✅ Phase 1: Introduction to Data Analytics
- Understanding Data and Its Various Types
- Exploring the Role of Data Analytics in Business Decision-Making
- Overview of the Data Analytics Process (Data Collection, Cleaning, Analysis, and Visualisation)
- Familiarising with Popular Data Analytics Tools like Excel, Python, R, and SQL
- Understanding Data Visualisation Tools like Tableau and Power BI
- Introduction to Statistical Concepts for Data Analysis
- Recognising the Ethics and Privacy Concerns in Data Analytics
- Exploring Real-World Applications of Data Analytics in Various Industries
✅ Phase 2: Data Collection and Cleaning
- Identifying Various Data Sources (APIs, Databases, Web Scraping, Sensors)
- Using Data Acquisition Tools like SQL, Python (Pandas), and R
- Performing Data Cleaning and Handling Missing Data
- Identifying and Treating Outliers using Statistical Methods
- Utilising Data Transformation Techniques like Scaling and Normalisation
- Detecting and Removing Duplicate Data
- Converting Data Types and Standardising Data Formats
- Applying Feature Engineering for Better Insights
- Using ETL (Extract, Transform, Load) Processes in Data Pipelines
- Validating Data Quality with Accuracy, Completeness, and Consistency Checks
✅ Phase 3: Data Analysis and Visualisation
- Performing Exploratory Data Analysis (EDA) to Discover Patterns and Trends
- Creating Visual Representations using Matplotlib and Seaborn
- Using Pandas for Data Manipulation and DataFrames Analysis
- Conducting Statistical Analysis for Informed Decision-Making
- Performing Hypothesis Testing to Validate Insights
- Understanding and Interpreting Correlation and Regression Analysis
- Creating Interactive Visualisations using Plotly and Power BI
- Generating Reports and Dashboards for Stakeholder Communication
- Recognising and Avoiding Common Data Visualisation Mistakes
- Drawing Actionable Insights from Visual Data Representation
✅ Phase 4: Data Modelling
- Introduction to Machine Learning and Its Applications
- Understanding Supervised and Unsupervised Learning
- Building Regression Models for Predicting Continuous Data
- Developing Classification Models for Categorical Predictions
- Exploring Algorithms like Linear Regression, Logistic Regression, and Decision Trees
- Evaluating Model Performance using Metrics like Accuracy, Precision, and Recall
- Performing Cross-Validation and Hyperparameter Tuning
- Implementing Models using Python with Scikit-Learn
- Understanding Overfitting and Underfitting Issues
- Using Model Deployment Tools for Real-World Applications
✅ Phase 5: Advanced Topics
- Performing Time Series Analysis for Forecasting and Trend Analysis
- Using Models like ARIMA, SARIMA, and LSTMs for Time Series Prediction
- Handling Large Datasets with Big Data Analytics using Hadoop and Spark
- Performing Real-Time Data Processing with Apache Kafka
- Creating Interactive Data Visualisations using Power BI and Tableau
- Building Automated Data Dashboards for Stakeholder Insights
- Applying Data Ethics Principles to Ensure Responsible Data Use
- Understanding Data Privacy Regulations like GDPR and CCPA
- Exploring the Role of Explainable AI (XAI) in Transparent Decision-Making
- Conducting Data Governance for Data Management and Security
🎯 Become proficient in Data Analytics with KSP Infosec and uncover valuable insights from data!