Data Analytics

• Introduction to Python
  • What is Python and why use it?
  • Setting up the Python environment
  • Basic syntax and execution flow
  • Writing your first Python script
• Variables and Data Types
  • Understanding variables and basic data types (integers, floats, strings)
  • Type casting and data type conversion
• Control Flow
  • Making decisions with if, elif, and else
  • Looping with for and while
  • Controlling loop flow with break and continue
• Data Structures (Part 1)
  • Lists: Creation, indexing, and list operations
  • Tuples: Immutability and tuple operations
• Data Structures (Part 2)
  • Sets: Usage and set operations
  • Dictionaries: Key-value pairs, accessing, and manipulating data
• Functions
  • Defining functions and returning values
  • Function arguments and variable scope
  • Anonymous functions: Using lambda
• File Handling
  • Reading from and writing to files
  • Handling different file types (text, CSV, etc.)
• Error Handling and Exceptions
  • Try and except blocks
  • Raising exceptions
  • Using finally for cleanup actions

• Introduction to Excel
  • Overview of Excel’s interface and features
  • Basic spreadsheet operations: entering data, formatting cells, sorting and filtering
  • Introduction to formulas and cell references
  • Summarizing data with SUM, AVERAGE, MIN, MAX, COUNT
• Working with Data
  • Data types and best practices for data entry
  • Using ranges, tables, and data validation
  • Understanding date and time functions
  • Conditional functions like IF, COUNTIF, SUMIF
• Mastering Excel Functions
  • Exploring logical functions: AND, OR, NOT
  • Mastering lookup functions: VLOOKUP, HLOOKUP, INDEX, MATCH
  • Nesting functions for complex calculations
  • Text functions to manipulate strings
• Data Visualization
  • Creating and customizing charts
  • Using conditional formatting to highlight data
  • Introduction to PivotTables for summarizing data
  • PivotCharts and slicers for interactive reports
• Advanced Data Analysis Tools
  • Exploring What-If Analysis tools: Data Tables, Scenario Manager, Goal Seek
  • Solving complex problems with Solver
  • Introduction to array formulas for complex calculations
• Introduction to Macros and VBA
  • Recording and running macros
  • Writing simple VBA scripts to automate repetitive tasks
  • Customizing the Excel environment with VBA
• Integration and Power Tools
  • Linking Excel with other Office applications
  • Using Power Query to import and transform data
  • Overview of Power Pivot for data modeling
  • An introduction to dashboard creation
• Capstone Project
  • Using Excel as part of a data analysis project
  • Integrating knowledge from Python and Excel to analyze a dataset
  • Presenting insights and telling stories with data

• Introduction to SQL and Database Concepts
  • Overview of relational databases
  • Basic SQL syntax and setup
  • SELECT and FROM clauses to retrieve data
  • Sorting and filtering data with ORDER BY and WHERE
• Working with SQL Joins and Aggregations
  • Understanding different types of joins: INNER, LEFT, RIGHT, and FULL
  • Using aggregate functions like COUNT, SUM, AVG, MIN, and MAX
  • Grouping data with GROUP BY
  • Filtering grouped data using HAVING
• Advanced SQL Operations
  • Subqueries: using subqueries in SELECT, FROM, and WHERE clauses
  • Common Table Expressions (CTEs) and WITH clause
  • Advanced data manipulation with INSERT, UPDATE, DELETE, and MERGE
• Mastering SQL Functions and Complex Queries
  • String functions, date functions, and number functions
  • Conditional logic in SQL with CASE statements
  • Advanced use of data types and casting
• Exploring SQL Window Functions
  • Introduction to window functions
  • Using OVER() with PARTITION BY, ORDER BY
  • Functions like ROW_NUMBER(), RANK(), DENSE_RANK(), LEAD(), LAG()
• SQL Performance Tuning
  • Understanding indexes, including when and how to use them
  • Query optimization techniques
  • Using EXPLAIN plans to analyze query performance
• Transaction Management and Security
  • Understanding transactions, ACID properties
  • Implementing transaction control with COMMIT, ROLLBACK
  • Basics of database security: permissions, roles
• Integrating SQL with Other Technologies
  • Linking SQL databases with programming languages like Python
  • Using SQL data in Excel via ODBC, direct queries
  • Introduction to using APIs with SQL databases for web integration
• Advanced Data Analytics Tools in SQL
  • Using analytical functions for deeper insights
  • Exploring materialized views for performance
  • Dynamic SQL for flexible query generation
• Capstone Project
  • Designing and implementing a database schema for a real-world application
  • Comprehensive data analysis using advanced SQL techniques
  • Integrating SQL knowledge with tools like Python and Excel to provide business solutions
  • Presenting findings and insights effectively

•  Understanding NoSQL
  • Overview of NoSQL
    • Definition and evolution of NoSQL databases.
    • Differences between NoSQL and traditional relational database systems (RDBMS).
  • Types of NoSQL Databases
    • Key-value stores, document stores, column stores, and graph databases.
    • Use cases and examples of each type.
• NoSQL Concepts and Data Models
  • NoSQL Data Modeling
    • Understanding NoSQL data modeling techniques.
    • Comparing schema-on-read vs. schema-on-write.
  • Advantages of NoSQL
    • Scalability, flexibility, and performance considerations.
    • When to choose NoSQL over a traditional SQL database.
• Getting Started with MongoDB
  • Installing MongoDB
    • Setting up MongoDB on different operating systems.
    • Understanding MongoDB’s architecture: databases, collections, and documents.
  • Basic Operations in MongoDB
    • CRUD (Create, Read, Update, Delete) operations.
    • Using the MongoDB Shell and basic commands.
• Working with Data in MongoDB
  • Data Manipulation
    • Inserting, updating, and deleting documents.
    • Querying data: filtering, sorting, and limiting results.
  • Indexing and Aggregation
    • Introduction to indexing for performance improvement.
    • Basic aggregation operations: $sum, $avg, $min, $max, and $group.

• Introduction to Exploratory Data Analysis
  • Overview of EDA
    • The importance and objectives of EDA.
    • Key steps in the EDA process.
• Data Handling with Pandas
  • Getting Started with Pandas
    • Introduction to Pandas DataFrames and Series.
    • Reading and writing data with Pandas (CSV, Excel, SQL databases).
  • Data Cleaning Techniques
    • Handling missing values.
    • Data type conversions.
    • Renaming and replacing data.
  • Data Manipulation
    • Filtering, sorting, and grouping data.
    • Merging and concatenating datasets.
    • Advanced operations with groupby and aggregation.
• Numerical Analysis with NumPy
  • Introduction to NumPy
    • Creating and manipulating arrays.
    • Array indexing and slicing.
  • Statistical Analysis with NumPy
    • Basic statistics: mean, median, mode, standard deviation.
    • Correlations and covariance.
    • Generating random data and sampling.
• Visualization Techniques
  • Using Matplotlib
    • Basics of creating plots, histograms, scatter plots.
    • Customizing plots: colors, labels, legends.
  • Advanced Visualization with Seaborn
    • Statistical plots in Seaborn: box plots, violin plots, pair plots.
    • Heatmaps and clustermaps.
    • Facet grids for multivariate analysis.

• Introduction to Machine Learning
  • Overview of Machine Learning*
    • Definitions and significance of machine learning.
    • Types of machine learning: supervised, unsupervised, and reinforcement learning.
  • Key Concepts and Terminology*
    • Features, labels, training sets, and test sets.
    • Overfitting and underfitting.
• Machine Learning with Python
  • Using Scikit-Learn
    • Introduction to the Scikit-Learn library.
    • Building simple models: linear regression and logistic regression.
  • Model Evaluation
    • Splitting data into train and test sets.
    • Understanding key metrics: accuracy, precision, recall, F1 score.

• Introduction to Power BI
  • Overview of Power BI
    • Introduction to business intelligence and the role of Power BI.
    • Differences between Power BI Desktop, Service, and Mobile.
  • Setting Up and Importing Data
    • Connecting to data sources like Excel and SQL databases.
    • Basic data transformations using Power Query.
• Data Modeling and Introduction to DAX
  • Creating Data Models
    • Understanding and creating relationships between tables.
    • Overview of data model best practices.
  • Introduction to DAX
    • Basic DAX functions for calculated columns and measures.
    • Introduction to more complex DAX expressions.
• Building Reports and Advanced DAX
  • Designing Interactive Reports
    • Choosing and configuring visualizations effectively.
    • Best practices in report layout and design.
  • Advanced DAX and Data Analysis
    • Time intelligence functions.
    • Practical examples of advanced DAX scenarios.
• Publishing, Sharing, and Capstone Overview
  • Publishing and Sharing
    • How to publish reports to Power BI Service.
    • Sharing dashboards and managing access.
  • Capstone Project Brief and Initiation
    • Overview of capstone project requirements.
    • Initial planning and dataset selection for the project.

• Introduction to Cloud Services
  • Cloud Computing Fundamentals
    • What is cloud computing?
    • Service models: IaaS, PaaS, SaaS.
    • Deployment models: public, private, hybrid cloud.
• Cloud Platforms Overview
  • Common Cloud Platforms
    • Brief overview of AWS, Azure, and Google Cloud Platform.
    • Key services from these platforms (e.g., AWS EC2, AWS S3, Azure VMs, Google Compute Engine).

• Introduction to Web Scraping
  • What is Web Scraping?
    • The legal and ethical considerations of scraping data from websites.
    • Common use cases in data analytics and business intelligence.
• Tools and Techniques
  • Using Python for Scraping
    • Introduction to BeautifulSoup and requests library.
    • Extracting data from HTML: tags, IDs, classes.
  • Handling Web Data*
    • Working with APIs using Python.
    • Cleaning and storing scraped data.

• Understanding ChatGPT
  • How ChatGPT Works
    • Introduction to natural language processing (NLP) and transformers.
    • Overview of the GPT architecture and training methods.
  • Setting Up ChatGPT
    • Accessing ChatGPT via API.
    • Basic configurations and settings for analytics use cases.
• Using ChatGPT in Data Cleaning
  • Automating Data Preprocessing
    • Using ChatGPT to identify and correct errors in datasets.
    • Examples of scripting ChatGPT to automate data cleaning tasks.
  • Text Data Manipulation
    • Leveraging ChatGPT for text normalization and extraction.
    • Generating summaries from large text datasets to identify trends and patterns.
• Advanced Data Analysis
  • Querying Data with ChatGPT
    • How to use ChatGPT to generate SQL queries.
    • Extracting data insights using conversational AI.
  • Enhancing Data Visualization
    • Integrating ChatGPT with visualization tools (e.g., Tableau, Power BI).
    • Generating narrative descriptions for charts and graphs.
• Implementing ChatGPT in Real-World Scenarios
  • Case Studies
    • Examples of businesses effectively using ChatGPT in their data analytics.
    • Discussing successful implementations and measured outcomes.
  • Ethical Considerations and Best Practices
    • Understanding the ethical implications of using AI in data analytics.
    • Best practices for maintaining data privacy and integrity.

Capstone Project: Apply Your Knowledge to Real-World Challenges

• • Overview
    • Our Data Analytics course culminates in a Capstone Project, a cornerstone of our hands-on learning approach. This project challenges you to apply the comprehensive skills you’ve acquired to solve real-world problems. Through this immersive experience, you’ll showcase your ability to integrate and utilize data analytics techniques in practical scenarios.
  • Objectives
    • Synthesize Learning: Draw upon the entire course’s content to demonstrate your comprehensive understanding of data analytics tools and methodologies.
    • Solve Complex Problems: Tackle a complex issue from a relevant industry, using data to drive decisions and create impactful solutions.
    • Professional Development: Prepare for the professional world with a project that mirrors the challenges and complexities of industry-specific analytics tasks.
• Project Scope
  Each student will select a project from a pool of options across various sectors such as finance, healthcare, retail, and more. These projects are designed in collaboration with industry leaders to ensure relevance and challenge. You will:
  
  • • Analyze large datasets to uncover trends, solve problems, and predict outcomes.
    • Create compelling visualizations and reports to communicate your findings effectively.
    • Implement predictive models that can be used to make data-driven decisions.
• Support and Resources
  Throughout the Capstone Project, you will receive:
  
  • Guidance from Experienced Mentors:* Regular sessions with industry experts who provide insights and feedback.
  • Peer Collaboration:* Opportunities to collaborate with classmates to exchange ideas and refine approaches.
  • Access to Advanced Tools:* Full access to all software and tools covered during the course, including Python, SQL, PowerBI, and MongoDB.
• Deliverables
  By the end of the project, you are expected to deliver:
  • A Comprehensive Analysis Report:* Documenting your data analysis process, insights, and recommendations.
  • A Data-Driven Presentation:* A final presentation to faculty and peers, showcasing your approach and solutions.
  • A Reflective Essay:* Reflecting on the project process, what you learned, and how you can apply these skills in your future career.
• Why This Matters
  The Capstone Project is not only a test of your analytical abilities but also a chance to experience the life of a data analyst. It’s an invaluable part of your portfolio that demonstrates your capability to potential employers. This project will equip you with confidence and practical skills to excel in your career, making you a sought-after professional in the field of data analytics.

Join us and turn your analytical skills into real-world success.