Track
Data Science
Duration
30 hours
Skill Level
Intermediate
Language
English
About this Course
Learn Python basics with a focus on data visualization. Build interactive charts and explore libraries like Matplotlib and Seaborn.
Learning Mode: Learn at ALC or at Home
Detailed Course Curriculum
Hands-on module breakdown aligned with MKCL production standards and industry requirements.
- Tableau: A Data Visualization Tool
- Exploring Data with Tableau
- Tableau Desktop and Tableau Public
- Tableau Public Installation and Interface Exploration
- Data Import and Exploration in Tableau
- Efficient Data Integration: Excel Sheets in Tableau for Analysis
- Introduction to Data Exploration in Tableau Public
- Sheet Connections in Tableau Public
- Pricing Structure for Tableau
- Sources of Data in Tableau
- Understanding Dimensions and Measures in Tableau
- Dimensional Analysis - Insights in Tableau
- Distinguish Discrete and Continuous in Tableau
- Discrete vs. Continuous
- An Example using Dimensions and Measures in Tableau
- Data Aggregations in Tableau
- Advanced Measures Exploration in Tableau
- Visualize with Tableau - Charts and Graphs
- Advanced Chart Creation in Tableau
- Tableau Public - Profile, Interactions, and Sharing
- Creating, Customizing, and Managing Reports in Tableau
- Exploring and Customizing Bar Charts in Tableau
- Measures for Customizing Bar Chart Labels in Tableau
- Construct Stacked Bar Charts for Deeper Insights
- Creating Continuous Line Charts with Tableau
- Crafting and Customizing Line Charts in Tableau
- Exploring Scatter Plots in Tableau
- Advanced Techniques for Scatter Plots and Circle Views in Tableau
- Create Dual Axis Charts for Comparative Analysis
- Dual and Combined Axes in Tableau
- Customizing Dual and Combined Axes
- Designing and Crafting Funnel Charts in Tableau
- Funnel Formatting and Final Touches
- Create Crosstabs for Data Comparison
- Develop Highlight Tables for Enhanced Visualization
- Modify Column Data Types for Accurate Analysis
- Manage Data: Renaming, Hiding, and Sorting
- Set Default Field Properties for Efficiency
- Implement Dimension Filters for Focused Analysis
- Apply Date Filters for Temporal Data Analysis
- Utilize Measure Filters for Quantitative Analysis
- Crafting Visualizations and Dashboards in Tableau
- Introduction to Action Filters in Tableau
- Create Interactive Filters for Dynamic Insights
- Apply Data Source Filters for Streamlined Data
- Context Filters for Targeted Insights in Tableau
- Calculated Fields and Top N Filters in Tableau
- Diverse Visualizations and Dashboard Layouts in Tableau
- Applying Filters and Adding Visualizations in Tableau
- Enhancing Visualizations with Action Filters in Tableau
- Advanced Action Filters in Tableau
- What can Python do?
- Why Python?
- Python Installation
- Print Statement with Multiple Techniques
- Displaying Name and Age with the format() Method
- Understanding and Utilizing Different Types of Comments
- Multi-line and DocString Comments
- Strings, Numeric, and Complex Data Types
- Lists, Tuples, Ranges, and Dictionaries in Python
- Indexing, Slicing, and Essential Methods
- String Functions and Boolean Operations
- Sets, Frozen Sets and Booleans in Python
- Understanding Byte, ByteArray, and Memory View
- Python Handling User Input with Ease
- Operations and Tuple Modifications
- Arithmetic and Assignment Operators
- Comparison and Logical Operators
- Understanding Rules and Examples for Python Indentation
- Structure in Loops
- Conditional Statements in Python
- Simple If and If-Else Statements
- Advanced If-Else and Nested IFs
- An Example with If and Its Related Statements
- Python’s While Loops
- Infinite Loops and Break Statements
- Python’s For Loop
- For Loops with Dictionaries and Sets
- Python’s Range Function Basics
- Advanced Techniques for Range Functions
- Break & Continue
- Assert
- Python Looping Essentials
- Advanced Looping Techniques in Python
- Create a Function
- Function Type
- Fundamentals of Variable Arguments in Python Functions
- Advanced Applications of Variable Arguments in Python
- Scope of a Function
- Function Documentations
- Lambda Functions & Map
- Basics to Advanced Applications in Python
- Functions for String Manipulation and Data Types
- Create a Module
- Python’s Standard Math Modules
- Time with Standard Modules
- Local to Global Variables in Python
- Advanced Local and Global Variables
- Python Errors - From Syntax to Runtime
- Error Handling in Python
- Python Exception Handling
- Type Errors and Custom Solutions
- File Handling - Reading, Writing, and Appending
- File Handling - Modes, Closing, and Best Practices
- Custom Exceptions in Python
- Implementing and Utilizing Custom Exceptions
- Python File Reading Essentials
- Reading and Analyzing Words and Lines in Python
- New-Style Classes in Python
- Python’s Class Hierarchy and Inheritance
- Creating Classes
- Instance Methods
- Inheritance in Python - Foundation of Superclass
- Subclassing and Method Overriding
- Polymorphism
- Python Exception Handling - Basics
- Creating and Utilizing Custom Exceptions in Python
- Namedtuple
- Operations, Instantiation, and Advanced Features
- Rotations and Element Access in Python
- Accessing and Modifying Mappings
- Custom ChainMap Class in Python
- Counter
- OrderedDict
- Defaultdict
- UserDict, UserList, and UserString
- Introduction and Installation
- DB Connection
- Getting Started with MySQL
- Crafting Tables in MySQL
- Inserting Data into MySQL Tables
- Reading, Updating, and Deleting Data in MySQL
- COMMIT & ROLLBACK operation
- MySQL Error Handling
- Handling Errors in MySQL Operations
- An Introduction to Built-in Iterators
- Custom Iterators for Squares
- Range-like Iterators
- Sleep
- Techniques for Measurement in Python
- Calculating Execution Time with Python’s timeit and time Module
- Time Representation in Python
- Creation to Arithmetic Operations
- Data Filtering in Python
- Filtering Non-Empty Strings and Unique Emails
- Python’s map, filter, and reduce
- Python’s map and star map for Enhanced Functionality
- Reduce
- Basic Decorators Functionality
- Decorators for Enhanced Functionality
- Frozen set
- Python’s Collections Module and Its Core Components
- Collections for Enhanced Data Handling and Manipulation in Python
- Python String Manipulation
- Handling Whitespace and Delimiters with Split()
- Identifying Various Date Formats in Python
- Email Validation with Regular Expressions in Python
- Power of Quantifiers in Regular Expressions
- Lazy and Non-Greedy Quantifiers in Regular Expressions
- Exploring Match, Search, and Fullmatch in Python
- Leveraging find all and finditer Functions in Python
- Search, Substitute, and Named Groups
- Search and Substitute Functions with Regular Expressions
- Advanced Replacement Techniques with sub N Function and Practical Examples
- Exploring Patterns, Classes, and Case-Insensitive Matching in Python
- Ranges, Delimiters, and Specific Patterns in Python
- Exploring Character Classes and Search Methods in Regular Expressions
- Managing Special Characters with Escape Sequences and Anchors
Eligibility Criteria
• Basic knowledge of computers and keen desire to build skills in this field.
• Open to students, job seekers, and working professionals.
• Open to students, job seekers, and working professionals.
Official Certification
• Official MKCL KLiC Certificate upon successful completion of the course and evaluations.
Work-Centric Learning Approach
• Step 1: Learners are given an overview of the course and its connection to life and work
• Step 2: Learners are exposed to the specific tool(s) used in the course through the various real-life applications of the tool(s).
• Step 3: Learners are acquainted with the careers and the hierarchy of roles they can perform at workplaces after attaining increasing levels of mastery over the tool(s).
• Step 4: Learners are acquainted with the architecture of the tool or tool map so as to appreciate various parts of the tool, their functions, utility and inter-relations.
• Step 5: Learners are exposed to simple application development methodology by using the tool at the beginner’s level.
• Step 6: Learners perform the differential skills related to the use of the tool to improve the given ready-made industry-standard outputs.
• Step 7: Learners are engaged in appreciation of real-life case studies developed by the experts.
• Step 8: Learners are encouraged to proceed from appreciation to imitation of the experts.
• Step 9: After the imitation experience, they are required to improve the expert’s outputs so that they proceed from mere imitation to emulation.
• Step 10: Emulation is taken a level further from working with differential skills towards the visualization and creation of a complete output according to the requirements provided. (Long Assignments)
• Step 11: Understanding the requirements, communicating one’s own thoughts and presenting are important skills required in facing an interview for securing a work order/job. For instilling these skills, learners are presented with various subject-specific technical as well as HR-oriented questions and encouraged to answer them.
• Step 12: Finally, they develop the integral skills involving optimal methods and best practices to produce useful outputs right from scratch, publish them in their ePortfolio and thereby proceed from emulation to self-expression, from self-expression to self-confidence and from self-confidence to self-reliance and self-esteem!
• Step 2: Learners are exposed to the specific tool(s) used in the course through the various real-life applications of the tool(s).
• Step 3: Learners are acquainted with the careers and the hierarchy of roles they can perform at workplaces after attaining increasing levels of mastery over the tool(s).
• Step 4: Learners are acquainted with the architecture of the tool or tool map so as to appreciate various parts of the tool, their functions, utility and inter-relations.
• Step 5: Learners are exposed to simple application development methodology by using the tool at the beginner’s level.
• Step 6: Learners perform the differential skills related to the use of the tool to improve the given ready-made industry-standard outputs.
• Step 7: Learners are engaged in appreciation of real-life case studies developed by the experts.
• Step 8: Learners are encouraged to proceed from appreciation to imitation of the experts.
• Step 9: After the imitation experience, they are required to improve the expert’s outputs so that they proceed from mere imitation to emulation.
• Step 10: Emulation is taken a level further from working with differential skills towards the visualization and creation of a complete output according to the requirements provided. (Long Assignments)
• Step 11: Understanding the requirements, communicating one’s own thoughts and presenting are important skills required in facing an interview for securing a work order/job. For instilling these skills, learners are presented with various subject-specific technical as well as HR-oriented questions and encouraged to answer them.
• Step 12: Finally, they develop the integral skills involving optimal methods and best practices to produce useful outputs right from scratch, publish them in their ePortfolio and thereby proceed from emulation to self-expression, from self-expression to self-confidence and from self-confidence to self-reliance and self-esteem!
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