Data Analytics

Explore data engineering, analytics pipelines, and SQL/Python-based roles. Understand what data careers look like at top US companies and what skills get you hired.

SQLPythonAnalytics PipelinesData Engineering

Duration

4 months

Starts

July 2026

Format

Live · Cohort

Tuition

$1,999

16-Week Curriculum

Week by week, from fundamentals to a job-ready profile — every module ships a hands-on deliverable.

Week	Module Focus	Topics Covered	Hands-on Deliverables	Job Readiness Outcome
1	Python for Data Science	Python refresh, Jupyter, NumPy basics, data types, functions	Python problem-solving notebook	Python confidence
2	Data Analysis with Pandas	Series, DataFrames, filtering, joins, groupby, missing values	Sales data analysis project	Data wrangling skills
3	Data Visualization	Matplotlib, charts, trends, distributions, dashboards	EDA dashboard notebook	Visualization skills
4	Statistics for Data Science	Probability, distributions, sampling, hypothesis testing, confidence intervals	Statistical case study	Statistical reasoning
5	SQL for Analytics	Advanced joins, window functions, aggregations, case statements	Business SQL case study	SQL interview readiness
6	Exploratory Data Analysis	Feature understanding, outliers, skewness, correlation, insights storytelling	EDA report	Analyst thinking
7	Machine Learning Foundations	Supervised vs unsupervised learning, train/test split, metrics	First ML notebook	ML fundamentals
8	Regression Algorithms	Linear regression, polynomial regression, regularization basics	House price prediction	Regression project
9	Classification Algorithms	Logistic regression, KNN, decision trees, random forest	Customer churn classifier	Classification confidence
10	Unsupervised Learning	Clustering, PCA, dimensionality reduction	Customer segmentation	ML breadth
11	Model Evaluation	Precision, recall, F1, ROC-AUC, cross validation, hyperparameter tuning	Optimized ML pipeline	Model improvement skills
12	Feature Engineering	Encoding, scaling, feature selection, pipelines	Production-ready preprocessing workflow	Enterprise ML workflow
13	Time Series and NLP	Forecasting basics, text preprocessing, TF-IDF, sentiment analysis	Sales forecast or review analyzer	Specialized DS skills
14	Deep Learning Basics	Neural networks, intro to TensorFlow, PyTorch, ANN basics	ANN classification notebook	Modern AI foundations
15	Model Deployment + MLOps Basics	API serving with FastAPI, Docker basics, experiment tracking intro	Deployed ML model API	Deployment readiness
16	Capstone + Placement Preparation	End-to-end project, GitHub portfolio, resume, case studies, mock interviews	Final capstone + portfolio	Data science job-ready

1
Python for Data Science
Python refresh, Jupyter, NumPy basics, data types, functions
Build: Python problem-solving notebook
Python confidence
2
Data Analysis with Pandas
Series, DataFrames, filtering, joins, groupby, missing values
Build: Sales data analysis project
Data wrangling skills
3
Data Visualization
Matplotlib, charts, trends, distributions, dashboards
Build: EDA dashboard notebook
Visualization skills
4
Statistics for Data Science
Probability, distributions, sampling, hypothesis testing, confidence intervals
Build: Statistical case study
Statistical reasoning
5
SQL for Analytics
Advanced joins, window functions, aggregations, case statements
Build: Business SQL case study
SQL interview readiness
6
Exploratory Data Analysis
Feature understanding, outliers, skewness, correlation, insights storytelling
Build: EDA report
Analyst thinking
7
Machine Learning Foundations
Supervised vs unsupervised learning, train/test split, metrics
Build: First ML notebook
ML fundamentals
8
Regression Algorithms
Linear regression, polynomial regression, regularization basics
Build: House price prediction
Regression project
9
Classification Algorithms
Logistic regression, KNN, decision trees, random forest
Build: Customer churn classifier
Classification confidence
10
Unsupervised Learning
Clustering, PCA, dimensionality reduction
Build: Customer segmentation
ML breadth
11
Model Evaluation
Precision, recall, F1, ROC-AUC, cross validation, hyperparameter tuning
Build: Optimized ML pipeline
Model improvement skills
12
Feature Engineering
Encoding, scaling, feature selection, pipelines
Build: Production-ready preprocessing workflow
Enterprise ML workflow
13
Time Series and NLP
Forecasting basics, text preprocessing, TF-IDF, sentiment analysis
Build: Sales forecast or review analyzer
Specialized DS skills
14
Deep Learning Basics
Neural networks, intro to TensorFlow, PyTorch, ANN basics
Build: ANN classification notebook
Modern AI foundations
15
Model Deployment + MLOps Basics
API serving with FastAPI, Docker basics, experiment tracking intro
Build: Deployed ML model API
Deployment readiness
16
Capstone + Placement Preparation
End-to-end project, GitHub portfolio, resume, case studies, mock interviews
Build: Final capstone + portfolio
Data science job-ready

Apply for this cohort

30 of 70 seats left · July 2026 – Nov 2026

Data Analytics

16-Week Curriculum

Python for Data Science

Data Analysis with Pandas

Data Visualization

Statistics for Data Science

SQL for Analytics

Exploratory Data Analysis

Machine Learning Foundations

Regression Algorithms

Classification Algorithms

Unsupervised Learning

Model Evaluation

Feature Engineering

Time Series and NLP

Deep Learning Basics

Model Deployment + MLOps Basics

Capstone + Placement Preparation