Data Analysis Course

Excel skills enable users to manage, modify, and analyze massive datasets with ease. It includes vital components like PivotTables and PivotCharts as well as sophisticated operations like VLOOKUP, INDEX, MATCH, and IF expressions. Advanced Excel is well known and frequently used in data analysis because of its adaptability, flexibility, and user-friendly interface.

• The basic IT functions
• Creating and using a range of name
• Locate formulas
• IFERROR
• VLOOKUP & HLOOKUP
• INDEX & MATCH
• Analyzing Your Data
  • Set up Pivot tables
  • Grouping your data
  • Amend Pivot Tables with new data
  • Use a slicer to filter your data
  • Combining slicers to more than one Pivot Table
  • Using a timeline
  • Organize a Pivot chart
• Reducing Your Audit Risk
  • Data Recognition
  • Use of Trace Precedents
  • Use of Trace Dependents
  • Eliminate Arrows
  • Flaws checking
  • Check out Formula
  • Watch Window
• Enhance Your Workflow
  • Set up Macro security
  • Recording Macros
  • How to edit Macro
  • Understanding the VBA edit window
  • Allow & run a Macro from the ribbon
  • Saving & using a Macro-enabled Workbook
  • Deleting your Macro
• Additional Topics
  • Scenarios
  • Goal Seek

The fundamental tools and methods for analyzing and interpreting data are provided by mathematics and statistics, which together constitute the cornerstone of data analytics. Professionals that are proficient in mathematics may identify trends, make precise forecasts, and use cutting-edge modeling approaches.

• Descriptive
• Variability
• Distribution
• Probability
• Linear Functions
• Linear Algebra
• Vectors
• Matrices
• Tensors

The fast retrieval and analysis of data from relational databases is made possible by SQL (Structured Query Language), which is a key component of data analysis. It makes it easier to analyze massive datasets accurately, and knowing SQL is essential for successful data analysis procedures. It is important to have a thorough grasp of SQL in order to deal with data efficiently and get insightful information.

• DDL and DML in MySQL and setup
• ERD Diagrams and Relational Mapping
• Data normalization
• Basic Queries
• Database Manipulation
• Table Manipulation
• Relational Algebra
• Advanced SQL - Joining, Subquery, Views
• Database Security
• Multiple Activities to Perform

Developers may build a strong portfolio displaying their coding skills and making their work available to a larger audience by using Git and GitHub. This platform provides an effective way to exchange code, cooperate with other members of the coding community, and showcase one's abilities, eventually promoting one's professional development and exposure.

Python, a popular programming language, is significant in data analysis because of its adaptability, sizable library, and simple syntax. Data analysts use Python to manage data effectively, carry out data analysis and manipulation activities, build predictive models, and produce aesthetically pleasing visualizations.

• Python Setup and What is Python?
• Data Types and Syntax
• Comparison Operators
• Python Loop
• Python Statements
• Logical Operators
• Methods and Functions
• Error and Exception Handling
• Modules Packages and libraries
• Debugging
• Advanced python Modules (DateTime)
• File Management
• Multiple Activities to Perform
• Multiple Projects to Build

Machine learning, a key component of artificial intelligence, enables computers to learn from data and generate predictions on their own without human interaction. By gaining important insights from large datasets and creating prediction models, it plays a significant part in data analysis.

• Data Preprocessing
• Supervised Learning
• Unsupervised Learning
• Regression Models
  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Random Forest Regression
  • Topics such as
    • Assessing a Regression Model
    • Bias vs Variance
    • Regularisation
    • Gradient Descent
• Classification Models
  • Decision Tree Classification
  • K-Nearest Neighbor
  • Logistic Regression
  • Naïve Bayes
  • Random Forest Classification
  • Support Vector Machines
  • Additional Topics
    • Assessing a Classification Model
    • Adaboost
    • Gradient Boosting
    • XGBoost
    • Grid Search CV
• Clustering Models
  • Hierarchical
  • K-Means Clustering
• Association
  • Apriori
  • Eclat
• Build Dashboards for Data Visualisation
• Solved Sample Code Files for easy practice
• Access to Multiple Datasets
• 7+ Real world data projects

R is a highly regarded programming language in the realm of data analysis for its powerful statistical analysis capabilities, comprehensive library support, and graphics facilities. It is favored by statisticians and data analysts for its proficiency in data manipulation, statistical model building, and advanced data visualization.

• R Introduction
• R Installing
• R Syntax
• Comments, Variables and Data types in R
• Numbers, Math, Strings, Booleans and Operators in R
• IF, IF Else, Else if and Nested IF in R
• Loops in R
• Functions in R
• Data Structures in R
• Graphics in R
• R Statistics
• Final Project of Data Analysis with R

ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are data integration processes used to extract data from various sources, transform it into a desired format, and load it into a target data warehouse or data lake for analysis and reporting. Both ETL and ELT play a crucial role in consolidating, cleaning, and preparing data for business intelligence and analytics purposes.

• Introduction to ETL/ELT
  • Extract
  • Transform
  • Load
• Software Tools
  • Talend Studio
  • Apache Hadoop
  • Apache Kafka
• Real-world data projects

Power BI is a powerful data visualization tool that enables analysts to create interactive dashboards, reports, and visualizations. With its user-friendly interface and robust features, Power BI empowers users to transform raw data into insightful and visually engaging representations. Analysts can easily explore data, identify trends, and communicate findings effectively, making Power BI an indispensable tool for data-driven decision-making.

• Introduction to Power BI
  • Overview of Power BI
  • Advantages of Power BI
  • Power BI components
  • Power BI service vs Power BI desktop
• Data Sources and Connections
  • Data sources overview
  • Connecting to different data sources
  • Working with data in Power BI
• Data Transformation and Modeling
  • Data transformation and cleaning
  • Data modeling
  • Creating relationships between data tables
• Visualization
  • Basic visualization types
  • Customizing visualizations
  • Working with visuals and filters
• DAX Functions
  • Introduction to DAX functions
  • DAX formulas and expressions
  • Aggregating and summarizing data with DAX
• Sharing and Collaboration
  • Sharing and publishing reports
  • Managing access to reports
  • Collaboration with Power BI
• Advanced Topics
  • Advanced data modeling
  • Custom visuals and extensions
  • Power BI embedded
• Case Studies and Hands-On Projects
  • Real-world case studies
  • Hands-on projects with Power BI

Tableau is a user-friendly business intelligence and data visualisation application that creates interactive visualisations from data to help organisations make decisions based on that data.

• Tableau Installation

  Installing Tableau on your computer will allow you to undertake data analysis.

• Tableau UI

  You will learn how to utilise the Tableau user interface to do research and create data visualisations.

• Tableau UI Components

  Utilising elements for building visualisations, including as sheets and legends, using Tableau UI components.

• Tableau Marks Card

  Manages how data points look in a display.

• Tableau Functions

  Understanding built-in data manipulation algorithms using Tableau Functions.

• Filters in Tableau

  Using tools for data focus and visualisation refinement, such as filters in Tableau.

• Forecasting with Tableau

  Utilise automated predictions to forecast future data patterns when forecasting using Tableau.

• Parameters

  Customising an interactive visualisation by working with dynamic values.

• Measures

  The process for using quantitative data in calculations.

• Dimensions

  Data categorization and grouping using categorical variables.

Project In Tableau

• House Data Dashobard - Project I
• Jobs Data Dashboard - Project II

Learn Tkinter, a well-known GUI toolkit, to create one-of-a-kind bespoke programs with eye-catching interfaces. Additionally, find out how to use Python's integrated SQLite database to quickly save and retrieve data within your programs, improving their usability and usefulness. You gain the knowledge and abilities necessary to develop data-driven, interactive apps that match your unique needs thanks to this add-on module.