engaging introduction to data science. It strikes a nice balance of explaining fundamental data science concepts and theories, while also equipping readers with hands-on practice with Python. No previous experience is required to get started with this book. Tuckfield guides you from basic ideas like collecting and exploring data, to more advanced machine learning techniques for classification, regression, clustering, and recommender systems. Along the way, Dive Into Data Science teaches you how to think like a data scientist.
Exploratory data analysis
Exploratory data analysis (EDA) is a crucial first step in any data science project. Before building models or developing algorithms, data scientists need to familiarize themselves with the data they are working with. Exploratory analysis helps uncover hidden patterns, insights, and anomalies that would otherwise be missed.
Dive Into Data Science provides a solid intro to EDA with Python libraries. You will learn to determine central tendencies and statistics with the pandas library and visualize data with Matplotlib and Seaborn. You’ll investigate correlations between different features, create heatmaps, and break down the data into subsets to further investigate it.
Dive Into Data Science uses the real-world example of a bicycle-sharing company. You have data on how many people are renting your bikes at different times of day. You must analyze the data to find relevant patterns that can help you make better business decisions.
Forecasting with simple models
Forecasting is one of the most important and valuable applications of data science. The ability to predict future trends based on historical data and patterns enables organizations to make better decisions. Data scientists use a variety of forecasting techniques like regression to predict future outcomes.
In Dive Into Data Science, you learn to forecast sales for a car dealership company. You have historical sales data and want to predict how many cars you’ll need to store for each month. Before doing the forecasting, you’ll prepare the data with Python. Data preparation includes cleaning and formatting data, handling missing values, duplicates, incorrect entries, etc.
Then you’ll plot the data and start forecasting future sales with simple linear regression models. Tuckerfield then shows you how you can try different techniques to improve your model’s performance while avoiding overfitting. You’ll finally compare your models and choose the best one to forecast sales.
Samples, populations, and testing
Hypothesis testing is a fundamental part of data science. You form hypotheses by making educated guesses about populations based on sample data. And then you use different techniques to determine whether the sample evidence supports or contradicts the hypothesis. Hypothesis tests are used to make inferences beyond the immediate data and reduce uncertainty. Hypothesis testing techniques are the basis of A/B tests, where data scientists propose hypotheses that one variant will outperform the other on some metric.
Dive Into Data Science teaches you these concepts through practical examples. You get to divide customers of a marketing campaign into different segments and compare them across different variables.
You’ll learn about populations, samples, confidence intervals, p values, statistical significance, and other data science and statistic concepts.
In the course of your journey, you’ll learn more Python libraries and functions and continue to visualize data and build models. You’ll also learn about some of the pitfalls and sensitivities of running statistical tests, such as how sample sizes affect statistical significance.
Machine learning and advanced topics
As you build your skills and knowledge, Dive Into Data Science introduces you to machine learning with Python libraries such as scikit-learn. You get to predict customer churn using logistic regression models, predict website ad revenue through supervised learning, use k-nearest neighbors to forecast article performance, and compare different machine learning algorithms such as decision trees, random forests, and artificial neural networks.
You’ll also get familiarized with unsupervised learning, where you need to figure out patterns in unlabeled data. You’ll use clustering techniques to group customers based on different characteristics.
Finally, you’ll learn some complementary skills such as web scraping with Python’s Beautiful Soup library, creating recommender systems through collaborative filtering, and an intro to natural language processing with word2vec.
Take the next steps in data science with Python
One thing I didn’t like about Dive Into Data Science was the explanations of basic Python programming concepts. I expect anyone who wants to get started with data science to know the basics of at least one programming language, preferably Python. I think the book spent too much space explaining Python installation or explaining things such as list comprehensions.
Dive Into Data Science is not a definitive guide and won’t make you a pro data scientist. But it packs a lot of information for 272 pages and is definitely a good place to start data science. If you want to go deeper into data science with Python, I suggest looking at Data Science From Scratch or Principles of Data Science.