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ROADMAP: Techniques to Know

For Python Data Analytics, Business Intelligence, Machine Learning, and more.

This page highlights core techniques and concepts professionals apply across real-world analytics projects. Check the boxes as you add skills.

Core Analytics Techniques

  • [ ] Descriptive statistics - Mean, median, mode, standard deviation
  • [ ] Data visualization and exploration - Charts, graphs, and summary views
  • [ ] Filtering, sorting, and slicing data - Extract specific subsets for analysis
  • [ ] Grouping and aggregation - Summarize data by categories (e.g., SUM, AVG)
  • [ ] Pivot tables, cross-tabulation, and summaries - Reshape and aggregate data
  • [ ] Basic SQL querying (SELECT, WHERE, JOIN) - Retrieve and combine datasets

Data Preparation Techniques

  • [ ] Data cleaning - Handle typos, inconsistent values
  • [ ] Handling missing values - Fill, drop, or flag missing data
  • [ ] Detecting and filtering outliers - Identify and handle extreme values
  • [ ] Deduplication - Remove duplicate records
  • [ ] Type conversion and normalization - Ensure consistency and accuracy
  • [ ] Encoding categorical variables - One-hot, label, or ordinal encoding
  • [ ] Feature creation and transformation - Generate new variables for analysis
  • [ ] Merging and joining datasets - Combine data from multiple sources
  • [ ] Standardizing units and formats - Align dates, currencies, and scales.
  • [ ] ETL (Extract, Transform, Load) and ELT - Move and prepare data for analysis

Data Modeling & Warehousing

  • [ ] Star and snowflake schema design - Organize data for efficient queries
  • [ ] Fact and dimension table definitions - Support multi-dimensional analysis
  • [ ] Creating and populating a data warehouse - Structure and store historical data

OLAP Processing

  • [ ] Slicing - Filter data along one dimension, creating a 2D view (e.g., sales for "Region A")
  • [ ] Dicing - Filter data along multiple dimensions, creating a sub-cube (e.g., sales for "Region A," "Electronics," "2023")
  • [ ] Roll-up - Aggregate data to a higher level (e.g., Daily to Monthly or Store to Region)
  • [ ] Drill-down - Expand data to a more detailed level (e.g., Year to Quarter to Month)

Warehouse Management

  • [ ] Designing efficient queries - Optimize data retrieval
  • [ ] Managing Slowly Changing Dimensions (SCD) - Track historical changes over time

Business Intelligence & Reporting

  • [ ] Defining KPIs and metrics - Key performance indicators and measurements
  • [ ] Designing dashboards for clarity and impact - Visual insights at a glance
  • [ ] Building interactive reports - Filters, slicers, and dynamic views
  • [ ] Storytelling with data and visual narratives - Communicate insights effectively
  • [ ] Refreshing and automating reports - Ensure data stays up-to-date
  • [ ] Data blending - Combine data from multiple sources
  • [ ] User access control and data security - Manage permissions and protection

Machine Learning & Prediction

  • [ ] Classification and regression basics - Predict categories or numeric values
  • [ ] Splitting data into training and test sets - Prepare for evaluation
  • [ ] Feature selection and feature engineering - Improve model quality
  • [ ] Evaluating models - Metrics like accuracy, precision, recall, F1
  • [ ] Avoiding overfitting and using cross-validation - Improve generalization
  • [ ] Hyperparameter tuning - Optimize model parameters
  • [ ] Model deployment basics - Make models accessible and usable

Applied Analytics

  • [ ] Web scraping and text mining (NLP) - Extract and analyze text data
  • [ ] Time series forecasting - Predict future values over time
  • [ ] Recommendation systems - Suggest items based on user patterns
  • [ ] Streaming data analytics - Real-time insights from continuous data
  • [ ] *Kafka and event-driven pipelines - High-volume real-time processing
  • [ ] Graph analytics - Discover relationships in connected data

Advanced & Emerging Techniques

  • [ ] Semantic search and embeddings - Context-aware search and retrieval
  • [ ] Integrating AI/LLMs in workflows - Enhance analytics workflows with generative tools
  • [ ] Data orchestration (Prefect / Airflow) - Automate and schedule pipelines