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Data Analytics
Data Analytics with Machine Learning Certification
Boost up Program for Mastermind & Growth Hack
On Campus
Course Duration- 180 Hourse
Placementship Plus
Course Objectives:
Understand key data analytics terms & frameworks.
Use Power Query for data cleaning & transformation.
Build data models with relationships & hierarchies.
Learn Power BI security like row-level security & sharing.
#1 Data Analytics with Machine Learning
Data Analytics with Machine Learning Technology
Module 1: Excel for Data Analytics
- Introduction to Excel for Analytics
- Importance of Excel in Data Analysis
- Overview of Excel Interface
- Data Management Techniques
- Importing Data from Various Sources
- Data Cleaning and Preprocessing
- Handling Missing Values
- Removing Duplicate
- Filtering and Sorting Data
- Data Analysis Functions
- Statistical Functions (AVERAGE, MEDIAN, STDEV)
- Logical Functions (IF, AND, OR)
- Lookup Functions (VLOOKUP, HLOOKUP, INDEX-MATCH)
- Data Visualization
- Creating Charts (Column, Line, Pie)
- Using Pivot Tables and Pivot Charts
- Conditional Formatting for Insights
- Data Analysis Toolpak
- Performing Regression Analysis
- Descriptive Statistics
- Histogram and Moving Average
- Data Aggregation
- GROUP BY, HAVING, and Aggregate Functions (SUM, COUNT, AVG)
- Joins (INNER, LEFT, RIGHT, FULL) to combine tables
- Data Manipulation
- INSERT, UPDATE, DELETE Statements
- Using Subqueries and Common Table Expressions (CTEs)
- Database Management
- Basic Database Design Principles
- Indexing for Performance Improvement
Module 2: Python for Data Analytics
- Introduction to Python
- Python Installation and Environment Setup
- Overview of Jupyter Notebooks
- Python Core (Full Syllabus)
- Data Manipulation with Pandas
- Introduction to DataFrames and Series
- Data Cleaning and Transformation
- Handling Missing Data and Outliers
- Exploratory Data Analysis (EDA)
- Descriptive Statistics with Pandas
- Data Visualization using Matplotlib, Seaborn and Plotly
- Correlation Analysis and Heatmaps
- Statistical Analysis
- Hypothesis Testing with SciPy
- Regression Analysis using StatsModels
- Introduction to Machine Learning
- Basic Concepts and Applications
- Supervised vs. Unsupervised Learning
- Ability to conduct data analysis and visualization using Python libraries.
Module 3: SQL for Data Analytics
- Introduction to SQL
- Understanding Relational Databases
- SQL Installation and Environment Setup
- Basic SQL Queries
- SELECT, WHERE, ORDER BY, and LIMIT
Module 4: Tableau for Data Visualization
- Introduction to Tableau
- Overview of Tableau Desktop Interface
- Connecting to Data Sources (Excel, SQL, etc.)
- Creating Visualizations
- Building Basic Charts (Bar, Line, Scatter)
- Using Filters, Parameters, and Calculated Fields
- Dashboard Design
- Principles of Effective Dashboard Design
- Creating Interactive Dashboards
- Advanced Visualization Techniques
- Mapping and Geographic Analysis
- Using Tableau Prep for Data Cleaning
- Sharing and Publishing
- Sharing Dashboards via Tableau Server/Public
- Best Practices for Presentation
- Conclusive Result
- Proficiency in creating and sharing interactive visualizations using Tableau.
Module 5: Power BI for Business Intelligence
- Introduction to Power BI
- Overview of Power BI Desktop
- Connecting to Data Sources
- Data Transformation with Power Query
- Data Cleaning and Shaping
- Merging and Appending Queries
- Creating Reports and Dashboards
- Building Interactive Visualizations
- Using DAX for Calculated Columns and Measures
- Publishing and Sharing Reports
- Overview of Power BI Service
- Sharing Reports and Collaboration Features
- Data Governance and Security
- Best Practices for Data Governance in Power BI
- Conclusive Result
- Ability to create, publish, and share interactive reports in Power BI.
Module 6: Introduction to Machine Learning
- Overview of Machine Learning
- Supervised vs. Unsupervised Learning
- Key Terminology (Features, Labels, Models)
- Data Preparation
- Data Cleaning and Preprocessing
- Feature Engineering and Selection
- Model Training and Evaluation
- Training vs. Testing Data
- Evaluation Metrics (Accuracy, Precision, Recall)
- Common Algorithms
- Linear Regression and Classification
- Decision Trees and K-Means Clustering
- Conclusive Result
- Understanding basic machine learning concepts and techniques.
Module 7: Ethical Considerations in Data Analytics
- Understanding Data Ethics
- Importance of Ethics in Data Analytics
- Overview of Ethical Guidelines
- Data Privacy and Security: An ISO 9001 : 2008 Certified
- Key Regulations (GDPR, CCPA)
- Best Practices for Data Protection
- Bias and Fairness in Data
- Identifying Bias in Data and Algorithms
- Techniques to Mitigate Bias
- Data Governance
- Frameworks for Effective Data Governance
- Importance of Transparency and Accountability
- Conclusive Result
- Awareness of ethical considerations and best practices in data analytics.
Module 8: Capstone Project
- A comprehensive project that requires the application of skills and tools learned throughout the course.
Module 9: Final Assessment
- A comprehensive exam covering all tools and modules.
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Total Program Fee:
₹30,500 ₹25,500
- Live instruction from Industry Veterans
- Vibrant community just like a College Campus
- Hands-on curriculum with Real-Life Projects
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Data Analytics And Machine Learning
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