1
Python for Data Roles
Python syntax, data types, functions, OOP basics, exception and file handling, virtual environments, Jupyter, debugging.
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Learning objectives:
• Write Python with correct syntax and data types
• Use functions and OOP basics
• Handle exceptions and files
• Use virtual environments and Jupyter
• Debug effectively
2
NumPy Deep Dive
Arrays, broadcasting, vectorization, matrix operations, performance optimization.
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Learning objectives:
• Work with NumPy arrays and broadcasting
• Apply vectorization and matrix operations
• Optimize performance
3
Pandas Advanced
DataFrames, GroupBy, merging & joining, pivot tables, multi-indexing, window operations, cleaning pipelines, large datasets.
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Learning objectives:
• Master DataFrames, GroupBy, merging, joining
• Use pivot tables and multi-indexing
• Apply window operations and cleaning pipelines
• Handle large datasets
4
SQL Foundations
SELECT, WHERE, GROUP BY, HAVING, ORDER BY.
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Learning objectives:
• Write SELECT, WHERE, GROUP BY, HAVING, ORDER BY
5
SQL Intermediate
Joins (all types), subqueries, CASE, aggregate functions, indexing.
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Learning objectives:
• Use all join types, subqueries, CASE
• Apply aggregate functions and indexing
6
SQL Advanced
Window functions, CTE, recursive queries, query optimization, performance tuning, database design basics.
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Learning objectives:
• Use window functions, CTE, recursive queries
• Optimize queries and tune performance
• Apply database design basics
7
Advanced Excel
Pivot tables, lookup functions, Power Query, data cleaning, financial modeling basics.
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Learning objectives:
• Use pivot tables and lookup functions
• Apply Power Query and data cleaning
• Use financial modeling basics
8
Power BI / Tableau
Dashboard creation, data modeling, DAX basics, KPI dashboards, interactive reports, storytelling.
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Learning objectives:
• Create dashboards and data models
• Use DAX basics and KPI dashboards
• Build interactive reports and storytelling
9
Descriptive Statistics
Mean, median, variance, standard deviation, skewness, correlation.
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Learning objectives:
• Calculate mean, median, variance, standard deviation
• Use skewness and correlation
10
Probability
Random variables, probability distributions, conditional probability, Bayes theorem.
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Learning objectives:
• Work with random variables and distributions
• Apply conditional probability and Bayes theorem
11
Inferential Statistics
Sampling, confidence intervals, hypothesis testing, p-values, A/B testing, ANOVA, chi-square.
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Learning objectives:
• Apply sampling and confidence intervals
• Use hypothesis testing, p-values
• Design A/B tests and use ANOVA, chi-square
12
Exploratory Data Analysis
Data profiling, missing value treatment, outlier detection, correlation analysis, feature engineering.
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Learning objectives:
• Profile data and treat missing values
• Detect outliers and analyze correlation
• Perform feature engineering
13
Business Analytics
KPI identification, funnel analysis, cohort analysis, customer segmentation, revenue analytics, retention analysis.
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Learning objectives:
• Identify KPIs and perform funnel analysis
• Use cohort analysis and customer segmentation
• Apply revenue and retention analytics
14
Experimentation & Product Analytics
A/B testing design, metrics definition, experiment evaluation, causal inference basics.
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Learning objectives:
• Design A/B tests and define metrics
• Evaluate experiments and apply causal inference basics
15
ML Foundations
ML workflow, train/test split, cross validation, bias-variance tradeoff.
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Learning objectives:
• Follow ML workflow and use train/test split
• Apply cross validation and understand bias-variance
16
Supervised Learning
Linear regression, logistic regression, decision trees, random forest, gradient boosting, evaluation metrics.
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Learning objectives:
• Apply linear/logistic regression, trees, RF, gradient boosting
• Use evaluation metrics
17
Unsupervised Learning
K-Means, hierarchical clustering, PCA, anomaly detection.
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Learning objectives:
• Apply K-Means, hierarchical clustering, PCA
• Detect anomalies
18
Model Tuning & Feature Engineering
Hyperparameter tuning, feature selection, regularization, imbalanced data handling.
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Learning objectives:
• Tune hyperparameters and select features
• Apply regularization and handle imbalanced data
19
Time Series Analysis
Trend & seasonality, ARIMA, SARIMA, forecasting, Prophet.
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Learning objectives:
• Identify trend and seasonality
• Apply ARIMA, SARIMA, Prophet for forecasting
20
NLP Basics
Text cleaning, TF-IDF, sentiment analysis, text classification.
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Learning objectives:
• Clean text and use TF-IDF
• Perform sentiment analysis and text classification
21
Recommender Systems
Collaborative filtering, content-based filtering, matrix factorization.
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Learning objectives:
• Apply collaborative and content-based filtering
• Use matrix factorization
22
ETL & Data Pipelines
ETL concepts, Airflow basics, data transformation, batch vs streaming.
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Learning objectives:
• Apply ETL concepts and Airflow basics
• Transform data and understand batch vs streaming
23
Big Data Fundamentals
Hadoop overview, Spark basics, PySpark, distributed data.
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Learning objectives:
• Understand Hadoop and Spark basics
• Use PySpark and distributed data
24
Data Warehousing
Star schema, snowflake schema, data lakes, data modeling.
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Learning objectives:
• Design star and snowflake schemas
• Work with data lakes and data modeling
25
Cloud Basics
AWS, GCP, Azure basics, cloud storage, cloud databases.
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Learning objectives:
• Use AWS, GCP, Azure basics
• Work with cloud storage and databases
26
Model Deployment
Flask/FastAPI, Docker basics, REST APIs, CI/CD basics.
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Learning objectives:
• Deploy with Flask/FastAPI and Docker
• Expose REST APIs and use CI/CD basics
27
Data Governance
Data quality, data privacy, GDPR basics, data lineage.
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Learning objectives:
• Ensure data quality and privacy
• Apply GDPR basics and data lineage
28
Responsible AI
Bias detection, explainability, fairness metrics.
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Learning objectives:
• Detect bias and apply explainability
• Use fairness metrics
29
Portfolio Development
GitHub projects, Kaggle projects, case studies, end-to-end projects.
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Learning objectives:
• Build GitHub and Kaggle projects
• Complete case studies and end-to-end projects
30
Interview Preparation
SQL interview questions, case study interviews, product thinking, ML and statistics interview questions.
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Learning objectives:
• Prepare for SQL, case study, product thinking
• Answer ML and statistics interview questions