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Data Analytics Course - Notes

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Published in: Data Analysis Certification
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Core Services: Data mining, data analysis, statistical analysis, data visualization, predictive analytics, machine learning. Industry Focus: Healthcare, finance, retail, marketing, manufacturing, etc. Unique Selling Points: Proprietary methodologies, industry-specific expertise, advanced tools, or a focus on specific data types. Showcasing the Value Proposition Problem-Solving: How do you help clients solve their business challenges using data? Data-Driven Decisions: Emphasize the importance of informed decision-making based on data insights. Competitive Advantage: Explain how data analytics can give clients a competitive edge. Client Success Stories Case Studies: Share specific examples of how you've helped clients achieve their goals. Testimonials: Include quotes from satisfied clients. Are you drowning in data but struggling to extract meaningful insights? Our data analytics experts can help you turn raw data into actionable intelligence. With our proven methodologies and industry-specific expertise, we'll uncover hidden trends, optimize processes, and drive data-driven decision-making. Let's talk about how we can help your business succeed.

Sadhvi A / Coimbatore

year of teaching experience

Qualification:

Teaches: Advanced Excel, Basic Computer, MS Office, Business Analytics, Content Writing, Data Analysis Certification, Machine Learning, Power BI Certification, Cloud Computing, Data Structures, DBMS & RDBMS, MongoDB, PL/SQL, Tableau, JELET, NATA Exam, Software Engineering, C / C++, Python Programming, Coding & Programming, Artificial Intelligence, Data Science, ACS, Digital Marketing, Node JS, React JS

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  1. Introduction: Welcome to the Data Analytics course at Sadhvi Academy! In today's data-driven world, analyzing and interpreting data has become a vital skill for businesses across all industries. This comprehensive 180-hour course is designed to equip you with the necessary skills and tools to excel in data analysis using various platforms such as Excel, MySQL, Power Bl, Tableau, and Python. By the end of this course, you will not only master these tools but also gain hands-on experience with real- world data analysis projects. Data Analytics Syllabus (180hrs) 1- EXCEL (20hrs) 1: Introduction to Excel for Data Analysis Overview of Excel's role in data analysis. Navigating the Excel interface. Basic Excel operations and shortcuts. Introduction to datasets. Data Entry and Basic Functions Entering and formatting data. Basic formulas and functions (SUM, AVERAGE, COUNT). Using relative and absolute cell references. Introduction to named ranges. 2: Data Cleaning and Preparation Identifying and handling missing data. Data validation techniques. Text functions (LEFT, RIGHT, MID, LEN, TRIM, CONCATENATE). Removing duplicates. 3: Sorting and Filtering Data Sorting data (single and multiple columns). Filtering data using AutoFilter. Advanced filtering techniques.
  2. Introduction to slicers. 4: Data Visualization with Charts • Creating basic charts (bar, line, pie). Customizing chart elements (titles, labels, colours). Introduction to combo charts. Using sparklines for data trends. 5: PivotTables and Pivot Charts Creating and customizing PivotTables. Grouping and summarizing data in PivotTables. Creating Pivot Charts. Using slicers with PivotTables and Pivot Charts. 6: Advanced Excel Functions LOOKUP functions (VLOOKUP, HLOOKUP, XLOOKUP). INDEX and MATCH functions. Advanced date and time functions. Conditional functions (IF, SUMIF, COUNTIF). 8: Data Analysis Tools Introduction to Excel's Analysis ToolPak. Descriptive statistics using Data Analysis tools. Performing correlation and regression analysis. Using Goal Seek and Solver. 9: Introduction to Power Query Overview of Power Query. Importing data from various sources. Basic data transformation and cleansing. Combining multiple datasets. 10: Advanced Power Query Techniques Advanced data transformation techniques. Creating custom columns.
  3. Merging and appending queries. Using parameters in queries. 11: Introduction to PowerPivot • Overview of PowerPivot. Creating a Data Model. Importing data into PowerPivot. Relationships between tables. 12: Advanced PowerPivot Techniques Creating calculated columns and measures. Using DAX (Data Analysis Expressions) for advanced calculations. Time intelligence functions in PowerPivot. Optimizing the Data Model. Creating PivotTables from the Data Model. Advanced PivotChart techniques. Using slicers and timelines with PowerPivot. Enhancing visualizations with conditional formatting. 13: What if Analysis 14: Excel Dashboards 2 - MvSQL (20hrs) 1: Introduction to Databases and SQL Understanding databases and their importance What is SQL and RDBMS? Overview of MySQL installation and setup 2: Basic SQL Commands Basic SQL syntax Creating databases and tables Data types in MySQL
  4. • Primary keys and foreign keys 3: Inserting and Modifying Data • • INSERT INTO statement Handling NULL values UPDATE and DELETE statements 4: Querying Data • Basic SELECT statements Using WHERE clause for filtering Logical operators: AND, OR, NOT Using functions: COUNT, SUM, AVG, MIN, MAX GROUP BY and HAVING clauses 5: Joining Tables INNER JOIN LEFT JOIN, RIGHT JOIN Practical examples of joining tables 6: Sub queries and Nested Queries 7: Indexes and Performance Tuning Importance of indexes Creating and dropping indexes Query optimization techniques Using EXPLAIN to analyse queries 8: Views What are views? Creating and managing views Benefits and limitations of using views 9: Stored Procedures and Functions Introduction to stored procedures Creating and executing stored procedures
  5. • Introduction to user-defined functions 10: Transactions and Error Handling • • Introduction to transactions COMMIT and ROLLBACK commands Error handling in SQL using TRY...CATCH blocks 11: User Management and Security Creating and managing users in MySQL Granting and revoking privileges Database security basics 12: Backup and Recovery Importance of backups Backup strategies Using MySQL dump for backups Restoring data from backups 13: Advanced Query Techniques Advanced querying techniques Using window functions Advanced aggregate functions 14: Data cleaning with MySQL 15 - Data Analysis Project 3 - Powerbi (20hrs) 1: Introduction to Power Bl and Data Visualization • Overview of Power Bl and its components Installation and setup Tour of Power Bl interface Loading data into Power Bl Data Loading and Basic Transformations
  6. Connecting to different data sources (Excel, CSV, databases) Basic data transformations (cleaning, shaping) Introduction to Power Query Editor 2 - Introduction to Data Visualization Introduction to data visualization principles Creating basic charts (bar charts, line charts, pie charts) Formatting visualizations Working with advanced visuals (scatter plots, maps, treemaps) Applying filters and slicers Advanced chart types (waterfall charts, funnel charts, gauges, KPls) 3 - Advanced Formatting Techniques • Conditional formatting Themes and templates • Customizing tooltips 4 - Data Modelling in PowerBl 5 - Storytelling with Data Using bookmarks and report navigation Building narrative-driven reports Sample Dashboard Demo Story telling with data 6 - DAX Fundamentals 7 - Advanced Data Transformations • Merging and appending queries 8 - Introduction to Power Bl Paginated Reports Understanding paginated reports Creating and formatting paginated reports Using Report Builder 9 - PowerBl Exam Overview and Preparation Tips & Power Bl Service
  7. Overview of Microsoft Certified Exam • Study resources and tips Understanding exam format and question types Time management strategies 10 - Sales Analysis Project Loading sales data into Power Bl Creating KPls (Key Performance Indicators) Analysing sales trends and patterns Visualizing sales performance 4 - Tableau (20hrs) 1 - Tableau Introduction Tableau - Overview Tableau - Environment Setup Tableau - Get Started Tableau - Navigation Tableau - Design Flow Tableau - File Types Tableau - Data Types Tableau - Show Me Tableau - Data Terminology 2 - Tableau Data Sources Tableau - Data Sources Tableau - Custom Data View Tableau - Extracting Data Tableau - Fields Operations Tableau - Editing Metadata Tableau - Data Joining Tableau - Data Blending 3 - Tableau Worksheets
  8. Tableau - Add Worksheets Tableau - Rename Worksheet Tableau - Save & Delete Worksheet Tableau - Reorder Worksheet Tableau - Paged Workbook 4 - Tableau Calculations Tableau - Operators Tableau - Functions Tableau - Numeric Calculations Tableau - String Calculations Tableau - Date Calculations Tableau - Table Calculations Tableau - LOD Expressions 5 - Tableau Sort & Filters - Part 1 Tableau - Basic Sorting Tableau - Basic Filters Tableau - Quick Filters Tableau - Context Filters 6 - Tableau Sort & Filters - Part 2 Tableau - Condition Filters Tableau - Top Filters Tableau - Filter Operations 7 - Tableau Charts - part 1 Tableau - Bar Chart Tableau - Line Chart Tableau - Pie Chart Tableau - Crosstab Tableau - Scatter Plot Tableau - Bubble Chart Tableau - Bullet Graph
  9. 8 - Tableau Charts - part 2 Tableau - Box Plot Tableau - Tree Map Tableau - Bump Chart Tableau - Gantt Chart Tableau - Histogram Tableau - Motion Charts Tableau - Waterfall Charts 9 - Tableau Dashboard and Formatting Tableau - Dashboard Tableau - Formatting Tableau - Forecasting Tableau - Trend Lines 10 — Tableau Dashboard Demo 5 - Python Fundamentals (16hrs) 1: Introduction to Python What is Python? Python for Data Science Overview Installing Python and setting up the environment Introduction to IDES (Jupyter Notebook, VS Code) Running your first Python program Jupyter notebook Installation 2: Basic Syntax and Data Types Basic syntax and writing Python scripts Data types: integers, floats, strings, and Booleans Variables and type conversion Input and output functions 3: String methods
  10. 4: Operators 5: Control Flow statements Conditional statements (if, elif, else) Loops: for and while Break and continue statements 6: Control Flow statements Practice 7: Functions 8: Data Structures - Lists and Tuples 9: Data Structures - Dictionaries and Sets 10: File Handling Reading from and writing to files Working with text and CSV files Using with statement for file operations Basic file handling errors and exceptions 12: Error Handling and Exceptions Introduction to exceptions Try, except, else, and finally blocks Raising exceptions Common exceptions and handling strategies 13: Object-Oriented Programming Basics Introduction to object-oriented programming (OOP) Creating classes and objects Class attributes and methods Inheritance and polymorphism 15: Advanced Python Concepts Decorators and generators Context managers
  11. Regular expressions 12 — Python for Data Science (24hrs) Module 2: Data Analysis with NumPy Module 3: Data Analysis with Pandas (4 hours) Module 4: Data Visualization with Matplotlib (4 hours) Module 5: Data Visualization with Seaborn (2 hours) Introduction to Statistics for Data Analysis (3 hours) • Descriptive statistics 2: Probability fundamentals (3hrs) Probability fundamentals Distribution types Hypothesis Testing and Statistical Inference Understanding hypothesis testing Types of hypothesis tests Confidence intervals and p-values Module 8: Machine Learning with Scikit-Learn (6 hours) 1: Introduction to Machine Learning (1 hour) • What is Machine Learning? Types of Machine Learning (supervised, unsupervised) Overview of common algorithms Introduction to Neural Networks Basics of neural networks Understanding deep learning Overview of popular frameworks (TensorFlow, Keras) 2 - Linear Regression with House Price prediction 3 - Random forest Algorithm with Car Evaluation Data Conclusion
  12. At the end of this course, you will have developed a deep understanding Of key data analysis tools and techniques. Whether you aim to work in business intelligence, data science, or any field where data plays a pivotal role, this course will serve as a strong foundation for your career. We are excited to embark on this learning journey with you at Sadhvi Academy and look forward to seeing your success!

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