atomcamp

Data Science Bootcamp Curriculum

Curriculum Highlights

Basics of Data Science

Python for AI

Machine Learning

NLP, LLMs,
Computer Vision

Curriculum Overview

  • Basic Data and Statistical Concepts

    • Data Literacy
    • Statistical Foundations
    • Descriptive Statistics
    • Statistical Inference and Sampling Techniques
    • Regression Analysis
  • Data Cleaning, Preparation, and Management

    • Data relationships, data shapes, and primary/unique keys and identifiers
    • Basic data cleaning
    • Data extraction from external sources
    • Sorting, Filtering, and Merging Data
    • Common excel formulas and functions
  • Further Concepts in Data Cleaning

    • Excel Functions for Data Cleaning (E.g. Left, Right, Concatenate, etc.)
    • Conditional Formatting
    • Data Validation and Error Checking
  • Basic Data Processing

    • IF and Nested IF statements
    • Absolute and Relative cell references
  • Data Analysis with Intermediate Excel

    • Processing large datasets
    • Find, Find & Replace
    • Advanced Commands (VLookUp, HLookUp, Index, Match)
  • Introduction to Pivot Tables

    • Navigating Pivot Tables Interface
    • Creating basic Pivot Tables
    • Field Settings and layout options
    • Working with pivot table data
  • Advance Data Analysis with Pivot Tables

    • Advanced pivot table functions
    • Data slicing
    • Pivot charts and visualizations
    • Advanced Pivot table visualizations
  • Further Data Visualization on Excel

    • Intermediate and Advanced Excel Charting Techniques
    • Excel Add-ins for advanced visualization
    • Data Analysis and Visualization on Google Sheets
  • Overview

    • Introduction to Data Warehousing
    • Definition and purpose
    • Key concepts: OLTP vs. OLAP
    • What is ETL?
    • Overview of Microsoft Tools for Data Warehousing
    • Introduction to Excel, SQL Server, SSIS, and SSRS
  • Data Warehousing Terminology and Concepts

    • Understanding Data Warehousing Terminology
    • Fact tables, dimension tables, star and snowflake schemas
    • Exploring the differences between OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing)
    • Use cases and examples
  • Installing and Setting Up SQL Server and SSMS

    • Installation of Microsoft SQL Server and SQL Server Management Studio (SSMS)
    • Guided installation process
    • Overview of SSMS interface and features
    • Introduction to SQL Server databases
    • Creating and managing databases in SSMS
  • Introduction to Microsoft Business Intelligence Tools

    • Installing and using SSIS (SQL Server Integration Services) and SSRS (SQL Server Reporting Services)
    • Overview of SSIS and SSRS interfaces
    • Exploring the capabilities of each tool
    • Basic concepts of ETL (Extract, Transform, Load) with SSIS
  • Data Extraction and Transformation with SSIS

    • Introduction to different flat file formats (CSV, TXT, Excel)
    • Data extraction using SSIS
    • Connecting to various data sources
    • Extracting data from flat files and databases
    • Data cleaning and transformation using SSIS
    • Applying basic transformations (Data Conversion, Conditional Split, etc.)
    • Handling common data quality issues
  • Data Preparation with Microsoft Excel

    • Cleaning CSV files using Microsoft Excel
    • Removing duplicates, handling missing values, data formatting
    • Preparing data for loading into SQL Server
    • Validating data and exporting it for SSIS processes
  • Building ETL Workflows with SSIS

    • Creating efficient ETL workflows with SSIS
    • Designing ETL packages
    • Avoiding common errors in ETL processes
    • Best practices for ETL process optimization
    • Error handling, logging, and performance tuning
  • SQL-Based Data Analysis and Reporting with SSRS

    • Introduction to SSRS and report creation
    • Overview of report types and their purposes
    • Designing and building basic reports
    • Practicing data analysis with SQL queries in SSRS
    • Writing SQL queries to retrieve and analyze data
    • Integrating SQL queries with SSRS reports
  • Advanced Data Transformation Techniques with SSIS

    • Implementing complex transformations in SSIS
    • Using advanced SSIS components (Lookups, Merge Joins, etc.)
    • Data enrichment and aggregation techniques
  • Installing SQL Workbench

  • Introduction to SQL

    • Overview of Database Management Systems(DBMS)
    • Basic SQL syntax and structure
    • Flow of SQL commands
    • Data Types in SQL
    • Running SQL commands using SELECT statement
    • Retrieving data from tables
  • Filtering, Sorting, and Aggregating Data

    • Using WHERE clause to filter data based on conditions
    • Sorting query results using ORDER BY clause
    • Limiting the number of results with LIMIT clause
    • Using aggregate functions for summary statistics
    • Grouping query results using GROUP BY clause
    • Filtering grouped data with HAVING clause
  • Table Joins and Case Statements

    • Case statements
    • Understanding relationships between tables
    • Using joins to combine data from multiple tables
  • Advanced Query Techniques

    • Further practice with different types of joins
    • Working with subqueries
    • Working with derived tables
    • Common table expressions (CTEs)
  • Window Functions and Data Modification

    • Working with window functions
    • Ranking data using RANK and DENSE_RANK
    • Modifying data in tables using ALTER, RENAME, INSERT, UPDATE, DELETE
    • Using UNION, INTERSECT, and EXCEPT to combine query results
  • Creating New Tables

    • Further practice with window functions
    • Creating new tables
    • Inserting values into new tables
    • Modifying and updating new tables
  • Advanced Topics and Artificial Intelligence

    • Working with Primary Keys
    • Auto-Increments
    • Updating Tables
    • Indexing
    • Using AI in SQL programming
  • Date Variables and Artificial Intelligence

    • Dealing with date variables in SQL data
    • Setting variables in SQL
    • Using AI in SQL programming
    • Wrapping up
  • Introduction to PowerBI/ Connecting & Shaping Data

    • Download and install Power BI Desktop, and adjust the settings.
    • Understand the role that Power BI plays within the broader Microsoft ecosystem
    • Explore core components of the Power BI Desktop interface
    • Review the business intelligence workflow.
    • Explore Power BI’s query editor and understand the role that Power Query plays in the larger BI workflow
    • Introduce different types of connectors and connectivity modes available for getting data into Power BI
    • Review tools for checking data quality and key profiling metrics like column distribution, empty values, errors, and outliers
    • Transform tables using text, numerical and date/time tools, pivot and group records, and create new conditional columns
    • Practice combining, modifying, and refreshing queries
  • Data Modeling

    • Understand the basic principles of data modeling, including normalization, fact & dimension tables, and common schemas
    • Create table relationships using primary and foreign keys, and discuss different types of relationship cardinality
    • Configure report filters and trace filter context as it flows between related tables in the model
    • Explore data modeling options like hierarchies, data categories, and hidden fields
  • DAX

    • Introduce DAX fundamentals and learn when to use calculated columns and measures
    • Understand the difference between row context and filter context, and how they impact DAX calculations
    • Learn DAX formula syntax, basic operators and common function categories (math, logical, text, date/time, filter, etc.)
    • Explore nested functions, and more complex topics like iterators and time intelligence patterns
  • Data Visualization

    • Review frameworks and best practices for visualizing data and designing effective reports and dashboards
    • Explore tools and techniques for inserting, formatting and filtering visuals in the Power BI Report view
    • Add interactivity using tools like bookmarks, slicer panels, parameters, tooltips, and report navigation
    • Learn how to configure row-level security with user roles
    • Optimize reports for mobile viewing using custom layouts
  • Introduction to Spatial Analysis

    • Understanding Spatial Data: Definition, significance, and examples of spatial data.
    • Spatial Data Types: Differentiating between vector and raster data.
    • Applications of Spatial Analysis: Overview of how spatial analysis is used in various fields such as environmental science, urban planning, and public health.
    • Introduction to GIS: Understanding Geographic Information Systems and their role in spatial analysis.
  • Introduction to QGIS

    • Getting Started with QGIS: Installing QGIS and familiarizing with the interface.
    • Basic Operations: Opening and viewing spatial data, navigating the map canvas, and managing layers.
    • Data Import and Export: How to import and export different spatial data formats in QGIS.
  • Creating Shapefiles (Point, Line, and Polygon)

    • Understanding Shapefiles: The structure and components of shapefiles.
    • Creating New Shapefiles: Step-by-step guide to creating point, line, and polygon shapefiles.
    • Editing Shapefiles: Adding, modifying, and deleting features in shapefiles.
  • Learning Basic Cartography

    • Map Elements: Understanding essential map elements (title, legend, scale, north arrow).
    • Design Principles: Color theory, symbolization, and layout design for clear and effective map-making.
    • Creating Maps: Practical exercises to design maps using QGIS.
  • Learning Basic Vector Analysis Tools

    • Spatial Queries: Selecting features based on spatial relationships (e.g., proximity, intersection).
    • Buffer Analysis: Creating buffers around features and understanding their applications.
    • Overlay Analysis: Performing spatial operations like intersect, union, and difference.
  • Creating Heatmaps

    • Understanding Heatmaps: Concept and applications of heatmaps in representing data density.
    • Generating Heatmaps in QGIS: Step-by-step guide to creating heatmaps from point data.
    • Customization: Adjusting parameters to refine the heatmap visualization.
  • Using Google Maps to Create Maps in QGIS

    • Integrating Google Maps: Methods to use Google Maps as basemaps in QGIS projects.
    • Georeferencing: Aligning external map images with spatial data using georeferencing tools.
    • Practical Exercise: Creating a map project in QGIS with Google Maps as the base layer.
  • Introduction to Raster Data

    • Understanding Raster Data: Definition, structure, and examples of raster data.
    • Raster Analysis Tools: Introduction to basic raster operations (e.g., map algebra, reclassification).
    • Working with Satellite Imagery: Basics of accessing and analyzing satellite imagery in QGIS.
  • Real-world Examples of GIS in Various Industries and Fields

    • Environmental Management: Use cases of GIS in conservation, pollution monitoring, and resource management.
    • Urban Planning: Applications in zoning, infrastructure development, and traffic management.
    • Public Health: Spatial analysis in epidemiology, access to healthcare facilities, and health outcome mapping.
    • Agriculture: Precision farming, crop yield analysis, and soil mapping.
  • Introduction to Business Intelligence

    • Concepts and Importance of BI: Understanding BI, its components, and its importance in decision-making.
    • BI vs. Data Science: Differentiating BI from Data Science and understanding their intersection.
    • Overview of BI Tools: Introduction to popular BI tools and technologies (e.g., Tableau, Power BI).
  • Data Warehousing and ETL Processes

    • Data Warehousing Concepts: Understanding data warehousing, data marts, and the role they play in BI.
    • ETL Processes: Introduction to Extract, Transform, Load (ETL) processes and tools.
    • Hands-on ETL Project: Practical exercise involving data extraction, data cleansing, transformation, and loading into a data warehouse.
  • SQL for Business Intelligence

    • Advanced SQL Queries: Writing complex SQL queries for data analysis and reporting.
    • SQL for Data Manipulation: Techniques for data manipulation and preparation for BI applications.
    • Hands-on SQL Project: Using SQL to solve a business problem and prepare data for analysis.
  • Data Visualization and Dashboarding

    • Principles of Data Visualization: Best practices for designing effective and informative visualizations.
    • Introduction to Tableau/Power BI: Getting started with a BI tool, setting up, and basic functionalities.
    • Dashboard Creation: Designing and developing interactive dashboards for business reporting.
  • Analytical Reporting and Decision Making

    • Creating Reports: Techniques for creating comprehensive and insightful reports.
    • Storytelling with Data: Learning how to tell a compelling story using data visualizations and reports.
    • Decision Making with BI: Understanding how to use BI reports and dashboards to make informed business decisions.
  • Advanced BI Tools and Techniques

    • Predictive Analytics in BI: Introduction to incorporating predictive analytics into BI for forecasting.
    • Real-Time BI: Understanding real-time BI and analytics for dynamic decision-making.
    • Hands-on Project with Advanced Tools: Applying predictive analytics and real-time data in BI projects.
  • Implementing BI Solutions

    • BI Strategy and Implementation: Planning and executing a BI project from start to finish.
    • Managing BI Projects: Best practices for managing BI projects and ensuring their success.
    • Case Study: Analysis of a successful BI implementation in a business.
  • Business Intelligence in Practice

    • Industry-Specific BI Applications: Exploring how BI is used in different industries such as finance, healthcare, retail, and more.
    • Emerging Trends in BI: Discussion on AI, machine learning in BI, and future directions.
    • Group Project: Developing a BI solution for a real-world business problem.
  • Installation

    • Introduction to the Python programming language and its applications
    • Setting up the Python environment: installation of Python and necessary libraries
    • Configuring the development environment: IDEs, text editors, and Jupyter Notebook
  • Python Basics

    • Introduction to Python: history, features, and advantages
    • Expressions and operators: arithmetic, assignment, comparison, and logical
    • Understanding type() function and type inference
    • Introduction to data structures: lists, tuples, and dictionaries
  • Python Basics

    • Recap of Python basics
    • Working with arithmetic operators: addition, subtraction, multiplication, division, modulus, and exponentiation
    • Using comparison operators: equal to, not equal to, greater than, less than, etc.
    • Logical operators: and, or, and not
    • Exploring advanced data types: sets and strings manipulation
  • Expressions, Conditional Statements & For Loop

    • Evaluating expressions: operator precedence and associativity
    • Introduction to conditional statements: if, elif, and else
    • Executing code based on conditionals.
    • Understanding the flow of control in conditional statements
    • Iteration using the for loop: range(), iteration over lists, and strings.
  • While loop, Break and Continue Statements, and Nested Loops

    • Working with while loop: syntax, conditions, and examples
    • Combining loops and conditionals
    • Using the break statement to exit loops prematurely.
    • Utilizing the continue statement to skip iterations.
    • Implementing nested loops for complex iterations
  • Functions

    • Introduction to functions: purpose, advantages, and best practices
    • Defining and calling user-defined functions
    • Parameters and arguments: positional, keyword, and default values
    • Return statement and function output.
    • Variable scope and lifetime
    • Function documentation and code readability
  • Exception Handling and File Handling

    • Understanding exceptions: errors, exceptions, and exception hierarchy
    • Handling exceptions using try-except blocks: handling specific exceptions, multiple exceptions, and else and finally clauses.
    • Raising exceptions and creating custom exception classes
    • File handling in Python: opening, reading, writing, and closing files.
    • Working with different file modes and file objects
  • Python Modules: NumPy and Matplotlib

    • Introduction to the NumPy module: features and applications
    • Working with multidimensional arrays: creation, indexing, slicing, and reshaping
    • Performing element-wise operations: arithmetic, logical, and statistical
    • Overview of the Matplotlib module: data visualization and plotting
    • Customizing plots: line properties, markers, colors, labels, and legends
  • Mathematics for Data Science

    • Vectors and Matrices: Definition, addition, scalar multiplication.
    • Matrix Multiplication: Concept and application in data transformation and neural networks.
    • Eigenvalues and Eigenvectors: Importance in dimensionality reduction techniques like PCA
    • Derivatives and Gradients: Understanding how changes in input affect changes in output.
    • Partial Derivatives and the Gradient Vector: Application in gradient descent and optimization.
    • Introduction to Optimization: Concept of loss functions and how gradients are used to minimize them.
    • Probability Theory: Basics and conditional probability.
    • Bayes' Theorem: Importance in machine learning for making predictions.
    • Descriptive Statistics: Mean, median, mode, variance, and standard deviation.
    • Distributions: Normal distribution and its significance in machine learning.
    • Beyond Gradient Descent: Introduction to stochastic gradient descent and mini-batch gradient descent.
    • Regularization Techniques: L1 (Lasso) and L2 (Ridge) regularization to prevent overfitting.
    • Introduction to Convex Optimization: Understanding convex functions and their relevance to machine learning optimization.
  • Introduction and Missing Value Analysis

    • Introduction to Exploratory Data Analysis (EDA)
    • Importance of EDA in data analysis
    • Steps involved in EDA
    • Handling missing values: identification, analysis, and treatment strategies
    • Imputation techniques for missing values
  • Data Consistency, Binning, and Outlier Analysis

    • Data consistency checks using fuzzy logic
    • Binning and discretization techniques for continuous variables
    • Outlier detection and analysis methods
    • Handling outliers: techniques for treatment or removal
  • Feature Selection and Data Wrangling

    • Importance of feature selection in EDA
    • Feature selection techniques: filter methods, wrapper methods, and embedded methods
    • Data wrangling: cleaning and transforming data for analysis
    • Handling categorical variables: encoding techniques
  • Inference, Hypothesis Testing, and Visualization

    • Inference and hypothesis testing in EDA
    • Common statistical tests: t-test, chi-square test, ANOVA, etc.
    • Visualization techniques for EDA: histograms, box plots, scatter plots, etc.
    • Hands-on practical session for complete EDA using a dataset
  • Machine Learning Performance Metrics and Naive Bayes

    • Evaluation metrics for classification problems: accuracy, precision, recall, F1 score, etc.
    • Introduction to Naive Bayes algorithm and its applications
    • Implementing Naive Bayes for classification tasks
  • Logistic Regression, SVM, Decision Trees, and Random Forests

    • Logistic Regression: theory, interpretation, and applications
    • Support Vector Machines (SVM): concepts, kernels, and use cases
    • Decision Trees: construction, pruning, and interpretability
    • Random Forests: ensemble learning and feature importance
    • Bagging and Boosting: techniques for improving model performance
  • Clustering Introduction, Partitioning Algorithms, and Cluster Evaluation

    • Introduction to clustering: unsupervised learning technique
    • Partitioning algorithms: K-means, K-medoids
    • Hierarchical clustering: agglomerative and divisive approaches
    • Density-based clustering: DBSCAN, OPTICS
    • Cluster evaluation metrics: silhouette coefficient, Davies-Bouldin index
  • Regression and Evaluation of Regression Methods

    • Introduction to regression analysis
    • Linear regression: assumptions, interpretation, and model evaluation
    • Evaluation metrics for regression: mean squared error, R-squared, etc.
    • Other regression methods: polynomial regression, ridge regression, lasso regression.
  • Introduction to NLP and Text Normalization

    • Overview of Natural Language Processing (NLP)
    • Techniques for text normalization: lowercasing, punctuation removal, etc.
  • Text Representation and Tokenization

    • Introduction to vectors in NLP: Bag of Words and Count Vectorizer
    • Basics of tokenization and stopword removal
  • Stemming, Lemmatization, and N-gram Language Models

    • Understanding and applying stemming and lemmatization
    • Introduction to N-gram language models
  • Markov Models and Language Model Evaluation

    • Basics of Markov models in NLP
    • Techniques for evaluating language models: probability smoothing and performance metrics
  • Text Classification Fundamentals

    • Overview of Text Classification
    • Introduction to Naive Bayes and Sentiment Classification
  • Advanced Classifiers and Vector Semantics

    • Generative vs. discriminative Classifiers
    • Understanding vector semantics and embeddings: TF-IDF theory and vector similarity
  • Neural Word Embeddings and Sequence Models

    • Introduction to neural word embeddings: Word2Vec and GloVe
    • Exploring sequence of words in NLP tasks
  • Transformers and Large Language Models

    • Overview of transformers and their impact on NLP
    • Introduction to large language models (LLMs) and their applications.
  • Introduction to Deep Learning

    • Overview of deep learning, its importance in computer vision, key concepts, and architectures.
    • Code along session for building Deep Neural Network from scratch
  • Deep Learning Hyperparameter Tuning

    • Strategies for optimizing hyperparameters like learning rate, batch size, and regularization to improve model performance.
  • Introduction to Convolutional Neural Networks (CNNs)

    • Explanation of CNNs, their architecture, and their role in image processing.
    • Code along session on Convolutional Neural Networks
  • Building Custom Image Classification Models

    • Step-by-step guide to creating and training a custom image classifier using a CNN.
  • Transfer Learning and Introduction to Object Detection

    • Introduction to transfer learning, its applications, and an overview of object detection techniques.
  • Hands-on with YOLO Object Detection

    • Practical session on using the YOLO (You Only Look Once) algorithm for object detection.
  • Custom Training YOLO model

    • Detailed guidance on training a YOLO model with a custom dataset for specific object detection tasks.
  • Using State-of-the-Art Models for Real-World Applications

    • Exploring and implementing advanced models in computer vision for practical use cases.
  • Introduction to OpenCV

    • Introduction to OpenCV, its libraries, and its importance in computer vision tasks.
  • Image Pre-processing and Pre-build Algorithms in OpenCV

    • Hands-on session on image pre-processing techniques and using built-in algorithms in OpenCV.
  • Advance guided project with OpenCV

    • Capstone project where participants apply learned techniques in a guided project using OpenCV.
  • Introduction to MLOps and AI/NLP Fundamentals

    • Overview of MLOps and its importance in the AI lifecycle
    • Current trends in AI
    • Setting up the development environment
  • Deep Dive into Machine Learning Models for NLP

    • Understanding NLP models (llama2, GPT, Mistral, etc.)
    • Introduction to Hugging Face Transformers and Datasets
    • Hands-on: Building a simple NLP model with Hugging Face
  • Introduction to FastAPI for ML Model Deployment

    • Basics of API development with FastAPI
    • Deploying a simple ML model with FastAPI
    • Hands-on: Creating your first ML API with FastAPI
  • Advanced FastAPI Features for Production-Ready APIs

    • Authentication and authorization in FastAPI
    • Hands-on: Enhancing your ML API with advanced features
  • Introduction to Docker for AI Applications

    • Basics of Docker and containerization
    • Building Docker images for AI/ML applications
    • Hands-on: Containerizing your FastAPI application
  • Leveraging Lang Chain and LangSmith for Enhanced NLP Applications

    • Introduction to Lang Chain and its Ecosystem
    • Overview of LangSmith for debugging, testing, evaluating, and monitoring LLM applications
    • Hands-on: Integrating Lang Chain with your NLP models and using LangSmith for enhanced capabilities
  • Advanced Model Deployment with Hugging Face and Lang Chain

    • Integrating Hugging Face models for advanced NLP capabilities
    • Exploring Lang Chain for building complex NLP applications
    • Hands-on: Deploying a Hugging Face model via FastAPI with LangSmith integration
  • Deploying ML Models on Google Cloud

    • Overview of Google Cloud Platform (GCP) for ML
    • Introduction to Google Cloud Run
    • Hands-on: Deploying your Dockerized FastAPI application on GCP with LangSmith monitoring.
  • Email Writing

    • Basics of Professional Email Communication: Structure, tone, and etiquette.
    • Writing Effective Subject Lines: Techniques to ensure your emails are opened.
    • Emails for Networking: Approaching professionals and mentors in data science/AI.
    • Follow-up Emails: Strategies for following up without being intrusive.
  • Report Writing + Presentations

    • Structure of a Data Science Report: Elements including abstract, methodology, results, and conclusion.
    • Visualizing Data: Incorporating charts, graphs, and other visual tools to enhance comprehension.
    • Creating Engaging Presentations: Tips for PowerPoint, storytelling, and engaging your audience.
    • Presentation Skills: Delivering your message confidently, handling Q&A sessions.
  • LinkedIn Optimization

    • Building a Professional Profile: Key components of a LinkedIn profile for data science/AI professionals.
    • Networking Strategies: Connecting with industry professionals and joining relevant groups.
    • Content Sharing and Creation: Establishing thought leadership by sharing insights, articles, and engaging with community content.
  • Resume/CV Writing

    • Tailoring Your Resume for Data Science/AI: Highlighting relevant skills, projects, and experiences.
    • Action Verbs and Quantifiable Achievements: Demonstrating impact in previous roles or projects.
    • Design and Layout: Making your resume/CV visually appealing and easy to read.
  • Cover Letter

    • Structure of a Cover Letter: Introduction, body, and closing.
    • Customizing Your Message: Researching the company and role to personalize content.
    • Highlighting Fit and Value: Articulating how your skills and experiences align with the job requirements.
  • Freelancing

    • Getting Started with Freelancing: Platforms for data science/AI freelancers, setting up a profile.
    • Finding Projects and Clients: Strategies to secure freelance work and build a portfolio.
    • Pricing Your Services: Understanding market rates and value-based pricing.
    • Client Management: Communicating effectively and managing expectations.
  • Kaggle for Data Science

    • Introduction to Kaggle: Overview of the platform, competitions, datasets, and notebooks.
    • Participating in Competitions: Tips for success, collaboration, and learning from the community.
    • Building a Portfolio: Using Kaggle to showcase your skills and projects to potential employers.
  • GitHub

    • Why GitHub for Data Scientists: Importance of version control and code sharing.
    • Creating and Managing Repositories: Best practices for organizing and documenting projects.
    • Collaborating on Projects: Contributing to open-source projects and collaborating with others.
    • GitHub as a Portfolio: Presenting your work and contributions to potential employers.
  • How to Crack Data Science Interviews

    • Understanding the Interview Process: Types of interviews (technical, behavioral, case studies).
    • Preparing for Technical Interviews: Common questions, coding challenges, and statistical questions.
    • Behavioral Interview Preparation: Crafting your story, STAR method for responses.
    • Mock Interviews: Practicing with peers or mentors to gain confidence.
  • Global Market Understanding

    • Data Science/AI Trends: Understanding global trends and emerging technologies.
    • Cultural Competence: Working in multicultural teams and serving diverse user bases.
    • Regulatory Environment: Overview of data privacy laws and ethical considerations in different regions.
  • AI Product Development

    • From Idea to Product: Ideation, validation, and development processes.
    • User-Centric Design: Incorporating user feedback and UX/UI principles.
    • Product Management for AI: Unique challenges in managing AI projects, iteration, and deployment.
    • Metrics and Performance: Evaluating the success and impact of AI products.
  • Storytelling Using Data

    • Principles of Data Storytelling: Crafting narratives that resonate with your audience.
    • Visual Narrative Techniques: Using data visualizations effectively in your story.
    • Engaging Presentations: Combining data, visuals, and narrative for impactful presentations.
  • Intro to Data Commons

    • Understanding Data Commons: Concept, importance, and examples.
    • Accessing and Contributing to Data Commons: Guidelines and best practices.
    • Leveraging Data Commons: How data scientists can use these resources for research and development.
Status: Siap