Track
Data Science
Duration
60 hours
Skill Level
Intermediate
Language
English
About this Course
Master advanced analytics and visualization techniques using Excel and other platforms.
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
- Discover Google Sheets Features
- Navigate Google Sheets with Ease
- Create and Save Your First Spreadsheet
- Spreadsheet Formatting
- Advanced Spreadsheet Formatting
- Collaborate and Share Spreadsheets Efficiently
- Basic Formula and Functions
- Introduction to Absolute and Mixed References
- Common Math Functions and Operations
- COUNTIF, SUMIF, and SUMIFS Functions
- Text Manipulation Functions
- LEFT, RIGHT, MID, and FIND Functions
- Date and Time Functions in Google Sheets
- NETWORKDAYS and TEXT Functions
- IF Statements, and Nested IFs
- VLOOKUP and HLOOKUP Functions
- Sort and Filter Data for Enhanced Insights
- Data Validation and Data Integrity
- Date and Text Validation
- Analyze Data Efficiently with PivotTables
- Date Data Grouping, Extracting and Filtering
- Visualize Data with Charts and Graphs
- Creating Stacked Columns, Combo Charts, and Line Graphs
- Conduct Advanced Data Analysis Techniques
- Enhance Teamwork with Comments and Tools
- Track Changes with Revision History
- Automate with Google Apps Script Basics
- Create Custom Functions and Macros
- Create Macros to Automate Task
- Automate Routine Tasks in Google Sheets
- Setting Triggers for Spreadsheet Opening
- Apply Advanced Cell Formatting Techniques
- Implement Conditional Formatting for Insights
- Create Custom Conditional Formats for Data
- Use Templates for Consistent Data Reports
- Master Advanced Formatting for Professional Reports
- Import Data Seamlessly from Various Sources
- Export Data in Multiple Formats for Use
- Query Data within Google Sheets for Insights
- Connect Sheets with Apps for Streamlined Workflow
- Clean and Transform Data for Analysis
- Create Interactive Charts for Dynamic Insights
- Utilize Sparklines for Compact Data Visualization
- Implement GeoMapping for Geographic Data Analysis
- Customize Column Charts
- Chart Customization for Enhanced Data Storytelling
- Analyze Trends with Advanced Visualization
- Automate Reports for Up-to-Date Dashboards
- Integrate Sheets with Data Pipelines for Efficiency
- Optimizing Google Sheets to Overcome Limitations
- Function Optimization and Execution Control
- Apply Advanced Tips for Efficient Data Analysis
- Recap: Transform Data Entry into Actionable Insights
- Why Choose Looker Studio?
- Setting Up Looker Studio
- Exploring the Interface
- Creating Your First Visualization
- Saving the Look in Looker
- Introduction to Filtering
- Filtering in Looker Using Measures
- Overview of Visualization in Looker
- Creating Bar and Column Charts
- Adding Dimensions, Pivoting, and Grouping
- Grid Layout, Pivoting, and Spacing
- Designing Line Charts
- Pie Charts for Data Distribution
- Introduction to Scatterplots
- Advanced Scatterplot - Trend Lines, Reference Lines, and Saving
- Utilizing GeoMaps
- GeoMap Scale, Position, Zoom, and Saving
- Single-Value Visualization
- Introduction to Customs in Looker
- Creation and Interpretation of Table Calculations
- Permissions, Limitations, and Key Distinctions
- Custom Dimension Creation
- Binning as a Custom Dimension
- Grouping Data with Custom Dimensions
- Developing Custom Measures
- Look View Mode in Looker
- Data and Filters in View Mode
- Different Options in Look View Mode
- Customizing Dashboard Layout and Design
- Filter Adding to the Dashboard
- Optimizing Dashboards with Filters, Linked Filters, and Tile Management
- Setting Up Tiles and Filters Dashboard
- Creating and Managing Folders
- Downloading the Data from Looker
- Sharing Looks and Sending Mails from Looker
- Sharing Dashboards and Sending Mails from Looker
- Creating Boards in Looker
- Overview of R
- Introduction to Data Types in R
- Understanding Data Type Casting
- Introduction to Variables in R
- Variable Methods and Naming Conflicts
- Operators in R
- Reading Data Files in R
- The Reader Package, CSV Files, and read_lines Function
- Data Import with read_table and read_CSV
- Writing Data inside R
- Introduction to Decision Making
- Nested If-Else-If Statement, Switch Statement
- Introduction to Loops
- Nested For Loops, Break Statement & Next Statement
- Repeat Loops & While Loops
- Introduction to String Functions
- Standardizing Text Case in R
- Creating Date Objects in R
- Formatting Dates & Handling Time Data
- Calculating Date and Time Differences
- Math Functions in R
- Introduction to Arrays & Multidimensional Structures
- Accessing Elements, Rows & Columns
- Introduction to Lists in R
- Manipulating Lists
- Introduction to Data Frames in R
- Multiple Activities using Data Frames in R
- Introduction to Vectors in R
- Manipulation of Vectors and Factors
- Introduction to Matrices in R
- Accessing Matrix Elements & Modifying Matrices
- Combining Matrices & Creating Special Matrices
- Introduction to Charts and Graphs, Bar Plots, Histograms
- Box Plots, Multiple Box Plots, Scatter Plots, Heat Maps & 3D Graphs
- Scatter Plots
- 3D Scatter Plots, Box Plots, Colored Box Plots & Multiple Box Plots in One Plot
- Bar Plots & Labeled Bar Plots
- Grouped Bar Plots, Stacked Bar Plots & Histograms
- Conducting T-tests
- Working with Excel Files in R
- Managing CSV Files in R
- Introduction to Advanced Data Import Techniques
- Reading XML Files & Reading Data from Websites
- Understand ChatGPT-4’s Capabilities
- Understand ChatGPT-4’s Limitations
- Set Up ChatGPT-4 for Efficient Data Analytics
- Create Datasets Using Diverse Sources and Techniques
- Clean Data by Identifying and Resolving Irregularities
- Classify Data Types and Structures for Analytics
- Learn Basic Data Transformation Techniques
- Execute Simple Data Queries Using ChatGPT-4
- Data Analysis and Exploration with ChatGPT
- Cleaning, Analysis, and Visualization with ChatGPT
- Analyze Market Trends with ChatGPT-4
- Perform Real-time Data Processing and Analysis
- Apply Predictive Analytics in Business Decision-Making
- Utilize ChatGPT-4 for Understanding Financial Reports
- Missing Values and Outliers
- Normalize and Standardize Data for Consistency
- Engineer Features to Enhance Data Analysis
- Preprocess Text Data for Effective Analysis
- Understanding Date and Time Data with ChatGPT
- Extracting, Analyzing, and Enhancing Date of Birth Data
- Prepare Data for In-Depth Analysis
- Apply Effective Data Visualization Principles
- Create Basic Charts and Graphs Using ChatGPT-4
- Form Histograms & Bar Charts
- Generate Advanced Visualizations: Heatmaps and Boxplots
- Build Interactive Dashboards and Compelling Data Stories
- Real-Time Dashboards with ChatGPT
- Apply Best Practices in Data Presentation
- Perform Exploratory Data Analysis with ChatGPT-4
- Analyze Data Statistically and Summarize Metrics
- Introduction, Anomalies, Patterns, and ChatGPT Exploration
- Spotlight on Anomaly Detection and Box Plot Analysis
- Analyze Correlation and Causation in Data Sets
- Insights on Correlation and Causation
- Extract Inferences from Exploratory Data Analysis
- Understanding Employee Attrition and Strategies for Retention
- Perform Regression Analysis with ChatGPT-4
- Apply Classification Techniques in Data Analytics
- Conduct Time Series Analysis and Forecasting
- Implement Clustering
- Conduct Sentiment Analysis and Study Consumer Behavior
- Generate Custom Code for Data Analytics with ChatGPT-4
- Seamlessly Integrate ChatGPT-4 with Analytical Tools
- Master Advanced Data Querying and Retrieval with ChatGPT-4
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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