Course Details
- Introduction to Data Analytics
- Overview of Data Analytics Life Cycle
- Key Skills Required for Data Analytics
- Types of Data Analytics (Descriptive, Diagnostic, Predictive, Prescriptive)
- Data Analytics vs. Data Science
Data Analytics Basics
- What is Data Analytics?
- Definition and Importance of Data Analytics
- Data Analytics vs. Business Intelligence
- Types of Data (Structured vs. Unstructured, Qualitative vs. Quantitative)
- The Role of Data Analytics in Decision Making
- Data Analytics Process
- Understanding the Analytics Workflow (Data Collection, Cleaning, Analysis, Visualization, Interpretation)
- Steps in Data Analytics (Data Collection, Data Preparation, Data Exploration, Data Modeling, Data Visualization, Communication)
- Tools and Technologies for Data Analytics
- Overview of Popular Data Analytics Tools (Excel, R, Python, SQL, Tableau, Power BI)
- Open-Source vs. Commercial Tools
- Understanding the Role of Cloud in Data Analytics (AWS, Google Cloud, Azure)
Data Collection & Data Sources
- Types of Data Sources
- Primary vs. Secondary Data
- Structured vs. Unstructured Data Sources
- Internal Data (Company Databases, Logs, CRM)
- External Data (Market Data, Social Media, APIs)
- Data Collection Methods
- Surveys, Interviews, and Observations
- Web Scraping (Data Extraction from Websites)
- Data from Sensors or IoT Devices
- APIs and Data Feeds (JSON, XML)
- Connecting to Databases (SQL, NoSQL)
- Data Storage and Management
- Relational Databases (SQL) vs. NoSQL Databases
- Data Warehouses vs. Data Lakes
- Cloud Storage vs. On-premise Storage
- Data Governance (Data Quality, Data Privacy, Compliance)
Data Cleaning & Preprocessing
- Why Data Cleaning is Important
- The Importance of Clean Data in Analytics
- Impact of Dirty Data on Results
- Data Cleaning Techniques
- Handling Missing Data (Imputation, Deletion)
- Removing Duplicates
- Data Transformation (Scaling, Normalization)
- Identifying and Handling Outliers
- Formatting Issues (Date Formats, Text Formatting)
- Data Preprocessing Tools and Libraries
- Using Python Libraries (Pandas, NumPy) for Data Cleaning
- Using R for Data Cleaning
- Power Query in Excel/Power BI for Data Transformation
Exploratory Data Analysis (EDA)
- What is Exploratory Data Analysis?
- Overview and Importance of EDA
- Descriptive Statistics (Mean, Median, Mode, Standard Deviation, Variance)
- Visualizing Data
- Data Visualization Techniques (Histograms, Box Plots, Bar Charts, Scatter Plots)
- Using Visualization Tools (Tableau, Power BI, Matplotlib, Seaborn)
- Correlation Analysis and Heatmaps
- Data Distribution and Relationships
- Identifying Patterns in Data
- Exploring Data with Univariate, Bivariate, and Multivariate Analysis
- Identifying Skewness and Distribution of Data
- Hypothesis Testing
- Understanding Statistical Hypothesis Testing (Null and Alternative Hypothesis)
- p-values and Significance Levels
- T-tests, Chi-Square Tests, ANOVA
Statistical Analysis
- Basic Statistical Concepts
- Population vs. Sample
- Descriptive vs. Inferential Statistics
- Probability Distributions (Normal Distribution, Binomial, Poisson)
- Inferential Statistics
- Confidence Intervals
- Hypothesis Testing (Z-test, T-test, ANOVA)
- Regression Analysis (Linear and Logistic Regression)
- Advanced Statistical Methods
- Multivariate Analysis (Principal Component Analysis, Factor Analysis)
- Time Series Analysis
- Bayesian Analysis
Predictive Analytics
- What is Predictive Analytics?
- Overview of Predictive Analytics and Its Use Cases
- The Role of Machine Learning in Predictive Analytics
- Types of Predictive Models
- Regression Models (Linear Regression, Multiple Regression)
- Classification Models (Logistic Regression, Decision Trees, Random Forests)
- Time Series Forecasting (ARIMA, Exponential Smoothing)
- Building Predictive Models
- Data Splitting: Training vs. Test Data
- Model Training and Validation
- Cross-validation Techniques
- Model Evaluation Metrics (Accuracy, Precision, Recall, F1-Score, AUC-ROC)
- Tools for Predictive Analytics
- Python (scikit-learn, Statsmodels)
- R (caret, randomForest, e1071)
- Machine Learning in Power BI
Prescriptive Analytics
- What is Prescriptive Analytics?
- Overview of Prescriptive Analytics and Decision Support
- Use Cases in Optimization and Simulation
- Optimization Techniques
- Linear Programming and Integer Programming
- Optimization Algorithms (Simplex Method, Gradient Descent)
- Simulation Techniques
- Monte Carlo Simulations
- Sensitivity Analysis
- Decision Trees and Recommender Systems
- Building Decision Trees for Decision Making
- Collaborative Filtering for Recommender Systems
- Association Rule Mining (Apriori, Eclat)
Data Visualization
- What is Data Visualization?
- The Importance of Data Visualization in Storytelling
- Visualization vs. Presentation
- Best Practices for Data Visualization
- Choosing the Right Visualization for the Data
- Designing Effective and Clear Dashboards
- Avoiding Chartjunk and Misleading Graphs
- Interactivity and Dynamic Dashboards
- Popular Data Visualization Tools
- Tableau (Overview, Visuals, Dashboards)
- Power BI (Building Reports, Dashboards, Data Modeling)
- Python (Matplotlib, Seaborn, Plotly)
- R (ggplot2, plotly)
- Excel (Charts, Pivot Tables, Power Pivot)
Big Data Analytics
- Introduction to Big Data
- What is Big Data? Characteristics of Big Data (Volume, Velocity, Variety, Veracity)
- Big Data Technologies (Hadoop, Spark, NoSQL Databases)
- Processing Big Data
- Data Streaming vs. Batch Processing
- Using Apache Hadoop and Spark for Big Data Processing
- Real-Time Analytics with Apache Kafka
- Data Storage for Big Data
- Data Lakes vs. Data Warehouses
- NoSQL Databases (MongoDB, Cassandra)
Advanced Analytics Techniques
- Machine Learning in Data Analytics
- Supervised vs. Unsupervised Learning
- Common Algorithms (K-Nearest Neighbors, Decision Trees, Support Vector Machines)
- Model Tuning and Hyperparameter Optimization
- Ensemble Methods (Bagging, Boosting, Random Forest)
- Deep Learning
- Introduction to Neural Networks and Deep Learning
- Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN)
- Natural Language Processing (NLP) in Analytics
- Text Analytics and Sentiment Analysis
- Text Mining Techniques (Tokenization, Lemmatization, Stemming)
- Sentiment Analysis (Using NLP and Machine Learning for Sentiment Extraction)
- Word Cloud Visualizations
Data Analytics and Business Intelligence
- Business Intelligence (BI) Overview
- The Role of Data Analytics in Business Intelligence
- BI vs. Data Analytics: Differences and Synergies
- Data Analytics in Decision Making
- How Data Analytics Supports Business Decisions
- Case Studies of Data-Driven Decision Making
- Real-World Applications of Data Analytics
- Marketing Analytics (Customer Segmentation, Campaign Analysis)
- Financial Analytics (Risk Management, Fraud Detection)
- Healthcare Analytics (Predictive Health Models, Patient Outcomes)
- Supply Chain Analytics (Inventory Management, Demand Forecasting)
- Retail Analytics (Sales Optimization, Customer Preferences)
Data Analytics Career & Skills
- Key Skills for Data Analysts
- Data Wrangling and Data Cleaning
- Statistical and Mathematical Proficiency
- Programming Skills (Python, R, SQL)
- Data Visualization and Reporting Tools
- Business Acumen and Communication Skills
- Certifications and Training
- Popular Data Analytics Certifications (Microsoft Certified: Data Analyst Associate, Google Data Analytics, SAS Certified Data Scientist)
- Online Courses and Resources (Coursera, edX, LinkedIn Learning, DataCamp)
- Data Analyst Career Path
- Entry-Level vs. Senior Data Analyst Roles
- Salary Expectations and Job Growth in Data Analytics
- Developing a Portfolio and Gaining Experience
Data Analytics How To
- How to Perform EDA (Exploratory Data Analysis)
- How to Build Predictive Models in Python or R
- How to Create Interactive Dashboards in Tableau or Power BI
- How to Use SQL for Data Querying and Manipulation
- How to Clean and Prepare Data Using Python (Pandas, NumPy)
- How to Implement Regression and Classification Models
- How to Automate Data Analytics Reports
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