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DSML Course
Data Science with Machine Learning Certification
Boost up Program for Mastermind & Growth Hack
On Campus
Placementship Plus
Course Duration- 180 Hours
Course Objectives:
Understand key data analytics terms and frameworks.
Learn to clean, transform, and shape data using Power Query.
Build data models with relationships and hierarchies, following best practices.
Explore Power BI data security, including row-level security and sharing options.
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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
- Conclusive Result
- Proficiency in data manipulation, analysis, and visualization using Excel
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
- Conclusive Result
- 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
- 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
- Conclusive Result
- Ability to write and optimize SQL queries for data retrieval and manipulation
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
- 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: Statistical Foundations for Data Science
- Introduction to Statistics
- Descriptive vs. Inferential Statistics
- Probability Distributions (Normal, Binomial, Poisson)
- Hypothesis Testing
- Null and Alternative Hypotheses
- Types of Errors and p-values
- Statistical Modeling
- Linear Regression Analysis
- ANOVA and Chi-Square Tests
- Conclusive Result
- Proficiency in applying statistical methods to data analysis
Module 9: Advanced Machine Learning
- Ensemble Methods
- Random Forest, Gradient Boosting
- Bagging vs. Boosting Techniques
- Deep Learning Introduction
- Neural Networks Basics
- Frameworks (TensorFlow, Keras)
- Natural Language Processing (NLP)
- Text Preprocessing and Tokenization
- Sentiment Analysis and Topic Modeling
- Conclusive Result
- Understanding and applying advanced machine learning techniques
Course Conclusion
- Capstone Project
- A comprehensive project that requires the application of skills and tools learned throughout the course
- Final Assessment
- A comprehensive exam covering all tools and modules
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Online
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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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