Online Training-Online Mode-Online
Data Analytics
Data Analytics Course
Description
This course introduces participants to the fundamentals of Data Analytics. Through practical exercises and real-world case studies, students will learn data exploration, cleaning, visualization, and analysis techniques. Utilizing popular tools and programming languages like Python and SQL, participants will gain hands-on experience in extracting insights from structured and unstructured data. By the end of the course, students will be equipped with the skills to apply Data Analytics methodologies, enabling them to make informed decisions and solve complex business problems.
Key Highlights
1. Real project experience under the guidance of domain experts.
2. Regular assessments and assignments to track progress and plan remedial classes.
3. Build your profile on job portals including LinkedIn.
4. Increase your presence on community platforms for developers like GitHub, codementor and many more.
5. Logical and Reasoning test, Personal HR Round and Machine test.
6. Nurture your startup ideas with the support from our mentor network of successful entrepreneurs.
7. Handholding support for the next 1 year after course completion.
8. Certification from NSDC
9. 1 Month Paid Internship
What You will Learn?
- Data exploration
- Data cleaning
- Data visualization
- and Data analysis techniques
Topics for this course
Total learning: 200 Topics
Introduction to Excel
Excel Interface
Basic Functions and Formulas
Basic Data Entry and Formatting
Advanced Formulas
Data Management
Conditional Formatting
Data Analysis and Visualization
Charts and Graphs
Pivot Tables
Data Validation
Complex Formulas
1. Introduction to SQL Server
• Overview of SQL Server:
o Editions and Features
o SQL Server Architecture: Instances, Databases, Tables
• Installing and Configuring SQL Server:
o Installation steps and options
o Configuration settings, SQL Server Management Studio (SSMS) introduction
2. SQL Server Basics
• Introduction to Databases and Tables:
o Creating and managing databases
o Designing and creating tables
• Basic SQL Commands:
o SELECT, INSERT, UPDATE, DELETE
o Filtering and sorting data with WHERE, ORDER BY
• Data Types and Constraints:
o Common data types (INT, VARCHAR, DATE, etc.)
o Constraints: PRIMARY KEY, FOREIGN KEY, UNIQUE, CHECK
3. Advanced SQL Queries
• Joining Tables:
o INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN
o Self-joins and cross joins
• Subqueries and Derived Tables:
o Writing subqueries in SELECT, WHERE, and FROM clauses
• Set Operations:
o UNION, INTERSECT, EXCEPT
4. Data Integrity and Transactions
• Transactions:
o Understanding transactions and ACID properties
o Using BEGIN TRANSACTION, COMMIT, and ROLLBACK
• Error Handling:
o Using TRY...CATCH for error handling in T-SQL
5. Indexing and Performance Tuning
• Indexes:
o Types of indexes (Clustered, Non-Clustered)
o Creating and managing indexes
6. Security and Permissions
• User Management:
o Creating and managing SQL Server logins and users
o Roles and permissions (fixed and user-defined)
• Security Best Practices:
o Managing access to databases and tables
o Data encryption and auditing
7. Case Studies and Practical Applications
• Real-world Scenarios:
o Implementing database solutions for business problems
• Capstone Project:
o A comprehensive project that involves designing, implementing, and managing a SQL Server database system
1. Introduction to Power BI
o What is Power BI?
o Components: Power BI Desktop, Power BI Service, Power BI Mobile
• Getting Started:
o Installing Power BI Desktop
o Navigating the Power BI interface
• Connecting to Data Sources:
o Importing data from Excel, SQL Server, web data sources, and more
o Understanding different data connection types
2. Data Loading and Transformation
• Power Query Editor:
o Introduction to Power Query
o Basic data transformation tasks: filtering, sorting, and renaming
o Advanced transformations: merging queries, appending queries
• Data Shaping:
o Data types and data cleaning
o Creating calculated columns and measures
3. Data Modelling
• Data Relationships:
o Understanding relationships between tables
o Creating and managing relationships
• Data Modelling Concepts:
o Creating and using data models
• DAX Basics:
o Introduction to Data Analysis Expressions (DAX)
o Basic DAX functions: SUM, AVERAGE, COUNT, IF
4. Data Visualization
• Creating Visualizations:
o Types of visuals: bar charts, line charts, pie charts, tables, matrices, maps
o Adding and formatting visuals
• Interactive Reports:
o Adding slicers, filters, and drill-throughs
o Using bookmarks and buttons for interactive reports
• Custom Visuals:
o Importing and using custom visuals from the Power BI marketplace
5. Advanced Data Modelling and DAX
• Advanced DAX Functions
• Performance Optimization:
o Optimizing DAX queries
o Best practices for improving model performance
6. Power BI Service
• Publishing and Sharing:
o Publishing reports to Power BI Service
o Sharing reports and dashboards with others
• Workspaces and Apps:
o Creating and managing workspaces
o Creating and distributing Power BI apps
• Data Refresh and Scheduling:
o Configuring data refresh settings
o Setting up scheduled data refreshes
7. Data Analysis and Exploration
• Advanced Analytics:
o Using Key Influencers visual
o Creating and using analytics features like trend lines, forecasting
• What-If Analysis:
o Creating what-if parameters and scenarios
• Using R and Python:
o Integrating R and Python scripts for advanced analytics
8. Power BI Administration and Security
• User Management:
o Managing user roles and permissions
• Data Governance:
o Managing datasets, data sources, and reports
o Implementing and monitoring data governance policies
9. Power BI Integration
• Integration with Other Tools:
o Integrating Power BI with Excel, SQL
• Embedding Power BI:
o Embedding Power BI reports into web applications and other platforms
10. Case Studies and Practical Applications
• Real-world Scenarios:
o Implementing Power BI for business reporting and analysis
• Capstone Project:
o Developing a comprehensive Power BI solution involving data import, transformation, visualization, and deployment
1. Introduction to Python for Data Analytics
• Overview of Python:
o Python installation and environment setup (Anaconda, Jupyter Notebook)
o Basic Python syntax and data types
• Introduction to Data Analytics:
o Understanding the role of data analytics in decision-making
o Key libraries and tools for data analytics in Python
2. Python Basics for Data Analysis
• Data Structures:
o Lists, tuples, dictionaries, and sets
o Data manipulation and operations
• Control Flow:
o Conditional statements (if, elif, else)
o Loops (for, while)
• Functions and Modules:
o Defining and using functions
o Importing and using modules
3. Data Manipulation with Pandas
• Introduction to Pandas:
o Series and DataFrames: creation, manipulation, and operations
o Indexing, selecting, and filtering data
• Data Cleaning:
o Handling missing data (imputation, removal)
o Data transformation (normalization, standardization)
• Data Aggregation and Grouping:
o GroupBy operations and aggregation functions
o Pivot tables and cross-tabulations
4. Data Visualization
• Introduction to Visualization Libraries:
o Overview of Matplotlib, Seaborn, and Plotly
• Basic Visualizations:
o Creating line plots, bar charts, histograms, scatter plots
o Customizing plots: titles, labels, legends
• Advanced Visualizations:
o Heatmaps, pair plots, violin plots
o Interactive visualizations with Plotly
5. Stats for Data Analytics
6. Statistical Analysis and Hypothesis Testing
• Introduction to Statistics:
o Probability distributions (normal, binomial, Poisson)
o Sampling and estimation
• Hypothesis Testing:
o Formulating and testing hypotheses
o T-tests, chi-square tests, ANOVA
• Correlation and Regression Analysis:
o Pearson and Spearman correlation coefficients
o Linear regression: fitting models and interpreting results
7. Time Series Analysis
• Time Series Data:
o Working with date and time data
o Time series decomposition (trend, seasonality, residuals)
• Time Series Forecasting:
o Moving averages, exponential smoothing
o ARIMA models and forecasting with statsmodels
8. Machine Learning Basics
• Introduction to Machine Learning:
9. Data Pipelines and Automation
• Data Pipeline Concepts:
o Creating data pipelines using Python
o Automating data processing tasks
• Working with APIs:
o Fetching data from APIs (requests library)
10. Case Studies and Real-world Applications
• Industry Applications:
o Analysing financial data, marketing data, or healthcare data
• Capstone Project:
o A comprehensive project involving data collection, cleaning, analysis, and visualization
Opportunities after this course completion
Price: INR 5500 INR 6000
This Course Includes
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Skill level : Advanced
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Duration : 2 Months
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Chapters : 4
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Topics : 200
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Language : English
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Certificate : Yes
