Data Analytics → Probability Concepts and Distributions

Curriculum

27 Sections
104 Lessons
12 Weeks

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Introduction to Data Analytics
4
- 1.1
  Overview of Data Analytics
- 1.2
  Importance of Data Analytics in Business
- 1.3
  Types of Data Analytics (Descriptive, Diagnostic, Predictive, Prescriptive)
- 1.4
  Real-world Applications of Data Analytics
Understanding Data Types and Sources
4
- 2.1
  Types of Data: Quantitative vs. Qualitative
- 2.2
  Structured vs. Unstructured Data
- 2.3
  Primary vs. Secondary Data
- 2.4
  Data Sources: Surveys, Databases, APIs, and Web Scraping
Data Collection Techniques
4
- 3.1
  Methods of Data Collection (Surveys, Interviews, Observations)
- 3.2
  Designing Effective Surveys and Questionnaires
- 3.3
  Using APIs for Data Acquisition
- 3.4
  Introduction to Web Scraping Tools
Data Cleaning and Preparation
4
- 4.1
  The Importance of Data Cleaning
- 4.2
  Common Data Quality Issues (Missing Values, Duplicates, Outliers)
- 4.3
  Data Cleaning Techniques using Python (Pandas)
- 4.4
  Data Transformation and Normalization
Exploratory Data Analysis (EDA)
4
- 5.1
  Understanding EDA and Its Importance
- 5.2
  Visualization Techniques (Histograms, Box Plots, Scatter Plots)
- 5.3
  Summary Statistics (Mean, Median, Mode, Variance)
- 5.4
  Using Python Libraries (Matplotlib, Seaborn)
Introduction to Statistical Concepts
4
- 6.1
  Descriptive Statistics vs. Inferential Statistics
- 6.2
  Probability Concepts and Distributions
- 6.3
  Hypothesis Testing Fundamentals
- 6.4
  Confidence Intervals and P-Values
Advanced Statistical Techniques
4
- 7.1
  Correlation vs. Causation
- 7.2
  Regression Analysis (Simple and Multiple Regression)
- 7.3
  ANOVA (Analysis of Variance)
- 7.4
  Chi-Square Tests for Categorical Data
Data Visualization Principles
4
- 8.1
  Importance of Data Visualization
- 8.2
  Principles of Effective Visualization
- 8.3
  Choosing the Right Chart Types
- 8.4
  Introduction to Data Visualization Tools (Tableau, Power BI)
Data Visualization with Python
4
- 9.1
  Visualizing Data with Matplotlib and Seaborn
- 9.2
  Creating Interactive Visualizations with Plotly
- 9.3
  Building Dashboards with Dash
- 9.4
  Customizing Visualizations for Better Insights
Introduction to Databases
4
- 10.1
  Understanding Relational Databases
- 10.2
  Introduction to SQL (Structured Query Language)
- 10.3
  Basic SQL Queries (SELECT, WHERE, JOIN)
- 10.4
  Data Manipulation with SQL (INSERT, UPDATE, DELETE)
Advanced SQL for Data Analytics
4
- 11.1
  Complex Queries and Subqueries
- 11.2
  Aggregate Functions and Grouping Data
- 11.3
  Window Functions and CTEs (Common Table Expressions)
- 11.4
  Performance Optimization Techniques in SQL
Introduction to Excel for Data Analytics
4
- 12.1
  Using Excel for Data Analysis
- 12.2
  Key Functions and Formulas (VLOOKUP, IF, PivotTables)
- 12.3
  Data Visualization in Excel
- 12.4
  Automating Tasks with Macros and VBA
Introduction to Business Intelligence (BI)
4
- 13.1
  Understanding BI and Its Role in Data Analytics
- 13.2
  Overview of BI Tools (Tableau, Power BI, Qlik)
- 13.3
  Building BI Dashboards
- 13.4
  Case Studies on BI Implementation
Predictive Analytics Fundamentals
4
- 14.1
  Understanding Predictive Analytics
- 14.2
  Key Techniques in Predictive Modeling
- 14.3
  Introduction to Machine Learning Concepts
- 14.4
  Overview of Algorithms Used in Predictive Analytics
Introduction to Machine Learning
4
- 15.1
  Types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning)
- 15.2
  Key Machine Learning Algorithms (Linear Regression, Decision Trees, K-Means)
- 15.3
  Model Evaluation Metrics (Accuracy, Precision, Recall, F1 Score)
- 15.4
  Tools and Libraries for Machine Learning (Scikit-learn)
Time Series Analysis
4
- 16.1
  Understanding Time Series Data
- 16.2
  Key Concepts: Trend, Seasonality, Noise
- 16.3
  Time Series Forecasting Techniques (ARIMA, Exponential Smoothing)
- 16.4
  Applications of Time Series Analysis
Data Analytics in Marketing
4
- 17.1
  Role of Data Analytics in Marketing Strategies
- 17.2
  Customer Segmentation and Targeting
- 17.3
  A/B Testing and Experimentation
- 17.4
  Measuring Marketing ROI through Analytics
Data Analytics in Finance
4
- 18.1
  Applications of Data Analytics in Finance and Investment
  10 Minutes0 Questions
- 18.2
  Risk Analysis and Management
  10 Minutes0 Questions
- 18.3
  Forecasting Financial Performance
  10 Minutes0 Questions
- 18.4
  Fraud Detection Techniques
  10 Minutes0 Questions
Data Ethics and Privacy
4
- 19.1
  Importance of Data Ethics in Analytics
- 19.2
  Understanding Data Privacy Regulations (GDPR, CCPA)
- 19.3
  Ethical Considerations in Data Collection and Usage
- 19.4
  Best Practices for Ethical Data Analytics
Real-World Case Studies
4
- 20.1
  Analyzing Real-World Data Analytics Projects
- 20.2
  Group Discussion on Case Studies
- 20.3
  Lessons Learned from Successful Data Analytics Implementations
- 20.4
  Identifying Key Performance Indicators (KPIs)
Capstone Project Introduction
4
- 21.1
  Overview of Capstone Project Requirements
- 21.2
  Selecting a Project Topic and Data Source
- 21.3
  Project Planning and Scope Definition
- 21.4
  Forming Project Teams
Capstone Project Work Session 1
4
- 22.1
  Data Collection and Preparation for Capstone Project
- 22.2
  Conducting Exploratory Data Analysis
- 22.3
  Initial Findings Presentation and Feedback
- 22.4
  Iterative Improvement of Analysis
Capstone Project Work Session 2
4
- 23.1
  Advanced Analytics Techniques Application
- 23.2
  Visualization and Reporting of Findings
- 23.3
  Integrating Feedback from Previous Sessions
- 23.4
  Preparing for Final Presentation
Capstone Project Finalization
4
- 24.1
  Final Review of Capstone Projects
- 24.2
  Refining Analysis and Visualizations
- 24.3
  Preparing Documentation and Reports
- 24.4
  Final Team Meetings for Presentation Preparation
Capstone Project Presentations
4
- 25.1
  Presentation of Capstone Projects to Peers and Instructors
- 25.2
  Q&A Session for Each Project
- 25.3
  Constructive Feedback and Evaluation
- 25.4
  Discussion on Project Outcomes and Insights
Career Development in Data Analytics
4
- 26.1
  Overview of Career Paths in Data Analytics
- 26.2
  Building a Professional Portfolio and Resume
- 26.3
  Preparing for Data Analytics Interviews
- 26.4
  Networking and Professional Development Opportunities
Course Wrap-Up and Future Trends
4
- 27.1
  Recap of Key Learnings Throughout the Course
- 27.2
  Discussion on Future Trends in Data Analytics (AI, Big Data, etc.)
- 27.3
  Resources for Continued Learning
- 27.4
  Final Q&A and Course Feedback Session

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Descriptive Statistics vs. Inferential Statistics

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