Data Analytics Bootcamp

• Introduction to Data Literacy and Data?

• • • Definition and Characteristics of Data
    • Types of Data: Structured, Unstructured, Semi-Structured
    • Examples of Data in Various Contexts (e.g., Business, Healthcare, Education)

       

• Data Collection and Sources

• • • Methods of Data Collection: Surveys, Interviews, Observations, Sensors, etc.
    • Primary vs. Secondary Data Sources
    • Understanding Bias and Reliability in Data Collection

       

• Data Representation Techniques

• • • Introduction to Data Visualization
    • Principles of Effective Data Visualization
    • Types of Charts and Graphs: Bar Charts, Line Graphs, Pie Charts, Scatterplots
    • Choosing the Right Visualization for Different Types of Data

       

• Introduction to Data Analysis

• • • Overview of Data Analysis Process
    • Descriptive vs. Inferential Statistics
    • Key Statistical Measures: Mean, Median, Mode, Standard Deviation, Range

• Introduction to Excel for Data Analysis

• • • Basics of Excel: Data Entry, Formulas, Functions
    • Sorting and Filtering Data
    • Introduction to PivotTables for Data Summarization and Analysis

• Data Ethics and Privacy

• • • Understanding Data Privacy and Security
    • Ethical Considerations in Data Collection, Analysis, and Sharing

• Real-world Applications of Data Literacy

• Case Studies: Examples of Data Literacy in Different Industries (e.g., Marketing, Healthcare, Education)

Introduction to Data Communication

• • Importance of effective data communication in decision-making.

     

    Understanding Data Interpretation
    • Identifying trends, patterns, outliers, and correlations in data.
    • Explaining the significance of findings to stakeholders.

       

      Storytelling with Data
      • Emphasizing the importance of narrative structure in data communication.
      • Structuring presentations or reports in a narrative format.

        Data Narrative Techniques

        • Crafting compelling data narratives using anecdotes, case studies, and real-world examples.

           

          Data Communication Best Practices
          • Avoiding jargon and using plain language to communicate effectively.
          • Providing context to help stakeholders understand the significance of the data.

             

            Data Presentation Skills
            • Public speaking tips for delivering presentations confidently.
            • Slide design principles for creating visually appealing presentations.

               

              Data Storyboarding
              • Introduction to storyboarding as a planning tool for data presentations.
              • Creating a visual roadmap for organizing key points in a presentation.

                 

                Feedback and Iteration
                • Importance of seeking feedback from stakeholders.
                • Incorporating feedback into data communication efforts for continuous improvement.

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.

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

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 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.

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

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 Analysis 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.
  
  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.