Data Science Roadmap

Course Objective:

• Build a strong foundation in Python, mathematics, statistics, SQL, and data handling.
• Learn to collect, clean, explore, visualize, and communicate insights from real datasets.
• Develop practical machine learning skills from baseline models through model evaluation and tuning.
• Explore time series, NLP, computer vision, deep learning, and big-data concepts.
• Learn reproducible workflows, deployment, monitoring, responsible AI, and professional practices.
• Build portfolio-ready projects and prepare for entry-level and advanced Data Science roles.

1

Introduction to Data Science

• What is Data Science?
• Data Science vs Data Analytics vs AI vs ML
• Data Science lifecycle
• Types of data: structured, semi-structured, unstructured
• Business problems and use cases
• Roles: Data Analyst, Data Scientist, ML Engineer
• CRISP-DM workflow
• Setting learning goals

2

Computer & Programming Foundations

• Computer fundamentals and file systems
• Installing Python and an IDE (VS Code/Jupyter)
• Command line basics
• Python interpreter and scripts
• Variables and data types
• Operators and expressions
• Input/output
• Comments and code style
• Debugging and error messages

3

Python Programming Essentials

• Strings and string methods
• Lists, tuples, sets, dictionaries
• Conditions and loops
• Functions and parameters
• Scope and return values
• Comprehensions
• Modules and packages
• Exceptions
• File handling: CSV, TXT, JSON
• Virtual environments and pip
• Object-oriented programming basics

4

Data Structures & Algorithms Basics

• Time and space complexity intuition
• Arrays/lists and strings
• Stacks and queues
• Dictionaries and sets
• Searching and sorting concepts
• Recursion basics
• Choosing appropriate data structures

5

Mathematics for Data Science

• Arithmetic, ratios, percentages
• Algebra and equations
• Functions and graphs
• Exponents and logarithms
• Summation notation
• Vectors and matrices
• Matrix operations
• Dot products
• Derivatives and gradients intuition
• Optimization basics

6

Statistics & Probability

• Population vs sample
• Mean, median, mode
• Variance and standard deviation
• Percentiles and IQR
• Probability rules
• Conditional probability and Bayes' theorem
• Common distributions: normal, binomial, Poisson
• Sampling and sampling bias
• Central Limit Theorem
• Confidence intervals
• Hypothesis testing and p-values
• Type I/II errors
• Correlation vs causation
• A/B testing fundamentals

7

Data Collection & Wrangling

• CSV, Excel, JSON, APIs, databases
• Reading and writing datasets
• Data types and schema inspection
• Missing-value handling
• Duplicate detection
• Outlier investigation
• Data cleaning strategies
• Data transformation
• String/date parsing
• Data validation and quality checks
• Reproducible cleaning pipelines

8

NumPy for Numerical Computing

• ndarrays and dimensions
• Array creation and indexing
• Slicing and boolean masks
• Vectorization
• Broadcasting
• Aggregation functions
• Random number generation
• Linear algebra operations
• Performance compared with Python loops

9

Pandas for Data Analysis

• Series and DataFrames
• Import/export CSV and Excel
• Selecting, filtering and sorting
• loc and iloc
• Missing data treatment
• GroupBy and aggregation
• Merge, join and concatenate
• Reshaping: pivot and melt
• Datetime and time-series handling
• Apply, map and vectorized operations
• Categorical data
• Efficient workflows

10

SQL & Database Skills

• Relational database concepts
• Tables, rows, columns and keys
• SELECT, WHERE, ORDER BY
• DISTINCT, LIMIT and aliases
• Aggregate functions
• GROUP BY and HAVING
• INNER, LEFT, RIGHT and FULL joins
• Subqueries and CTEs
• CASE expressions
• Window functions
• Date and string functions
• Normalization basics
• Indexes and query plans
• Connect Python to SQL

11

Exploratory Data Analysis (EDA)

• Define questions before analysis
• Understand dataset shape and types
• Univariate analysis
• Bivariate and multivariate analysis
• Distribution analysis
• Outlier exploration
• Correlation analysis
• Segment and cohort comparisons
• Data leakage awareness
• EDA checklist and findings summary

12

Data Visualization

• Chart selection principles
• Matplotlib fundamentals
• Seaborn statistical plots
• Line, bar, scatter and histogram charts
• Box, violin and heatmap plots
• Subplots and annotations
• Color, labels and accessibility
• Misleading charts to avoid
• Storytelling with data
• Build a concise analytical report

13

Business & Product Analytics

• Translate business questions into metrics
• KPIs and metric definitions
• Funnel analysis
• Cohort and retention analysis
• Customer segmentation
• Revenue and profitability metrics
• Experiment design
• A/B test interpretation
• Stakeholder communication
• Recommendations with assumptions and limitations

14

Data Sources & APIs

• HTTP and REST API basics
• Requests library
• JSON parsing
• Pagination and rate limits
• Authentication and API keys
• Web data collection ethics
• HTML parsing basics
• Public datasets and data portals
• Data licensing and privacy
• Automated data ingestion

15

Machine Learning Foundations

• What is machine learning?
• Supervised vs unsupervised learning
• Regression vs classification
• Train/validation/test split
• Features and labels
• Baseline models
• Overfitting and underfitting
• Bias-variance tradeoff
• Cross-validation
• Data leakage
• Preprocessing pipelines
• Scikit-learn workflow

16

Supervised Learning: Regression

• Linear regression
• Multiple linear regression
• Polynomial features
• Regularization: Ridge and Lasso
• Decision tree regression
• Random forest regression
• Gradient boosting basics
• XGBoost/LightGBM concepts
• Regression metrics: MAE, MSE, RMSE, R²
• Residual analysis
• Feature importance and interpretation

17

Supervised Learning: Classification

• Logistic regression
• K-nearest neighbors
• Decision trees
• Random forests
• Support Vector Machines
• Naive Bayes
• Gradient boosting classifiers
• Confusion matrix
• Precision, recall and F1
• ROC-AUC and PR-AUC
• Class imbalance and resampling
• Threshold tuning
• Probability calibration

18

Unsupervised Learning

• Clustering use cases
• K-means and choosing K
• Hierarchical clustering
• DBSCAN
• Cluster evaluation and silhouette score
• PCA for dimensionality reduction
• t-SNE and UMAP overview
• Anomaly detection
• Association rules and market basket analysis

19

Feature Engineering & Model Selection

• Numerical scaling and transformations
• Categorical encoding
• Date/time features
• Text-derived features
• Feature selection
• Pipeline construction
• Hyperparameter tuning
• GridSearchCV and RandomizedSearchCV
• Nested validation intuition
• Model comparison
• Explainability with permutation importance and SHAP overview

20

Time Series & Forecasting

• Time-series components and stationarity
• Trend and seasonality
• Time-based train/test split
• Lag and rolling features
• Moving averages
• Exponential smoothing
• ARIMA/SARIMA concepts
• Forecast evaluation: MAE, RMSE, MAPE caveats
• Backtesting
• Forecasting business scenarios

21

Specializations: NLP & Computer Vision

• Text cleaning and tokenization
• Bag of words and TF-IDF
• Text classification
• Embeddings and transformer concepts
• Image representation basics
• Image preprocessing and augmentation
• CNN concepts
• Transfer learning overview
• Evaluation and responsible use

22

Deep Learning Foundations

• Neural network building blocks
• Perceptrons and activation functions
• Loss functions
• Backpropagation intuition
• Optimizers: SGD and Adam
• Batching and epochs
• Regularization and dropout
• PyTorch or TensorFlow basics
• Training and validation curves
• GPU basics and experiment tracking

23

Big Data & Data Engineering Concepts

• Data warehouse vs data lake vs lakehouse
• ETL and ELT
• Batch vs streaming processing
• File formats: CSV, JSON, Parquet
• Partitioning and columnar storage
• Apache Spark and PySpark basics
• Distributed computing concepts
• Workflow orchestration overview
• Data pipeline monitoring

24

Cloud, Deployment & MLOps

• Cloud fundamentals: AWS/Azure/GCP overview
• Notebook-to-script workflow
• Git and GitHub
• Environment and dependency management
• Build a prediction API with FastAPI/Flask
• Model serialization
• Docker fundamentals
• Batch and online inference
• CI/CD concepts
• Model versioning and experiment tracking
• Monitoring drift and performance
• Retraining and rollback strategies

25

Responsible Data Science & Professional Practice

• Privacy and data minimization
• Security and access control
• Bias and fairness assessment
• Explainability and transparency
• Consent and data licensing
• Reproducibility and documentation
• Communicating uncertainty
• Peer review and code review
• Working with stakeholders
• Ethical deployment checklist

26

Portfolio Capstone & Career Preparation

• Choose a meaningful problem and dataset
• Write a problem statement and success metric
• Create a reproducible data pipeline
• Perform EDA and document insights
• Build baseline and improved models
• Evaluate with suitable metrics
• Explain limitations and risks
• Create a dashboard or app
• Publish code, README and report
• Present results in a short demo
• Resume and GitHub portfolio
• Practice SQL, Python, statistics and ML interviews

Skills You'll Gain:

• Python programming and data manipulation with NumPy and Pandas.
• SQL querying, data cleaning, validation, and exploratory analysis.
• Statistical reasoning, probability, hypothesis testing, and experiment analysis.
• Data visualization, dashboarding concepts, and communicating findings.
• Building, evaluating, tuning, and explaining machine learning models.
• Foundational knowledge of deep learning, NLP, computer vision, and forecasting.
• Data pipeline, cloud, deployment, and MLOps fundamentals.
• Responsible, reproducible, and well-documented project practices.
• A portfolio of end-to-end projects suitable for demonstrating skills to employers.

Duration:

Typically 4 to 6 months with consistent study and project practice; the pace depends on prior programming and mathematics experience.

Certification:

Complete the learning modules and capstone projects, and earn a course completion certificate if offered by your training provider. Consider recognized platform certificates where relevant.