Data Science & Advanced Analytics
40 HoursHybrid4.8
Build strong data analysis and analytical skills using Python, SQL, statistics, and advanced data visualization tools.
About this course
Build strong data analysis and analytical skills using Python, SQL, statistics, and advanced data visualization tools. Learn how to collect, clean, process, and analyze data to discover meaningful patterns and business insights. Gain hands-on experience with Excel, Power BI, Tableau, machine learning, predictive analytics, and business forecasting. Develop practical skills in data engineering, cloud data platforms, and end-to-end data science projects to support data-driven decision-making.
Curriculum & Topics
9 modules · 63 topics
- 1.Python Fundamentals, Advanced Programming & Object-Oriented Programming
- 2.NumPy, Pandas & Data Manipulation
- 3.Data Structures, Functions, Modules & Exception Handling
- 4.Data Import, Export, File Handling & Data Integration
- 5.Data Cleaning, Transformation & Preprocessing
- 6.Python Environments, Virtual Environments & Dependency Management
- 1.Descriptive Statistics, Measures of Central Tendency & Dispersion
- 2.Probability Theory, Conditional Probability & Bayes' Theorem
- 3.Statistical Distributions and Sampling Techniques
- 4.Hypothesis Testing, Confidence Intervals & Statistical Significance
- 5.Correlation, Covariance & Regression Analysis
- 6.Linear Algebra and Calculus Fundamentals for Data Science
- 7.Statistical Inference and Business Decision-Making
- 1.Data Collection from CSV, Excel, SQL Databases & APIs
- 2.Data Cleaning, Missing Values & Duplicate Handling
- 3.Outlier Detection and Data Quality Assessment
- 4.Exploratory Data Analysis (EDA) and Pattern Identification
- 5.Feature Engineering, Encoding & Data Transformation
- 6.Data Aggregation, Grouping and Analytical Operations
- 7.Exploratory Analysis Using Pandas, NumPy & Visualization Libraries
- 1.SQL Queries, Filtering, Sorting, Joins & Aggregations
- 2.Subqueries, Common Table Expressions (CTEs) & Window Functions
- 3.Advanced SQL for Analytical Reporting
- 4.Database Design, Normalization & Relational Data Modeling
- 5.Data Extraction, Transformation & Loading (ETL)
- 6.Working with APIs, JSON and Structured Data Sources
- 7.Data Pipelines, Data Warehousing & Data Integration Fundamentals
- 1.Data Visualization Using Matplotlib, Seaborn & Plotly
- 2.Dashboard Design, KPI Tracking & Performance Reporting
- 3.Microsoft Excel: PivotTables, Advanced Formulas & Power Query
- 4.Microsoft Power BI: Data Modeling, DAX & Interactive Dashboards
- 5.Tableau: Calculated Fields, Parameters & Dashboard Actions
- 6.Data Storytelling, Report Design & Business Insights
- 7.Visualization Best Practices and Stakeholder Reporting
- 1.Exploratory Statistical Analysis and Relationship Testing
- 2.Hypothesis Testing: t-Tests, Chi-Square Tests & ANOVA
- 3.Regression Analysis and Model Interpretation
- 4.A/B Testing, Experimental Design & Statistical Significance
- 5.Sampling Bias, Confounding Variables & Statistical Limitations
- 6.Time Series Analysis, Trend Identification & Seasonality
- 7.Business Forecasting and Statistical Decision Support
- 1.Supervised and Unsupervised Learning Fundamentals
- 2.Linear Regression, Logistic Regression & Regularization
- 3.Decision Trees, Random Forests & Ensemble Methods
- 4.Clustering Algorithms: K-Means, Hierarchical Clustering & DBSCAN
- 5.Feature Selection, Model Training & Hyperparameter Tuning
- 6.Model Evaluation, Cross-Validation & Performance Metrics
- 7.Forecasting, Classification, Customer Segmentation & Predictive Modeling
- 1.Big Data Fundamentals and Distributed Processing Concepts
- 2.Apache Spark and PySpark Fundamentals
- 3.Data Warehousing, Data Lakes & Lakehouse Architecture
- 4.Cloud Data Services on Microsoft Azure, AWS & Google Cloud
- 5.ETL/ELT Pipeline Design and Workflow Automation
- 6.Data Quality Monitoring, Scheduling & Pipeline Reliability
- 7.Cloud Data Governance, Access Control & Data Security
- 1.End-to-End Data Science Project Lifecycle
- 2.Business Problem Definition, Data Collection & Analytical Planning
- 3.Customer Churn, Sales Forecasting & Customer Segmentation Projects
- 4.Exploratory Analysis, Feature Engineering & Model Development
- 5.Model Interpretation, Validation & Performance Reporting
- 6.Data Governance, Privacy, Ethics & Responsible Analytics
- 7.Project Documentation, Git/GitHub & Reproducible Workflows
- 8.Capstone Project: Business Insights, Predictive Model & Final Presentation
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