Data Science with Python Syllabus

Duration: 3.5 Months

Python Course Module 1:

  • Python- overview
  • Python – Environment setup
  • Python- Basic syntax
  • Python-variable types
  • Assigning Values to Variables
  • Multiple Assignment
  • Standard Data Types

Python Basic Operators

  • Arithmetic Operators
  • Comparison (Relational) Operators
  • Assignment Operators
  • Logical Operators
  • Bitwise Operators
  • Membership Operators
  • Identity Operators
  • Operators Precedence

Python-Decision Making

  • If statement
  • If-else statement
  • Nested if else

Python-Loops

  • while loop
  • for loop
  • nested loop
  • break statement
  • continue statement
  • pass statement

Python –Numbers

Number type conversion

  • Random Number Functions
  • Mathematical Constants

Python – Strings

  • Updating Strings
  • Escape Characters
  • String Special Operators
  • String Formatting Operators
  • Unicode String

Python Lists

  • Indexing, Slicing, and Matrixes
  • Updating, deleting

Python – Tuples

  • Access,Update, Delete Tuple Elements
  • Indexing, Slicing, and Matrixes
  • Built-in Tuple Functions

Python – Dictionary

  • Access, Update, Delete Dictionary Elements
  • Properties of Dictionary Keys

Python – Date & Time

  • Date, time
  • Calendar , Tick

Python – Functions

  • Passing argument
  • Returning value
  • Using list, tuple, dictionary with function
  • Anonymous Functions
  • Scope of variable

Python – Modules

  • The import Statement
  • Namespaces and Scoping
  • Packages in Python

Python – Files I/O

  • Reading, writing file
  • Opening, closing, opening modes
  • file Object Attributes

Python – Exceptions

  • try-except Clause
  • try-finally Clause
  • except Clause with Multiple Exceptions
  • Argument of an Exception
  • User-Defined Exceptions

Python Advance

Python – class/objects

  • Overview of OOP Terminology
  • Class, object, constructor
  • Inheritance,
  • Destroying Objects (Garbage Collection)
  • Overriding Methods
  • Overloading Operators
  • Data Hiding
  • Other topics

Other topics

Module 2: Regression and Anova

  • Regression
  • ANOVA
  • R square
  • Correlation and causation

Module 3: Exploratory data analysis

  • Data visualization
  • Missing value analysis
  • The correction matrix
  • Outlier detection analysis
  • Supervise Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Algorithms
  • Linear & polynomial regression
  • support vector machine
  • decision tree
  • random forest
  • logistic regression
  • k-nearest neighbors
  • bagging & boosting

End to End project Development

  • Django

Computer Vision

  • Techniques
  • Image classification
  • object detection
  • semantic segmentation

Natural Language Processing Techniques

  • Techniques
  • Named Entity Recognition(NER)
  • Tokenization
  • Stemming and Lemmatization
  • Bag of Words
  • Natural language generation

Module4: Power BI Syllabus

Introduction to Power BI

  • Overview of Power BI: Understanding the components (Power BI Desktop, Power BI Service, Power BI Mobile)
  • Power BI Interface: Navigating the Power BI Desktop environment
  • Power BI Desktop vs Power BI Service: Key differences and use cases
  • Power BI File Types: .pbix, .pbit, .csv, .xlsx

Data Loading & Transformation (Power Query)

  • Power Query Editor: Basic data transformations
    • Removing columns, Renaming columns
    • Changing data types, Removing duplicates
    • Filtering and Sorting data
  • Importing Data: Connecting to various data sources (Excel, CSV, SQL, Web, etc.)
  • M Language Basics: Introduction to Power Query M language for advanced data transformation

Data Modeling

  • Creating Relationships: Building relationships between tables (One-to-One, One-to-Many, Many-to-Many)
  • Star Schema and Snowflake Schema: Best practices for data modeling
  • Managing Relationships: Relationship properties, Cross-filter direction
  • Creating Calculated Columns & Measures: Introduction to DAX (Data Analysis Expressions)

Data Visualization

  • Power BI Visualizations: Overview of available visualizations (Bar charts, Line charts, Pie charts, Tables, Matrix, etc.)
  • Custom Visuals: Using custom visuals from AppSource
  • Building Reports: Creating interactive reports with visuals
  • Adding Titles, Tooltips, Filters, and Slicers
  • Conditional formatting, Data labels, and Themes
  • Interactive Features: Drill-down, Drill-through, and Hierarchies

Advanced DAX (Data Analysis Expressions)

  • Basic DAX Functions: SUM, AVERAGE, COUNTROWS, etc.
  • Filter Context and Row Context: Understanding row and filter context in DAX
  • Time Intelligence Functions: DATESYTD, SAMEPERIODLASTYEAR, etc.
  • Advanced DAX Functions: CALCULATE, FILTER, ALL, RELATED, and more
  • Dynamic Measures: Creating dynamic KPIs, performance metrics

Data Refresh & Security

  • Scheduled Data Refresh: Setting up data refresh in Power BI Service
  • Row-Level Security (RLS): Implementing RLS to restrict data access based on user roles
  • Configuring Data Sources: Managing credentials and data source settings

Power BI Dashboarding & Reporting

  • Creating Dashboards: Pinning visuals and reports to dashboards
  • Dashboard Interactivity: Using slicers and filters on dashboards
  • Publishing & Sharing Dashboards: Best practices for sharing dashboards with stakeholders
  • Subscriptions & Alerts: Setting up report subscriptions, email alerts

Power BI Integration

  • Power BI and Excel Integration: Importing and analyzing Power BI data in Excel
  • Power BI and SharePoint Integration: Embedding Power BI reports in SharePoint Online
  • Power BI Embedded: Embedding Power BI visuals in other applications

Capstone Project

  • End-to-End Power BI Project:
  • Data Collection and Import
  • Data Transformation using Power Query

Data Science Based project work

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