Data Analytics is a process of examining, cleaning, transforming and interpreting data.
- Helps businesses to identify patterns, draw conclusions and improve overall performance.
- For example, an e-commerce business can analyze real-time sales and inventory data to automatically adjust product prices based on demand. while a Bank can analyze transactions in real time to detect and block potentially fraudulent payments.
Foundations
This section explains the basic concepts of data, types of analytics and the difference between Data Science and Data Analytics.
Excel
This section covers how Excel is used for data cleaning, analysis, formulas, pivot tables, charts and dashboards.
- Introduction
- Basic Excel Formulas
- Sorting
- Filtering
- Conditional formatting
- Data Validation
- Removing duplicates
- Lookup functions: VLOOKUP, HLOOKUP, INDEX & MATCH
- Text functions: LEFT, RIGHT, MID, CONCATENATE
- IF Function
- Date Functions
- Creating pivot tables
- Charts
- Dashboards
Python
This section introduces Python basics and explains how it is used for data analysis and visualization.
- Introduction
- Download and Install
- Variables
- Data Types
- Operators
- Conditional Statements
- Loops
- Functions
- String
- Lists
- Dictionary
Data Analysis Libraries
This section focuses on important Python libraries used for data manipulation, numerical analysis, visualization and basic modeling.
- Pandas: Data manipulation and analysis
- NumPy: Numerical operations and matrix handling
- Matplotlib/Seaborn: Data visualization
Reading and Loading Datasets
This section explains how to import data from different file formats and prepare it for analysis.
Data Preprocessing
This section covers techniques used to clean, transform and prepare data before analysis.
Data Visualization
This section explains how charts and graphs are used to present data clearly and highlight key insights.
SQL
SQL is essential for working with structured data stored in databases. This section focuses on querying, filtering, aggregating and optimizing data for analysis.
- Introduction
- Installing MySQL/PostgreSQL
- CREATE DATABASE
- Queries
- Operators
- Aggregate functions
- Joins
- Subqueries
- Window Functions
- Date and Time Functions
- Data Cleaning: Duplicates, Missing values & Type casting
- Performance Basics: Indexes & Query optimization
Mathematics & Statistics
Mathematics and statistics provide the core logic behind data analysis.
Probability
- Basic probability: Sample space, Types of events, Probability Rules
- Conditional Probability
- Bayes' Theorem
- Probability distributions
Statistics
- Descriptive Statistics: Mean, Median, Mode, Variance, Standard deviation
- Inferential Statistics: Confidence Interval, Hypothesis Testing, Central Limit Theorem
- Skewness and Kurtosis
- Tests: T-test, F-Test, Z-test, Chi-square Test
- Correlation: Pearson, Spearman
Linear Algebra & Calculus
- Vectors
- Matrices
- Dot Product
- Linear Mapping
- Solving systems of linear equations
- Calculus: Differentiation, Gradient, Chain Rule
Exploratory Data Analysis (EDA)
Exploratory Data Analysis (EDA) helps understand data through summaries and visualizations. It is used to identify patterns, trends and potential issues in data.
- Introduction
- Univariate, Bivariate and Multivariate data analysis
- Visualization: Histograms, Boxplots, Q-Q plots
- Correlation and Covariance
- Cross-tabulation
- Factor & Canonical Correlation Analysis
Power BI
Power BI helps transform raw data into interactive dashboards and reports. This section focuses on data modeling, DAX calculations and visual storytelling.
- Introduction
- Data Sources and its type
- Power Query
- Data Modeling
- Merging & Appending queries
- Data Analysis Expressions (DAX)
- Creating measures using DAX
- Calculated columns using DAX
- Data Visualization With Multiple Charts
- Filters in Power BI
- Slicer In Power BI
- Dashboards
- Publishing & Sharing reports
- Row-Level Security (RLS)
Tableau
Tableau is a popular data visualization tool used to explore data and build interactive dashboards. It enables analysts to communicate insights effectively through visuals.
- Introduction
- Connecting to data sources
- Data Types
- Calculated fields
- Set in Tableau
- Operators
- Visualization
- Filtering in Visualization
- Dashboard in Tableau
- Layout & formatting in Dashboard
Projects
This section includes practical projects to apply the concepts covered in the tutorial.