Data Analytics Course
Data Analytics Training in Pune
Enroll in Tech Bunny’s Data Analyst Course in Pune to acquire the skills and knowledge essential for success in data analytics. Our extensive program covers the latest tools and techniques, including SQL, Excel, and Python for data handling, as well as Tableau, Power BI, and Alteryx for data visualization. Gain the expertise to tackle real-world business challenges using a variety of data analytics tools. Join our data analytics courses in Pune today and embark on your journey to becoming a proficient data analyst. At Tech Bunny, we provide hands-on training with real-world datasets, ensuring you get practical experience alongside theoretical knowledge. Our experienced instructors, who are industry experts, will guide you through complex data analysis processes, making sure you understand every concept thoroughly.
Choose top-rated data analytics classes near me and enroll in a data analyst course near me to boost your skills and career prospects.
Lectures: 18-24
Duration: 52-60 hours
Mode of Training: Online
About the course
What is Data Analytics all about?
Data analytics involves the systematic computational analysis of data. It is used to discover patterns, correlations, and trends in data, and to extract valuable insights that inform decision-making. Here’s a deeper look into what data analytics entails:
1. Data Collection
Data analytics begins with gathering data from various sources. This data can be structured (e.g., databases, spreadsheets) or unstructured (e.g., text, images).
2. Data Cleaning
Raw data often contains errors, duplicates, or inconsistencies. Data cleaning involves preprocessing the data to correct or remove these issues, ensuring the data is accurate and ready for analysis.
3. Data Exploration
This step involves initial data examination to understand its structure, distribution, and key characteristics. Tools like descriptive statistics and data visualization techniques are used to summarize the data.
4. Data Analysis
The core of data analytics, this stage uses various techniques to delve deeper into the data:
- Descriptive Analytics: Summarizes past data to understand what happened.
- Diagnostic Analytics: Examines data to determine why something happened.
- Predictive Analytics: Uses historical data to make predictions about future events.
- Prescriptive Analytics: Recommends actions based on the data analysis.
5. Data Visualization
Data visualization involves creating visual representations of the data, such as charts, graphs, and dashboards, to make the insights more accessible and understandable.
6. Data Interpretation
Interpreting the results of the analysis is crucial. It involves drawing meaningful conclusions and making data-driven decisions.
7. Reporting
The final insights are compiled into reports that communicate the findings to stakeholders, helping them understand the data and take informed actions.
Which are the different types of Data Analytics?
Data Analytics majorly have 4 verticals, which are
- Descriptive Analytics: It helps the organizations or developers answer the various questions regarding the happenings or the situations
- Diagnostic Analytics: It helps the developers and the organizations to understand why the situation occurred or why it happened.
- Predictive Analytics: It helps organizations understand the trend and predict what will happen in the future.
- Prescriptive Analytics: It helps the organizations know what steps should be taken to the situations and happenings which might occur in the future.
Why choose Tech Bunny for Data Analytics Course?
1. Comprehensive Curriculum: Tech Bunny offers a well-rounded Data Analytics course covering essential topics such as data handling, statistical analysis, machine learning, data visualization, and more. Our curriculum is designed to provide both theoretical knowledge and practical skills.
2. Expert Instructors: Learn from industry experts with extensive experience in data analytics. Our instructors bring real-world insights and hands-on expertise to the classroom, ensuring you receive top-notch education and mentorship.
3. Hands-on Learning: Our course emphasizes practical learning through real-time projects, case studies, and interactive sessions. You’ll work on real-world datasets and apply your skills to solve practical problems, making you industry-ready.
4. State-of-the-Art Resources: Gain access to cutting-edge tools and technologies used in the data analytics field. From software like Python and R to libraries such as Pandas, NumPy, and Matplotlib, you’ll be well-equipped with the resources needed for success.
5. Industry-Relevant Certifications: Earn a certification from Tech Bunny, recognized and valued by employers in the industry. Our partnerships with leading organizations and institutions add further credibility to your credentials.
6. Flexible Learning Options: We offer both online and offline training options to suit your schedule and learning preferences. Whether you prefer the convenience of online learning or the interactive experience of classroom sessions, we have you covered.
7. Strong Placement Support: Benefit from our robust placement support services, including resume building, interview preparation, and job placement assistance. Our dedicated placement team works tirelessly to connect you with top employers in the industry.
8. Proven Track Record: Tech Bunny has a reputation for excellence in IT training. Our alumni have gone on to secure prominent positions in leading companies, and our courses are highly rated for their quality and effectiveness.
9. Continuous Learning and Support: Stay updated with the latest trends and advancements in data analytics through our continuous learning programs. We offer workshops, webinars, and alumni support to keep you ahead in your career.
10. Community and Networking: Join a vibrant community of learners, alumni, and professionals. Engage in networking opportunities, participate in events, and be part of a supportive ecosystem that fosters growth and collaboration.
Choose Tech Bunny for your Data Analytics course and embark on a journey to become a skilled and sought-after data analytics professional.
How Tech Bunny provide the training?
1. Expert-Led Instruction: Our courses are taught by industry experts with extensive experience in their respective fields. Instructors bring real-world knowledge and practical insights to ensure you receive high-quality education.
2. Comprehensive Curriculum: Each course is meticulously designed to cover all necessary concepts, tools, and technologies. The curriculum is regularly updated to keep pace with industry trends and advancements.
3. Hands-On Learning: We emphasize practical, hands-on training. You will work on real-time projects, case studies, and exercises that simulate actual industry scenarios, helping you apply theoretical knowledge in a practical context.
4. Interactive Classes: Our training sessions are highly interactive, encouraging active participation from students. Whether online or offline, you’ll engage in discussions, Q&A sessions, and collaborative activities.
5. Flexible Learning Options: Tech Bunny offers both online and offline training options to cater to different learning preferences and schedules. Online classes provide the flexibility to learn from anywhere, while offline classes offer a traditional classroom experience.
6. State-of-the-Art Tools and Technologies: Students get access to the latest tools and technologies relevant to their field of study. This includes software, programming languages, and analytical tools essential for modern IT and data analytics roles.
7. Real-Time Projects: You will work on real-time projects that reflect current industry challenges and requirements. These projects help you build a robust portfolio and demonstrate your skills to potential employers.
8. Continuous Assessment and Feedback: Our training includes regular assessments through quizzes, assignments, and projects. Instructors provide continuous feedback to help you improve and stay on track with your learning goals.
9. Support and Mentorship: Students receive ongoing support and mentorship from instructors and peers. Our community is dedicated to helping each other succeed, providing guidance and assistance whenever needed.
10. Placement Assistance: Tech Bunny offers robust placement support, including resume building, interview preparation, and job placement services. Our dedicated placement team works to connect you with leading employers in the industry.
11. Certification: Upon successful completion of the course, students receive a certification from Tech Bunny, recognized and valued by employers. This certification validates your skills and knowledge in the respective field.
12. Alumni Network: Join a growing network of Tech Bunny alumni who have successfully transitioned into prominent roles in the industry. Benefit from networking opportunities, events, and continuous learning resources.
By combining expert instruction, practical experience, and robust support, Tech Bunny ensures you are well-prepared to excel in your chosen field.
Any Questions? Ask Us!!
Give Us A Chance To Prove It!
Industry Oriented Syllabus
Syllabus for Data Analytics Course at Tech Bunny
Mastering Data Domain with SQL
Introduction to SQL: Learn the Lang. of Data
- SQL Basics for Data Analysis:
- Introduction to SQL
- Setting up the SQL environment
- Basic SQL Commands
- Creating and Deleting Databases and Tables
- Importing and Exporting Data from CSV Files
- Fundamentals of SQL Query:
- Anatomy of SQL Query
- SQL Data Types and Operators
- Filtering and Sorting Data in SQL
- Aggregate functions in SQL
- Dealing With Multiple Tables:
- Grouping Data – GROUP BY
- HAVING
- Subqueries
- Joining tables using INNER JOIN, LEFT JOIN, RIGHT JOIN & FULL OUTER JOIN
- Alias in SQL queries
- Working with Multiple Tables Using Subqueries
- Using Set Operators
- Aggregating Data from Multiple Tables using GROUP BY and HAVING.
- Advanced SQL Joins:
- Advanced Join Techniques
- Joining multiple tables
- Handling duplicate records and eliminating duplicates
- Using UNION and UNION ALL to combine data from multiple tables
- SQL Basics for Data Analysis:
SQL In-Built Functions
- Type Casting & Math Functions:
- Mathematical Functions
- Type Conversion Functions
- Using CASE Statements to Perform Conditional Operations
- DateTime & String Functions:
- Working with date/time data in SQL
- Date/time functions
- Formatting date/time data
- String manipulation functions (e.g. UPPER, LOWER, LEFT, RIGHT, etc.)
- Regular expressions in SQL for string operations
- Using CONCAT_WS to concatenate strings with a separator
- Window Functions:
- Syntax of Windows Function
- Ranking functions (e.g. ROW_NUMBER, RANK, DENSE_RANK, etc.)
- Aggregate functions using windows (e.g. SUM, AVG, MAX, MIN, etc.)
- Partitioning data for window functions
- Understanding the difference between row-based and aggregate-based window functions
- Type Casting & Math Functions:
SQL for Data Preparation
- Complex queries using CTE & Pivoting:
- Common Table Expressions(CTE)
- Recursive CTEs for Hierarchical Data
- Combining CTEs with Window Functions and Subqueries
- Understanding the Performance Implications of CTEs
- Database Management & Schema Design:
- Understanding The Relational Model and Database Schema Design
- Normalization and Denormalization of Database Tables
- Database Administration Tasks
- Implementing Indexes and Constraints for Data Integrity
- Designing Efficient Database Queries for Performance Optimization
- Complex queries using CTE & Pivoting:
Microsoft Excel
Introduction to Excel
- Excel Interface Overview
- Navigating the Ribbon
- Understanding Worksheets and Workbooks
- Creating and Saving Workbooks
- Data Entry and Editing
- Customizing the Quick Access Toolbar
- Excel File Formats
Excel Formula
- Basic Arithmetic Formulas (SUM, SUBTRACT, MULTIPLY, DIVIDE)
- AutoSum Function, Cell References (Relative, Absolute, Mixed)
- Common Statistical Functions (AVERAGE, MEDIAN, MODE)
- Text Formulas (CONCATENATE, LEFT, RIGHT)
- Date Formulas (TODAY, NOW, DATE)
- Logical Formulas (IF, AND, OR)
Formula Based Formatting
- Conditional Formatting Rules
- Highlighting Cells Based on Values
- Data Bars, Color Scales, and Icon Sets
- Custom Conditional Formatting
- Managing Conditional Formatting Rules
- Using Formulas in Conditional Formatting
- Applying Conditional Formatting to Pivot Tables
Conditional Statements & Logical Operators
- IF Statements, Nested IF Statements, AND, OR Operators, NOT Operator
- Combining Multiple Conditions
- Using IF with Text, Numbers, and Dates
- Conditional Formatting with Logical Operators
Text Functions
- CONCATENATE (or CONCAT), LEFT, RIGHT, MID, LEN, and TRIM
- UPPER, LOWER, PROPER
- SUBSTITUTE and REPLACE
- FIND and SEARCH
- TEXT Function for Formatting Numbers and Dates
Date and Time Functions
- TODAY and NOW
- DATE and TIME
- DAY, MONTH, YEAR, HOUR, MINUTE, SECOND
- DATEDIF, NETWORKDAYS, EDATE, and EOMONTH
Excel Tables
- Creating and Formatting Excel Tables
- Table Styles and Options
- Sorting and Filtering Tables
- Adding and Removing Table Rows/Columns
- Structured References
- Table Calculations and Totals
- Converting Table to Range
Basic and Advanced Table Operations
- Creating Dynamic Tables
- Using Table Formulas
- Advanced Filtering Techniques
- Using Slicers with Tables
- Advanced Sorting Techniques
- Working with Subtotals
- Merging and Splitting Tables
Data Pivoting with Different Examples
- Creating Pivot tables, PivotTable Fields and Areas
- Grouping Data in PivotTables
- Pivotable Calculations (Sum, Count, Average)
- Creating Pivot Charts
- Filtering Pivot tables with Slicers and Timelines
- Advanced Pivotable Techniques (Calculated Fields, Custom Calculations)
Cell Reference Functions
- Understanding Cell References
- Using Relative References
- Using Absolute References
- Mixed References
- INDIRECT Function
- OFFSET Function
- Linking Cells Between Sheets and Workbooks
LookUp and VLookUp
- Introduction to LookUp Functions
- VLOOKUP Basics
- HLOOKUP Basics
- Using VLOOKUP with Exact and Approximate Match
- Combining VLOOKUP with Other Functions
- Common VLOOKUP Errors and Fixes
- Alternatives to VLOOKUP (INDEX and MATCH)
Excel Data Visualization
- Creating Basic Charts (Column, Line, Pie)
- Customizing Chart Elements (Titles, Legends, Labels)
- Using Sparklines for Miniature Charts
- Creating Combo Charts
- Using Conditional Formatting for Data Visualization
- Creating Heat Maps
- Using Data Bars and Color Scales
Introduction to Power Query
- What is a Power Query?
- Power Query Interface Overview
- Loading Data into Power Query
- Basic Data Transformation (Filter, Sort, Remove Columns)
- Combining Data from Multiple Sources
- Data Profiling in Power Query
- Saving and Loading Data to Excel
Power Query Editor
- Navigating the Power Query Editor
- Applying Basic Transformations (Replace Values, Remove Duplicates)
- Advanced Transformations (Pivot/Unpivot, Group By)
- Creating Custom Columns
- Merging Queries
- Appending Queries
- Managing Query Steps
Basic Charts in Excel
- Creating Column Charts
- Creating Line Charts
- Creating Pie Charts
- Creating Bar Charts
- Creating Area Charts
- Creating Scatter Plots
- Customizing Chart Types and Styles
Formatting Charts
- Creating Column Charts
- Creating Line Charts
- Creating Pie Charts
- Creating Bar Charts
- Creating Area Charts
- Creating Scatter Plots
- Customizing Chart Types and Style
Creating Reports in Excel with Dataset
- Data Preparation and Cleaning
- Using PivotTables for Reports
- Combining Multiple Data Sources
- Adding Visual Elements (Charts, Sparklines)
- Creating Interactive Dashboards
- Automating Report Updates
- Distributing and Sharing Reports
Microsoft Power BI Tool
Introduction to Power BI
- Overview of Power BI and its components
- Power BI service vs. Power BI Desktop
- Benefits of using Power BI for data analytics
- Key features and capabilities of Power BI
- Understanding the Power BI ecosystem
- User interface and navigation
- Basic concepts and terminology in Power BI
Installation of Power BI Desktop
- System requirements for Power BI Desktop
- Step-by-step installation guide
- Initial setup and configuration
- Updating Power BI Desktop
- Troubleshooting common installation issues
- Licensing and subscription options
- Importing sample datasets for practice
Connecting Power BI with Other Sources
- Supported data sources in Power BI
- Connecting to databases, cloud services, and files
- Data import methods and best practices
- Managing data connections
- Data refresh and scheduling
- Handling data privacy and security
- Using Power BI gateways
Basic Visualization in Power BI
- Types of visualizations available in Power BI
- Creating basic charts and graphs
- Customizing visualizations (colors, labels, titles)
- Using slicers and filters
- Creating and managing dashboards
- Adding interactivity to visualizations
- Publishing and sharing reports
Advanced Visualization in Power BI
- Using advanced chart types (waterfall, funnel, gauge)
- Creating and using bookmarks
- Implementing drill-through functionality
- Using custom visuals from the marketplace
- Creating dynamic visualizations with DAX
- Advanced interactivity techniques
- Combining multiple visualizations in a single report
Advanced DAX
- Introduction to DAX: Calculated Columns, Measures, and Tables
- Time Intelligence Functions
- Filtering Functions
- Advanced Calculations and Complex DAX Queries
- Optimizing DAX Queries for Performance
- Practical Applications of DAX in Data Models
Introduction to Power Query
- Overview of Power Query and its interface
- Connecting to various data sources
- Understanding the M language
- Basic data import and transformation
- Using the query editor for data preparation
- Managing and combining queries
- Best practices for efficient data queries
Tableau Tool
Tableau Basics
- Introduction to Tableau and its components
- Tableau Desktop vs. Tableau Public
- Key features and benefits of Tableau
- Understanding the Tableau interface
- Basic concepts and terminology in Tableau
- Connecting to data sources
- Creating your first visualization
Design Features
- Overview of Tableau design principles
- Customizing visualizations (colors, labels, titles)
- Using shapes and images in dashboards
- Applying themes and templates
- Formatting numbers, dates, and text
- Enhancing aesthetics with backgrounds and borders
Time Series Analysis
- Understanding time series data
- Creating time series visualizations
- Using date functions in Tableau
- Time series forecasting
- Analyzing trends and seasonality
- Implementing moving averages and running totals
- Comparing time periods
Aggregation and Granularity
- Understanding aggregation in Tableau
- Adjusting granularity of data
- Using calculated fields for aggregation
- Aggregating data in visualizations
- Customizing aggregation settings
- Drilling down and rolling up data
- Aggregation best practices
Filters
- Types of filters in Tableau
- Applying filters to visualizations
- Using quick filters and filter actions
- Creating dynamic and cascading filters
- Managing filter hierarchies
- Filter optimization techniques
- Best practices for using filters
Maps and Scatter Plots
- Creating and customizing maps
- Using geographic data in Tableau
- Implementing map layers and controls
- Creating scatter plots and bubble charts
- Analyzing data with scatter plots
- Adding interactivity to maps and scatter plots
- Combining maps with other visualizations
Joining and Union
- Understanding joins and unions in Tableau
- Types of joins (inner, left, right, full)
- Performing data joins in Tableau
- Using unions to combine datasets
- Managing data relationships
- Handling data blending
- Best practices for joins and unions
AI Analytics Platform – Alteryx
Introduction to Alteryx
- Overview of Alteryx
- Key Features and Benefits
- Use Cases and Applications
- Comparison with Other Data Analytics Tools
- Alteryx Designer Interface
- Licensing and Pricing
- Alteryx Community and Resources
Installation of Alteryx
- System Requirements
- Step-by-Step Installation Guide
- Post-Installation Configuration
- Licensing and Activation
- Troubleshooting Common Installation Issues
- Updating Alteryx
- Uninstalling Alteryx
In/Out Tools
- Input Data Tool
- Output Data Tool
- Directory Tool
- Text Input Tool
- Download Tool
- SharePoint Input Tool
- Blob Input Tool
Preparation Tools
- Data Cleansing Tool
- Filter Tool
- Select Tool
- Sort Tool
- Sample Tool
- Unique Tool
- Generate Rows Tool
Join Tools
- Join Tool
- Union Tool
- Find Replace Tool
- Append Fields Tool
- Make Group Tool
- Fuzzy Match Tool
- Spatial Match Tool
Transform Tools
- Cross Tab Tool
- Transpose Tool
- Formula Tool
- Multi-Field Formula Tool
- Running Total Tool
- Tile Tool
- Imputation Tool
Reporting Tools
- Table Tool
- Charting Tool
- Layout Tool
- Render Tool
- Report Map Tool
- Text Box Tool
- Email Tool
In-Database Tools
- Connect In-DB Tool
- Data Stream In Tool
- Filter In-DB Tool
- Formula In-DB Tool
- Join In-DB Tool
- Summarize In-DB Tool
- Write Data In-DB Tool
Documentation Tools
- Comment Tool
- Tool Container
- Block Until Done Tool
- Message Tool
- Summarize Tool
- Tool Annotation
- Metadata Output Tool
Full Workflow
- Designing a Workflow
- Best Practices for Workflow Management
- Debugging and Error Handling
- Workflow Automation
- Performance Optimization
- Sharing & Collaboration
- Real-World Workflow Examples
- Designing a Workflow