Data Science: Data Engineering, Analytics, and Machine Learning

This course provides a comprehensive introduction to the interdisciplinary field of data science, bridging computer science, statistics, and domain expertise. You will learn how to extract, clean, and manipulate messy datasets, uncover patterns through exploratory data analysis, build predictive statistical models, and communicate data-driven insights effectively. This curriculum equips you with the modern technical stack required to solve complex problems using data.

Core Topics

• Data Wrangling and Cleaning: Importing data from diverse sources (CSV, SQL databases, APIs), handling missing values, filtering outliers, merging datasets, and reshaping data using libraries like Python’s pandas.

• Exploratory Data Analysis (EDA) and Visualization: Uncovering underlying distributions, checking correlations, and building insightful visual narratives using tools like Matplotlib and Seaborn.

• Applied Probability and Statistics for Data Science: Hypothesis testing, probability distributions, confidence intervals, and statistical feature selection.

• Introduction to Machine Learning: Supervised learning (linear and logistic regression, decision trees, random forests) and unsupervised learning (K-means clustering, dimensionality reduction).

• Data Storytelling and Ethics: Communicating technical findings to non-technical stakeholders through dashboards and reports, while navigating data privacy, bias, and ethical considerations.

FAQ

• What is the difference between Data Science and Data Analytics? Data analytics focuses primarily on examining historical data to answer specific business questions through queries and dashboards. Data science encompasses analytics but goes further into building predictive machine learning models, writing advanced algorithms, and handling unstructured data.

• What programming languages are essential for Data Science? Python and R are the industry standards. Python is heavily favored for its versatility, deep machine learning ecosystem (Scikit-Learn, TensorFlow), and data libraries (pandas, numpy), while R is widely used for statistical research.

• Do I need a background in advanced mathematics to start? A foundational understanding of linear algebra, basic calculus, and core statistics is necessary to understand how machine learning models optimize and make predictions, though modern libraries handle most of the heavy computational lifting.