Data professionals are widely sought after in various industries due to the increasing reliance on data analysis for decision-making and AI applications. As such, aspiring data scientists should equip themselves with several key skills concerning data extraction and analysis. This handy collection of how-to guides offers extensive tutorials on mastering SQL, Python, data cleaning and processing techniques, data wrangling with Python and Pandas, and exploratory data analysis.
The first guide presents an in-depth approach to mastering SQL, a language crucial for working with databases. It touches on fundamentals like SQL commands, sorting, joins, subqueries, and window functions and emphasizes the practical application of SQL in solving business problems. Online platforms, such as HackerRank and PGExercises, are recommended for practice and for data science interview preparation.
Next, the guide on Python delves into the process of picking up this popular programming language, from learning the basics online to leveraging Python libraries for data analysis, machine learning, and web scraping. Hints on using coding practice projects to showcase Python skills in an online portfolio are shared, alongside free and paid resource recommendations at every step.
The third guide covers data cleaning and preprocessing – necessary parts of any data science project. Evolutionary data analysis, addressing missing values and outliers, classifying data in training and test sets, feature scaling, and handling encoding categorical features and imbalanced data are some of the topics discussed. Example codes using Python libraries like Pandas and scikit-learn help illustrate these preprocessing tasks.
The fourth guide addresses data wrangling with Python and Pandas – the process of reshaping and preparing raw data for analysis. It starts with Python fundamentals, SQL, and web scraping before moving on to loading, filtering, exploring, cleaning, and combining datasets. It recommends building an interactive data dashboard using Streamlit to show off data analysis skills.
Lastly, the guide on exploratory data analysis (often the first step in an analysis process) explains using Python for data collection, cleaning, visualization to recognize patterns and outliers, and conducting univariate, bivariate, and multivariate analysis to detect relationships between variables.
The process of becoming a capable data scientist entails mastering SQL and Python, maintaining high-quality datasets through informed cleaning techniques, properly wrangling data with Pandas, and conducting thorough exploratory data analysis. After acquiring these skills and gaining experience through projects (begin with simple ones before tackling complex tasks), share your experience through platforms like Medium and continue keeping abreast of emerging techniques to hone skills.
The collection of guides is shared by Abid Ali Awan, a certified data scientist with a Master’s degree in technology management and a bachelor’s degree in telecommunication engineering. He regularly writes technical blogs on AI and data science. His future goal is helping students suffering from mental illness using an AI product built on a graph neural network.
