What Data Science Concepts Are Very Hard to Learn Yourself?

Data science is a multifaceted field that encompasses a wide array of techniques and concepts. While some aspects, such as statistical features and probability distributions, may be more straightforward to grasp, others present significant challenges. This article delves into some of the most difficult data science concepts and explores strategies for tackling them effectively.

Data Cleaning and Database Management

Data science begins with clean, reliable data. Managing a database efficiently is crucial, and it involves more than just storing data. Understanding how fields, tables, and keys connect is vital. Ensuring that your data is well-organized and meets type and edit checks is essential, as improperly formatted data can cause system crashes.

Challenge: Dealing with poorly structured databases where fields such as Customer-Number and Cust-Number may be inconsistently used in multiple tables. This inconsistency can lead to confusion and errors.

Solution: A structured approach is necessary. Begin by documenting your database schema, including all tables, keys, field names, and types. Dumping your schema and storing it in a tool like MS Access can be helpful for running reports and performing lookups on table keys and fields.

Dimensionality Reduction

Dimensionality Reduction is a key concept in data science, especially when working with high-dimensional data. The technique involves reducing the number of feature variables to improve model performance.

Challenge: Understanding and implementing essential techniques like Principal Component Analysis (PCA), which creates vector representations of features to show their importance to the output.

Solution: Familiarize yourself with the underlying mathematics and the intuition behind PCA. Practical implementation can be done using various libraries in Python, such as scikit-learn, which provide robust tools for performing PCA and other dimensionality reduction techniques.

Over and Under Sampling

Over and under sampling are important techniques for balancing class distributions in classification problems. Over-sampling involves duplicating minority class instances, while under-sampling selects a subset of the majority class instances.

Challenge: Choosing the right strategy, especially when the class imbalance is significant. The wrong approach can lead to biased models that do not accurately represent the minority class.

Solution: Start with a basic understanding of where over and under sampling can be applied. Implement these techniques using libraries like imbalanced-learn in Python. It's often beneficial to experiment with different sampling strategies and evaluate their impact on model performance.

Bayesian Statistics

Bayesian Statistics is a powerful but often misunderstood concept in data science. Unlike frequentist statistics, which relies on empirical evidence, Bayesian statistics incorporates prior knowledge to make inferences.

Challenge: Grasping the principles of Bayesian inference, particularly the role of prior and posterior distributions, can be challenging.

Solution: Start with the basics of probability and then move on to Bayesian principles. Utilize resources such as online courses, textbooks, and tutorials. Practical experience, such as applying Bayesian models to real-world problems, will reinforce your understanding.

Statistical Features

Statistical features are fundamental in data exploration. Common techniques like bias, variance, mean, and median are easily understood and implemented in code.

Challenge: Interpreting complex statistical concepts in the context of data science.

Solution: