Introduction To Data Science

Explore what data science is through a supermarket profit scenario, revealing how structured and unstructured data drive analytics, models, and predictions to boost decision-making.

Learn how data science uses structured and unstructured data to build models that predict weather and calamities, guide self-driving car decisions, and enable decision making in online shopping.

Identify analytics problems, determine the required data sets and variables, collect and cleanse data from diverse sources, and select models to communicate findings via visualizations.

Explore the life cycle of data science, covering data discovery (structured and unstructured data), data preparation, planning and building models, operationalize, and communicating results.

Explore the four key data science components: data, programming, statistics and probability, and machine learning, along with structured and unstructured data, and how Python supports data management and analysis.

data scientists apply machine learning to recommendation systems, education, biotechnology, weather forecasting, and social media analytics, using content-based and collaborative filtering to model user data.

Outline the essential skills for data science, including programming (R, Python), statistics, data wrangling, AI, ML, DL, data and communication skills, and visualization tools like matplotlib and Tableau.

Explore tools used in data science, including Python, R, Google Analytics, Apache Spark, D3.js, Tableau, and Excel, and learn how they support data processing, visualization, and analysis.

Data science and business intelligence differ. Business intelligence uses dashboards and reports for decisions; data science applies predictive models, machine learning, and big data tools to structured and unstructured data.

Explore how data science differs from machine learning, highlighting data-centric processing, predictive analysis, and the three learning paradigms: supervised, unsupervised, and reinforcement.

Discover how data science differs from artificial intelligence, with data science emphasizing preprocessing, analysis, visualization, and prediction, while AI focuses on autonomous, cognition-emulating predictive models.

Distinguish big data's volume, variety, veracity, and velocity from data science by showing how big data manages vast data with Apache, Hadoop, and Cassandra, while data science analyzes insights.

Explore linear regression to model the relationship between input variables and a dependent variable, derive the equation y = c + b x, and forecast outcomes using predictors.

Learn logistic regression, a data science algorithm using the logistic function to produce 0-1 probabilities for binary outcomes, enabling threshold-based classification like predicting whether a patient is sick.

Explore how a decision tree uses a root node, internal nodes, branches, and leaf nodes to classify instances and predict outcomes.

Learn how the k-means clustering algorithm groups data by iteratively updating centroids and assigning points to the nearest centroid. Explore applications in document classification, customer segmentation, and delivery optimization.

Explore common data science challenges, from identifying real-time issues and extracting insights to data availability, privacy, tool limitations, and time-consuming deployment for analysis and prediction.

Compare data science designs with business intelligence, machine learning, artificial intelligence, and big data; review examples of beta sites, and outline the algorithms and challenges of data science.