What You'll Learn

By the end of this course, you'll be able to:

Perform exploratory data analysis

Apply statistical methods

Build predictive models

Create compelling visualizations

Communicate data insights

Follow data science workflows

Course Modules

A comprehensive curriculum designed for practical application.

1

Data Science Overview

  • The data science process
  • Tools and environments
  • Types of analysis
  • Career paths
2

Data Collection

  • APIs and web scraping
  • Databases
  • File formats
  • Data quality
3

Data Cleaning

  • Missing values
  • Outliers
  • Inconsistencies
  • Python libraries
4

Exploratory Analysis

  • Descriptive statistics
  • Distributions
  • Correlations
  • Visual exploration
5

Statistical Inference

  • Hypothesis testing
  • Confidence intervals
  • P-values
  • Statistical power
6

Regression Analysis

  • Linear regression
  • Logistic regression
  • Model diagnostics
  • Interpretation
7

Classification

  • Decision trees
  • Logistic regression
  • SVM
  • Model evaluation
8

Clustering

  • K-means
  • Hierarchical
  • DBSCAN
  • Evaluation metrics
9

Dimensionality Reduction

  • PCA
  • t-SNE
  • Feature selection
  • Interpretation
10

Time Series

  • Trends and seasonality
  • Forecasting methods
  • ARIMA
  • Prophet
11

Data Visualization

  • Matplotlib
  • Seaborn
  • Interactive plots
  • Storytelling
12

Big Data

  • Spark basics
  • Distributed computing
  • Data lakes
  • Processing pipelines
13

Communication

  • Presentations
  • Reports
  • Dashboards
  • Stakeholder management
14

Capstone Project

  • End-to-end project
  • Real dataset
  • Presentation
  • Peer review

Enroll in Data Science

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