Python Course in Gurgaon
Master Python and get prepared for Python official certifications with our role based courses tailored to your specific needs
- Enroll for Cloud Architecture, Developer, Operations, DevOps, AI / ML, Networking and Security Certifications
- Experience blended learning through interactive offline and online sessions.
- Job Assured Course
- Course Duration - 2 months
- Get Trained from Industry Experts
Train using realtime course materials using online portals & trainer experience to get a personalized teaching experience.
Active interaction in sessions guided by leading professionals from the industry
Gain professionals insights through leading industry experts across domains
Enhance your career with 42+ in-demand skills and 20+ services
Python Course Overview
This Python live-learning courses cover deep into designing, developing, deploying, and managing scalable solutions and infrastructure automation across platforms using Python, equipping you for success in today’s fast-evolving technology landscape
- Benefit from ongoing access to all self-paced videos and archived session recordings
- Success Aimers supports you in gaining visibility among leading employers
- Get prepared for Python official certifications with our role based courses tailored to your specific needs
- Engage with real-world capstone projects
- Engage in live virtual classes led by industry experts, complemented by hands-on projects
- Learn 42+ In-Demand Skills & 20+ Services
- Job interview rehearsal sessions
Basic, high-level knowledge of Cloud services and terminology. Ideal starting point for Python Certification for those with no prior IT/cloud experience transitioning to cloud careers, or business professionals seeking foundational cloud literacy.
Familiarity with Python Flask, Django, Web2Py (AI), Pyramid, and generative Python fundamentals to implement in real-world applications
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What is Databricks Spark Developer?
Databricks Certified Spark Developer are essential for driving AI innovation in software development and testing. They manage the full deployment lifecycle—deploying apps, building and maintaining pipelines for web applications and K8s workflows, and automating development and testing processes. By streamlining workflows and resolving challenges in webapp deployment on cloud, and maintenance, they help organizations deliver reliable, high-performance applications faster and more efficiently.
The role of Databricks Spark Developer ?
Databricks Certified Spark Developer in software development and testing oversees the end-to-end lifecycle of web applications, from development to deployment and system performance optimization. Key responsibilities include:
Exploring Emerging Technologies: Leveraging Cloud technologies, Cloud networking, and security techniques to enhance efficiency and streamline deployment workflows.
Scalable Data, AI & WebApps Development: Designing and implementing web applications that address critical business needs.
Seamless Deployment: Coordinating web deployment with infrastructure management for smooth delivery.
Workflow Optimization: Creating, analyzing, and refining automation scripts and deployment workflows to maximize productivity.
For professionals aspiring to excel in this field, the Success Aimers Databricks Spark Developer Course provides hands-on training to master these skills. The program equips you to confidently manage deployment lifecycles, deployment pipelines, automation, and deployment processes, positioning you as a high-impact Databricks Certified Spark Developer in software development and testing.”**
Who should take this Databricks Certified Spark Developer course?
The Databricks Certified Spark Developer Course is tailored for professionals aiming to accelerate their careers in Cloud, data, and technology-driven sectors. It is particularly valuable for roles including:
Cloud Team Leaders
Software and DevOps Developers
Cloud Engineers and IT Managers
Cloud & Infrastructure Engineers
Cloud Researchers and Application Engineers
This program equips participants with the skills to lead DevOps & infrastructure initiatives, implement advanced deployment workflows, and drive innovation in software development and testing.
What are the prerequisites of Databricks Certified Spark Developer Course?
Prerequisites for the Azure Certified Cloud Engineer Certification Course”
To ensure a seamless learning experience, candidates are expected to have:
Educational Background:Â An undergraduate degree or high school diploma in a relevant field.
Technical Foundation: Knowledge of IT, software development, or data science fundamentals.
Programming Skills:Â Basic proficiency in languages such as Python or JavaScript.
Cloud Familiarity:Â Experience with cloud platforms like AWS or Microsoft Azure.
Meeting these prerequisites enables learners to effectively grasp advanced Cloud concepts, including DevOps tool, pipeline workflows, Webapp deployment, and automation throughout the course.
What are the prerequisites of Databricks Certified Spark Developer Course?
- Azure Certified Cloud Engineer
- SRE Reliability Engineer
- Cloud Solutions Release Manager
- Infrastructure/Cloud Automation Engineer
- Cloud Engineer / Cloud Architect
- Cloud Infrastructure Engineer
- Cloud Deployment Engineer
- Â
| Training Options | Weekdays (Mon-Fri) | Weekends (Sat-Sun) | Fast Track |
|---|---|---|---|
| Duration of Course | 3 months | 4 months | 1 month |
| Hours / day | 1-2 hours | 2-3 hours | 5 hours |
| Mode of Training | Offline / Online | Offline / Online | Offline / Online |
Python Course Curriculum
This Databricks Cloud Certification training enhances your career after choosing the relevant certification path based on the roles. You can practice with hands-on labs and capstone projects and gain proficiency with Databricks tools. After completion of the course, you can leverage Job assistance services and enhance career prospects.
Below are the leading cloud career roles and the corresponding Databricks certification path. You can choose the role(s) that progress your Databricks certification journey toward your goals. These learning paths are recommendations, not requirements.
Python
Getting started with Python
- Python Installation
- Python Interpreter
- iPython
Strings – Working with Textual Data
Integers and Floats – Working with Numeric Data
Python Standard Datatypes
- Lists, Tuples, Dictionary and Sets
- Access Items from List, Tuples & Dictionary
- Unpack Tuples
- Add & remove items from List, Tuples, Sets & Dictionary
- Dictionaries – Working with Key-Value Pairs
- Slicing Lists and Strings
- Sorting Lists, Tuples and Objects
- String Formatting – Advanced Operations for Dicts, Lists, Numbers and Dates
Advanced Datatypes in Python
- Deque Objects
- orderedDict
- defaultDict
- Set & FrozenSet
- namedTuples
Python Control Statements
- Conditional and Booleans – If, Else and Elif Statements
- Loops and Iterations – For/While Loops
- While Loops
- Decision Making
Functions
Default Arguments
Keyword-only Arguments
Variable-Length
Function Annotations
Anonymous Functions
- map() function
- map() with Lambda Function
- Connect Open-Source Models like Llama-3 with Auto GPT
- filter()
- filter with Lambda
- reduce()
- reduce() with Lambda Function
Built in Functions
Decorator Functions
Functions with arguments & decorators
Passing parameters to a Decorator Function
Higher Order Functions (HOF)
Recursive Functions
F-Strings – How to use them and Advanced String Formatting
Generators
Decorators – Dynamically Alter the Functionality of our Functions
Decorators with Arguments
Python Modules
- Introduction to modules
- Using Modules
- Custom Modules
- Third-Party Library Integration
- Import Modules and Exploring the Standard Library
- OS Module – Use Underlying Operating System Functionality
- Datetime Module – How to work with Dates, Times, Timedeltas and Timezones
- Generate Random Numbers and Data using the random module
- CSV Module – How to Read, Parse and Write CSV Files
- Statistics module
- Math Module
- Request Module
Python Virtual Environment & Development IDE’s
- Setting up a Python Development in Eclipse
- Virtualenv and why should use virtual environments
- How we manage multiple projects, Virtual Environments and Environment Variables
- Jupyter Notebook: Introduction, Setup and Walkthrough
Pip – In-depth look at the package management system
Python Comprehensions: How they work and why we should be using them
- List Comprehensions
- Set Comprehensions
- Dictionary Comprehensions
Python File Handling
- File Objects – Reading and Writing to Files
- Renaming & Deleting Files
- Python Directories
- File Methods
- OS File/Directory Methods
- Automate Parsing and Renaming of Multiple Files
Python Regular Expression
- re Module – How to write and match Regular Expressions (Regex)
- RegEx Functions
OOP’s (Object Oriented Programming)
- OOPs: Classes and Instances
- OOPs: Class Variables
- OOPs: Access Modifiers
- Python Constructors
- OOPs: classmethods and statismethods
- OOPs: Inheritance – Creating Subclasses
- OOPs: Special (Magic/Dunder) Methods
- OOP’s: Property Decorators – Getters, Setters, and Deleters
- Python: Polymorphism
- Python: Dynamic Binding
- Python: Encapsulation
- Python: Packages
- Python: Singleton & Wrapper Class
- Python: Enums
- Python: Reflection
Python Errors & Exceptions
- Exceptions
- Try-except block
- Try-finally block
- Raising Exceptions
- Exception Chaining
- Nested Try
- User-Defined Exception
- Assertions
- Built-in-Exceptions
Python Multithreading
Thread Life Cycle
Creating a Thread
Starting a Thread
- Fundamentals of the Databricks Lakehouse Platform
- Persistence Volume Claim (PVC)
- Fundamentals of Delta Lake
- Fundamentals of Lakehouse Architecture
- Fundamentals of Databricks SQL
- Fundamentals of Databricks Machine Learning
- Fundamentals of the Databricks Lakehouse Platform Accreditation
- Fundamentals of Big Data
- Fundamentals of Cloud Computing
- Fundamentals of Enterprise Data Management Systems
- Fundamentals of Machine Learning
- Fundamentals of Structured Streaming
Joining Threads
- Spark Ecosystem
- Quick Reference Spark Architecture
- Introduction to Apache Spark Architecture
- Apache Spark API’s
- Starting Spark 2.0 – APIs for Datasets & DataFrames have merged
- Delta Lake Rapid Start with Python
- Delta Lake Rapid Start with Spark SQL
Thread Scheduling
- Infrastructure Management
- Workspace Collaboration
- Automation
- Security
Thread Pools
- Azure Databricks Cloud Architecture & System Integration Fundamentals
- Azure Databricks Cloud Architecture and System Integration Fundamentals
- Databricks on Google Cloud: Cloud Architecture & System Integration
- Configuring Workspace Access Control Lists (ACLs)
- Databricks Command Line Interface Fundamentals
- Databricks Datadog Integration
- Easy ETL with Auto Loader
- Introduction to Cloning with Delta Lake
- Introduction to Databricks Connect
- Introduction to Databricks Repos
- Introduction to Delta Live Tables
- Introduction to Multi-Task Jobs
- Lake House with Delta Lake Deep Dive
- Propagating Changes with Delta Change Data Feed
- Quick Reference: CI/CD
- Structured Streaming on Azure Databricks
- Streaming DELTA Tables
- Compliance & Optimization
Thread Priority
Daemon Thread
- Isolation Unit
- Workspace ID
- Locked Resources
- Organize Assets
- Access Controls
Synchronizing Threads
- Inter-Thread Communication
- Thread Deadlock
- Interrupting a Thread
- Semaphores
- Barrier Objects
- Countdown Latch
Python Networking
- Socket Programming
- URL Processing
- Generics
Serialization
Python Templating
Web Scrapping with Beautiful Soup and Requests
Working with JSON data using Json module
Complete Overview: Creating a Database, Table and running queries
Logging Basics – Logging to Files, Setting Levels, and Formatting
Logging Advanced – Loggers, Handlers, and Formatters
Hiding Passwords and Secrets Keys in Environment Variables
Unit Testing our code with the unittest Module
Easily Manage Packages and Virtual Environment
Web Scrapping with Requests-HTML
How to send Emails using Python – Plain Text, Adding Attachments, HTML Emails & more
Creating an API Key and Querying the API
How to use ChatGPT as a powerful Tool for programming
EDA (Exploratory Data Analysis - NumPy, Pandas, Matplot, Seaborn)
EDA – Exploratory Data Analysis - NumPy
- NumPy Overview
- Array Slicing and Indexing
- Array Manipulation Functions
- Additional Array Creation Functions
- Array Arithmetic and Mathematical Functions
- IO Functions in NumPy
EDA – Exploratory Data Analysis – Pandas (Data Manipulation Library)
- Pandas Overview
- Introduction to Series
- Introduction to DataFrames
- Selecting Data 1
- Selecting Data 2
- Data Manipulation 1
- Data Manipulation 2
- Data Aggregation and Grouping
- Data Cleansing
- Combining DataFrames
- Windowing Operations
EDA – Exploratory Data Analysis – DataFrames and Datasets
- Datasets & CSV
- pd.read_csv & DataFrames
- Inspecting DataFrames: head(), tail(), etc.
- Datatypes and info()
- The House Sales Dataset Walkthrough
- The Titanic Passenger Dataset Walkthrough
- Non-comma Separators: Netflix Dataset
- Overriding Headers: Country Population Dataset
EDA – Exploratory Data Analysis – Basic DataFrame Methods and Computations
- Min & Max
- Sum & Count
- Mean, Median, & Mode
- Describe With Numeric Values
- Describe With Objects (Text) Values
EDA – Exploratory Data Analysis – Series & Columns
- Selecting A Single Column
- A Closer Look at Series
- Important Series Methods
- unique & nunique
- nlargest & nsmallest
- Selecting Multiple Columns
- The powerful value_counts() method
- Using plot() to visualize!
EDA – Exploratory Data Analysis – Indexing & Sorting
- set_Index Basics
- set_index: The World Happiness Index Dataset
- setting index with read_csv
- sort_values intro
- sorting by multiple columns
- sorting text columns
- sort_index
- Sorting and Plotting!
- loc
- iloc
- loc & iloc with Series
EDA – Exploratory Data Analysis – Filtering DataFrames
- Filtering DataFrames with A Boolean Series
- Filtering With Comparison Operators
- The Between Method
- The isin() Method
- Combining Conditions Using AND (&)
- Combining Conditions Using OR (|)
- Bitwise Negation
- isna() and notna() Methods
- Filtering + Plotting Examples
EDA – Exploratory Data Analysis – Adding & Removing Columns
- Dropping Columns
- Dropping Rows
- Adding Static Columns
- Creating New "Dynamic" Columns
- Finding The Highest price/sqft homes
- Finding Largest Bitcoin Price Changes
EDA – Exploratory Data Analysis – Updating Values
- Renaming Columns and Index Labels
- The replace() method
- Updating Values Using loc[]
- Updating Multiple Values Using loc[]
- Making Updates With loc[] and Boolean Masks
EDA – Exploratory Data Analysis – Working with Types and NA
- Casting Types With astype()
- Introducing the Category Type
- Casting With pd.to_numeric()
- dropna() and isna()
- fillna()
EDA – Exploratory Data Analysis – Working with Dates and Times
- Converting With pd.to_datetime ()
- Specifying Fancy Formats With pd.to_datetime()
- Dates and DataFrames
- The Useful dt Properties
- Comparing Dates
- Finding StarLink Flybys in UFO Dataset
- Date Math & TimeDeltas
- Billboard Charts Dataset Exploration
EDA – Exploratory Data Analysis – Grouping & Aggregating
- Introducing Groupby
- Exploring Groups
- Split-Apply-Combine
- Using The Agg Method
- Agg with Custom Functions
- Named Aggregation
EDA – Exploratory Data Analysis - Matplot
- Matplotlib Overview
- Choosing the Right Chart Type
- Creating a Plot Area 1
- Creating a Plot Area 2
- Bar Plots
- Line Plots
- FIFA 21 Player Dataset
- Scatter Plots
- Histograms
- Box Plots and Violin Plots
- Style and Presentation
EDA – Exploratory Data Analysis - Seaborn
- Seaborn Overview
- Categorical Plots
- Relational Plots
- Distribution Plots
- Regression Plots
- Matrix Plots
- Multi Plot Grids
- Style and Presentation
EDA – Exploratory Data Analysis – Plotly Express
- Plotly Express Overview
- Interactive Charts in Plotly Express
- 3D Charts
Hours of content
Live Sessions
Software Tools
After completion of this training program you will be able to launch your carrer in the world of AI being certified as Databricks Certified Spark Developer Associate course.
With the Databricks Certified Spark Developer Associate course in-hand you can boost your profile on Linked, Meta, Twitter & other platform to boost your visibility
- Get your certificate upon successful completion of the course.
- Certificates for each course
- Azure BLOB Storage
- AWS Polly
- Azure Databricks
- Azure SQL Server
- Azure Functions
- Azure App Services
- Azure Logic Apps
- Azure Document DB
- Azure Fabric
- Azure Kubernetes Service (AKS)
- Azure Container Registry (ACR)
- API Management
- Azure Search
- Azure Moniter
45% - 100%
Designed to provide guidance on current interview practices, personality development, soft skills enhancement, and HR-related questions
Receive expert assistance from our placement team to craft your resume and optimize your Job Profile. Learn effective strategies to capture the attention of HR professionals and maximize your chances of getting shortlisted.
Engage in mock interview sessions led by our industry experts to receive continuous, detailed feedback along with a customized improvement plan. Our dedicated support will help refine your skills until your desired job in the industry.
Join interactive sessions with industry professionals to understand the key skills companies seek. Practice solving interview question worksheets designed to improve your readiness and boost your chances of success in interviews
Build meaningful relationships with key decision-makers and open doors to exciting job prospects in Product and Service based partner
Your path to job placement starts immediately after you finish the course with guaranteed interview calls
Why should you choose to pursue a Databricks Certified Spark Developer Associate Course with Success Aimers?
Success Aimers teaching strategy follow a methodology where in we believe in realtime job scenarios that covers industry use-cases & this will help in building the carrer in the field of Databricks & also delivers training with help of leading industry experts that helps students to confidently answers questions confidently & excel projects as well while working in a real-world
What is the time frame to become competent as a Databricks Certified Spark Developer Associate?
To become a successful Databricks Certified Spark Developer required 1-2 years of consistent learning with dedicated 3-4 hours on daily basis.
But with Success Aimers with the help of leading industry experts & specialized trainers you able to achieve that degree of mastery in 6 months or one year or so and it’s because our curriculum & labs we had formed with hands-on projects.
Will skipping a session prevent me from completing the course?
Missing a live session doesn’t impact your training because we have the live recorded session that’s students can refer later.
What industries lead in Databricks implementation?
Manufacturing
Financial Services
Healthcare
E-commerce
Telecommunications
BFSI (Banking, Finance & Insurance)
“Travel Industry
Does Success Aimers offer corporate training solutions?
At Success Aimers, we have tied up with 500 + Corporate Partners to support their talent development through online training. Our corporate training programme delivers training based on industry use-cases & focused on ever-evolving tech space.
How is the Success Aimers Databricks Certified Spark Developer Associate Course reviewed by learners?
Our Databricks Certified Spark Developer Associate Course features a well-designed curriculum frameworks focused on delivering training based on industry needs & aligned on ever-changing evolving needs of today’s workforce due to Cloud Computing (Azure)
Also our training curriculum has been reviewed by alumi & praises the thorough content & real along practical use-cases that we covered during the training. Our program helps working professionals to upgrade their skills & help them grow further in their roles…
Can I attend a demo session before I enroll?
Yes, we offer one-to-one discussion before the training and also schedule one demo session to have a gist of trainer teaching style & also the students have questions around training
programme placements & job growth after training completion.
What batch size do you consider for the course?
On an average we keep 5-10 students in a batch to have a interactive session & this way trainer can focus on each individual instead of having a large group
Do you offer learning content as part of the program?
Students are provided with training content wherein the trainer share the Code Snippets, PPT Materials along with recordings of all the batches
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