Data Analysis is essential for interpreting and extracting insights from data. This course covers data analytics basics, visualization, and key tools like Excel, SQL, and Python to help you make data-driven decisions.

No strict prerequisites are required, though a basic understanding of cloud platforms and data management concepts is helpful. Familiarity with data storage and database systems will be advantageous but is not mandatory.

Module 1: Introduction to Data Analytics

Part 1) Introduction to Data Analytics

• What is Data Analytics?

  • Definition and scope of data analytics
  • Key concepts and terminologies
  • Roadmap to become Data Analytics expert.

• Importance and Applications in Various Industries

  • How data analytics is transforming different sectors (e.g., finance, healthcare, retail, etc.)
  • Case studies of successful data analytics implementations.

• Overview of the Data Analysis Process

  • Steps in the data analysis process: data collection, data cleaning, data exploration, data modelling, and data visualization
  • Understanding the data analytics lifecycle

• Types of Analytics

  • Descriptive analytics: summarizing historical data
  • Diagnostic analytics: understanding why something happened
  • Predictive analytics: forecasting future trends
  • Prescriptive analytics: suggesting actions based on predictions

Part 2) Data Types and Data Collection

• Understanding Different Types of Data

  • Structured data: data organized in rows and columns (e.g., databases)
  • Unstructured data: data without a predefined format (e.g., text, images)
  • Semi-structured data: data with some organizational properties (e.g., XML, JSON)

• Methods of Data Collection

  • Primary data collection methods: surveys, interviews, observations
  • Secondary data collection methods: existing databases, reports, online resources
  • Introduction to big data and its characteristics (volume, velocity, variety, veracity)

• Introduction to Data Sources

  • Internal data sources: company databases, CRM systems
  • External data sources: social media, public datasets, web scraping

Part 3) Data Quality and Data Governance

• Importance of Data Quality

  • Impact of data quality on analysis outcomes
  • Key dimensions of data quality: accuracy, completeness, consistency, timeliness

• Data Cleaning Techniques

  • Identifying and handling missing data
  • Dealing with outliers and anomalies
  • Data transformation and normalization

• Data Governance Principles

  • Establishing data governance frameworks
  • Roles and responsibilities in data governance
  • Ensuring data privacy and security
  • Compliance with regulations and standards (e.g., GDPR, HIPAA)

Module 2: Python for Analytics

Part 1) Python Basics

• Introduction to Python

  • Installing Python and Setting Up the Environment
    • Downloading and installing Python
    • Setting up an integrated development environment (IDE) like Jupyter Notebook or PyCharm
    • Introduction to Python interactive shell and scripts

• Python Syntax and Basic Programming Concepts

  • Basic syntax and structure of Python programs
  • Variables, data types, and operators
  • Control flow statements: if-else, for loops, while loops
  • Functions and modules

• Data Structures and Libraries

  • Lists, Tuples, Dictionaries, and Sets
    • Understanding and using lists for ordered collections
    • Tuples for immutable sequences
    • Dictionaries for key-value pairs
    • Sets for unordered collections of unique items
  • Introduction to Libraries: NumPy and pandas
    • Overview of the NumPy library for numerical operations
    • Introduction to pandas for data manipulation and analysis
    • Installing and importing these libraries

• Data Manipulation with pandas

  • Reading and Writing Data
    • Loading data from various file formats (CSV, Excel, JSON)
    • Saving data to different formats
  • DataFrames and Series
    • Understanding pandas DataFrames and Series
    • Creating and manipulating DataFrames and Series
  • Data Cleaning and Transformation
    • Handling missing data
    • Filtering and selecting data
    • Applying functions to data
    • Merging and joining datasets

Part 2) Advanced Python for Analytics

• Data Visualization with Matplotlib and Seaborn

  • Creating Basic Plots: Line, Bar, Scatter, and Histograms
    • Introduction to Matplotlib for basic plotting
    • Creating line plots, bar charts, scatter plots, and histograms
  • Customizing Plots and Using Seaborn for Advanced Visualizations
    • Customizing plot appearance (labels, titles, legends, colors)
    • Introduction to Seaborn for statistical data visualization
    • Creating advanced visualizations with Seaborn

• Working with APIs and Web Scraping

  • Introduction to APIs
    • What are APIs and how they work
    • Using Python libraries to interact with APIs
  • Retrieving Data from APIs
    • Making API requests using the requests library
    • Parsing and processing JSON responses
    • Example of accessing a public API to retrieve data
  • Web Scraping Basics
    • Introduction to web scraping and its applications
    • Using libraries like BeautifulSoup and Scrapy
    • Extracting data from HTML pages

Module 3: Excel for Analytics

Part 1) Excel Basics

• Introduction to Excel

  • Excel interface and basic functions
  • Working with spreadsheets and data entry

• Data Manipulation in Excel

  • Sorting, filtering, and conditional formatting
  • Data validation and pivot tables

• Advanced Excel Functions

  • VLOOKUP, HLOOKUP, and INDEX-MATCH
  • Using macros and VBA for automation

Part 2) Data Visualization in Excel

• Creating Charts and Graphs

  • Bar charts, line charts, and pie charts
  • Customizing charts for better visualization

• Dashboard Creation

  • Combining charts and tables
  • Using slicers and timelines for interactive dashboards

Module 4: SQL for Data Analytics

Part 1) SQL Basics

• Introduction to SQL

  • What is SQL and its Importance in Data Analytics
    • Definition of SQL (Structured Query Language)
    • Historical background and development of SQL
    • Importance of SQL in managing and analyzing data in relational databases
  • SQL Syntax and Basic Queries
    • Basic structure and syntax of SQL queries
    • Writing simple queries to retrieve data from a database
    • Examples of basic SQL queries

• Data Retrieval and Manipulation

  • SELECT, FROM, WHERE, and JOIN Clauses
    • Using the SELECT statement to retrieve data
    • Specifying the source table with the FROM clause
    • Filtering results with the WHERE clause
    • Combining data from multiple tables using various types of JOINs (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN)
  • Inserting, Updating, and Deleting Data
    • Using the INSERT INTO statement to add new records
    • Updating existing records with the UPDATE statement
    • Deleting records with the DELETE statement

• Aggregations and Grouping

  • Using Aggregate Functions: COUNT, SUM, AVG, MIN, MAX
    • Explanation and use cases of aggregate functions
    • Writing queries to calculate counts, sums, averages, minimums, and maximums
  • GROUP BY and HAVING Clauses
    • Grouping data with the GROUP BY clause
    • Filtering groups with the HAVING clause
    • Examples of grouping and aggregation queries

Part 2) Advanced SQL

• Advanced Query Techniques

  • Subqueries and Nested Queries
    • Definition and use cases of subqueries
    • Writing nested queries for complex data retrieval
    • Correlated vs. uncorrelated subqueries
  • Common Table Expressions (CTEs)
    • Introduction to CTEs and their benefits
    • Writing and using CTEs for readable and maintainable SQL code
    • Recursive CTEs for hierarchical data

• SQL Functions and Stored Procedures

  • Built-in SQL Functions
    • Overview of common built-in SQL functions (e.g., string functions, date functions, numeric functions)
    • Practical examples of using built-in functions in queries
  • Creating and Using Stored Procedures
    • Introduction to stored procedures and their advantages
    • Writing and executing stored procedures
    • Passing parameters to stored procedures

• Data Warehousing and SQL Optimization

  • Introduction to Data Warehousing Concepts
    • Overview of data warehousing and its role in data analytics
    • Key components of a data warehouse (e.g., fact tables, dimension tables)
    • Introduction to data warehouse schemas (e.g., star schema, snowflake schema)
  • Query Optimization Techniques
    • Importance of query optimization for performance
    • Best practices for writing efficient SQL queries
    • Use of indexes, query execution plans, and other optimization techniques

Module 5: Azure Cloud for Data Analytics

Part 1) Introduction to Azure Cloud

• Overview of Azure Cloud

  • What is Cloud Computing?
    • Definition and advantages of cloud computing
    • Comparison of cloud computing with traditional on-premises computing
  • Introduction to Azure Services
    • Overview of Azure’s key services and features
    • Understanding Azure’s global infrastructure and regions

• Azure Data Services

  • Azure SQL Database
    • Features and benefits of Azure SQL Database
    • Use cases for Azure SQL Database in data analytics
  • Azure Data Lake and Azure Blob Storage
    • Introduction to Azure Data Lake and its architecture
    • Azure Blob Storage for unstructured data storage
    • Comparison between Azure Data Lake and Azure Blob Storage

• Azure Machine Learning and Analytics

  • Introduction to Azure Machine Learning
    • Key features and capabilities of Azure Machine Learning
    • Examples of machine learning applications using Azure
  • Using Azure Data Factory for ETL Processes
    • Overview of Azure Data Factory
    • Building ETL pipelines with Azure Data Factory

Part 2) Hands-on with Azure

• Setting Up Azure Environment

  • Creating and Managing Azure Resources
    • Steps to create an Azure account and access the Azure portal
    • Managing resources using Azure Resource Manager (ARM)
  • Cost Management and Optimization
    • Understanding Azure pricing and billing
    • Best practices for cost management and optimization in Azure

• Working with Azure Data Services

  • Connecting and Using Azure SQL Database
    • Setting up and configuring Azure SQL Database
    • Connecting to Azure SQL Database using SQL Server Management Studio (SSMS) and other tools
  • Storing and Retrieving Data from Azure Data Lake
    • Setting up Azure Data Lake Storage
    • Uploading and retrieving data using Azure Storage Explorer and Azure CLI

Module 6: ETL (Extract, Transform, Load)

Part 1) Introduction to ETL

• ETL Concepts

  • What is ETL?
  • Importance of ETL in Data Analytics

• Popular ETL Tools

  • Overview of Azure-Based ETL Tools:
    • Azure Data Factory
    • Azure Databricks
    • Azure Synapse Analytics
  • Various ETL Tools Available in the Market

• Hands-on with an ETL Tool (e.g., Azure Data Factory)

  • Setting Up Azure Data Factory
  • Creating Simple ETL Jobs

Part 2) Advanced ETL Techniques

• Data Transformation and Cleaning

  • Using ETL Tools for Data Transformation
  • Data Cleaning Techniques

• Integrating Multiple Data Sources

  • Extracting Data from Different Sources
  • Combining and Loading Data into Target Database

Module 7: Power BI for Data Visualization

Part 1) Introduction to Power BI

• Power BI Overview

  • What is Power BI?
  • Power BI interface and basic functions

• Data Import and Transformation

  • Importing data from various sources
  • Data transformation using Power Query

• Creating Visualizations

  • Basic visualizations: bar charts, line charts, pie charts
  • Customizing visualizations

• Paginated Reports

  • Introduction to Paginated Reports
  • Getting Started with Power BI Report Builder
  • Data Sources and Datasets
  • Designing Reports
  • Formatting Reports
  • Parameters and Filters
  • Exporting and Printing Reports
  • Publishing and Sharing Reports
  • Performance Optimization

• Understanding Power BI

  • Fundamentals
  • Installation and Pre-requisites
  • Know the Interface
  • Set up the environment
  • Getting the data

• Getting Data into Model

  • Performing Basic Transformations
  • Row Operations
  • Columns Operations
  • Text Operations
  • Numeric Operations
  • Conditional Formatting
  • Pivoting — unpivoting
  • Applying filters

• Visualizations

  • Column Charts
  • Line Charts
  • Combination Charts
  • Table
  • Maps
  • Forecast
  • Cards
  • Buttons
  • Pie [Donut]

• Interactions

  • Navigations
  • Drill through
  • Dynamic display of charts
  • Interactivities using Filters
  • Buttons

• Power BI Service

  • Knowing the Interface
  • Publishing Reports
  • Exploring Workspaces
  • Data Gateways
  • Dashboard
  • Scheduled Refresh
  • Apps
  • Row Level Security
  • Page Level Security

Module 8: Case Study and Practical Applications

• Implementing a data visualization project using Power BI
• Practical session with hands-on exercises

Module 9: Core Skill and LinkedIn Profile Optimization

• Certifications
• Networking
• Resume preparation
• Best practices on how to optimize your LinkedIn profile
• Continuous Learning
• Domain Knowledge
• Communication Skills
• Collaborate with cross-functional teams

Module 10: Certifications

• Cracking Microsoft Certified: Power BI Exam — Resources, Strategy, Mindset
• Preparation Strategy