Data Science Interactive Graph Curriculum

Data Scientist

The master roadmap to becoming a professional Data Scientist — covering applied statistics, SQL data engineering, exploratory analysis, production ML pipelines, and causal experimentation.

5 Phases

10 Topics

~156h Total Time

Study Progress: 0 of 10 completed • 0h of ~156h 0%

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Recommended Next Step Beginner ⏱️ 3 hours

Intelligent Agents & Problem Formulation

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Intermediate

Advanced

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Curriculum Start

01 Phase 01 0/2 completed

Phase 1: Statistics & Mathematical Intuition

Probability theory, statistical distributions, hypothesis testing, and quantitative reasoning for data-driven decisions.

beginner Recommended • 2 weeks

Descriptive & Inferential Statistics

Measures of central tendency (mean, median, mode), dispersion (variance, standard deviation, IQR), skewness, kurtosis, and the Central Limit Theorem (CLT).

intermediate Recommended • 2 weeks

Probability Distributions & Hypothesis Testing

Normal, Binomial, Poisson, and Student's t-distributions; formulating null vs alternative hypotheses, Type I/II errors, p-values, t-tests, and ANOVA.

02 Phase 02 0/2 completed

Phase 2: SQL & Data Engineering Foundations

Querying relational databases, complex joins, analytical window functions, and data cleaning pipelines.

beginner Recommended • 2 weeks

Modern SQL for Data Science

SELECT, filtering, GROUP BY, aggregations, multi-table JOINs (INNER, LEFT, FULL OUTER), Common Table Expressions (CTEs), and subqueries.

intermediate Recommended • 10 days

Advanced SQL Window Functions & Aggregations

ROW_NUMBER(), RANK(), DENSE_RANK(), LEAD(), LAG(), running totals, moving averages, and cumulative distribution calculations.

03 Phase 03 0/2 completed

Phase 3: Python Data Analysis & Visualization

High-performance data wrangling with Pandas, numerical computing with NumPy, and visual storytelling with Seaborn.

beginner Recommended • 2 weeks

Pandas & NumPy for Data Manipulation

Loading multi-format data (CSV, Parquet, JSON, SQL), handling nulls, vectorized string operations, datetime manipulation, and pivot tables.

intermediate Recommended • 10 days

Exploratory Data Analysis & Visual Storytelling

Matplotlib, Seaborn, distribution plots, correlation heatmaps, pairplots, box plots for outlier detection, and communicating statistical insights.

04 Phase 04 0/2 completed

Phase 4: Predictive Modeling & Experimentation

Applying supervised learning models to solve business problems, feature engineering, and conducting rigorous A/B tests.

intermediate Recommended • 3 weeks

Applied Machine Learning for Business

Linear/Logistic Regression, Random Forests, XGBoost, Scikit-Learn pipelines, customer churn prediction, and lifetime value (LTV) modeling.

advanced Recommended • 2 weeks

A/B Testing & Causal Inference

Randomized controlled trials (RCTs), sample size determination, statistical power, minimum detectable effect (MDE), and causal inference (propensity score matching).

05 Phase 05 0/2 completed

Phase 5: Big Data & Production Analytics

Distributed computing with Apache Spark, cloud data warehouses, and automated analytics pipelines.

advanced Recommended • 2 weeks

Distributed Data Processing with PySpark

Resilient Distributed Datasets (RDDs), Spark DataFrames, distributed aggregation, partitioning strategies, and big data ETL workflows.

intermediate Optional • 10 days

Cloud Data Warehousing & Analytics Engineering

Snowflake, Google BigQuery, dbt (data build tool), columnar storage architectures, data modeling (star and snowflake schemas), and metric layers.

Curriculum Breakdown

Phase 1: Statistics & Mathematical Intuition

0/2 completed

Probability theory, statistical distributions, hypothesis testing, and quantitative reasoning for data-driven decisions.

Descriptive & Inferential Statistics

beginner ⏱️ 2 weeks

Measures of central tendency (mean, median, mode), dispersion (variance, standard deviation, IQR), skewness, kurtosis, and the Central Limit Theorem (CLT).

Key Outcomes:

✓ Understanding sampling distributions and standard error

✓ Applying Central Limit Theorem to sample means

✓ Detecting skewed distributions and applying log / power transforms

Links: Statistics and Probability (Khan Academy) Fundamental Math for Data Science (Codecademy)

Probability Distributions & Hypothesis Testing

intermediate ⏱️ 2 weeks

Normal, Binomial, Poisson, and Student's t-distributions; formulating null vs alternative hypotheses, Type I/II errors, p-values, t-tests, and ANOVA.

Key Outcomes:

✓ Formulating statistical null and alternative hypotheses

✓ Interpreting p-values and confidence intervals accurately

✓ Conducting one-sample, two-sample, and paired t-tests

Links: StatQuest: Hypothesis Testing and p-values NPTEL Swayam Mathematics Course

Phase 2: SQL & Data Engineering Foundations

0/2 completed

Querying relational databases, complex joins, analytical window functions, and data cleaning pipelines.

Modern SQL for Data Science

beginner ⏱️ 2 weeks

SELECT, filtering, GROUP BY, aggregations, multi-table JOINs (INNER, LEFT, FULL OUTER), Common Table Expressions (CTEs), and subqueries.

Key Outcomes:

✓ Writing multi-join aggregation queries for customer analytics

✓ Structuring complex analysis using Common Table Expressions (WITH clauses)

✓ Optimizing query execution plans and index usage

Links: SQL Tutorial for Beginners (W3Schools) Mode Analytics SQL Tutorial for Data Analysis

Advanced SQL Window Functions & Aggregations

intermediate ⏱️ 10 days

ROW_NUMBER(), RANK(), DENSE_RANK(), LEAD(), LAG(), running totals, moving averages, and cumulative distribution calculations.

Key Outcomes:

✓ Computing rolling metrics and retention cohorts with window functions

✓ Partitioning data across customer categories without collapsing rows

✓ Calculating period-over-period growth rates

Links: PostgreSQL Official Documentation: Window Functions

Phase 3: Python Data Analysis & Visualization

0/2 completed

High-performance data wrangling with Pandas, numerical computing with NumPy, and visual storytelling with Seaborn.

Pandas & NumPy for Data Manipulation

beginner ⏱️ 2 weeks

Loading multi-format data (CSV, Parquet, JSON, SQL), handling nulls, vectorized string operations, datetime manipulation, and pivot tables.

Key Outcomes:

✓ High-performance data transformation on large DataFrames

✓ Handling missing data: forward fill, backward fill, and model imputation

✓ Reshaping data using melt, pivot_table, and stack/unstack

Links: Python for Data Science (Full Video Tutorial) Data Science Introduction (W3Schools)

Exploratory Data Analysis & Visual Storytelling

intermediate ⏱️ 10 days

Matplotlib, Seaborn, distribution plots, correlation heatmaps, pairplots, box plots for outlier detection, and communicating statistical insights.

Key Outcomes:

✓ Identifying multicollinearity and feature interactions visually

✓ Designing publication-quality visualizations for stakeholders

✓ Interactive dashboards with Plotly and Streamlit

Links: Seaborn Example Gallery IBM Data Science Professional Certificate

Phase 4: Predictive Modeling & Experimentation

0/2 completed

Applying supervised learning models to solve business problems, feature engineering, and conducting rigorous A/B tests.

Applied Machine Learning for Business

intermediate ⏱️ 3 weeks

Linear/Logistic Regression, Random Forests, XGBoost, Scikit-Learn pipelines, customer churn prediction, and lifetime value (LTV) modeling.

Key Outcomes:

✓ Building production Scikit-Learn pipelines with preprocessors

✓ Feature importance and SHAP (SHapley Additive exPlanations) values

✓ Calibrating probabilities for business decision thresholds

Links: Google Data Analytics Professional Certificate Scikit-Learn Machine Learning in Python

A/B Testing & Causal Inference

advanced ⏱️ 2 weeks

Randomized controlled trials (RCTs), sample size determination, statistical power, minimum detectable effect (MDE), and causal inference (propensity score matching).

Key Outcomes:

✓ Calculating required sample size based on alpha, beta, and MDE

✓ Addressing peeking problems and novelty effects in online experiments

✓ Difference-in-differences and quasi-experimental methods

Links: Udacity: A/B Testing by Google Quick 5 Minute Intro to Data Science

Phase 5: Big Data & Production Analytics

0/2 completed

Distributed computing with Apache Spark, cloud data warehouses, and automated analytics pipelines.

Distributed Data Processing with PySpark

advanced ⏱️ 2 weeks

Resilient Distributed Datasets (RDDs), Spark DataFrames, distributed aggregation, partitioning strategies, and big data ETL workflows.

Key Outcomes:

✓ Parallel transformations across distributed compute clusters

✓ Avoiding expensive shuffles and data skews in Spark jobs

✓ Interfacing PySpark with AWS S3 / Google Cloud Storage

Links: Apache Spark Python API Documentation

Cloud Data Warehousing & Analytics Engineering

intermediate Optional ⏱️ 10 days

Snowflake, Google BigQuery, dbt (data build tool), columnar storage architectures, data modeling (star and snowflake schemas), and metric layers.

Key Outcomes:

✓ Transforming raw lake data into modeled dimension and fact tables

✓ dbt testing and automated data quality validation

Links: dbt (data build tool) Getting Started Guide

Beginner 45 min

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Data Scientist

Intelligent Agents & Problem Formulation

Phase 1: Statistics & Mathematical Intuition

Descriptive & Inferential Statistics

Probability Distributions & Hypothesis Testing

Phase 2: SQL & Data Engineering Foundations

Modern SQL for Data Science

Advanced SQL Window Functions & Aggregations

Phase 3: Python Data Analysis & Visualization

Pandas & NumPy for Data Manipulation

Exploratory Data Analysis & Visual Storytelling

Phase 4: Predictive Modeling & Experimentation

Applied Machine Learning for Business

A/B Testing & Causal Inference

Phase 5: Big Data & Production Analytics

Distributed Data Processing with PySpark

Cloud Data Warehousing & Analytics Engineering

Curriculum Breakdown

Phase 1: Statistics & Mathematical Intuition

Descriptive & Inferential Statistics

Probability Distributions & Hypothesis Testing

Phase 2: SQL & Data Engineering Foundations

Modern SQL for Data Science

Advanced SQL Window Functions & Aggregations

Phase 3: Python Data Analysis & Visualization

Pandas & NumPy for Data Manipulation

Exploratory Data Analysis & Visual Storytelling

Phase 4: Predictive Modeling & Experimentation

Applied Machine Learning for Business

A/B Testing & Causal Inference

Phase 5: Big Data & Production Analytics

Distributed Data Processing with PySpark

Cloud Data Warehousing & Analytics Engineering

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