Pythonsoft

Data Analytics

Data Analytics  Syllabus

PythonSoft LLP

www.pythonsoft.org

1. Introduction to python

• History of Python
• Why to learn python
• How is Python Different from other programming languages
• Installing Python and path setting
• How to run python programs

2. Basics of Python

• Variable
• Identifier
• Keywords
• Statements & Comments
• Indentation
• Data types(number,string,list,tuple,set,dictionary)
• Static Typing vs Dynamic Typing
• Input and output

3. Operators

• Arithmetic operator
• Relational Operator
• Assignment Operator
• Logical operator
• Bitwise operator
• Membership Operator
• Identity Operator

4. Control Flow

• If statement
• If - else
• If – elif -else
• Nested if – else
• While loop
• for – in loop
• Nested loop
• Loop with else
• Pass statement
• Break and continue

5. Functions

• Function Basics
• Defining function
• Function call
• Return statement
• Function parameters
• Call by value or call by reference
• Local and global variable
• Recursion
• Anonymous (lambda) function

6. Modules

• Defining module
• How to create module
• Importing module
• Dir ()
• Module search path
• Reloading a module
• Sys module
• Os module
• Namespace

7. Packages

• Defining package
• How to create package
• Importing package
• Installing third party packages

8. Numeric Types

• Numeric type basics
• Numbers
• Hexadecimal, Octal and Binary Notation
• Complex Numbers
• Type casting
• Numeric Functions
• Random number generation

9. String

• Defining a string
• Different ways to create string
• Accessing elements of string
• Escape sequence
• Raw string
• String methods
• String formatting Expressions

10. List

• Defining a list
• Creating list
• Accessing list elements of list
• Deleting list
• List methods
• Functions used with list
• List comprehension
• Implementation of stack and queue using list
• Use of Zip ()
• Matrix operations like addition ,multiplication using list
• Numpy array

 

 

11. Tuple

• Defining a tuple
• Creating a tuple
• Accessing elements of tuple
• Immutability
• List vs tuples
• Tuple Methods
• Functions used with tuple
• Advantage of Tuple

12. Dictionary

• Defining a dictionary
• Creating a dictionary
• Accessing elements of dictionary
• Deleting a dictionary
• Dictionary methods
• Dictionary Comprehension

13. Set

• Defining a set
• Creating set
• Set operations
• Set methods
• Set comprehension

14. File Handling

• Defining a file
• Types of file
• File operations
• Opening a File
• Closing file
• File modes
• File attributes
• Writing to file
• Reading from file
• Appending to file
• File positions
• Binary file
• Pickle module

15. Exception Handling

• Defining an exception?
• Default exception handler
• Exception handling techniques
• Detecting Exception (try)
• Catching exceptions (catch)
• Catching multiple exceptions
• Raising exception (raise)
• Finally block
• User defined exceptions

 

16. Object Oriented Programming

• Oop concepts
• Defining a class
• Creating object
• Method vs function
• Calling methods
• Instance attribute vs class attribute
• Instance method vs class method
• Private attribute and method
• Static method
• Method Overloading
• Constructor
• List of objects
• Inheritance
• Method overriding
• Operator overloading
• Abstract method
• Abstract class

 

17.Stack ,Queue 

• Stack  data structure
• Push ,pop operations on stack
• Applications of stack
• Queue Data structure 
• Insert and delete operations
• Applications of Queue

18. Multithreading

• Process based multi-tasking
• Thread based multi-tasking
• Creating a Thread without using class
• Creating thread using class
• Sleep () method
• Join () method
• Getting and setting name of Thread
• Synchronization
• Lock concept
• Is_Alive () method
• Active_count () method
• Enumerate () method
• Current_thread () method
• Daemon Thread

19. GUI Programming with Tkinter

• Introduction to tkinter
• Creating a window
• Tkinter widgets
  • Label
  • Button
  • Entry
  • Message box
  • List
  • Radio Button
  • Check Button
• Creating Frame
• Create  a Random Number Picker
• Create a calculator

 

20. Database Programming

• Introduction to SQLite module
• Connecting to database by using sqlite3
• Creating table by sqlite3
• Performing SQL operations
• Introduction to MySQL
• Installing MySQL
• Creating database using MySQL
• Connecting MySQL database from python
• Creating table
• Performing SQL operations

 

#  Data Analysis  using Python

1 Data analytics ,Data Science,Machine Learning  and AI

• What is data Science ?
• Data Analytics vs Data Science
• What is machine learning?
• Types of Machine learning
• Supervised learning
• Unsupervised learning
• Reinforcement Learning
• What is deep learning
• What is AI
• Difference between Machine learning, deep learning and AI
• Project life cycle
• Applications of Machine learning and deep learning
• Python packages for data science ,machine learning and deep learning2.

2. Introduction to Data Analytics

• Over view of data Analytics
• Application s of data Analytics
• Data Analytics Life Cycle

3. Numpy Array and its use

• Introduction to Numpy
• Different ways for Creating numpy array
• Array(),Arange(),linspace(),zeros(),ones()
• Advantage of Numpy array over List
• Mathematical operation on numpy array
• Reshaping numpy array
• Concatenation and splitting operation
• Trigonometric functions
• Matrix operations using numpy

 

3. Data collection and manipulation

• Pandas series
• Pandas data frame
• Reading CSV files
• Parameters of read_csv()
• Read excel files
• Handling missing values
• Data cleaning and pre processing

4. Exploratory Data Analysis (EDA)

Data visualization using matplotlib and seaborn

• Barplot
• Scatter plot
• Pie chart
• Lineplot
• Heat map etc

 

5. Data Preprocessing

• Data transformation
• Feature scaling
• Handling missing values  and outliers

6. Predictive Modelling

• • Introduction to machine learning
  • Supervised and unsupervised learning
  • Model evaluation and selection

7. Regression Analysis

• • Linear regression
  • Multiple regression
  • Logistic regression

8.Classification techniques

• • Decision trees
  • Random forests
  • Support vector machines

 

9.clustering

• • K-means clustering
  • Evaluation of clustering algorithms

 

10.Text Analysis

• • Text preprocessing
  • Sentiment analysis
  • Topic modeling

11.case Studies and Project

• • Real-world case studies in data analytics
  • Hands-on projects to apply learned concepts
  • Presentation and discussion of project outcomes

SQL

1.Introduction to SQL

• • Overview of SQL and its importance in data analytics
  • Understanding relational databases and SQL terminology
  • Setting up SQL environment

 

2.Basic SQL Queries

•        over view of sql and its importanace in analtyics
  • Understanding relational databases and SQL terminology
  • Setting up SQL environment

3.Data Manipulation

• • INSERT, UPDATE, and DELETE statements
  • Modifying data in tables
  • Managing transactions and data integrity

 

4.Join and sub queries

• • Inner, outer, left, and right joins
  • Using subqueries for complex queries
  • Understanding join performance considerations

 

5.Aggreation Functions

• • GROUP BY and HAVING clauses
  • Aggregate functions (SUM, AVG, COUNT, MIN, MAX)
  • Performing calculations on grouped data

6.Data Analysis using SQL

• • Analyzing data distributions and patterns
  • Using SQL for exploratory data analysis
  • Extracting insights from relational databases

7.Data visualization and Reporting

• • Integrating SQL with visualization tools (Power BI)
  • Generating reports using SQL queries
  • Presenting insights from SQL data analysis

8.Real world applications and case studies

• • Applying SQL for specific data analytics tasks
  • Case studies from various industries
  • Hands-on projects to solve data analytics challenges

 

Power BI

1.Introduction to Power BI

• • Overview of Power BI and its capabilities
  • Understanding Power BI Desktop vs. Power BI Service
  • Exploring Power BI interface and components

2.Getting started with power BI desktop

• •  Installing and configuring Power BI Desktop
  • Connecting to data sources (Excel, databases, web sources)
  • Importing data into Power BI Desktop

3.Data preparation and Transformation

• • Understanding data shaping and transformation
  • Cleaning and transforming data using Power Query Editor
  • Merging and appending queries

4.Data Modeling

• • Introduction to data modeling concepts
  • Creating relationships between tables
  • DAX (Data Analysis Expressions) fundamentals

5.Creating Visualizations

• • Overview of Power BI visualization types
  • Building basic visualizations (bar charts, line charts, pie charts, etc.)
  • Enhancing visualizations with formatting and interactions

6.Advanced visualizations

 

• • Using custom visuals from the Power BI marketplace
  • Implementing slicers, filters, and drill-downs

Creating hierarchies and matrices

7.Introduction to power BI service

• • Overview of Power BI Service and its features
  • Publishing reports and dashboards to Power BI Service
  • Sharing and collaboration options in Power BI Service

8.Data analysis and insights

• • Analyzing data using Power BI visuals
  • Implementing calculated columns and measures in DAX
  • Creating key performance indicators (KPIs)

9.Dashboard Design and Development

• • Design principles for effective dashboards
  • Building interactive dashboards in Power BI
  • Dashboard navigation and layout optimization

10. case studies and projects

• • Real-world case studies demonstrating Power BI applications
  • Hands-on projects to apply learned concepts
  • Presentation and discussion of project outcomes

 

 

 

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