About Course
Master Data Analytics and learn how to collect, analyze, visualize, and interpret data to make smarter business decisions. This course is designed for beginners, students, working professionals, business owners, and anyone looking to build practical data analytics skills.
Start with the fundamentals of data analytics, including data collection, cleaning, and analysis. As you progress, you’ll learn data visualization, dashboard creation, statistical analysis, business reporting, and industry-standard tools used by data professionals.
Through step-by-step lessons, practical exercises, and real-world projects, you’ll gain the confidence to analyze business data, uncover valuable insights, and solve real-world problems. You’ll also discover more professional courses and resources available at BeyondUni to continue building your data analytics and technology skills.
What Will You Learn?
- Data Analytics Fundamentals
- Data Collection & Cleaning
- Data Organization & Preparation
- Data Analysis Using Excel & Analytics Tools
- Charts, Dashboards & Data Visualization
- Business Reporting
- Descriptive & Inferential Statistics
- Trend & Performance Analysis
- Data-Driven Decision Making
- Real-World Datasets & Case Studies
- Portfolio Projects
- Industry Best Practices
Course Content
Module 1 Introduction
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What Is Data Analytics?
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Career Opportunities in Data Analytics in 2026
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Skills Required to Become a Data Analyst
Module 2 Excel Basics
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Basics
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Worksheet Basics
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Data Formats
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Data Handling Basics Cut, Copy & Paste
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Saving and Printing Basics
Module 3 Essential Formulas
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Basic Formula Operations
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Mathematical Functions 1
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Mathematical Functions 2
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Textual Functions Part 1
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Textual Functions 2
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Logical Functions
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Date-Time Functions
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Lookup functions (VLOOKUP,HLOOKUP,INDEX-MATCH)
Module 4 Xlookup – Only for Excel 2021 and Office 365
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XLOOKUP as a replacement of VLOOKUP
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Handling NA and approximate match in XLookup
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Wildcard matching in XLOOKUP
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Search modes
Module 5 Data Tools
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Sorting, Filtering and Data Validation
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Text-to-columns and remove duplicates
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Advanced Filter option
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Formatting Data and Tables
Module 6: Excel Charts
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1. Importance of Data Visualization
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2. Elements of Charts
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3. The Easy way of Creating Charts
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4. Bar and Column Charts
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5. Formatting Charts Part 1
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6. Formatting Charts Part 2
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7. Line Charts
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8. Area Charts
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9. Pie and Doughnut Charts
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10. Scatter plot or XY Chart
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11. Waterfall Charts
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12. Sparklines
Module 7 Pivot tables
Your Assignment Essential Formulas + xlookup
Module 8 Pivot Tables and Pivot Charts
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Pivot Tables
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Pivot Charts
Your Assignment Excel charts, Pivot tables and charts
Module 9 Map Charts- Only for Excel 2019 & above
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Maps Chart
Module 10 Using ChatGPT with Excel
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Use ChatGPT For Excel Problems
Module 11 Macros
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Macros
Module 12 Importing Data from External Sources
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Importing Data from Websites Excel 2019 and 365
Module 13 SQL Introduction
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Introduction
Module 16 Installation and getting started
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Installing SQL Server and SSMS in your system
Module 17 Database Basics
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1. Sorting, Filtering and Data Validation
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2. Text-to-columns and remove duplicates
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3. Advanced Filter option
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4. Formatting Data and Tables
Module 18 Fundamental SQL Statements
Your Assignment Fundamental SQL statements
Module 19 Restore and Backup
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Restore and Backup
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Backup and Restore-Adventure Works DB
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Creating tables using CSV files
Module 20 Selection Commands: Filtering
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IN
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BETWEEN
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LIKE
Module 21 Selection Commands:Ordering
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Side Lecture Commenting in SQL
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ORDER BY
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TOP (LIMIT)
Your Assignment Selection commands: filtering and ordering
Module 22 Aliasing
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AS
Module 23 Aggregate Commands
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COUNT
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SUM
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AVG
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MIN MAX
Module 24 GROUP BY
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GROUP BY
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HAVING
Module 25 Conditional Statement
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CASE WHEN
Your Assignment Group by & conditional statements
Module 26 JOINS
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Introduction to Joins
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Concepts of Joining and Combining Data
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Preparing the Data
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INNER JOIN
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LEFT JOIN
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RIGHT JOIN
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FULL OUTER JOIN
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CROSS JOIN
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INTERSECT
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EXCEPT
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UNION ALL
Module 27 Assignment Joins
Module 28 Subqueries
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Subquery in WHERE Clause
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Subquery in FROM clause
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Subquery in SELECT Clause
Module 29 Views and Indexes
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VIEW
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INDEX
Your Assignment Subqueries & views
Module 30 String Functions
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LENGTH
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UPPER LOWER
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REPLACE
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TRIM LTRIM RTRIM
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Concatenation
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SUBSTRING
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LIST AGGREGATION
Module 31 Mathematical Functions
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CEIL FLOOR
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RANDOM
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ROUND
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POWER
Module 32 DATE TIME Functions
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Current date time
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AGE – DATEDIFF
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EXTRACT – DATE AND TIME PARTS
Your Assignment Date and time functions
Module 33 Pattern(String)Matching
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PATTERN MATCHING BASICS
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PATTERN MATCHING PART 1
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advance pattern matching part 2
Your Assignment Pattern (string) matching
Module 34 Window functions
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Introduction to window functions
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Introduction to Row numbers
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Implementing row number in SQL
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RANK and DENSERANK
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NTILE
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AVERAGE Window Function
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COUNT WINDOW FUNC
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SUM TOTAL
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RUNNING TOTAL
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LAG and LEAD
Module 35 Data type conversion functions
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Converting number date to string and string to date
Your Assignment Data type conversion functions
Module 36 Deep dive into Python
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Python introduction and installation
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Anaconda installation
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Jupyter Walkthrough
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Data Types and Basics of OOP
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Numbers
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Maths
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Operator precedence
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Strings Introduction
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Type conversion and Escape Sequences
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Strings 101
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Booleans
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Lists
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List Methods
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List Patterns and Unpacking
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None
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DIctionary
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Dict Methods
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Tuples
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Sets
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Set Methods
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Conditional Logic
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Truthy and Falsy
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Ternary Operator
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Short Circuiting ( Optional )
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Logical Operators
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Exercise – Logical Operators
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== vs is
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For Loop
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Iterables
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Exercise – Tricky Counter
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range() and enumerate()
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While Loop
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Break , continue , pass
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Exercise – Our first GUI
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Exercise – Find Duplicates
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Functions
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Parameters & Arguments
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Keyword arguments and Default parameters
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Return
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Funtions vs Methods
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Docstring
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Clean Code
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args and kwargs
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Exercise – Functions
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Scope
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Global and nonlocal
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Pure Functions
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Map()
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Filter() & Zip()
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Reduce()
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Lambda
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Exception Error handling
Module 37 Projects
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Project Objective and Creating Tables in SQL
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SQL Oueries across multiple tables for data extraction
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Analysis, Validation and Visualization using Excel
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Creating a Risk Prediction model in Python
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Deploying Healthcare Risk stratification App
Module 38 Modules, Packages and Libraries
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Modules
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Packages
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Ways to Import
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Built -in Modules
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Python Libraries
Module 39 Numpy
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Numpy Introduction
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Numpy Datatypes and Attributes
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Creating Numpy Arrays
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Numpy Random Seed
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Viewing Arrays and Matrices
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Manipulation of Arrays ( Arithmetic )
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Manipulating Arrays ( Aggregate )
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Variance and Standard Deviation
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Reshaping and Transpose
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Dot product
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Comparison Operator and Sorting
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Image to Numpy Array
Module 40 Data Analysis using Pandas
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Pandas Overview
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Series, Data Frames and CSV
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Describing Data with Pandas
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Selecting and Viewing Data
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Asking the right questions
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Manipulating Data using Pandas
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Manipulating Data with Pandas 2
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Apply Function
Module 41 Data Visualization using Matplotlib, Seaborn and Plotly
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Matplotlib Introduction
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Using Matplotlib
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Matplotlib Workflow (Explicit Method)
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Scatter and Bar plot
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Histograms and Subplots
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Subplots Alternate Method
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Plotting from Pandas Series
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Plotting from Pandas DataFrame
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Plotting from Pandas DataFrame 2
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Plotting from Pandas DataFrame 3
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Plotting from Pandas DataFrame 4
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Plotting from Pandas DataFrame 4
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Plotting from Pandas DataFrame 6
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Customizing our Plots
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Customizing our Plots 2
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Seaborn Introduction
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Seaborn Univariate Analysis
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Seaborn Bivariate Analysis
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Seaborn Plotting Relationships
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Plotly Express
Module 42 Power BI
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1. Introduction to Power BI
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2. Data Transformation + Data Types
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3. Replace Text + Extract + Split
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4. Trim + Clean
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5. Assignment
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6. Data Modelling
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7. Visualization
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8. ETL
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9. Column Chart
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10. Pie Chart
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11. Line Chart
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12. Table
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13. Slicers
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14. Filters
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15. Tree Visualization
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16. Logical DAX
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17. Text DAX
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18. DateTime DAX
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19. Problem-1
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20. Problem-2
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21. Problem-3
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22. Problem-4
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23. Problem-4 Extended
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24. Problem-5
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25. Problem-6
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26. Bookmarks
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27. RLS
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28. Field Parameters
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29. AI Visuals + Quick Measures
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30. Folder Problem
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31. SQL+PowerBI
Module 43 Job Readiness & Placement Support
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1. How to build a Resume
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2. Customizing the Resume as per specific Job
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3. Interview Preparation Guidelines
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4. Short Mock Interview Flow
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5. How to Apply for Jobs
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6. Profile Enhancement and Upskilling
