Python Course Details- Duration, Syllabus, Jobs, And Salary

Web development. Application development and other web platforms are seeing the rise. As the requirement of the organization is mounting more and more; the demand for web developers is also filling up the employment bracket in the market. Today the web development is a way through which one can easily reach the indigent market and provide the services and the products they are looking for. So let’s explore the python details related to the learning method, fees, course duration, jobs, and salary.

What is Python Programming?

Python developed by Guido Van Rossum in 1991 is currently a widely-used programming language that is used for the development of web applications and websites. It offers server-side development and can be run on different platforms such as Windows, Mac, Linux, and others. Today those course is offered by the institute with the aim to help you learn the complete work process involved with python. So if you are looking to learn and grow your skills in it you need to enroll in Python Course Online as it offers the perfect way through which you can easily learn the course from home.

Need of Python

The development of python is completely defined for better readability, completing the commands, and to be used as a general-purpose programming. Today python is used by many experts who are engaged with the development of the online platform. Software engineers, data scientists, and coders use this language to perform the roles such as testing, coding, developing software, data security, debugging, data storage, and much more.

Course Duration

The course duration for the certificate and diploma differs as both have different course structures. With a diploma, you will learn more components attached to python programming whereas with the certificate you will learn core values and the basic programming involved with Python.

• Course duration for a certificate course in python is up to 6 months
  
• Course duration for getting a diploma in Python is 1-2 years

Python Syllabus

Updated Python Course syllabus

• Introduction to Data Analytics
• What Is Data Analytic?
• Common Terms in Data Analytics
• What Is Data?
• Classification Of Data
• Relevance In Industry and Need of The Hour
• Types Of Problems and Business Objectives in Various Industries
• How Leading Companies Are Harnessing the Power Of Analytics?
• Critical Success Drivers.
• Overview Of Data Analytics Tools & Their Popularity.
• Data Analytics Methodology & Problem-Solving Framework.
• List Of Steps In Data Analytics Projects
• Python for Data Analyst

Introduction To Python

• Installation and Working with Python
• Understanding Python variables
• Python basic Operators
• Understanding the Python blocks.

Python Keyword and Identifiers

• Python Comments, Multiline Comments.
• Python Indentation
• Understating the concepts of Operators
• Arithmetic
• Relational
• Logical
• Assignment
• Membership
• Identity

Introduction To Variables

• Variables, expression condition and function
• Global and Local Variables in Python
• Packing and Unpacking Arguments
• Type Casting in Python
• Byte objects vs. string in Python
• Variable Scope

Python Data Type

• Declaring and using Numeric data types
• Using string data type and string operations
• Understanding non-numeric data types
• Understanding the concept of Casting and Boolean.
• Strings
• List
• Tuples
• Dictionary
• Sets

Control Structure & Flow

• Statements – if, else, elif
• How to use nested IF and Else in Python
• Loops
• Loops and Control Statements.
• Jumping Statements – Break, Continue, pass
• Looping techniques in Python
• How to use Range function in Loop?
• Programs for printing Patterns in Python
• How to use if and else with Loop
• Use of Switch Function in Loop
• Elegant way of Python Iteration
• Generator in Python
• How to use nested Loop in Python
• Use If and Else in for and While Loop
• Examples of Looping with Break and Continue Statement
• How to use IN or NOT IN keyword in Python Loop.

Python Function, Modules And Packages

• Python Syntax
• Function Call
• Return Statement
• Arguments in a function – Required, Default, Positional, Variable-length
• Write an Empty Function in Python –pass statement.
• Lamda/ Anonymous Function
• *args and **kwargs
• Help function in Python
• Scope and Life Time of Variable in Python Function
• Nested Loop in Python Function
• Recursive Function and Its Advantage and Disadvantage
• Organizing python codes using functions
• Organizing python projects into modules
• Importing own module as well as external modules
• Understanding Packages
• Random functions in python
• Programming using functions, modules & external packages
• Map, Filter and Reduce function with Lambda Function
• More example of Python Function

List

• What is List.
• List Creation
• List Length
• List Append
• List Insert
• List Remove
• List Append & Extend using “+” and Keyword
• List Delete
• List related Keyword in Python
• List Revers
• List Sorting
• List having Multiple Reference
• String Split to create a List
• List Indexing
• List Slicing
• List count and Looping
• List Comprehension and Nested Comprehension

Tuple

• What is Tuple
• Tuple Creation
• Accessing Elements in Tuple
• Changing a Tuple
• Tuple Deletion
• Tuple Count
• Tuple Index
• Tuple Membership
• TupleBuilt in Function (Length, Sort)

Dictionary

• Dict Creation
• Dict Access (Accessing Dict Values)
• Dict Get Method
• Dict Add or Modify Elements
• Dict Copy
• Dict From Keys.
• Dict Items
• Dict Keys (Updating, Removing and Iterating)
• Dict Values
• Dict Comprehension
• Default Dictionaries
• Ordered Dictionaries
• Looping Dictionaries
• Dict useful methods (Pop, Pop Item, Str , Update etc.)

Sets

• What is Set
• Set Creation
• Add element to a Set
• Remove elements from a Set
• PythonSet Operations
• Frozen Sets

Strings

• What is Set
• Set Creation
• Add element to a Set
• Remove elements from a Set
• PythonSet Operations

Python Exception Handling

• Python Errors and Built-in-Exceptions
• Exception handing Try, Except and Finally
• Catching Exceptions in Python
• Catching Specific Exception in Python
• Raising Exception
• Try and Finally

Python File Handling

• Opening a File
• Python File Modes
• Closing File
• Writing to a File
• Reading from a File
• Renaming and Deleting Files in Python
• Python Directory and File Management
• List Directories and Files
• Making New Directory
• Changing Directory

Python Database Interaction

• SQL Database connection using
• Creating and searching tables
• Reading and Storing config information on database
• Programming using database connections

Reading An Excel

• Working With Excel
• Reading an excel file using Python
• Writing to an excel sheet using Python
• Python| Reading an excel file
• Python | Writing an excel file
• Adjusting Rows and Column using Python
• ArithmeticOperation in Excel file.
• Play with Workbook, Sheets and Cells in Excel using Python
• Creating and Removing Sheets
• Formatting the Excel File Data
• More example of Python Function

Complete Understanding Of OS Module Of Python

• Check Dirs. (exist or not)
• How to split path and extension?
• How to get user profile detail?
• Get the path of Desktop, Documents, Downloads etc.
• Handle the File System Organization using OS
• How to get any files and folder’s details using OS?

Statistics for Data Analyst

Introduction To Statistics

• Categorical Data
• Numerical Data
• Mean
• Median
• Mode
• Outliers
• Range
• Interquartile range
• Correlation
• Standard Deviation
• Variance
• Box plot

Understanding Statistics

• Descriptive Statistics
• Sample vs Population Statistics
• Random variables
• Probability distribution functions
• Expected value
• Normal distribution
• Gaussian distribution
• Z-score
• Spread and Dispersion
• Correlation and Co-variance

Data Pre-Processing & Data Mining

• Data Preparation
• Feature Engineering
• Feature Scaling
• Datasets
• Dimensionality Reduction
• Anomaly Detection
• Parameter Estimation
• Data and Knowledge
• Selected Applications in Data Mining

EDA (Exploratory Data Analysis)

• Need for structured exploratory data
• EDA framework for exploring the data and identifying any problems with the data
• (Data Audit Report)
• Identify missing data
• Identify outliers’ data
• Imbalanced Data Techniques

Data Analysis and Visualization

Data Analysis And Visualization Using Pandas.

• Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to files
• How to get record specific records Using Pandas Adding & Resetting Columns, Mapping with function
• Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalue’s
• Exploring the Data Plotting, Correlations, and Histograms
• Getting statistical information about the data Analysis Concepts, Handle the None Values
• Reading files with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
• Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
• Applying formulas on the columns Basic Grouping, Concepts of Aggre gate Function
• Complete Understanding of Pivot Table Data Slicing using iLoc and Loc property (Setting Indices)
• Under sting the Properties of Pivot Table in Pandas Advanced Reading CSVs/HTML,
• Binning, Categorical Data
• Exporting the results to Excel Joins
• Python | Pandas Data Frame Inner Join
• Under sting the properties of Data Frame Left Join (Left Outer Join)
• Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
• Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
• Pandas | Find Missing Data and Fill and Drop NA Appending Data Frame and Data
• Pandas | How to Group Data How to apply Lambda / Function on Data Frame
• Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)

Data Analysis And Visualization Using NumPy

• Introduction to NumPy Numerical Python
• Importing NumPy and Its Properties
• NumPy Arrays
• Creating an Array from a CSV
• Operations an Array from a CSV
• Operations with NumPy Arrays
• Two-Dimensional Array
• Selecting Elements from 1-D Array
• Selecting Elements from 2-D Array
• Logical Operation with Arrays
• Indexing NumPy elements using conditionals
• NumPy’s Mean and Axis
• NumPy’s Mode, Median and Sum Function
• NumPy’s Sort Function and More

Data Analysis And Visualization Using MatPlotLib

• Bar Chart using Python MatPlotLib
• Column Chart using Python MatPlotLib
• Pie Chart using Python MatPlotLib
• Area Chart using Python MatPlotLib
• Scatter Plot Chart using Python MatPlotLib
• Play with Charts Properties Using MatPlotLib
• Export the Chart as Image
• Understanding plt. subplots () notation
• Legend Alignment of Chart using MatPlotLib
• Create Charts as Image
• Other Useful Properties of Charts.
• Complete Understanding of Histograms
• Plotting Different Charts, Labels, and Labels Alignment etc.

Introduction To Data Visualization With Seaborn

• Introduction to Seaborn
• Making a scatter plot with lists
• Making a count plot with a list
• Using Pandas with seaborn
• Tidy vs Untidy data
• Making a count plot with a Dataframe
• Adding a third variable with hue
• Hue and scattera plots
• Hue and count plots
• Visualizing Two Quantitative Variables
• Introduction to relational plots and subplots
• Creating subplots with col and row
• Customizing scatters plots
• Changing the size of scatter plot points
• Changing the style of scatter plot points
• Introduction to line plots
• Interpreting line plots
• Visualizing standard deviation with line plots
• Plotting subgroups in line plots
• Visualizing a Categorical and a Quantitative Variable
• Current plots and bar plots
• Count plots
• Bar plot with percentages
• Customizing bar plots
• Box plots
• Create and interpret a box plot
• Omitting outliers
• Adjusting the whisk
• Point plots
• Customizing points plots
• Point plot with subgroups
• Customizing Seaborn Plots
• Changing plot style and colour
• Changing style and palette
• Changing the scale
• Using a custom palette
• Adding titles and labels Part 1
• Face Grids vs. Axes Subplots
• Adding a title to a face Grid object
• Adding title and labels Part 2
• Adding a title and axis labels
• Rotating x-tics labels
• Putting it all together
• Box plot with subgroups
• Bar plot with subgroups and subplots
• Well done! What’s next?

 Eligibility to learn Python

Well, to learn Python it is important to have little awareness about the programming concepts and have an overview of writing the programs. Well, python does not require any important eligibility criteria. The Eligibility for both certification and diploma is different for those who are looking to do certification must have completed their studies in 10th and for those who are looking to learn diploma in python must have completed their 10th and 12th in order to learn the course.

Job Opportunities in Python

There is a huge demand for python programmers and developers and the need are increasing with promising career opportunities. This makes it a most desirable career to learn Application development, testing, scriptwriting, application upgrades. The Python Job profile available in the market are:

• Python developer
  
• Software Engineer
• Data analyst
• Data scientist
• Research Analyst
• Software developer
• Product manager
• Financial Advisor
• Data journalist
• Trainer

Python Developer Salary 

Here's a table that outlines Python Developer Salary based on experience, job roles, and locations:

Key Insights:

• Entry Level: Python developers with little to no experience earn between ₹4-7 lakhs annually in India and $55,000-80,000 in the US and Europe.
• Mid-Level: Developers with 2-5 years of experience typically earn ₹7-12 lakhs in India and $80,000-120,000 in the US.
• Senior Level: Senior Python developers can command salaries upward of ₹12-25 lakhs or more in India and over $120,000 in the US.
• Specialized Roles: Roles like Full Stack Developer, Data Scientist, and Machine Learning Engineer tend to offer higher pay due to their specialized skills.

You May Also Read:

Python Programming for Beginners

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Data Scientist Qualifications

Data Science Interview Questions and Answers

Data Science Bootcamp

Skills to Learn Along with Python Training

Completing the Advanced Python Course is not enough as one needs to learn more tools and components to have better and complete control in working with the programming language. Here are some of the skill sets that you must get involved with to learn python.

• Object Relational mapper
  
• Deep learning
• Analytical skills
• Communication skills
• Data science
• Designing skills
• Expertise in Core Python
• Knowledge in the web framework

There are many other skills sets that you can explore as soon as you start learning python. So to start with enrolling for the python training and gain complete exposure to such skills so as to implement the same while completing the web development projects. With this, we come to the end of this page. Hope it has provided the complete knowledge and information that you were looking for, in case of any other doubt you can register your name with the institute and grab the information from the expert counselors.

Conclusion

Now that you have a complete idea of the Python Certification Course Fees, course structure, and the job roles involved in it. So, to start with simply enroll in the classes or you can also explore the course by enrolling for the free live demo sessions as it offers a perfect way through which you can understand the course structure and the modules you will learn before joining the training.