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Suitable for both beginners and advanced learners, offering a smooth learning curve.
Data analytics is a transformative field that empowers organizations to extract valuable insights from vast datasets. By leveraging statistical methods, machine learning algorithms, and visualization tools, data analytics illuminates patterns and trends, enabling informed decision-making. It plays a pivotal role across industries, driving efficiency, optimizing processes, and uncovering opportunities for innovation. In a data-driven era, proficiency in data analytics is a valuable skill, offering the means to turn raw data into actionable intelligence
Your responsiveness to student queries, both during live sessions and through online platforms, contributed to a positive and interactive learning atmosphere
- Overview of Python
- What is procedural, functional Object-oriented
- Memory management in python
- Interpreter vs compiler in python
- Different Applications where Python is Used
- Discuss Python Scripts on UNIX/Windows/Mac
- Values, Datatypes, Variables
- Operands and Expressions
- Conditional Statements
- Practice questions
- Loops
- Command Line Arguments
- ✓ What is Database
- ✓ SQL vs No-SQL
- ✓ SQL Server (MySQL) Database
- ✓ CRUD operations
- ✓ Key, Schema & its properties
- ✓ Operation with Excel sheet
- Basics of Excel
- Data Insertion and its operations
- Basic Report Designing
- Visual Sync & Grouping
- Hierarchies & Filters
- Bookmarks, Azure, Modeling & Visualization
- Query & DAX Functions
- Cloud, Excel & RLS
- Basic Mathematics
- Algebra Concept
- Calculus & Integration
- Probability
- Statistics
- Operation on Mathematics
- Practice question on mathematics
- Basics Functionalities of Data object
- Merging of Data Objects
- Concatenation of Data Objects
- Types of Joins on Data Objects
- Exploring & Analyzing Dataset
- Matplotlib library
- Grids, axes, plots
- Markers, Colors, fonts & Styling
- Types of Plots - bar graphs
- Types of Plots - bar charts, histograms
- Contour plots
- on Matplotlib
- Series in pandas
- Data Frames
- Data Frame & its operation
- Missing Data
- Groupby in Pandas
- Merging, joining & Concatenating
- Data Input & Output
- Exercise based on pandas
- NumPy Arrays
- Array Iteration
- Arrays Index Slicing
- Operations on Arrays
- Reading & writing Arrays on files
- Functions
- Function Parameters
- Global Variables
- Variable Scope and Returning Values
- Lambda Functions
- Object Oriented Concepts
- Standard Libraries
- Sessions Used in Python
- The Import Statements
- Session Search Path
- Package Installation Ways
- Errors and Exception Handling
- Handling Multiple Exceptions
- Tuples and its methods
- Lists and its methods
- Dictionary and its methods
- Sets and its methods
- Numbers
- Math & Its Operations
- ✓ About Time series analysis
- ✓ Components of time series
- ✓ White Noise
- ✓ Models & its types
- ✓ Practice questions
- ✓ Stationarity, ACF & PACF
- ✓ About Model Selection
- ✓ Cross -validation
- ✓ Boosting and its working
- ✓ Boosting Algorithms & its type
the Data Analysis course is an invaluable resource for anyone looking to master the fundamental skills needed to analyze and interpret data effectively