Data Science Fundamentals
The course teaches data science fundamentals, covering data collection, cleaning, exploratory analysis, statistical concepts, basic machine learning, and visualization, using light Python code with pandas, matplotlib, and scikit‑learn, empowering learners to turn raw data into insights.
Who Should Take This
Individuals aiming to become data scientists or analysts, with little to no prior programming experience, who want a solid conceptual foundation and practical Python snippets, should enroll. It suits recent graduates, career‑switchers, and junior analysts seeking to understand data pipelines, statistical reasoning, and introductory machine‑learning workflows.
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
66 learning goals
1
Data Collection & Cleaning
3 topics
Data Sources & Acquisition
- Identify common data sources including APIs, databases, flat files, web scraping, and surveys and describe the characteristics of each
- Describe structured, semi-structured, and unstructured data formats and explain when each format is appropriate for analysis
- Apply data import techniques using pandas to load CSV, JSON, and Excel files and perform initial data inspection with shape, dtypes, and info methods
- Evaluate data quality dimensions including completeness, consistency, accuracy, and timeliness and describe how to assess each before beginning analysis
Data Cleaning Techniques
- Identify common data quality issues including missing values, duplicates, inconsistent formats, and outliers
- Apply missing data handling strategies including deletion, imputation with mean/median/mode, and flag-based approaches using pandas
- Analyze the impact of different missing data mechanisms (MCAR, MAR, MNAR) on the validity of imputation strategies
- Apply outlier detection methods including z-score, IQR, and visual inspection to identify and handle anomalous data points appropriately
Data Wrangling & Transformation
- Apply pandas operations including filtering, grouping, merging, and pivoting to reshape datasets for analysis
- Apply data type conversions, string operations, and datetime parsing to standardize messy real-world data columns
- Evaluate trade-offs between wide and long data formats and determine the appropriate shape for different analytical and visualization tasks
2
Exploratory Data Analysis
3 topics
Summary Statistics & Distribution
- Describe measures of central tendency (mean, median, mode) and dispersion (variance, standard deviation, IQR) and when each is most informative
- Apply pandas describe, value_counts, and quantile methods to generate summary statistics and identify distributional characteristics
- Analyze the effect of outliers and skewness on summary statistics and recommend robust alternatives when distributions are non-normal
- Apply skewness and kurtosis measures to characterize distribution shapes and determine appropriate transformation strategies
Pattern Discovery
- Apply correlation analysis using Pearson and Spearman coefficients to identify linear and monotonic relationships between variables
- Identify patterns, trends, and anomalies in data through systematic EDA workflows including univariate, bivariate, and multivariate analysis
- Evaluate whether observed patterns in exploratory analysis are likely genuine signals or artifacts of sampling, confounding, or data collection bias
Hypothesis Generation
- Formulate testable hypotheses from EDA findings and describe how to transition from exploratory to confirmatory analysis
- Analyze the dangers of HARKing (hypothesizing after results are known) and explain how data dredging inflates false positive rates
3
Statistical Foundations
4 topics
Probability Basics
- Describe basic probability concepts including sample spaces, events, conditional probability, and independence
- Apply Bayes' theorem to update prior beliefs with new evidence in practical scenarios such as diagnostic testing and spam filtering
Common Distributions
- Describe the normal, binomial, and Poisson distributions including their parameters, shapes, and real-world applications
- Apply the central limit theorem to explain why sample means approximate a normal distribution regardless of the population shape
Inferential Statistics
- Describe hypothesis testing including null and alternative hypotheses, p-values, significance levels, and Type I and Type II errors
- Apply t-tests and chi-squared tests to determine whether observed differences between groups are statistically significant
- Construct and interpret confidence intervals for population parameters and explain how sample size affects interval width
- Analyze the limitations of p-value-based hypothesis testing including multiple comparison problems and the difference between statistical and practical significance
- Apply A/B testing methodology to compare two treatments including sample size calculation, randomization, and result interpretation
Sampling Methods
- Describe sampling methods including simple random, stratified, cluster, and systematic sampling and explain when each is appropriate
- Apply stratified sampling to ensure representative subgroups in datasets used for model training and evaluation
- Analyze how sampling bias introduces systematic errors in data analysis and describe strategies for detecting and mitigating sampling bias
4
Machine Learning Basics
5 topics
ML Concepts & Workflow
- Describe the machine learning workflow including problem framing, data preparation, model training, evaluation, and iteration
- Distinguish between supervised learning (classification, regression) and unsupervised learning (clustering, dimensionality reduction) and identify appropriate use cases for each
- Apply the train-test split methodology and explain why evaluating on training data produces misleadingly optimistic performance estimates
Supervised Learning Basics
- Apply linear regression using scikit-learn to predict continuous outcomes and interpret coefficients as feature importance indicators
- Apply logistic regression and decision tree classifiers using scikit-learn to binary classification problems and compare their outputs
- Evaluate classification models using accuracy, precision, recall, F1-score, and ROC-AUC and explain when each metric is most appropriate
- Apply feature importance from trained models to explain which variables drive predictions and communicate findings to stakeholders
Unsupervised Learning Basics
- Apply k-means clustering to segment data into groups and use the elbow method and silhouette scores to choose the number of clusters
- Describe principal component analysis (PCA) as a dimensionality reduction technique and explain how variance retention guides component selection
- Analyze clustering results to determine whether discovered segments represent meaningful groups or artifacts of algorithm assumptions
Overfitting & Model Selection
- Describe overfitting and underfitting including the bias-variance trade-off and how model complexity affects generalization
- Apply cross-validation techniques to estimate model generalization performance and select between competing models
- Analyze learning curves to diagnose whether a model suffers from high bias or high variance and recommend corrective actions
Feature Engineering Basics
- Describe feature engineering including encoding categorical variables, scaling numerical features, and creating derived features from raw data
- Apply one-hot encoding, label encoding, and standardization using scikit-learn preprocessing pipelines to prepare data for machine learning
- Analyze the impact of feature selection on model performance and apply correlation-based and importance-based methods to reduce dimensionality
5
Data Visualization
3 topics
Visualization Principles
- Describe fundamental visualization principles including Tufte's data-ink ratio, pre-attentive attributes, and the importance of honest axis scaling
- Identify common visualization pitfalls including truncated axes, misleading color scales, and chartjunk that distort data interpretation
- Evaluate competing visualizations of the same dataset and recommend improvements based on clarity, accuracy, and audience appropriateness
Chart Types & Selection
- Describe common chart types including bar, line, scatter, histogram, box plot, and heatmap and explain which data relationships each reveals
- Apply matplotlib and seaborn to create publication-quality visualizations with appropriate titles, labels, legends, and color palettes
- Select the most effective chart type for a given data question considering variable types, relationship complexity, and audience expertise
- Apply interactive visualization concepts including tooltips, filtering, and drill-down to enable exploratory data analysis for non-technical audiences
Data Storytelling
- Apply narrative structure to data presentations including context setting, insight highlighting, and actionable recommendation framing
- Analyze how different audiences (technical vs executive vs public) require different visualization complexity and narrative emphasis
6
Ethics & Bias in Data Science
3 topics
Bias in Data & Models
- Identify types of bias in data science including selection bias, measurement bias, confirmation bias, and algorithmic bias
- Analyze how biased training data propagates through machine learning models to produce discriminatory predictions in domains like hiring, lending, and criminal justice
- Apply bias detection techniques including demographic parity, equalized odds, and disparate impact analysis to evaluate model fairness
- Evaluate the tension between model accuracy and fairness and describe approaches for achieving acceptable trade-offs in real-world applications
Privacy & Responsible Data Use
- Describe data privacy principles including informed consent, data minimization, anonymization, and the distinction between PII and non-PII
- Apply anonymization and pseudonymization techniques to protect individual privacy while preserving analytical utility of datasets
- Evaluate the re-identification risks of anonymized datasets and describe how auxiliary data can compromise privacy protections
Reproducibility & Transparency
- Describe the reproducibility crisis in data science and identify practices that support reproducible analysis including version control, environment management, and documentation
- Apply reproducibility best practices including random seed setting, dependency pinning, and notebook documentation to ensure analyses can be independently verified
Hands-On Labs
15 labs
~390 min total
Console Simulator
Code Sandbox
Practice in a simulated cloud console or Python code sandbox — no account needed. Each lab runs entirely in your browser.
Scope
Included Topics
- Data collection methods and data cleaning techniques, exploratory data analysis (EDA) workflows, descriptive and inferential statistics foundations, supervised and unsupervised machine learning basics, data visualization principles and chart selection, ethics and bias in data science, pandas and matplotlib fundamentals, scikit-learn classification and regression basics, data wrangling and transformation, missing data handling, feature selection basics, model evaluation metrics
Not Covered
- Deep learning and neural network architectures
- Big data frameworks (Apache Spark, Hadoop, Flink)
- Advanced time series analysis (ARIMA, Prophet)
- Natural language processing beyond basic text preprocessing
- Cloud-based ML services (SageMaker, Vertex AI, Azure ML)
- Database administration and SQL optimization
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