The Women in Data Bootcamp is designed for women interested in data analysis and seeking to enhance their skills. Whether you’re a beginner with a passion for data or a professional looking to advance your career, this 12-week program offers hands-on learning, experienced women instructors, and a comprehensive curriculum.

• Understanding of computer systems
• Minimum of one-year technical experience
• Programming experience with Python & SQL would be a plus.
• Open to women of all backgrounds and experience levels.

Course Outline:

Women in Data Bootcamp Curriculum:

Introduction to Data Analysis

• Overview of data analysis and its applications
• Types of data: structured, unstructured, and semi-structured
• Introduction to data cleaning and preprocessing techniques
• Exploratory data analysis using visualization tools

Data Collection and Cleaning

• Data collection methods and sources
• Data quality assessment and cleaning techniques
• Handling missing data and outliers
• Data transformation and feature engineering

Databases – Relational

• Database Concepts
• SQL Queries
• Relational Concepts – Normalization

Data Visualization and Storytelling

• Principles of effective data visualization
• Visualization tools and libraries (e.g., Matplotlib, Tableau)
• Storytelling with data: conveying insights and narratives
• Interactive data visualization techniques

Statistical Analysis

• Descriptive statistics and measures of central tendency
• Probability distributions and hypothesis testing
• Correlation and regression analysis

Python Programming

• Basics of Python Programming
• Datatypes
• Functions and Modules
• File Handling
• Important Packages used in ML/ DL

Introduction to Machine Learning

• Supervised learning algorithms: linear regression, logistic regression
• Model evaluation and validation techniques
• Feature selection and dimensionality reduction
• Introduction to classification algorithms: decision trees and random forests
• Bias Variance Tradeoff
• Overfitting and Underfitting
• Unsupervised Learning Techniques – Clustering
• Distance Metrics used in clustering
• Prompt Engineering

Big Data and Cloud Computing

• Introduction to big data concepts and challenges
• Distributed computing frameworks: Hadoop and Spark
• Cloud computing platforms for data analysis
• Handling and processing large-scale datasets

Capstone Data Project

• Hands-on data projects using real datasets
• Applying data analysis and machine learning techniques
• Collaborating in teams and project management
• Presentation of project findings and insights

Career Development and Graduation

• Personality Assessment and personal leadership
• Understanding Culture and Team
• Improve Communication skills
• Graduation ceremony and celebration of achievements

Please note that the curriculum is designed to provide a comprehensive overview of the topics covered in the Women in Data Bootcamp. The actual content and specific tools used may vary based on the expertise of the instructors and the latest industry trends.

Learning Topics

Agile Scrum Methodology

• Scrum Introduction
• Scrum Team
• Scrum Artifacts
• Sprint Increment
• Spring planning
• Backlog
• Retrospective
• Project description and Case Study
• Practice exam and Knowledge check
• Certification (optional)

 Data Engineering Principles

• Data engineering to prepare data for downstream needs
• Build pipelines for batch processing and streaming processing
• Understanding different types of data

SDLC (Software Development Life Cycle)

• Software Engineering – Intro
• SDLC
• Various phases in detail
• Models
• Waterfall
• Spiral
• Prototyping
• Agile
• Software metrics (Size, FP)

SQL

• SQL Fundamentals
• Writing SQL Queries
• Working Tables and Indexes
• Predefined SQL functions
• Uses for SQL
• A/C/I/D
• Data Models
• Database Terminology
• Normalization
• First, Second & Third Normal Forms
• ANSI SQL
• Standardization & SQL Dialects
• DDL, DML, DTL, DQL, and DCL
• Data Integrity
• Types of Integrity
• Constraints
• Data Types
• Triggers
• What are Joins?
• Join Syntax
• Join Predicates
• Types of Joins
• The ‘using’ keyword

Python Programming – Fundamentals

Set Up

• Set up development environment – Jupyter notebooks
• Using python shell
• Executing python script
• Understanding python strings
• Print statements in python

Data Structures in python

• Integers
• Lists
• Dictionaries
• Tuple
• Sets
• File
• Mutable and Immutable structures

Selection and Looping Constructs

• If/else/elif statements
• Boolean type
• “in” membership
• For loop
• While Loop
• List and Dictionary Comprehension

Functions

• Defining functions
• Variable scope – Local and Global
• Arguments
• Polymorphisms

Modules

• Creating modules
• Importing Modules
• Different types of imports
• Dir and help
• Examining some built-in modules

Classes & Exceptions

• Object Oriented Programming Introduction
• Classes and Objects
• Polymorphism – Function and Operator Overloading
• Inheritance

Cloud Computing Foundations (GCP)

• Cloud Computing Overview
• Security with Google’s Cloud Infrastructure
• Understanding resource hierarchy
• IAM – Identity and Access Management
• Different IAM Roles
• Connecting to Google Cloud Platform
• Understanding different compute options
• Working with different Relational and NoSQL databases on GCP
• GCP Data Warehouse: BigQuery

Big Data Overview

• History and background of Big Data and Hadoop
• 5 V’s of Big Data
• Big Data Distributions in Industry
• Big Data Ecosystem before Apache Spark
• Big Data Ecosystem after Apache Spark
• Comparison of MapReduce Vs Apache Spark
• Big Data Ecosystem after Apache Spark
• Spark Clusters

Getting started with Apache Spark

• Understanding Apache Spark Components and Libraries
• Introduction to Pyspark
• Explore using Pyspark in Databricks Cloud Environment
• Pyspark code examples
• Working with Jupyter Notebook

Working with Spark SQL

• Getting started with Spark SQL
• Spark Context and Spark Session
• Performing basic data transformations with Spark SQL CLI
• Managing Tables with Spark SQL
• Spark SQL functions

What is “Data Science”?

• Overview of Data Science
• The Difference Between Business Analytics (BI), Data Analytics and Data Science
• The Field of Data Science
• The Data Science Process
• Define the Problem
• Get the Data
• Explore the Data
• Clean the Data
• Model the Data
• Communicate the Findings
• Identifying a problem and asking good questions

Descriptive Statistics Fundamentals

• Central Tendency
• Mean
• Median
• Mode
• Spread of the Data
• Variance
• Standard Deviation
• Range
• Relative Standing
• Percentile
• Quartile
• Inter-quartile Range

Inferential Statistics Fundamentals

• Inferential Statistics
  • Normal Distribution
  • Central Limit Theorem
  • Standard Error
  • Confidence Intervals
  • Other Distributions
  • Samples
  • Hypothesis Testing
• Perform statistical analysis on a given data set.

Data Exploration and Preparation

• Data Exploration
  • Describe
  • Merging
  • Grouping
  • Evaluating Features
• Data Visualization
  • Line Chart
  • Scatterplot
  • Pairplot
  • Histogram
  • Density Plot
  • Bar Chart
  • Boxplot
  • Customizing Charts
• Perform Exploratory Data Analysis
• Data Cleaning
  • Dropping Rows
  • Imputing Missing Values
  • Feature Evaluating
• Feature Engineering
• Data Transformation
• • One-Hot Encoding
  • Standardization
  • Normalization
• Test/Train Split
• Model Training

Machine Learning Overview

• History and Background of AI and ML
• Compare AI vs ML vs DL
• Describe Supervised and Unsupervised learning techniques and usages
• Machine Learning patterns
• Classification
• Clustering
• Regression
• Gartner Hype Cycle for Emerging Technologies
• Machine Learning offerings in Industry
• Discuss Machine Learning use cases in different domains
• Understand the Data Science process to apply to ML use cases
• Understand the relation between Data Analysis and Data Science
• Identify the different roles needed for successful ML project
• Prepare machine learning data using pipelines – Data manipulation

Generative AI Essentials

• Introduction to Generative AI
• Prompt Engineering
• Do’s and Don’ts of Generative AI
• Applications of Generative AI

Capstone Project & Use Case

• Project Overview
• Complete projects to get experience and practice
• Presentation of project findings and Insights
• Industry Use Case Studies

Certification (Optional)

• Certification Overview
• Identify the right certification for you
• Tips to prepare for certification

Training material provided: Yes (Digital format)