Data Analytics is a process of examining, cleaning, transforming and interpreting data to discover useful information, draw conclusions and support decision-making. It helps businesses and organizations understand their data better, identify patterns, solve problems and improve overall performance.
Foundations of Data Analytics
Foundations of Data Analytics cover the basic concepts required to understand data types, analytical methods and workflows. This section builds a strong base for working with data and drawing meaningful insights.
Mathematics & Statistics for Data Analytics
Mathematics and statistics provide the core logic behind data analysis. This section helps in understanding data patterns, measuring uncertainty and making data-driven decisions
- Linear Algebra : Vectors, Matrices, Linear Combinations, Matrices Operations, Solving systems of linear equations,
- Probability: Random Variables, Sample space, Types of events, Probability Rules, Conditional Probability, Bayes' Theorem, Probability distributions
- Statistics: Descriptive Statistics, Inferential Statistics, Skewness & Kurtosis, Confidence intervals, Hypothesis testing, P-value, Type I and II errors, T-test, F-Test, Z-test, Chi-square Test, ANOVA, Pearson, Spearman.
- Calculus : Differentiation, Gradient, Gradient Descent, Chain Rule
Python for Data Analytics
Python is a widely used language in data analytics for data cleaning, analysis and visualization. Its simple syntax and rich libraries make it ideal for handling real-world datasets efficiently.
- Introduction
- Download and Install
- Variables
- Data Types
- Operators
- Conditional Statements
- Loops
- Functions
- String
- Lists
- Dictionary
Data Analysis Libraries
Gain hands-on experience with the most powerful Python libraries:
- Pandas: Data manipulation and analysis
- NumPy: Numerical operations and matrix handling
- Matplotlib/Seaborn: Data visualization
- Scikit-learn: Data preprocessing and statistical modeling
Reading and Loading Datasets
Reading and loading datasets is the first step in data analysis. This section focuses on importing data from various sources into tools like Python and Excel for analysis.
- Reading CSV, Excel & JSON files
- Exporting dataframes to CSV/JSON
- Slicing, Indexing, Manipulating & Cleaning DataFrames
Data Preprocessing
Data preprocessing involves cleaning and transforming raw data into a usable format. It ensures data quality and prepares datasets for accurate analysis.
- Introduction
- What is Data Cleaning
- Handling Missing Data
- Handling outliers
- Data Transformation
- Feature Engineering
- Data Sampling
Data Visualization
Data visualization uses charts and graphs to present data clearly. It helps analysts communicate insights and make data easier to understand.
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.
- Univariate, Bivariate and Multivariate data analysis
- Visualization: Histograms, Boxplots, Q-Q plots
- Correlation and Covariance
- Cross-tabulation
- Cluster Analysis, Factor & Canonical Correlation Analysis
Time Series Data Analysis
Time series analysis focuses on data collected over time. It helps identify trends and seasonality to support reporting and basic forecasting
- Define Time Series Data
- Data and Time function in Python
- Time Series Data Plotting
- Deal with missing values in a Time series
- Moving Averages : Stationarity, Seasonality, Trend
SQL for Data Analytics
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
- Filtering & Logic
- Aggregate functions
- Joins
- Subqueries
- Window Functions
- Date and Time Functions
- Data Cleaning: Duplicates, Missing values & Type casting
- Performance Basics: Indexes & Query optimization
Excel for Data Analytics
Excel is a beginner-friendly tool for data cleaning, analysis and visualization. It is widely used for quick analysis, reporting and dashboard creation.
- 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
Power BI for Data Analytics
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 for Data Analytics
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
Machine Learning for Data Analytics
Machine Learning helps data analysts discover patterns and make predictions from historical data. This section introduces core models used in analytics-focused ML tasks.
- Introduction
- Supervised learning
- Unsupervised learning
- Regression Techniques
- Linear Regression
- Logistic Regression
- Classification Algorithms
- Decision Trees
- Random Forest
- Model evaluation metrics
- Basic model deployment concepts
- Big Data Analytics
You are now ready to explore real-world projects. For detailed guidance and project ideas refer to below article: