TDS: Tools in Data Science

Topics covered

• Command Line and Shell

  • Terminal basics
  • Shell commands
  • File and directory operations
  • Pipes and command-line workflows

• Development Environment

  • Code editors and IDEs
  • Jupyter Notebook
  • Google Colab
  • Environment setup

• Version Control

  • Git basics
  • Repositories
  • Commits, branches and merges
  • GitHub workflows

• Data Formats

  • JSON
  • CSV
  • Markdown
  • Basic file conversions

Expected skills: Creating a reproducible working environment with the help of Terminal, Git, notebooks and development tools.

Topics covered

• Web Applications

  • Frontend and backend basics
  • HTTP requests and responses
  • APIs

• Deployment

  • Hosting applications
  • Static website deployment
  • Application deployment workflows

• Backend Tools

  • FastAPI basics
  • Creating API endpoints
  • Handling requests and responses

• Containers and Cloud

  • Docker fundamentals
  • Application packaging
  • Cloud deployment concepts

• Automation

  • GitHub Actions
  • Basic CI/CD workflows

Expected skills: Deploying a basic application or API and understanding our development-to-deployment workflow.

Topics covered

• AI-assisted Coding

  • AI coding assistants
  • Prompting for code generation
  • Context-aware coding

• Coding Workflows

  • Using AI inside IDEs
  • AI coding through command-line tools
  • Online coding assistants

• Testing and Debugging

  • Generating test cases
  • Debugging with AI
  • Reviewing AI-generated code

• Effective AI Usage

  • Breaking tasks into smaller prompts
  • Verifying generated code
  • Managing context and iterations

Expected skills: Writing code, debugging and verifying generated solutions using AI tools.

Topics covered

• LLM Fundamentals

  • Language models
  • Tokens and context windows
  • Model inputs and outputs

• Prompt Engineering

  • Instruction-based prompts
  • Few-shot prompting
  • Structured output

• LLM APIs

  • API requests
  • Authentication and API keys
  • Working with model responses

• LLM Applications

  • Text extraction
  • Text classification
  • Sentiment analysis
  • Summarization

• Advanced Applications

  • Function calling
  • Embeddings
  • LLM agents
  • Model evaluation

Expected skills: Create practical applications using LLM APIs and evaluate prompts and model outputs.

Topics covered

• Data Sources

  • Web pages
  • Files and datasets
  • APIs
  • Databases

• HTTP and APIs

  • GET requests
  • Query parameters
  • Headers and status codes
  • JSON responses

• Web Scraping

  • HTML structure
  • CSS selectors
  • Extracting information from web pages

• Data Collection Tools

  • Python requests
  • Command-line data retrieval
  • Crawling basics

• Working with Files

  • Downloading files
  • Extracting PDF content
  • Converting HTML to Markdown
  • Reading structured data

Expected skills: Collecting relevant data from web, APIs, files and databases and putting it in usable format.

Topics covered

• Data Cleaning

  • Missing values
  • Duplicate records
  • Inconsistent values
  • Data type conversion

• Data Transformation

  • Filtering and sorting
  • Grouping and aggregation
  • Reshaping data
  • Combining datasets

• Spreadsheet Tools

  • Excel data cleaning
  • Formulas and functions
  • Data transformation
  • Data aggregation

• Command-Line Data Processing

  • Text processing
  • Shell-based data preparation
  • File transformations

• Database-Based Preparation

  • SQL queries
  • DuckDB
  • Data transformation workflows

Expected skills: Making raw, messy datasets clean, structured and analysis-ready.

Topics covered

• Exploratory Data Analysis (EDA)

  • Understanding dataset structure
  • Descriptive statistics
  • Distribution analysis
  • Identifying outliers

• Data Analysis with Python

  • NumPy
  • Pandas
  • Filtering and aggregation
  • Correlation analysis

• SQL-Based Analysis

  • SELECT queries
  • JOIN operations
  • GROUP BY and HAVING
  • Aggregation functions

• Analytical Tools

  • DuckDB
  • Datasette
  • Querying and exploring datasets

• Statistical Analysis

  • Mean, median and standard deviation
  • Correlation
  • Trend identification

Expected skills: Exploring datasets to identify patterns, relationships and useful insights.

Topics covered

• Geospatial Analysis

  • Latitude and longitude
  • Geographic datasets
  • Spatial relationships
  • Mapping and geographic visualization

• Network Analysis

  • Nodes and edges
  • Network structures
  • Connectivity
  • Centrality concepts

• Time Series Analysis

  • Time-based datasets
  • Trends and seasonality
  • Forecasting with spreadsheet tools

• Specialized Analysis

  • Data aggregation
  • Comparing groups
  • Finding relationships
  • Analysing complex datasets

Expected skills: Analyzing geographic, network-based and time-dependent data and extracting insights from it.

Topics covered

• Visualization Fundamentals

  • Choosing suitable charts
  • Visual encoding
  • Comparing categories
  • Showing distributions and relationships

• Python Visualization

  • Matplotlib
  • Seaborn
  • Scatter plots
  • Histograms
  • Bar charts
  • Heatmaps

• Interactive Visualization

  • Interactive charts
  • Plotly concepts
  • Exploring data visually

• Visualization with AI

  • Generating charts using ChatGPT
  • Refining chart outputs
  • Checking visual accuracy

Expected skills: Creating appropriate charts according to the nature of the data and communicating visual patterns clearly.

Topics covered

• Communicating Insights

  • Identifying key findings
  • Building a data narrative
  • Choosing relevant metrics

• Visual Storytelling

  • Chart selection
  • Visual hierarchy
  • Explaining trends and comparisons

• Presentations

  • Data-driven slides
  • Visual summaries
  • Communicating conclusions

• LLM-Assisted Storytelling

  • Summarizing analytical results
  • Generating narrative drafts
  • Reviewing AI-generated explanations

Expected skills: Converting analytical findings into understandable stories, reports and presentations.

Topics covered

• End-to-End Workflow

  • Sourcing data
  • Cleaning and transforming data
  • Performing analysis
  • Creating visualizations

• Combining Tools

  • Python and SQL
  • APIs and spreadsheets
  • Command-line utilities
  • AI-assisted workflows

• Reproducibility

  • Organizing project files
  • Documenting workflows
  • Version control

• Practical Application

  • Solving open-ended data problems
  • Choosing appropriate tools
  • Validating outputs

Expected skills: Solving a complete data science task by combining multiple tools.

Topics covered

• Development and Deployment

  • Git and GitHub
  • APIs
  • Application deployment

• LLMs and AI Tools

  • Prompt engineering
  • API usage
  • AI-assisted coding

• Data Workflow

  • Sourcing
  • Preparation
  • Analysis
  • Visualization

• Practical Problem Solving

  • Selecting suitable tools
  • Handling messy data
  • Debugging
  • Verifying results

• Project Integration

  • Combining concepts
  • Building a complete workflow
  • Communicating the final output

Expected skills: Solving independent, end-to-end practical data science problems and choosing appropriate tools.